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Popul Health MetrPopulation Health Metrics1478-7954BioMed Central London 1478-7954-2-101553895010.1186/1478-7954-2-10ResearchAssessment of possible impact of a health promotion program in Korea from health risk trends in a longitudinally observed cohort Park J [email protected] SH [email protected] DW [email protected] University of Michigan, 1027 E. Huron, Ann Arbor, Michigan 48104-1688, USA2 134, Shinchon-Dong, Seodaemun-Gu, Yonsei University, Seoul, Korea2004 11 11 2004 2 10 10 15 12 2003 11 11 2004 Copyright © 2004 Park et al; licensee BioMed Central Ltd.2004Park et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Longitudinally observed cohort data can be utilized to assess the potential for health promotion and healthcare planning by comparing the estimated risk factor trends of non-intervened with that of intervened. The paper seeks (1) to estimate a natural transition (patterns of movement between states) of health risk state from a Korean cohort data using a Markov model, (2) to derive an effective and necessary health promotion strategy for the population, and (3) to project a possible impact of an intervention program on health status.
Methods
The observed transition of health risk states in a Korean employee cohort was utilized to estimate the natural flow of aggregated health risk states from eight health risk measures using Markov chain models. In addition, a reinforced transition was simulated, given that a health promotion program was implemented for the cohort, to project a possible impact on improvement of health status. An intervened risk transition was obtained based on age, gender, and baseline risk state, adjusted to match with the Korean cohort, from a simulated random sample of a US employee population, where a health intervention was in place.
Results
The estimated natural flow (non-intervened), following Markov chain order 2, showed a decrease in low risk state by 3.1 percentage points in the Korean population while the simulated reinforced transition (intervened) projected an increase in low risk state by 7.5 percentage points. Estimated transitions of risk states demonstrated the necessity of not only the risk reduction but also low risk maintenance.
Conclusions
The frame work of Markov chain efficiently estimated the trend, and captured the tendency in the natural flow. Given only a minimally intense health promotion program, potential risk reduction and low risk maintenance was projected.
Health Risk AppraisalMarkov ChainHealth PromotionHealth StatusRiskTrendSimulationNatural FlowTransition
==== Body
Background
Evidence was found that health promotion programs affect health risks in the US and in many other countries [1-4]. Also, a consistent association of higher risk individuals with higher medical costs implies a potential impact of risk reduction on cost moderation [5,6]. Musich et al. [7] showed that participation in health promotion programs can be effective in moderating medical costs. While most health promotion programs in the US focus on the improvement of health rather than direct economic benefits, many economic evaluations claim that there are transfers of benefits between participation, risk reduction and cost savings [8-12]. However, without identified health risks and a systematic evaluation of the needs to provide the quality programs in the Korean society, an implementation of such programs would be unlikely.
A previous study with a random sample from a 12-year cohort of civil servants in Korea [13] provided an insight that lifestyle factors predicted future medical utilization reasonably well. This suggests that risk status, measured by lifestyle and biometric factors at a point in time, could be used as a pivot to estimate future medical utilization as a result of risk progression.
Longitudinally observed cohort data can be utilized for health promotion and healthcare planning provided the health risk trend is estimated and it poses a general need for an intervention. This paper attempts to address an issue around the possibility of predicting the impact of a health promotion intervention by applying observed effectiveness data from a population with an intervention to the observed transitions of risk status in a Korean population in the absence of such a program.
Methods
To help in understanding the overall risk transitions in the Korean population and to implement an effective intervention program, a Markov chain model was utilized, assuming finite risk states at any point in time [14]. Today's weather affects tomorrow's but yesterday's may be already irrelevant to tomorrow's forecast. Stock price tomorrow may depend on the previous week's stock prices, not just today's. In estimating the chance of getting "sunny" weather tomorrow, most relevant information to it could be today's weather, where today's weather can be described as "rain", 'cloudy", or "sunny", for example. In general, a random process with fixed number of values could be numerically described, with the collection of possible values forming a "state space" (all possible weather, for example) and the possible values being "states" (rain, cloudy, or sunny, for example). Markov chain models the dependent structure of the future state of a random process on previous states as in the weather forecast example. In the current study, the Markov chain model can describe risk transitions over time when future risk transitions depend on the previous risk states. This modelling is used when a decision problem involves risk state change over time, and interest in the event. This also enables one to project the health status of a population [15-18].
This paper utilizes the observed transitions of measured health risks in a cohort of the Korean National Health Insurance Corporation registrants (KNHIC) over 5 years. This longitudinally-followed population trend, without a particular intervention or policy change in place, was used as a basis to estimate a natural history of risk flow. In addition, an intervened transition was simulated, given that a health promotion program was implemented for the KNHIC cohort to project a possible impact on the improvement of health status. Comparing the two transitions also provided directions for a health promotion program that might be implemented in this population.
Population
The study population consisted of the established KNHIC prospective cohort [13]. Registrants of KNHIC were invited to complete a health survey prior to each mandatory bi-annual physical examination. The respondents to this preliminary-health risk appraisal (p-HRA) in 1992, were followed bi-annually from 1996 to 2000. Reliability and validation tests were not carried out for the p-HRA. The criteria for inclusion in the study are: (1) actively employed over the period, (2) ages between 30 and 65 in 1996 (N = 180,767).
Similarly, a comparison population was selected for the simulation of a program effect from the large longitudinal database of University of Michigan-Health Risk Appraisal (UM-HRA) completers. The UM-HRA was originally a CDC version, which was modified to fit the national trend of cost, and to meet the guidelines over time. Additional conditions applied are: (1) participated in a health promotion program at a minimal intensity, (2) completed the UM-HRA at least three times during 1996–2000, (3) were insured by the same insurance plans during the corresponding years and (4) were actively employed over the period at the same industry, and (5) age under 65 years in 1996 (N = 15,793).
Questionnaire
The survey questionnaires (p-HRA), on health status, diet, and lifestyles, were sent to work places and homes to encourage a national health screening at designated health care facilities and to measure lifestyle related health behaviors, every two years for the KNHIC registrants. UM-HRA was used to appraise individual health status during the same period (1996–2000) for the US population. The validity of UM-HRA has been addressed elsewhere [19,20].
Health risks and costs
Three lifestyle-related health risks were measured by the corresponding questionnaires (p-HRA for the study cohort, and UM-HRA for the comparison population): physical activity, alcohol consumption, and smoking. In addition, pivotal measures of overall health were collected by the questionnaires: perceived health and medical conditions. Three biometric measures were obtained from the appropriate lab tests during physical examination for the KNHIC population and from the self-reported measures for the comparison population: systolic blood pressure (SBP) /diastolic blood pressure (DBP), total cholesterol, and body mass index (BMI) via height and weight measures. The risk criteria for the US population were defined (Table 1) following the published guideline by the US–CDC/Carter Center, and some were modified to fit better for prediction of healthcare costs (BMI and physical activity).
Table 1 Risk Evaluation Criteria and baseline characteristics. Individuals from US population were classified as Low, Medium, and High risk as in KNHIC population. Within each risk group, random samples were selected each time of sampling from US population stratified with age and gender once they met the similar risk profile of KNHIC. This bootstrap-sampled match would be used as a control (interven ed) population.
Baseline characteristics KNHIC population (N = 180,767) Comparative population with intervention (N = 180,767)*
Risks Criteria N (%) Criteria N (%)
Perceived Health Poor /Fair 24,290(13.4) Poor /Fair 31,814(17.6)
Exercise Less than 1/week 100,395(55.5) Less than 1/week 56,399(31.2)
Alcohol Drink>7/week1 17,554(9.7) Drink>14/week 16,630(9.2)
Smoking Current smoker 55,052(30.4) Current smoker 29,645(16.4)
BMI BMI>25.0 for male, >23.0 for female2 46,227(25.6) BMI>27.50 69,776(38.6)
BP SBP> 120 or DBP>803 91,924(63.9) SBP> 139 or DBP>89 48,084(26.6)
Cholesterol Cholesterol>2204 32,118(17.2) Cholesterol>239 7,954(4.4)
Medical condition Self-reported disease 9,280(5.1) Self-reported disease5 35,973(19.9)
Baseline Class Average Age = 40.0 Male (61%) Average Age = 40.0 Male (61%)
Low Risk (0–2) 62.9% 63.0%
Medium Risk (3) 22.2% 22.3%
High Risk (4+) 14.9% 14.7%
Note
1a drink of "Soju" = 2 drinks of wine/beer
2WHO guideline (1999) = 23.5 for Asians
3,4Korean Medical Association (2000) guideline
5 diabetes, heart problem, cancer, past stroke, bronchitis/emphysema
*Simulated data after adjusted for age, gender, and baseline risk for KNHIC population
Information on eight health risks for the study cohort were systematically evaluated and mapped to the measured risks by UM-HRA (Table 1). This was done according to: (1) the published guidelines for Asians, (2) empirical comparison of question by question, and (3) age and gender adjusted association to the respective healthcare costs distribution [13,21-25]. In addition, different risk criteria were applied to alcohol consumption and medical condition due to systematic difference in measurement. Corresponding health states per period were defined according to the distribution of the aggregated risk state (sum of individual risks variable states) as low (0–2 risks), medium (3 risks) and high (4+ risks).
Inpatient plus outpatient costs per annum were collected from KNHIC database for the medical utilization in association with health risks, and the inflation adjusted average 1996 costs (January 1st, 1996 through December 31, 1996) were used for the T1 costs. Similarly, average T2 costs were calculated from 1998 claims costs.
Program
Participants of the p-HRA were not given any further information on identified health risks. Neither was it used to gain access to any health intervention programs during the five years (1996–2000). In US, on the other hand, as part of an intervention program, the completers of UM-HRA were given individually tailored health information, followed by encouragement of participation in a health promotion program at no cost during 1996–2000. This nation-wide program included an annual mail-based HRA, personalized follow-up report, identification of top significant risks and referral to health resources. This was defined as minimal level intervention, which differs from KNHIC's p-HRA in providing health information and individual feedback.
Trend
Provided that the least resources were available for a health intervention of the KNHIC population, a projected health risk transition with the minimal level intervention was utilized for the simulation of possible impact on risk transition. Population health trends were followed over the three time frames (T1, T2, and T3). T1 refers to the baseline year, which is 1996 for both populations. T2 refers to the second time frame, which is 1998 for the KNHIC population, and 1997 or 1998 for the compared US population. T3 refers to the 3rd time frame, 2000 for the KNHIC population, and 1998 or 2000 for the US population. Population with minimal intervention was matched to the baseline characteristics of KNHIC population, using age, gender, and baseline risk distribution. Trend was defined as the risk state change in each population between the time-points while each transition was annualized. Each change of risk state was estimated and the parameters to trend from the matched intervened population were used to project the possible trend of KNHIC population, following such a program.
Analysis
An age/gender-cohort model was implemented, following observations on the natural flow of health status over three time frames, and it was compared to the corresponding age/gender-cohort of a US population with the matched risk distributions, where an intervention at minimal level was applied.
For a simulation of an intervened transition for KNHIC population, an adjusted estimation of intervened transition was obtained based on random samples drawn from the selected US sample of intervened employees (N = 15, 793). To simulate additional random samples to reduce variances in the estimate of parameters for transition, a Monte Carlo bootstrap [26,27] method was employed to the sub-populations with age, gender, and baseline risk matched to the KNHIC populations (N = 180, 767). For each 10 years apart (30–40, 41–50, 51–60) cohort from the matched boosted random samples of US population, risk transitions following a minimal intervention were estimated while controlling for age, gender, current and previous risk states, and previous year's medical claims costs. Similarly, natural flow of risk transitions was estimated from the observed KNHIC cohort data. The estimated parameters (covariate and baseline effect, corresponding mean, variance and covariance) and fitted model for the intervened transition was applied to the KNHIC population for the transition probabilities for each risk state at T3. Finally, an aggregated weighted probability for transition was calculated based on age-gender-risk state distribution.
Computations were carried out using a generalized linear model for the ordered categorical outcomes (low risk<medium risk<high risk), assuming a Markov chain model in which the order of dependency was learnt from the data (Figure 1). Figure 1 depicts the transition between risk states at each cycle and the dependency of risk progress on the previous risk state. All risk states are inter-connected and allow feedback cycles at each risk state (staying at the same state).
Figure 1 Markov transition with 3 risk states without an exit.
Assumptions of Markov chain model, and model fit diagnostics were evaluated for the KNHIC population. Stationarity was tested by likelihood ratio test of the fitted models using a generalized equation estimation with and without time-dependence while controlling for other measured covariates. Order of Markov chain was tested with likelihood ratio test and fit-diagnostics were run. Following the fitted Markov model, we presented the estimated transitions over three time-frames for the natural flow. Cochran-Armitage trend tests controlling for T1 or T2 risk state were performed to test the trend of dependency of future state on T2 or T1 risk state.
Average costs in each T1-T2 risk state pair were calculated from the corresponding years' average costs, adjusted for inflation. Healthcare costs were converted to ratios of average costs per T1-T2 risk transition state, compared to low (T1)-low (T2). Adjusting with any appropriate inflation rate in the future, these ratios can be used to project the cost savings over time by comparing the number of projected trends times cost ratios in natural and intervened flows. The projected percentage changes in the two trends (natural vs. intervened) were compared for an estimation of an intervention impact on risk change.
Results
Feasibility of Markov chain model
Individual baseline risk prevalence of the two cohorts is shown in Table 1. The simulated intervened cohort was adjusted for age, gender, and baseline risk states according to those of KNHIC data. Compared to the US population, lack of physical exercise, and smoking were significantly more prevalent among the Korean cohort at baseline. Biometric risks such as blood pressure and cholesterol were higher in the KNHIC population due to 100% compliance rate of lab test of the study population. Overweight and medical condition was significantly less among the Korean. However, overall baseline risk distributions based on the clustered state (low, medium or high) were about the same. A simulation of an intervention effect follows the adjusted baseline characteristics of the comparative study population and Table 1 shows similarity of the two populations at baseline such as age, gender and risk distribution for comparability.
Under the assumption of finite number of health risk states (low, medium and high states), Markovian models were examined for proper order estimation, an association structure, and stationarity. Following a likelihood ratio test, Markov chain (MC) order 2 was preferred for the risk transition of the non-intervened (KNHIC) population (Table 2). In other words, future state depends on the current and the most recent past states and when controlled for the dependency on risk state, overall future risk state depends on the individual risk factors far less significantly. This also assured that matching intervened trend is applicable to the KNHIC population without further concern on effect of inherent risk progress to future risk transition. Therefore, for the following estimations of transitions, the dependency specified in Table 2 was used.
Table 2 MC order test using cumulative logit model of becoming low risk at T3
Selected Predictors (Significant, P < 0.01) MC order = 1 MC order = 2
Male -0.563 -0.345
Age -0.023 -0.019
Baseline risk (t1) Low 2.73 1.545
Baseline cost (t1) -0.01 -0.011
Risk at t2 Low - 1.938
Model fit
log L -283085 -139246*
* Log likelihood ratio test of order 2 model, compared to order 1 model (287,678 >> χ2(2), pr<0.001) was significantly preferred.
Characteristics of the obtained trend
When Markov chain order 2 was assumed, higher T1 risk status, controlling for T2 risk state as in Table 3, was associated with lower percentage of being at low or lower risk state at T3 (T3 state = "0" if at low or lower state than the state at T2. T3 state = "1" if at high or higher state than the state at T2, Pr<0.001). Similarly, higher T2 risk state, controlling for T1 risk, was less likely to be at "0" at T3 (Pr<0.001). Due to a stronger dependency on T2 risk state, medium (T1)-low (T2) is more likely to be at low at T3 than low (T1)-medium (T2). However, additional dependency on the T1 risk state differentiates the trend percentages at "0" state (T3) from medium (T1)-low (T2) and high (T1)-low (T2).
Table 3 T1-T2 risk state by T3 risk state. Note that outcome is 0 if T3 state is low or lower than the state at T2, otherwise it is 1. The test for trend controlling for T1 risk state is: a>b>c; d>e>f; g>h>i (Pr>z<0.001). The test for trend controlling for T2 risk state is: a>d>g; b>e>h(Pr>z<0.001)c>f>i (Pr = 0.143) The superscripted numbers in parenthesis represent the order of trend which appeared to be significantly associated with the likelihood to be at "0" at T3.
Trend percentage at T3 per T1-T2 risk state
T1-T2 risk state (N = 180,767) (0) (1) Comparison of order Trend statistic (Z)
Low-Lowa 82.4(1) 17.6 Vs. d -68.2*
Low-Mediumb 52.5(3) 47.5 Vs. g -11.4*
Low-Highc 35.8(5) 64.2 Vs. e -44.7*
Medium-Lowd 58.6(2) 41.4 Vs. b -11.2*
Medium-Mediume 34.0(6) 66.0 Vs. h -18.0*
Medium-Highf 21.5(8) 78.5 Vs. i -27.7*
High-Lowg 43.5(4) 56.5 Vs. c -26.2*
High-Mediumh 22.6(7) 77.4 Vs. f -43.8*
High-Highi 11.7(9) 88.3 - -
* Significant with α = 0.01
Cochran-Armitage trend tests controlling for T1 risk state or T2 risk state show a strong declining trend of staying at "0" than that at T2 as in the order of appearance in Table 3. For example, low (T1)-low (T2) is more likely to be at low in T3, compared to low-medium and low-high. This tendency holds for: a>b>c; d>e>f; g>h>i with Pr<0.001, a>d>g; b>e>h with Pr<0.001 except c>f>i (Pr<z = 0.143), where a, b, c; d, e, f; g, h, and i corresponds to low-low, low-medium, low-high; medium-low, medium-medium, medium-high; high-low, high-medium, and high-high risk state, respectively. Also, overall consistency appears significant (one-sided, Pr <z: <0.001). Pairwise comparisons determined the order of likelihood to be at low or lower state at T3 (i.e. outcome = 0) in association with T1-T2 risk state. Low (T1)-low (T2) tops the order, with 82.4 % of the individuals being at low risk at T3, followed by medium (T1)-low (T2) with 58.6%, low (T1)-medium (T2) with 52.5% and so on (a>d>b>g>c>e>h>f>i, Pr<0.001).
Application of Markov chain order 2 for estimation of natural and reinforced transition flows
We applied the results from the previous sections (regarding the KNHIC population) to the observed risk transitions of the matched sample from a US active employee population, who participated in a minimal level program. Similar assumptions were tested and stationary Markov chain order 2 was also applied. The estimated transition probabilities without an intervention (KNHIC cohort) and those of the same people with a minimal level intervention are shown in Table 4. The higher probabilities to be at low risk state at T3 were shown in the intervened flow (except medium (T1)-high (T2) and high-low) compared to the natural flow.
Table 4 Estimated risk transition probability with and without intervention and medical utilization.
Behavioral health risk measured at two times (T1 and T2) T1-T2 Utilization Cost Ratiob KNHIC population without intervention KN HIC population with a simulated intervention at minimal levela
Risk state at T3 Risk state at T3
T1 T2 Low Medium High Low Medium High
Low Low 1.00 0.81 0.15 0.04 0.97 0.03 0.00c
Medium 1.14 0.53 0.34 0.13 0.69 0.11 0.20
High 1.58 0.38 0.32 0.30 0.65 0.34 0.01
Medium Low 1.16 0.60 0.30 0.10 0.65 0.33 0.02
Medium 1.23 0.35 0.44 0.21 0.39 0.32 0.29
High 1.52 0.23 0.35 0.42 0.05 0.73 0.22
High Low 1.51 0.46 0.31 0.23 0.34 0.65 0.01
Medium 1.52 0.25 0.38 0.37 0.34 0.65 0.01
High 1.79 0.14 0.27 0.59 0.16 0.30 0.54
aRisk transition probability was estimated using Markov model order 2, adjusted for age and gender distribution of KNHIC.
bComparing average cost in each T1-T2 risk state pair (e.g. high-medium) to that of the low-low (T1-T2).
cAll probabilities were rounded off at the 3rd decimal place (i.e.0.0014).
Overall, the likelihood of maintaining health at low risk (T1-T2-T3) is higher in the intervened transition (0.97 vs. 0.81). Also, the probabilities of being at high (T1-T2-T3) and at medium (T1-T2-T3) are lower in the intervened flow (0.54 vs. 0.59 at high, 0.32 vs. 0.44 at medium). Healthcare cost ratios in relation to the T1 -T2 risk states in the KNHIC population are also shown in the table. In general, cost ratios increased in the order of T1-T2 risk states and cost utilization of high-high group almost doubled (1.79) the cost of low-low.
After three years of projection, the transitions, presented in Table 5 show the estimated numbers in each risk state, following the natural and the intervened flows as in Table 4. Three-year forward projection of populations (non-intervened vs. intervened) was calculated by pre-multiplying the number of people in each T1-T2 risk group to the 3rd power of the Markov transition matrix of order 2. This is then collapsed by the baseline states as in Table 5 to show the net gain from T1 to T3. The difference was calculated as the projected counts per state from the baseline total counts, and percentage point changes were calculated (Table 6). Overall, there was 0.72% point net increase in high risk state following from natural flow and 5.01% points net decrease in the intervened flow. Low risk percentage decreased by 3.07% points following the natural flow but increased by 7.49% points in the intervened flow.
Table 5 Projection of population KNHIC following natural vs. intervened flow over 3 waves. Projected 3 – forward years based on MC-order2
Risk state At Baseline At T3α At T3β
Low 113,605 108,050 127,149
Medium 40,151 44,400 35,675
High 27,011 28,317 17,943
Table 6 Projection of population KNHIC following natural vs. intervened flow over 3 waves. Percentage Point change following Table 4-(a)
Risk state At Baseline At T3α At T3β
Low 113,605 -3.07% +7.49%
Medium 40,151 +2.35% -2.47%
High 27,011 +0.72% -5.01%
α,Following natural risk flow, βFollowing intervened flow
Discussion
Markov Chain Model and Transitions of Risk States
After controlling for age, gender and other covariates, such as past healthcare costs, variations within groups in predicting the future risk transition were left unexplained. This suggested an additional consideration of dependence on other factors such as past health risk history. This dependent structure of a natural risk flow was best fitted with a Markov model order 2 (due to limitation of the data, no higher order was tested). In other words, current risk states largely depend on the immediate past state and also depend on the one before that. The estimated probability was stable. There was no policy change or environmental effect, which is considered as a period effect, during the study period. In addition, as a result of testing a higher order MC for the available US data, order 2 was preferred to the order 3 (data not shown). Therefore, we concluded that an assumption of MC order 2 was plausible for the health risk transitions regardless of natural flow or reinforced flow at minimal intensity [28,29]. Also, although the time-frames of observations in the two population were different, time of observation turned out to be non-influential to the transition (stationary) and average years of observation was used to calculate an annualized transition for both transitions. Therefore, the difference in observed years would not have implications for the results.
Difference in Individual Risk Profile
Simulated data for a minimal program participation, adjusted to matched age, gender, and baseline risk distribution of KNHIC population for comparison, showed that sampling disparities were significantly reduced in overall distribution and demographics but still remained in individual risk factors. Exercise and smoking risks are relevant to environmental and cultural adaptation, which showed large differences in prevalence. Typically, overweight is the most prevalent risk factor in the US, and it appeared to be significantly higher even after matching overall risk distribution to the Korean population. Although there exist some differences, these biases in the data were assumed to be smoothed out by collapsing to the aggregated risk levels.
There may be inherent differences in risk transitions in the two populations due to two factors: (1) disparities in the individual risk factors, (2) systematic differences due to cultural and environmental factors. Due to the fact that estimation of risk transition was primarily dependent on the aggregated risk state rather than the individual risks (Table 2), the potential influences were minimal. Matching on individual risk factors additionally may reduce such a bias in the transition. However, individual risk profiles are inconsistent at times and risk transitions depend more on inter-associations of individual risk profiles. Therefore, matching on individual risk factor and transitions based on such profiles may even create another type of bias in the estimation of transition. On the other hand, by bootstrapping of random samples, matching on age, gender, and risk distribution, variations within each risk state have been reduced and even reduced the potential bias due to disparity in individual risk factors. The remaining disparity in the matched populations would become irrelevant when additional control for those remaining disparity factors is made in the model for the ordered categorical transition.
Implementing a program based on found dependent structure of risk transition
Without reinforcement, the male population was less likely to be in the lower risk states in future compared to the females from the same past (T1 and T2) risk state, given age (Table 2). This suggested an observed potential for improvement of men's health such as an emphasis on the cultural adaptation of health by changing organizations and communities to create supportive environment and re-orienting health services [30].
Aging has been found to be related to the risk transition because as one gets older, he/she is less likely to be in lower risk state in future without intervention (Table 2). There have been concerns about the elderly population, who are under-represented due to unequal resources, and less interest [31,32]. Studies in the US found that current elderly population is healthier than the elderly 10 years ago [31-33]. The observed trend among the elderly may be linked by the changes in health-related individual behaviors in the past. This indicates the potential impact of continuous health promotion on aging adults.
Although the immediate past risk states showed the highest importance, when they are the same, risk state at T1 plays a key role in predicting T3 risk state. In other words, individuals currently at a state may have different paths to the current risk state and the weight (by intensity or allocation of resources) of risk reduction program should differentiate this path-variability to maximize the impact (Tables 3,4). Following the natural flow, the likelihood of those at low at T2 to be at the same low in future (T3) is significantly less than that of those who were on intervention, regardless of their risk state at T1. This suggests a continuing effort on those who once impacted, to maintain their modified health practices until they adapt to their newly improved lifestyle (Table 4).
Over the period, although there was presumably an aging process in the natural flow, there exists a regression to the previous state. Especially with Markov chain order 2, in controlling for the T1 state, the tendency stays. This implies that even without any external reinforcement for a healthier state, low risk state largely tends to maintain its state, followed by the high risk state. Therefore, an intervention with minimal effort to sustain healthy lifestyle and behavior for those who are at low risk may yield substantial benefit in the long run (Tables 3,4). Likewise, an intervention program to break high-high cycle to lower the health risks and eventually to optimize the utilization of the health resources is anticipated to be effective and necessary. These, in association with the cost trend following the risk trend (Table 4), imply that moderation of healthcare costs is also achievable by a well-targeted health promotion program.
Projected effect of a minimal level intervention
For this study population, the minimal level intervention appeared to be effective in the low and medium baseline groups (Table 4). This is reasoned that the particular program, which was designed to impact people with minimal resources, turned out to be most effective on "low risk maintenance". Thus, efforts to reduce risks were rather undersized. However, people at the high baseline risk except high (T1)-high (T2), were less likely to stay at high risk (at T3) than the non-intervened, showing a potential improvement in risk reduction as well. This was found in similar contexts and quite consistent across studies [34,35]. As a conclusion, these findings suggest that with limited resources, a program that delivers an individual feedback upon completion of an HRA, identifying most significant risks and referring to available resources, could have an impact on the natural health risk flow, especially on low risk maintenance by enhancing awareness and self-efficacy in maintaining good health practice.
One of the study goals was to achieve estimate a natural flow of the study population, where a demand and necessity for health promotion program has been increased recently. Natural flow was coined by Edington [14] to describe a health trend in a population without any intervention for a reinforced effort to improve heath state. By comparing it to various simulated trends with an intervention to the population, one could assess program effectiveness in terms of health change, which was measured as the risk state. This again, with cost-ratios per risk transition as in Table 4, may be used to calculate the possible savings in association with any program of interest. For example, comparing to low-low group, low-high incurred 158% of the costs of low-low. Trend at low at T3 following the intervention was increased by 16% from low-low, comparing to natural flow. Therefore, the potentially incurred costs due to increased risk state could be saved proportionately by 14% (1.14:1.00, 12% of the low (T1)-low (T2) avoided transition to medium (T3)), 58% (1.58:1.00, 4% avoided transition to high (T3)) of the costs. This concept was used wherever health behavior change and lifestyle modification is applicable in an effort to promote and manage health. Thus, health trends following such a program could be compared to the expected natural flow for a demonstration of "effectiveness". In the current study, the estimated natural flow shows decreases in low risk state by 3.07 % whilst 7.49 % increases by following an intervention. The underlying risk progression may be likely explained elsewhere [37].
Recommendation
It is recommended to set a policy and allocate resources, tailored to the population profile of aggregated risk state, first. Targeting risk factors in the context of risk state or risk clusters could follow next. For example, an overweight population at high risk state requires more resources and more intensive interventions than the overweight population at low risk state. The study provides the base information when planning such a population-based intervention. This approach differentiates from those interventions targeting individual risk profiles, which could be inconsistent at times. Studies suggest that an inter-association over risks is more important in implementing an intervention than individual risks [38]. Some variables are highly variable such as medical conditions, depending on genetic traits more than the environments. This variation may indicate that the level of a risk factor may not be acceptable in one population while it was so in another. This varying manifestation could have an influence on implementing interventions targeting on particular risk factor, which is beyond the scope of the paper.
5. Conclusions
As a reasonable method to project the risk trends, a stationary Markov model of order 2 was fitted for the health risk transitions of the non-intervened population, suggesting a natural risk flow for the population. Utilizing this with the matched intervened population, the reinforced risk transitions of the KNHIC population were estimated. The significant difference in the transitions appeared to be for the low baseline risk population even with a minimal intensity intervention program. Therefore, the difference in the projected numbers by the two transitions (with /without interventions) showed a significant impact on low risk maintenance although higher risk population was also impacted to increase fewer risks and to moderate it to becoming "medium risk".
Conventionally, most programs in the past have focused on risk reduction although low risk maintenance has been raised as a practical issue. Since this paper suggests a strong dependence on the previous history of risk status and the instability of high (T1)-low (T2) risk population, compared to low-to-low or medium-to-low population, differentiating efforts for the low risk people (from low to low) and for those moved to low (from high to low), should be sought for maximum impact.
The studied intervention with minimal intensity was to diversify the beneficiaries of the program, to increase awareness, to continue educating and motivating individuals to adopt healthier behaviors by individualized feedback, and to provide resources [36]. The findings demonstrated that even such a minimally intense program could be effective in moderating health risks, preventing relapse and sustaining healthy behaviors over time.
Limitation and future research
Population characteristics on risk transition were assumed to be similar by adjusting for age, gender, if previous risk states were the same. However, in addition to the measured risks, diet and culturally inherited behavioral differences could make an inherent transitional difference in the two compared populations. Also, some risks such as disease were underestimated in the KNHIC population. Therefore, the presented natural flow estimation in Korean population can be adopted but utilized with caution.
It may be conjectured that there are some risk factors are more easily modifiable than others such as exercise while medical condition and overweight may not. The currently shown disparity in the two compared risk profiles may have induced somewhat less significant program impact on risk transition (lowering risk state) due to higher prevalence in medical conditions and overweight in the control population. This study tried to match two populations as close as possible and to model to reduce potential biases but comparing two populations requires more caution in further relevant studies.
Despite the fact that individual risk distribution is not consistent often time, not only an overall health status but a multivariate risk state (i.e. a matrix per person) could be utilized to identify an effect of each risk factor to their projected state in future, as in Manton and Stallard [37]. Based on the estimated transitions, prediction of the effect of different intervention models on the risk transitions and the impact on healthcare costs would be available. Validity of the p-HRA (KNHIC) was not tested although it was matched to and tested by the established UM-HRA. This will be validated and tested against UM-HRA upon availability of cohort data, which were followed longer time. Markov higher order model (3 +) with a longer follow-up time could be explored for exact consistency and stationarity for the cohort. Even the progression rate from no incidence state to symptomatic state utilizing the health care cost database can be added to the cohort to project the proper care of medical conditions. Inclusion of an "exit" state (such as death) and consideration of a reasonable compliance rate would possibly make the model robust over the longer time of an intervention. We will further investigate the possibility of matching individuals in the two compared populations across many covariates including individual risk factors and other confounders such as willingness to improve, to further reduce potential biases.
Competing interests
The authors declare that they have no competing interests.
Authors' Contributions
JP carried out the analyses and drafted the manuscript. SHJ collected, cleaned the data and provided initial analyses. DWE participated in the design of the analyses and provided revisions. All authors read and approved the final manuscript.
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-1-401556337510.1186/1742-4690-1-40CommentaryHTLV-1 p30II: selective repressor of gene expression Green Patrick L [email protected] Department of Veterinary Biosciences, The Ohio State University, Columbus, OH 43210, USA2 Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, OH 43210, USA3 Center for Retrovirus Research, The Ohio State University, Columbus, OH 43210, USA4 Comprehensive Cancer Center, Arthur G. James Cancer Hospital and Solove Research Institute, The Ohio State University, Columbus, OH 43210, USA2004 24 11 2004 1 40 40 9 11 2004 24 11 2004 Copyright © 2004 Green; licensee BioMed Central Ltd.2004Green; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Human T-lymphotropic virus type-1 (HTLV-1) is a complex retrovirus that causes adult T-cell leukemia/lymphoma (ATL) and is implicated in a variety of lymphocyte-mediated disorders. HTLV-1 pX ORF II encodes two proteins, p13II and p30II whose roles are beginning to be defined in the virus life cycle. Previous studies indicate the importance of these viral proteins in the ability of the virus to maintain viral loads and persist in an animal model of HTLV-1 infection. Intriguing new studies indicate that p30II is a multifunctional regulator that differentially modulates CREB and Tax-responsive element-mediated transcription through its interaction with CREB-binding protein (CBP)/p300 and specifically binds and represses tax/rex mRNA nuclear export. A new study characterized the role of p30II in regulation of cellular gene expression using comprehensive human gene arrays. Interestingly, p30II is an overall repressor of cellular gene expression, while selectively favoring the expression of regulatory gene pathways important to T lymphocytes. These new findings suggest that HTLV-1, which is associated with lymphoproliferative diseases, uses p30II to selectively repress cellular and viral gene expression to favor the survival of cellular targets ultimately resulting in leukemogenesis.
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The complex sequence of events set in motion by human T-lymphotropic virus type 1 (HTLV-1) to cause proliferation and ultimately transformation of T lymphocytes is beginning to be unraveled. Only recently has it become clear that viral encoded proteins, the so-called "accessory" gene products of this complex retrovirus, play an integral role in the pathogenic process. In addition to the structural and enzymatic gene products, HTLV-1 encodes regulatory and accessory proteins from four open reading frames (ORF) in the pX region between env and the 3' long terminal repeat (LTR) of the provirus [1,2]. The well studied Rex and Tax positive regulators are encoded in the ORF III and IV, respectively. Rex plays a critical role in nuclear export of unspliced or singly spliced viral mRNA [3,4]. Tax orchestrates multiple interactions with cellular transcription factors and activates transcription from the viral promoter and modulates the transcription or activity of numerous cellular genes involved in cell growth and differentiation, cell cycle control, and DNA repair [5,6]. Recent studies have indicated novel roles for pX ORF I and II gene products in the replication of HTLV-1 [7-9]. Although the study of these gene products were largely by-passed by virologists until the mid 1990's, they intensified when infectious molecular clones provided the tools to better understand their role in vivo. Both HTLV-1 pX ORF I and II mRNAs have been detected in infected cell lines and blood leukocytes from HTLV-1-infected subjects including ATL and HAM/TSP patients [10,11]. Also, immune responses of HTLV-1 infected patients and asymptomatic carriers indicate that these proteins are expressed in vivo [12-14].
Molecular clones of HTLV-1 with selective mutations of ORF I and II have revealed the requirement of p12I and p13II/p30II in the establishment of infection and maintenance of viral loads in a rabbit model of infection [15-17]. The nuclear and nucleolar localizing p30II has minimal homology to transcription factors Oct-1 and -2, Pit-1, and POU-M1 [18-21]. In addition, the protein co-localizes with p300 in the nucleus and physically interacts with CREB binding protein (CBP)/p300 and differentially modulates cAMP responsive element (CRE) and Tax response element-mediated transcription [21,22]. Intriguing recent reports also indicate a post-transcriptional role of HTLV-1 p30II and HTLV-2 p28II(homologous protein encoded in the HTLV-2 pX ORF II region), in repressing the export of tax/rex mRNA from the nucleus [23,24]. Thus, it appears that HTLV-1 has yet another multifunctional protein with transcriptional and post-transcriptional roles in regulating viral gene expression.
Microarrays are important tools to gain insight into changes in gene expression profiles of virus-infected cells. This approach has been primarily used to investigate gene expression in HTLV-1-immortalized/transformed cell lines or in cells from ATL patients [25-29]. In the report by Michael et al. [30] the authors used the Affymetrix U133A human gene chip to test the role of HTLV-1 p30II as a regulator of gene expression in Jurkat T cells. They identified alterations in gene expression profiles unique to cell cycle regulation, apoptosis, and T lymphocyte signaling/activation. Although p30II expression, as might be expected from earlier reports, resulted in a general repressive pattern of gene expression, their data indicated that the viral protein selectively spared or enhanced NFAT, NFκB, and AP-1 mediated transcription in T cells undergoing co-stimulation. Signaling pathways primarily affected by p30II as measured by luciferase reporters included both NFAT and NFκB, which increased from approximately 3 to 11 fold, depending on co-stimulatory treatment. Overall, this study supports earlier reports on the repressive role of HTLV-1 p30II in gene expression [21-24] and reveals new potential mechanisms by which p30II may play a role in HTLV-1 replication (figure 1). The effects of p30II appear to overlap or counteract the influence of other HTLV-1 regulatory proteins like Tax or other accessory proteins such as p12I. Further studies to test if these proteins act coordinately or synergistically will undoubtedly shed light on this issue. It is possible that HTLV-1 employs selective use of these viral proteins during various stages of the infection to promote cell proliferation, a hallmark of the diseases associated with the deltaretrovirus family. Whatever the outcome of these studies, it is clear that "accessory" proteins, like p30II, may have "essential" roles in the life cycle of HTLV-1.
Abbreviations
HTLV-1, human T cell lymphotropic virus type-1
ATL, adult T cell leukemia
HAM/TSP, HTLV associated myelopathy/tropical spastic paraparesis
ORF II, open reading frame II
LTR, long terminal repeat
CRE, cAMP responsive element
CREB, cAMP response element binding protein
NFAT, nuclear factor of activated T cells
NFκB, nuclear factor kappa B
AP-1, activator protein 1
Competing Interests
The author(s) declare that they have no competing interests.
Figure 1 Model for HTLV-1 p30II transcriptional and posttranscriptional gene regulation. The cell nucleus surrounded by the nuclear membrane and key components are shown. p30II can directly interact with CBP/p300 and modulate transcription of viral and/or cellular genes. At low concentration p30II may stabilize the transcription complex and potentiate transcription, whereas a high concentration it may compete for limited amounts of CBP/p300 and repress gene expression. p30II (as well as the homologous p28II of HTLV-2) specifically binds tax/rex mRNA and block its export, reducing Tax and Rex and ultimately repressing viral gene expression. This interaction may be directly linked to splicing factors and splicing and/or the juxtaposition of specific exon/exon junction sequences. Thus, p30II is a multifunctional protein with transcriptional and post-transcriptional roles in regulating viral and/or cellular gene expression.
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World J Surg OncolWorld Journal of Surgical Oncology1477-7819BioMed Central London 1477-7819-2-441558830110.1186/1477-7819-2-44ResearchSeroma formation after surgery for breast cancer Hashemi Esmat [email protected] Ahmad [email protected] Masoume [email protected] Mandana [email protected] Homeira [email protected] Ali [email protected] Iranian Center for Breast Cancer, Tehran, Iran2 Tehran University of Medical Sciences, Faculty of Medicine, Department of Surgery, Tehran, Iran2004 9 12 2004 2 44 44 8 9 2004 9 12 2004 Copyright © 2004 Hashemi et al; licensee BioMed Central Ltd.2004Hashemi et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Seroma formation is the most frequent postoperative complication after breast cancer surgery. We carried out a study to investigate the effect of various demographic, clinical and therapeutic variables on seroma formation.
Patients and methods
A retrospective cross sectional study of patients who underwent surgical therapy for breast cancer with either modified radical mastectomy (MRM) or breast preservation (BP) was carried out. The demographic data and clinical information were extracted from case records. Seroma formation was studied in relation to age, type of surgery, tumor size, nodal involvement, preoperative chemotherapy, surgical instrument (electrocautery or scalpel), use of pressure garment, and duration of drainage. The multiple logistic regression analysis was performed to estimate odds ratios.
Results
A total of 158 patients with breast cancer were studied. The mean age of the patients was 46.3 years (SD ± 11.9). Seventy-three percent underwent modified radical mastectomy and the remaining 27% received breast preservation surgery. Seroma occurred in 35% of patients. In multivariate logistic regression analysis an association of postoperative seroma formation was noted with modified radical mastectomy (OR = 2.83, 95% CI 1.01–7.90, P = 0.04). No other factor studied was found to significantly effect the seroma formation after breast cancer surgery.
Conclusion
The findings suggest that the type of surgery is a predicting factor for seroma formation in breast cancer patients.
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Background
Breast cancer is the second leading cause of cancer death among women. The surgical treatment of choice for these patients is either modified radical mastectomy or breast preservation depending upon stage of the disease. Seroma formation is the most frequent postoperative complication after breast cancer surgery. It occurs in most patients after mastectomy and is now increasingly being considered side effect of surgery rather than a complication however, all patients are not clinically symptomatic [1]. Seroma is defined as a serous fluid collection that develops under the skin flaps during mastectomy or in the axillary dead space after axillary dissection [2]. Incidence of seroma formation after breast surgery varies between 2.5% and 51% [3-5]. Although seroma is not life threatening, it can lead to significant morbidity (e.g. flap necrosis, wound dehiscence, predisposes to sepsis, prolonged recovery period, multiple physician visits) and may delay adjuvant therapy [6,7]. Fluid collection is ideally managed by repeated needle aspiration to seal the skin flaps against the chest wall. Several factors have been investigated as the cause of seroma formation these include age, duration of wound drainage, use of pressure garment, postoperative arm activity, preoperative chemotherapy, and use of electrocautery [3,8-12]. The present study was undertaken to identify risk-factors associated with seroma formation after breast cancer surgery.
Patients and methods
A cross sectional study of a consecutive sample of 158 patients attending the breast cancer clinic between January 2000 to October 2002 in Tehran, Iran, was carried out. All patients undergoing surgical therapy [modified radical mastectomy (MRM) or breast preservation (BP)] were included. Level II axillary lymph node dissection was performed for both groups. None of the patients underwent immediate reconstruction. The demographic data and clinical information were extracted from case records. Axillary seroma was defined as any clinically apparent fluid collection in the axilla or under the skin flaps and was treated with multiple needle aspirations. Seroma formation was studied in relation to age, type of surgery, tumor size, nodal involvement, preoperative chemotherapy, surgical instrument (electrocautery or scalpel), use of pressure garment, and duration of drainage. To analyze data univariate odds ratio (or relative risk) was calculated using Chi-square tests or regression analysis and this was followed by the multivariate logistic regression analysis to evaluate independent risk factors related to seroma formation. The variables of interest were selected in a single step (enter method), classification cut off was set at 0.5, probably of step for entry into the model was set at 0.05 and removal at 0.1, and the model was set to converge in maximum of 20 iterations. All variables under study were considered as independent predicting factors and seroma formation was considered as dependent variable for multivariate analysis. The study was approved by the institutional ethics committee.
Results
In all, 158 breast cancer patients were recruited into the study and 55 patients developed seroma, giving an overall incidence of 35% for seroma formation after breast surgery. The mean age of patients was 46.3 years (SD ± 11.9). One hundred and fifteen patients (73%) underwent MRM and BP was performed in 43 patients (27%). The axillary node involvement was significantly different between MRM and BP patients (χ2 = 4.52, df = 1, P = 0.03) indicating that those who underwent MRM had higher rate of positive axillary nodes compared to those who received BP (78% vs. 21% respectively). Thirty-one mastectomies were performed by scalpel dissection of the skin flap (20%) and 127 by cautery dissection (80%). Two closed suction drains were placed in all patients undergoing surgery. Sixty-six percent of patients (n = 104) were node positive and the remaining 34% (n = 54) were node negative. The patients' characteristics and univariate odds ratios are shown in Table 1.
Table 1 The characteristics of patients in seroma and no seroma groups and univariate odds ratio.
Seroma group (n = 55) n. (%) No seroma group (n = 103) n. (%) OR (95% CI)* p value**
Age (years) 0.22
<40 12 (21.8) 34 (33.0) 1.00 (ref.)
40–49 20 (36.4) 38 (36.9) 1.49 (0.63–3.49)
>50 23 (41.8) 31 (30.1) 2.10 (0.89–4.92)
Tumor size (cm) 0.64
<2 21 (38.2) 47 (45.6) 1.00 (ref.)
2–5 21 (38.2) 34 (33.0) 1.42 (0.67–3.01)
>5 13 (23.6) 22 (21.3) 1.26 (0.53–2.96)
Nodal involvement (n = 152) 0.31
No 14 (26.4) 34 (36.8) 1.00 (ref.)
Yes 39 (73.6) 65 (65.7) 1.45 (0.69–3.04)
Surgical procedure 0.03
Breast conservation 10 (18.2) 33 (32.0) 1.00 (ref.)
Modified radical mastectomy 45 (81.8) 70 (68.0) 2.12 (0.95–4.72)
Surgical instrument 0.06
Scalpel 8 (14.5) 23 (22.3) 1.00 (ref.)
Cautery 47 (85.5) 80 (77.7) 1.68 (0.70–4.07)
Neoadjuvant chemotherapy 0.22
No 46 (83.6) 93 (90.3) 1.00 (ref.)
Yes 9 (16.4) 10 (9.7) 1.82 (0.69–4.78)
Pressure garment 0.63
Yes 12 (21.8) 26 (25.2) 1.00 (ref.)
No 43 (78.2) 77 (74.8) 1.21 (0.55–2.63)
Axillary drainage time (days/n= 152) 0.64
>10 8 (15.1) 20 (20.2) 1.00 (ref.)
5–10 30 (56.6) 49 (49.5) 1.53 (0.59–3.90)
<5 15 (28.3) 30 (30.3) 1.25 (0.44–3.49)
* Odds ratios derived from univariate logistic regression analysis.
** P values derived from the Chi-squared test.
The results of multivariate logistic regression analysis indicated that only the surgical type was significantly associated with seroma formation (OR = 2.83, 95% CI 1.01–7.90, P = 0.04). Of patients with BP, 10 of 43 (23%) developed seroma, while those who underwent MRM 45 of 115 (39%) developed seroma. The seroma formation did not show any significant association with any other variables studied. The results of maultivariate analysis are shown in Table 2.
Table 2 Risk factors for seroma formation derived from the multivariate logistic regression analysis
β (SE) Wald P OR (95% CI)
Age (years)
<40 - - 1.00 (ref.)
40–49 0.68 (0.49) 1.92 0.16 1.99 (0.75–5.25)
>50 0.75 (0.48) 2.44 0.11 2.12 (0.82–5.44)
Tumor size (cm)
<2 - - 1.00 (ref.)
2–5 0.38 (0.44) 0.74 0.38 1.46 (0.61–3.49)
>5 0.08 (0.52) 0.03 0.88 1.09 (0.39–3.00)
Nodal involvement (n = 152)
No - - 1.00 (ref.)
Yes 0.35 (0.43) 0.66 0.41 1.42 (0.60–3.35)
Surgical procedure
Breast conservation - - 1.00 (ref.)
Modified radical mastectomy 1.04 (0.52) 3.97 0.04 2.83 (1.01–7.90)
Surgical instrument
Scalpel - - 1.00 (ref.)
Cautery 0.60 (0.51) 1.36 0.24 1.83 (0.66–5.07)
Neoadjuvant chemotherapy
No - - 1.00 (ref.)
Yes 0.33 (0.55) 0.37 0.54 1.40 (0.47–4.13)
Pressure garment
Yes - - 1.00 (ref.)
No 0.51 90.46) 1.28 0.26 1.67 (0.67–4.11)
Axillary drainage time (days/n= 152)
>10 - - 1.00 (ref.)
5–10 0.13 (0.59) 0.04 0.76 1.17 (0.41–3.32)
<5 0.16 (0.53) 0.09 0.82 1.14 (0.35–3.66)
Discussion
Breast cancer is the most common malignancy in women. Surgery is the mainstay of treatment. Modified radical mastectomy with or without reconstruction or breast preservation in addition to axillary lymph node dissection are common surgical procedures in breast cancer. Surgery of the axilla is associated with numerous complications, including infection, lymphedema of the ipsilateral upper extremity and collection of fluid in surgical site (seroma). Most common complication after breast cancer surgery is wound seroma. The exact etiology of seroma formation remains controversial. Several interventions have been reported with the aim of reducing seroma formation including the use of ultrasound scissors in performing lymphadenectomy [13], buttress suture [14], fibrin glue [15], fibrin sealant [16], bovine thrombin application [17], and altering surgical technique to close dead space [18]. However, it has been suggested that although the use of these interventions might reduce the risk of seroma formation, further studies are needed to verify the real impact on long-term morbidity of such techniques [19].
Several studies have been performed to investigate factors related to post-surgical seroma. These studies have observed that the early removal of drains might led to increased incidence of seroma [12], whereas others have shown that drains removal time had no influence on seroma formation [3]. The findings from our study also indicated that the length of time drains are left did not influence the seroma rate (Table 2). Similar observation was reported by a recent study where the use of drains did not prevent seroma formation. On the other hand it was associated with a longer postoperative hospital stay and more pain after surgery for breast cancer [16]. It has been suggested that the restriction of arm movements may also reduce the incidence of seroma formation [8]. This observation however was challenged by others who showed that there is no significant disadvantage in early arm motion [9]. Porter et al reported that the use of electrocautery to create skin flaps in mastectomy reduces blood lose but increased the rate of seroma formation [11]. In addition, an association of postoperative seroma formation with neoadjuvant chemotherapy was also noted [4]. Compression dressing to prevent seroma rate is a common method used by many surgeons. A study demonstrated that routine use of a pressure garment to reduce postoperative drainage after axillary lymph node dissection for breast cancer is not warranted [12]. However, we think that the use of pressure garment and prolonged limitation of arm activity not only reduces seroma formation but also may increase the incidence of seroma formation after removal of drain [12] and even might cause shoulder dysfunction [8].
In the present study no relationship was observed between the incidence of seroma and the patients' age, tumor size, and lymph node status. However, the study found that the MRM was associated with higher rate of postoperative seroma formation (OR = 2.83, P = 0.04). Similarly, Gonzalez et al, demonstrated that patients who underwent modified radical mastectomy had a greater incidence of seroma formation than patients who underwent breast preservation surgery [10]. They also showed that there was a direct correlation between age and the development of seroma [10]. A recent study by Lumachi et al indicated that the tumor size and total amount of drainage represented the principal factors of seroma formation following axillary dissection in patients underwent surgery for breast cancer [19].
The results of our study suggest that seroma formation after breast cancer surgery is independent of duration of drainage, compression dressing and other known prognostic factors in breast cancer patients except the type of surgery, i.e there is a 2.5 times higher risk of seroma formation in patients undergoing MRM compared to BP. The small sample size of present study is a limitation and hence the power of the study is low. A number of questions remain unanswered and more research is needed to answer these.
Conflict of interest
The authors declare that they have no competing interests.
Source of Funding
None
Contributors
EH designed the study, collected the data and wrote the first draft of the manuscript.
AK, MN, ME and HH all contributed to patient recruitment and the preparation of first draft of the manuscript.
AM contributed to the study design, data analysis, and edited the final version.
All authors read and approved of the manuscript.
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| 15588301 | PMC543447 | CC BY | 2021-01-04 16:38:38 | no | World J Surg Oncol. 2004 Dec 9; 2:44 | utf-8 | World J Surg Oncol | 2,004 | 10.1186/1477-7819-2-44 | oa_comm |
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Hum Resour HealthHuman Resources for Health1478-4491BioMed Central London 1478-4491-2-161558506010.1186/1478-4491-2-16ResearchA model for analysis, systemic planning and strategic synthesis for health science teaching in the Democratic Republic of the Congo: a vision for action Parent Florence [email protected] Gérard [email protected] Josué [email protected] Michèle [email protected] Yves [email protected]êque Alain [email protected] Danielle [email protected] Department of Epidemiology and Health Promotion, School of Public Health, Université Libre de Bruxelles (ULB), Brussels, Belgium2 CREFSS-c/o Ministry of Health, Kinshasa/Gombe, Democratic Republic of the Congo3 Centre de Pédagogie Universitaire, Université Catholique de Mons (FUCAM), Belgium2004 7 12 2004 2 16 16 22 7 2004 7 12 2004 Copyright © 2004 Parent et al; licensee BioMed Central Ltd.2004Parent et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The problem of training human resources in health is a real concern in public health in Central Africa. What can be changed in order to train more competent health professionals? This is of utmost importance in primary health care.
Methods
Taking into account the level of training of secondary-level nurses in the Democratic Republic of the Congo (DRC), a systemic approach, based on the PRECEDE PROCEED model of analysis, led to a better understanding of the educational determinants and of the factors favourable to a better match between training in health sciences and the expected competences of the health professionals. This article must be read on two complementary levels: one reading, focused on the methodological process, should allow our findings to be transferred to other problems (adaptation of a health promotion model to the educational sphere). The other reading, revolving around the specific theme and results, should provide a frame of reference and specific avenues for action to improve human resources in the health field (using the results of its application in health science teaching in the DRC).
Results
The results show that it is important to start this training with a global and integrated approach shared by all the actors. The strategies of action entail the need for an approach taking into account all the aspects, i.e. sociological, educational, medical and public health.
Conclusions
The analysis of the results shows that one cannot bring any change without integrated strategies of action and a multidisciplinary approach that includes all the complex determinants of health behaviour, and to do it within the organization of local structures and institutions in the ministry of health in the DRC.
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Background
A partnership of the Ministry of Health of the Democratic Republic of the Congo (DRC) – more specifically, the directorate that is in charge of health science education – the French-speaking community of Belgium and various education and training associations made it possible to set up and carry out a teaching innovation project to bolster human resources in the health sector. One of the major public health challenges in Africa is to find efficient ways to enhance human resources in the health sector.
The goal of the medical policy in the DRC is to promote the health of the population by providing high-quality health care that is complete and integrated and in which the community participates, within the general context of the fight against poverty [1]. With this intention, the Ministry of Health defined six strategic axes to support the reinforcement of primary health care:
• restructuring the health system according to political, legislative and administrative orientations as well as updating standards of services;
• increasing the availability of resources by implementing an adequate administrative process;
• establishing an integrated system of preventive and curative care and health promotion for the target groups;
• strengthening the programmes of support to health activities;
• coordinating, promoting intrasectoral and intersectoral collaboration and partnership for health;
• promoting a suitable environmental framework for health.
The Ministry attaches priority importance to delivering high-quality care and health services and by: reaffirming the strategy of primary health care (PHC) as a fundamental option of the national policy on health; reaffirming the health zones' or districts' achieving a minimum package of activities as an operational unit; and regular procurement of essential drugs, including biological products and other laboratory reagents.
Training of nurses in the DRC is organized in all provinces of the country and conducted through technical medical institutes (ITM), medical educational institutes (IEM) for the secondary level and higher institutes of medical technology (ISTM) for the higher level. In 1998 there were 308 ITM and ISTM in the country; to date there are 254 schools of nurses (male and female) at the secondary level in the country recognized by the DRC government.
According to the type of management, these institutions are classified as public, officially agreed and private schools. However, the autonomy of management for all these schools is extensive, given the near inexistence of government subsidy. The infrastructure and quality of training differ widely from one ITM to another.
As a rule, the solutions that one observes in public health are located in the area of further training for health professionals. To the extent that further training is important, it is disappointing to ascertain the low yields that these various training courses have in improving the quality of health care and services [2]. There is a lack of prior analysis of the main training needs when it comes to developing abilities and independence, a lack of these training programmes' integration into existing structures, and a lack of consistency between these schemes and a complex environment composed of many interacting players. Moreover, it is difficult, for many different reasons, to escape from the many vertical programmes (more than 40 in the DRC) that impose their own training modules on target audiences that have been set beforehand on their level.
When it comes to basic training, the young nurses who have just graduated from secondary school and make up the critical mass of health professionals in primary health care are commonly required by specific private or church employers to enrol in a full year of training after their academic studies in order to try to fill the gaps between their basic training and health professionals' actual training needs. Confusion on the part of the ministries and other forces involved is attested to by the absence of vision and lack of expertise in educational research and the lack of reforms of the educational curricula in order to keep pace with the fundamental changes that have resulted from the decentralization of primary health care.
In addition, several other problems make this succinct analysis more complex. The unemployment rate for the country's nursing school graduates is extremely high, even for those who graduate from the best schools in Kinshasa, the capital. The situation is compounded by the almost total absence of quality management mechanisms, especially when it comes to taking stock of the health workforce that exists. The situation is a complex one in which the expected changes are not clearly discerned.
The problem of health sector human resources is vast. The context that interests us in this article is that of the human resources who are in the front line when it comes to grappling with the various communities' health problems. These are the nurses who have completed (technical) secondary school courses. They are the main primary health care professionals in Central Africa, especially in the DRC [3]. This choice limits the initial problem to a specific target population. However, the approach that we envisioned can be transferred quite easily to the other sectors concerned by health manpower management (mainly registered nurses, doctors, laboratory technicians and other health professionals).
The question of research is at three levels: on the theoretical level, this article proposes importing a theoretical model from one field to another; on the methodological level, it uses the action research-like mode of data collection to better establish results; on the empirical level, in the DRC research is unusual ground from which to introduce an innovation.
This qualitative research pursues two objectives: to present a methodology (adaptation of a health promotion model to the educational sphere) and to study the results of its application (health science teaching in the DRC). The qualitative hypothesis that subtends this thinking is that using an analytical, systemic planning and strategic synthesis model based on a systemic and participatory approach on various strategic and operational levels will procure the necessary vision for changing the basic education and training of nurses in (technical) secondary schools in the DRC.
There is a lack of literature on experiments and experiences that use analytical or planning methods to understand complex social realities and consequently the adoption of strategic plans of action that should result from such experiences. It is thus important for all the players in the process to use the outcomes of the various stages of analysis and planning to produce an appropriate and adapted logical framework. It is necessary, however, to be able to set down on paper the methodology that is used to be able to construct a model that by the end of the process appears obvious for the actors involved, i.e. teachers, internship supervisors and school management, personnel within the ministry's Directorate for Health Education and project officers.
Methodology
Generally speaking, three relatively distinct stages were necessary, as follows:
1st methodological stage: set the strategic, managerial and operational levels
In the DRC, the Ministry of Health and more specifically its directorate in charge of health science education, is responsible for the basic training of secondary school nursing students and further training of health professionals (Figure 1).
Figure 1 Organizational structure of the Ministry of Health of the Democratic Republic of the Congo
The situation sometimes varies in neighbouring contexts. Thus in Rwanda, for example, the Ministry of Education is in charge of basic education and training in the technical schools and for the medical professions. In France and Belgium, the general choice was to have the Ministry of Education responsible for most of the training and education of health professionals.
We shall not discuss in this article the relevance of the place of oversight for this type of brief for training health professionals. We shall limit our remarks to the need to choose the best place for systemic planning and strategic vision in the existing context. So it is that in the 6th Directorate of the Ministry of Health of the DRC, which is in charge of paramedical secondary education, the need was perceived to develop systemic planning tools that would give a comprehensive, consistent vision of the sector's needs. While the 6th Directorate is indeed the strategic level, there was early involvement of an operational level, in the form of a sample of schools and teachers, and creation of a management unit to guide the implementation of the plans by the 6th Directorate and teachers from the grassroots.
It is important to remember that we are talking about systemic and operational planning, not just strategic planning [4]. For the strategic level, it is thus necessary to determine the organizational level that is close enough to operations on the ground, yet at the same time is independent enough to take specific, actual strategic directions.
2nd methodological stage: Choosing an analytical, planning and strategic synthesis model that fosters a systemic vision
This article follows on from the given that organizations and human beings are complex, and one way to have public health actions that heed this complexity is to use a systemic approach to analyse them [5].
Various models for a systemic approach exist. The approach that we chose to develop a logical framework for analysing, planning, and synthesising the work of the ministry's directorate in charge of health science education in the DRC is Green and Kreuter's PRECEDE PROCEED model [6,7]. The PRECEDE model emanates from a conceptual synthesis of the founding elements and roots of what would become health promotion.
The PRECEDE PROCEED planning model is welcome because of its multidisciplinary approach, based on the fields of epidemiology, social sciences, behavioural sciences, education and health administration. In a nutshell, the fundamental principles that gave rise to this approach come from the multifactoral nature of all problems. Once this has been posited, all efforts made to act upon behaviour, the environment and social factors must necessarily be multidimensional and multisectoral.
The acronym PRECEDE means "Predisposing, Reinforcing and Enabling Constructs in Educational/Environment Diagnosis and Evaluation", while the acronym PROCEED means "Policy, Regulatory and Organizational Constructs in Educational and Environmental Development".
The PRECEDE-PROCEED model emphasizes planning interventions by focusing on the expected outcomes of actions based on epidemiological, social, behavioural, environmental, educational, organizational, administrative and political diagnoses of a socio-health and/or educational situation. The stages in the construction of a systemic model for analysing the problem that interests us – health science teaching – are adapted as the process unfolds. One of this method's great potentials is its great flexibility, or its ability to adapt to the specific analyses' needs.
A systemic analysis and planning model is built dynamically, in a process that calls for continuous assessment. The model that the ministry's 6th Directorate came up with, and that is presented below, must change with changing knowledge in the area.
3rd methodological stage: allowing a participatory approach to using the model
It is important to stress the qualitative process of continual exchanges and constant observations among the players (teachers, internship supervisors, school management, ministry officers and donors) that made it possible to fill the gaps in the information-gathering process. All these elements are much more difficult to organize in one well-defined stage, but are indeed part of a process that stresses the participatory approach and comes under the third strand of the methodology being discussed. The development of the first model proceeded during the workshop held in Kinshasa at the starting of the project, with the participation of personnel from three pilot schools and the Ministry of Health in October 2002. The three-day workshop, with 40 participants from various institutional levels, permitted the establishment in December 2003 of strategic orientations and guidelines for the continued broadening of the programme.
All PRECEDE PROCEED models are built upon the players' actual experiences of the problem to solve. The clarification of the problem itself, which is part of the epidemiological and social diagnosis, comes out of a debate that must be conducted with all the parties concerned. This problem will gradually become more and more clear as its statement shuttles back and forth among the parties until it eventually satisfies the strategic and institutional level that is in charge of the programme and that the problem concerns directly. If, thanks to a resolutely participatory approach, all of the players adopt the use of a systemic approach on a real strategic, managerial and operational level, it will become a solid tool for the entire teaching body concerned.
Results and discussion
The presentation of results is at two levels: PRECEDE results and PROCEED results. The first are mainly descriptive (to tell and analyse the facts). PROCEED results are more normative, leading to certain recommendations for practitioners and other actors.
PRECEDE results
To facilitate presentation of the results and understanding of this coherent, overall vision of the interrelated elements, it was considered pertinent to retranscribe the full model as it exists for the 6th Directorate of the DRC's Ministry of Health.
To structure the results' presentation, we shall follow the order in which the model's construction progressed. The table must be read from right to left, starting with the epidemiological and social diagnosis, then going on to the behavioural diagnosis and from there to the analysis of the educational and environmental determinants of these behaviours, and then to end with the analysis of the institutional diagnosis (see Additional file 1).
Epidemiological and social diagnosis
In the Ministry of Health, all the players are concerned by the mortality and morbidity indicators in the country. For health professionals, the lack of quality of the service provision and care provided by their health system is an obvious cause of the people's lack of confidence in their health system [8]. However, to produce a verifiable systemic analysis and then effective strategic synthesis within the directorate that interests us, the problem of the directorate in charge of health science education must first be clarified in connection with this broader problem. Thus the mismatch between health science teaching and the competence that health professionals are expected to have was seen as connected to the lack of quality in health care and services. All the players on their various organizational levels – ministry staff, teachers, basic supervisors, donors and project officers – took this diagnosis on board as a major concern.
Behavioural diagnosis
Who are these players and what behaviour can explain, through a direct link, the diagnosis of inadequacy? In answering these questions with the players themselves, we discover that there are groups of players that are never clearly identified yet are clearly related to this problem of inadequacy. This is the case, for example, of the donors and nongovernmental organizations (NGOs).
Revealing all the groups of players makes it possible to see more easily why importance should be given to a multisectoral approach, especially one that covers teachers and medical and paramedical professionals. If the population is considered a group of players that is separate from the problem at hand, it will not be possible to take it into account in setting up action strategies, to the extent that the aim of such work is to better define people's expectations in terms of the quality of care and arrive at a better understanding of their behaviour.
During the discussions, the teachers felt that priority had to be given to separating the group of teachers from that of intern supervisors in order to better highlight the particularities and role of the field training. The school managements revealed their specific role in this problem of mismatches. Indeed, the teachers' and supervisors' behaviour is strongly linked to their own behaviour in dealing with changes [9]. We have presented one or the other behaviour for each of these groups of players as examples only.
Environmental diagnosis
This diagnosis allows for the factors that are linked to the environment and are direct causes of the epidemiological and social diagnosis. In a context such as that of the DRC, geopolitical and socioeconomic factors head the list, along with the health structures' inaccessibility. To take a more constructive approach without denying reality, it is necessary to focus the analysis of this diagnosis on the more targeted problem of the inadequacies in the training sector. This reveals variables that are more controllable for the levels that are concerned and that everyone agrees are connected to the problem. These are: the learning environment, teaching environment, class hours that facilitate or hamper certain types of learning, etc.
Educational and motivational diagnosis
The educational diagnosis enables one to home in on the educational and motivational determinants, which must not be overlooked when one goes on to an interventional phase. To the extent that the systemic approach gives significance to each group of players (teachers, learners and others) as well, as is the case in the DRC, it is fully possible to set up a frame of analysis, assessment and action-research that presents the variables and determinants in a PRECEDE model that are specific to each specific group (action-research framework). This is what was done in the DRC to follow the changes in teaching practices, in conjunction with each intervention that was identified, that were made in the specific group of teachers. The results show that it is relatively easy to separate the educational determinants from each other in order to facilitate subsequent reflection about the strategic action to take.
The predisposing factors that concern knowledge, experience, attitudes, perceptions and representations have a key place in relation to the behaviours of the players of interest to the Directorate for Education. This construction shows clearly that the training given is usually concerned with knowledge only and generally does not make use of the learners' life experiences.
The other important result is to be able to visualize the place of representations in a conceptual framework that will likewise be used for the action. For example, there are the various representations of learning theories when it comes to teaching methods or unfounded beliefs about the quality of care. Specific models exist that enable one to delve much deeper into perceptions and beliefs [10,11]. These are complementary research models. When we are seeking to develop a tool that can be used to construct an operational model for strategic choices of action to take on a high institutional level, the possibility of providing this place for representations and beliefs is already vital and elucidating.
The enabling factors in terms of actual competences (skilfulness, know-how and behaviour) are too often disregarded and underestimated in interventions. Incorporating them in this model thus enables the directorate in charge of this branch of education to check to what extent the projects, programmes and other support measures consider this priority strand in terms of development independence.
The reinforcing factors, which are sometimes also referred to as facilitating factors, are the determinants that act upon the positive feedback loops. The importance that all the players give to this type of variable in constructing the model confirmed the need that the directorate had already felt to find means to set up long-range monitoring mechanisms for the various activities engendered by the programme or by some more specific projects.
The model contains a certain number of variables. It is clear that it can be enriched in the course of the process through its use and the players' better discovery and gradual appropriation of its features.
Institutional diagnosis
In terms of results, the institutional diagnosis requires analysing the situation at the organizational level that corresponds to the level of the model's application. This is a national health science education programme under the Ministry of Health. As such, the institutional diagnosis stresses essential strategic variables if one wants to work on a well-knit, comprehensive set of changes. It thus entails the need to analyse the institutional standards when it comes to inspections and assessment, but also those governing health system management and health sector human resource quality management (for example, the existence or lack of a Nursing Board). This is also the level on which we shall discuss how the programme dovetails with other variables and determinants.
PROCEED results
Before strategic thinking can be put into place based on this situational analysis, it is possible to go on to a more dynamic reading of the relations between determinants and variables. So it is that the DRC's Ministry of Health directorate in charge of health science education foresees a certain number of strategic axes for action. The aim of the action is the problem's translation into an objective form. The directorate thus considered its main goal to be to improve the match between what is learnt in schools and health professionals' needs and the population's expectations.
To better understand how the reading of the conceptual model brought us to the action strategies, it is useful to stress an intermediate step that is summarized in Figure 2.
Figure 2 Visual summary of considered actors
A natural adaptation of the PRECEDE model was to define the groups of players by their behaviour. In this way, we obtained a better picture of the division of responsibilities to achieve a common goal and evidence of the need for interdisciplinary work [12]. A comprehensive reading of the PRECEDE model points us towards a strategic choice that integrates an institutional and educational approach from education with an epidemiological and social approach from health and welfare. The players in their respective environments are located between the two. This model shows the need to find a common thread between education and health needs that allows for the place and role of each group of players in their context.
The results bring to the fore a number of behaviours that attest to a lack of independence, absenteeism, lack of collaboration, failure to connect theory and practice, a lack of communication, ignorance of the teacher's role, etc., depending on the group. Examination of these results prompts us to stress the importance that must be given to the learning environment and, when it comes to action strategies, the importance to give to a learning environment that is in tune with the strategic axes that are selected, in this part just as in the other parts of the situation's analysis.
Given this finding and the need to link the educational and institutional diagnosis with the health problem (seen as an appropriate form for education), one proposed strategic hypothesis is to favour learning techniques that are based on active teaching methods [13].
In going consistently through the various diagnoses and organizational levels, this choice led the education directorate to think about changing its programmes and standards so as to base them on novel teaching concepts such as skills-based learning [14] and setting a skills reference framework on the basis of in-depth research done with the entire set of clearly identified target populations.
The reading of these results in terms of strategic action reveals the need to bolster the analytical and planning process that already exists within the directorate to pay more attention to the educational and environmental determinants for all the target populations concerned. To our mind, the success of the expected changes in terms of narrowing the gap between the "supply" and the "demand" hinges on this.
The following diagram summarizes the strategic axes that the Directorate for Health Science Education chose to achieve this objective on the basis of the PRECEDE analysis (Figure 3).
Figure 3 Strategic axes for action
This figure reveals four axes to be reinforced:
• to reinforce communication and coordination in conjunction with the other reinforcing factors: the pilot schools' teaching method committees, teaching monitoring and feedback, the setting-up of networks, etc.;
• to develop methods to enhance the learner's autonomy: active teaching, constructivist approach, interdisciplinary, critical spirit, etc.;
• to foster a learning environment that enables the learners to acquire knowledge: library, teaching materials, computer learning, computerized documentation centre, etc.;
• to provide institutional and structural support: standards and curricula in tune with teaching and organizational innovations and skills targets that fit health professionals' needs and meet the community's expectations.
The discussion will take place on two levels – the operational and the conceptual. On the operational level, we feel it is interesting not to dwell on the presentation of the model as it could have been applied, but on its actual application. The results are presented so as to allow the reader to understand how to organize the problems that are felt to exist in health science education in the DRC.
Even if the Directorate for Health Science Education is well aware of its problems, the systemic modelling of the interconnected variables and populations seems to give it a conceptual and operational tool that is useful on various levels, as follows:
• tool for dynamic analysis of the situation with regular updates;
• tool for systemic planning that also enables the directorate to put forward arguments in dealing with donors and NGOs in the sector;
• assessment tool that gives more importance to assessment criteria such as cohesiveness, consistency, relevance, appropriation, and comprehensiveness, i.e., process criteria;
• research and evaluation tool that can also promote a more quantitative approach to analysing the relations between variables and various diagnoses or within the same diagnosis;
• a dialogue-enhancing tool, for it gives the groups of players involved a vision of the planned change and a common objective.
To sum up, this is a tool that provides a certain guarantee that the strategy development process is informed, meets the needs and is complete [15].
The list of these advantages is obviously based on some baseline conditions: a participatory process in which the model is developed and operates and the appropriation of the concepts that subtend the model [16]. Even though it was more difficult to describe how the intervention strategies are set, based on the construction of this model, we should like to stress that a complete analysis of the situation that is based on this systemic approach usually reveals the relevant strategic axes on its own and despite the protagonists' limited ability to synthesize the situation.
On the conceptual level, the discussion will revolve around Figure 4.
Figure 4 Multi-field vision of the change
We observed through the PRECEDE analysis and then the PROCEED strategic reflection phase that many disciplines converged in order to lead us to this hypothesis and a common objective of needs-matching. Indeed, when action is carried out it will be a matter of achieving a gradual advance that occurs along the (horizontal and vertical) strategic axes defined earlier in this article. Moreover, we are confronted with strategic choices that involve at least three dimensions: a public health approach, an education approach and a sociological approach.
These three dimensions are part of the data collection process's success, as well as the success of the strategic choices that follow. This reinforces the fact that the PRECEDE PROCEED model comes from the development of an approach aimed at meeting the need for education and health promotion tools and methods. So it is that we see numerous applications of this process in technical health education establishments that spring from a true systemic analysis of the problems with full mastery of a structuring capacity, unlike some other models such as causal analysis (17).
Similarly, we can consider that defining a problem at an institutional and organizational level also requires the identification and involvement of all the parties concerned. We can also consider that the tools that help to understand the relations between elements and insist on a better search for behavioural determinants are prerequisites for organizational learning that has groups of players interact with each other. This is all the more true if the change that is ultimately expected (a match between two sides of the equation) is contingent on changes in the players' behaviour and practices, as is the case of health education.
In terms of limits of this research, it targets the analysis of an inadequacy within human resources' management in health, which is that of training of nurses from professional technical levels. Other levels of inadequacies are worthy to be analysed in a complementary way relating to other health professionals, the sectors of health and education planning. The Green model is complementary to the use of methodological dynamic references much as the management of the project cycle focuses on managing interventions or projects whose aim is to contribute to changing a situation from unsatisfactory to satisfactory. Its use within the framework of the project could obtain more means while enabling developments relating to action research. In this context, the contribution from other disciplines, such as psychology, could be reinforced.
Conclusions
With regard to the three levels of starting research – the theoretical, methodological and empirical – PRECEDE PROCEED analysis is a model that can be applied to varied situations and problems, although it must be used participatively and proactively in order to enhance its utility in specific circumstances as a personal transfer tool. On the empirical level, the will of all actors – and the Ministry of Health in particular – to have a clear vision of the projected change and manner of reaching that point, while integrating the complexity, was the element carrying the process. We advance the hypothesis that L. Green's systemic approach may become one of a set of active methods, such as problem-based learning, cooperative learning, or even project-based learning, to transfer to learners in nursing schools and sections in the DRC. Indeed, the ability to analyse and synthesize, but also to carry out education and health promotion actions, is essential.
List of abbreviations
DRC: Democratic Republic of Congo
PRECEDE: Predisposing, Reinforcing and Enabling Constructs in Educational/Environment Diagnosis and Evaluation
PROCEED: Policy, Regulatory and Organizational Constructs in Educational and Environmental Development
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
FP is responsible for this research. She initiated the project in DRC. She is a specialist in public health and pedagogy. She participated in the design and coordination of the study and drafted the manuscript.
JK and DB are two teachers in charge of the reform of the nursing programme in DRC. They set up the collection of data for this study and finalized the analysis.
YC and AL participated in the design of the study and the adaptation of de Green's model in the field of nursing training. YC participated in writing the manuscript.
MG specializes in management and pedagogy. She conceived the study with FP and participated in its design. DP took part in the elaboration of the methodology. She brought an expertise in health promotion and wrote part of the manuscript.
All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Table 1. PRECEDE model for health science teaching in the DRC oversized table
Click here for file
Acknowledgements
This research was conducted under the aegis of PROJECT CK 01/01: "Renforcement de la formation de base pour infirmièr(e)s des Instituts Techniques Médicaux (ITM) à Kinshasa en République Démocratique du Congo (RDC)", financed by the overseas education and training association Association pour la Promotion de l'Éducation et de la Formation à l'Étranger (APEFE). We thank APEFE (its director, Jean Gillet, and staff) for having given us the opportunity to initiate the project and carry through its action-research strands. We also thank all of the teachers of the medical technical institutes of Kinshasa who participated in the development of this research framework.
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| 15585060 | PMC543448 | CC BY | 2021-01-04 16:37:43 | no | Hum Resour Health. 2004 Dec 7; 2:16 | utf-8 | Hum Resour Health | 2,004 | 10.1186/1478-4491-2-16 | oa_comm |
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Respir ResRespiratory Research1465-99211465-993XBioMed Central 1465-9921-5-281558831910.1186/1465-9921-5-28ResearchCoccidioides posadasii infection alters the expression of pulmonary surfactant proteins (SP)-A and SP-D Awasthi Shanjana [email protected] D Mitchell [email protected] Jacqueline J [email protected] Department of Pathology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA2 Department of Microbiology and Immunology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA3 Center for Biomedical Inventions, University of Texas Southwestern Medical Center, Dallas, TX, USA2004 10 12 2004 5 1 28 28 19 8 2004 10 12 2004 Copyright © 2004 Awasthi et al; licensee BioMed Central Ltd.2004Awasthi et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Coccidioidomycosis or Valley Fever is caused by Coccidioides in Southwest US and Central America. Primary pulmonary infection is initiated by inhalation of air-borne arthroconidia. Since, lung is the first organ that encounters arthroconidia, different components of the pulmonary innate immune system may be involved in the regulation of host defense. Pulmonary surfactant proteins (SP)-A and SP-D have been recognized to play an important role in binding and phagocytosis of various microorganisms, but their roles in Coccidioides infection are not known.
Methods
In this study, we studied the changes in amounts of pulmonary SP-A, SP-D and phospholipid in murine model of Coccidioides posadasii infection, and binding of SP-A and SP-D to Coccidioidal antigens. Mice were challenged intranasally with a lethal dose of C. posadasii (n = 30 arthroconidia) and bronchoalveolar lavage fluid (BALF) samples were collected on day 10, post infection. In another group of animals, mice were immunized with protective formalin killed spherule (FKS) vaccine prior to infection. The concentrations of BALF SP-A, SP-D, total phospholipid were measured using enzyme linked immunosorbent assay and biochemical assays.
Results
We found that in lavage fluid samples of C. posadasii infected mice, the concentrations of total phospholipid, SP-A and SP-D were 17 % (SEM 3.5, p < 0.001), 38 % (SEM 5.8, p < 0.001) and 4 % (SEM 1.3, p < 0.001) of those in lavage fluid samples of non-infected control mice, respectively. However, the concentrations of SP-A and SP-D remained unchanged in BALF samples of C. posadasii protected mice after immunization with FKS vaccine. Also, we found that both SP-A and SP-D bind to Coccidiodal antigens.
Conclusion
Our results suggest that the C. posadasii infection perturbs the pulmonary SP-A, SP-D, and phospholipids, potentially enabling the disease progression and promoting fungal dissemination.
Surfactant proteinsCoccidioides posadasii
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Background
Coccidioidomycosis or Valley Fever is a fungal disease caused by the biphasic, highly virulent, soil-fungus Coccidioides immitis or posadasii [1]. It is endemic in the southwest regions of US, Northern Mexico and parts of Central America [2]. C. posadasii or C. immitis, are the most virulent fungal pathogens enlisted in Select Agent list and pose a risk for bioterrorism [3]. The primary infection is acquired by inhalation of air-borne, mycelial phase arthroconidium that converts into endosporulating spherule in the lung. Clinical manifestations of the disease range from pulmonary infection to a more severe fatal mycosis involving extra-pulmonary tissues in 1–10% of the infected people [1-4]. Previous studies suggest that Th1 cell mediated immunity protects individuals against Coccidioides [5,6]. However, information is lacking regarding the pulmonary innate immune components that may play a critical role in regulation of immune responses against Coccidioides.
At alveolar level in the lung, the innate immune system is composed of many cell types and chemical mediators, including surfactant. The pulmonary surfactant is a complex mixture of lipids (88–90%) and proteins (10–12%), synthesized by type II epithelial cells and Clara cells. It lines the alveoli, and helps in maintaining normal lung function [7]. Among four different surfactant proteins, surfactant proteins-A (SP-A) and D (SP-D) are members of the "Collectin" family [8]. In the past, several studies have suggested that both SP-A and SP-D play an important role in innate host defense against various viral, fungal and bacterial pathogens [9,10]. More evidence for the pulmonary collectins' role in host defense comes from studies on SP-A- deficient mice that are susceptible to intra-tracheal Group B Streptococci [11], Pseudomonas aeruginosa [12], and Respiratory Syncytial Virus [13]. Also, intranasally administered SP-D has been found to reduce replication of Respiratory Syncytial Virus in the lungs of infected mice [14]. Both SP-A and SP-D, have been classified as secretory pattern-recognition receptors that can bind to a variety of pathogens and help in clearance [9,15]. Recent evidences indicate that in addition to their pathogen recognition property, SP-A and SP-D also play an important role in stimulating immuno-regulatory pathways [15]. However, the collectins' role in coccidioidomycosis is not known.
This study focuses on analyzing the changes in amounts of the SP-A and SP-D in the bronchoalveolar lavage fluid (BALF) samples from mice infected with lethal dose of C. posadasii and C. posadasii protected mice after immunization with protective formalin killed spherule (FKS) vaccine, and binding of pulmonary collectins to Coccidioidal antigens.
Methods
Mice
BALB/c and C57BL6 mice (6 weeks old female) from Jackson Laboratory (Bar Harbour, ME) were used in this study. Both mouse strains are susceptible to C. posadasii infection. The BALB/c mice were used to study the changes in pulmonary surfactant after intranasal challenge with C. posadasii. And, the C57BL6 mice were used to study the changes in pulmonary surfactant after vaccination with protective FKS vaccine. Mice were housed in Biosafety Level-3 animal facility at UTHSCSA and provided with food and water ad libitum. All experimental animal care and treatment protocols were reviewed and approved by Institutional Animal Care and Use Committee.
Coccidioides posadasii
C. immitis (now posadasii) Silveira strain, cultured on 1 % glucose-0.5 % yeast extract agar (GYE), was used for infecting the mice [1]. The arthroconidia were harvested in endotoxin-free 0.15 M saline (Baxter Health Care Products, Deerfield, IL) from 6–8 weeks old mycelial phase cultures grown on GYE plates. The arthroconidia suspension was passed over a sterile cotton column to remove hyphal elements and arthroconidia were enumerated by hemacytometer counts. The viable cfu counts were confirmed pre- and post-infection by plate cultures on GYE agar. All the experiments with C. posadasii were carried out in Biosafety Level-3 facility at UTHSCSA.
Intranasal Challenge with C. posadasii Arthroconidia
Mice were anaesthetized after intramuscular injection of ketamine- xylazine (75 μg/g body weight ketamine and 10 μg/g body weight xylazine) and were then challenged intranasally with a lethal dose of arthroconidia (n = 30, fresh harvest of C. posadasii arthroconidia) suspended in endotoxin-free 0.15 M NaCl (Baxter Health Care Corp, Deerfield, IL) using sterile pyrogen-free microtip. Mice were held in an upright position for 1–2 min to resume normal breathing after injection. Control mice were challenged with equal volume of endotoxin-free 0.15 M NaCl.
Preparation of Coccidioide-FKS vaccine and Immunization of Mice
C. posadasii (strain Silveira)arthroconidia were used to prepare FKS as described earlier [16]. Briefly, arthroconidia were inoculated in modified Converse medium containing Tamol and cultured while shaking at 180 rpm at 40°C in 20 % CO2 incubator. The spherules were collected from the harvested culture, washed in endotoxin-free water and killed with 1 % formalin. FKS preparation was checked for sterility and lyophilized. C57BL6 female mice (age 6 weeks old) were immunized intramuscularly twice and subcutaneously once at one week interval with FKS (0.7 mg/dose each time). The mice in FKS immunized, infected group were then challenged intranasally with 30 C. posadasii arthroconidia, 15 days after last immunization, as described above.
Fungal Burden Assay
Mice were anaesthetized as mentioned above, prior to sacrifice on day 10, post intranasal infection. This standard procedure was used for intranasal injection since it does not cause respiratory depression during anaesthesia. The lung and spleen tissues were collected in sterile 0.15 M NaCl for studying fungal load.
The fungal burden was studied by plating ten fold dilutions of lung and spleen homogenates in 0.15 M saline on Mycosel agar plates (BD Biosciences, Franklin Lakes, NJ) and incubating for 72 h at 30°C. The cfu counts were recorded and normalized with organ weight.
Collection and Processing of BALF
At the time of necropsy, we collected BALF by injecting 1 ml endotoxin-free 0.15 M NaCl solution (Baxter Health Care Corp, Deerfield, IL) three times, via an angiocatheter (BD Biosciences, San Diego, CA) placed in the trachea. The volume of the input solution was kept constant (3 ml total) and approximately, 90–95 % of the solution was recovered consistently. The BALF was centrifuged at 500 rpm for 10 min at 4°C to remove cells. The cell free BALF supernatant was filtered through 0.2 μm syringe filters (Nalge Nunc International, Rochester, NY) and stored at -80°C for further analysis.
Total Protein and Lipid Analysis
The total protein concentration was measured in BALF specimens using micro bicinchonic acid protein assay kit (Pierce, Rockford, IL) against bovine serum albumin (BSA) standard protein. The total phospholipid content in lipid extracts of BALF specimens was determined using the method of Stewart, against Dipalmitoyl-phosphatidylcholine (DPPC, Avanti Polar Lipids, Alabaster, AL) standard solutions [17,18]. Briefly, the lipid extract of BALF specimens and DPPC standard solutions was completely dried under compressed nitrogen gas. The dried lipids were dissolved in chloroform and mixed with 1 ml of 2.7% ferric chloride and 3% ammonium thiocyanate in glass tubes. The mixture was vortex mixed for 1 min and centrifuged at 200 rpm for 5 min. The bottom red lower layer of phospholipids and ammonium ferro-thiocyanate complex was collected and absorbance was read at 488 nm.
Western Blotting
The BALF and lung tissue homogenate samples (total protein 10–50 μg) were run on 10% sodium dodecyl sulfate- polyacrylamide gel electrophoresis (SDS-PAGE) running gel and transferred on nitrocellulose membranes (Schleicher & Schuell, Keene, NH) overnight at 15 mA current. The nonspecific sites on the membrane with transferred proteins were blocked by 15% nonfat milk in Tris-buffered saline containing 0.05% tween 20 (TBST). The membrane was washed and incubated for 1 h with diluted (1:500) primary anti-human SP-A polyclonal antibody raised in rabbit (obtained from Dr. Richard J. King, UTHSCSA, San Antonio, TX) or anti-mouse SP-D antibody (kindly provided by Dr. Jo Rae Wright, Duke University Medical Center, Durham, NC). After washing the membrane with TBST, the membrane was incubated for 1 h with 1:10,000 diluted alkaline phosphatase conjugated anti-rabbit IgG raised in goat (Sigma Chemical Co, St. Louis, MO). The immunoreactive bands were observed by alkaline phosphatase conjugate system (Biorad, Hercules, CA). Purified human SP-A (kindly provided by Dr. Richard J. King, UTHSCSA, San Antonio, TX) and recombinant human SP-D (kindly provided by Dr. Erika C. Crouch, Washington University in St. Louis, St. Louis, MO) were run with the samples.
The Coccidioidal antigens: lysates and filtrates of Coccidioidin (CDN), prepared as a toluene-induced lysate of young C. posadasii mycelia (obtained from Dr. Rebecca A. Cox, UTHSCSA, San Antonio, TX, [19]) were also run to check the cross-reactivities of anti-SP-A and SP-D antibodies to fungal antigens.
Enzyme-Linked Immunosorbent Assay (ELISA) for SP-A and SP-D
The concentrations of SP-A and SP-D were measured in BALF samples as described earlier [20]. The antibodies against SP-A and SP-D reacted with 34 kDa (SP-A) and 43 kDa (SP-D) immunroreactive bands in BALF and lung tissue homogenates (Fig 1). For measuring the lavage concentrations of SP-A and SP-D, the indirect ELISA procedure was used [20]. Briefly, the wells of Immulon 4 strips (Dynatech, Chantilly, VA) were coated overnight with purified human SP-A or recombinant human SP-D antigens (standards) and diluted BALF (three different dilutions) in 0.1 M NaHCO3, pH 9.6. The wells were washed three times with deionized water, and nonspecific sites were blocked with a buffer containing 0.25% BSA, 0.05% tween 20, 0.17 M boric acid and 0.12 M NaCl, pH 8.5. The wells were washed and incubated for 2 h with rabbit anti-human SP-A or rabbit anti-mouse SP-D antibody. After washing the wells, the horseradish peroxidase conjugated anti-rabbit IgG antibody (Sigma, St. Louis, MO) was added. After incubation for 2 h, the wells were washed again and incubated with tetramethylbenzidine substrate reagent (Sigma Chemical Co. St. Louis, MO). The reaction was stopped by adding 50 μl of 2 N H2SO4 and read at 450 nm spectrophotometrically. The regression coefficient for a least-square linear fit to the standard curve of SP-A and SP-D was 0.99. The limits of detection for SP-A and SP-D were 2 ng/ml.
Figure 1 Western blot for (A) SP-A and (B) SP-D proteins in mouse lung. Lanes (a, b): 2.5 μg total lavage fluid protein (c, d): 100 μg of total lung tissue homogenate protein from two healthy, non-infected BALB/c mice, and (e): 10 ng purified human SP-A or recombinant SP-D protein.
Binding of SP-A and SP-D to Coccidioidal Antigens
A microtiter well based method [21] was used to study the SP-A and SP-D interactions with Coccidioidal antigens (CDN-lysate and CDN-filtrate). Briefly, microtiter wells (Immulon 4; Dynatech, Chantilly, VA) were coated with 50 μl of CDN-lysate (10 μg/ml diluted in 0.1 M NaHCO3 buffer, pH 9.6) or CDN-filtrate (10 μg/ml diluted in 0.1 M NaHCO3 buffer, pH 9.6) or BSA (10 μg/ml diluted in 0.1 M NaHCO3 buffer, pH 9.6) at room temperature. The nonspecific binding was blocked with phosphate buffered saline (pH 7.4) containing 0.1% triton-X 100 and 3% nonfat milk (buffer A). The purified human SP-A and recombinant human SP-D diluted in 20 mM Tris (pH 7.4) containing 0.15 M NaCl, 5 mM CaCl2 and 1 mg/ml BSA were then added to the wells and incubated for 3 h at 37°C. The wells were then washed with buffer A and incubated for 1 h at room temperature with diluted (1:1000 in buffer A) anti-SP-A and anti-SP-D antibodies. After washing the wells, the horseradish peroxidase conjugated anti-rabbit IgG antibody (Sigma, St. Louis, MO) was added. After incubation for 2 h, the wells were washed again and incubated with tetramethylbenzidine substrate reagent (Sigma Chemical Co. St. Louis, MO). The reaction was stopped by adding 2 N H2SO4 and read at 405 nm spectrophotometrically.
The coating of Coccidioidal antigens (CDN-lysate and CDN-filtrate) to the plates was confirmed using a positive control antibody that recognizes Coccidioidal antigens as described earlier [22]. The alkaline phosphatase-conjugated rat anti-mouse IgG antibody (Zymed, San Francisco, CA) served as secondary detection antibody.
Statistics
Statistical analyses of the data (t-test or ANOVA) were done using Prism Software (Graphpad Software, San Diego, CA). The p value <0.05 was considered significant.
Results
Pathological status
All of the C. posadasii infected mice survived till the day of sacrifice (day 10 post infection). However, the mice were lethargic and lost body weight (Table 1). Abscess like lesions were quite evident on gross examination of the lung. The total wet lung weights were increased in C. posadasii infected mice.
Table 1 Body weights (g) of C. posadasii infected and non-infected BALB/c mice (n = 10 of each type). Values are shown as Mean (SEM) of one representative experiment of two independent experiments.
Days post challenge → Mice ↓ Day 0 Day 10
Non-infected 18.36 (0.29) 19.46 (0.23) **
C. posadasii infected 17.77 (0.39) 16.35 (0.53) *, #
** p < 0.01 as compared to non-infected control mice at day 0.
*p < 0.05, # p < 0.0001 as compared to C. posadasii infected mice at day 0 and non-infected mice at day 10, respectively (t-test).
The mean protein content of BALF samples from infected mice was 788 μg versus 326 μg protein in BALF samples from non-infected saline injected control mice, after 10 days of intranasal infection (p < 0.05, Table 2). In contrast, the phospholipid concentration was reduced in BALF samples from C. posadasii infected mice (58 μg) when compared to non-infected saline injected controls (165 μg, p < 0.05).
Table 2 Total protein and phospholipid contents in BALF samples from non-infected and C. posadasii infected BALB/c mice (n = 5 of each type). Values are shown as Mean (SEM) from one represenative experiment of two independent experiments.
Mice Total protein (μg) Total phospholipid (μg)
Non-infected 326.0 (26.9) 165.5 (10.8)
C. posadasii infected 788.7 (248.6)* 58.7 (15.7)*
* p < 0.05 as compared to non-infected control mice (t-test).
The amounts of SP-A, SP-D and phospholipid are reduced in BALF samples from C. posadasii infected mice
The anti-human-SP-A and anti-mouse SP-D antibodies recognized 34 kDa and 43 kDa monomer bands of SP-A and SP-D in BALF and lung tissue samples (Fig 1). The upper bands of approximately 68 kDa and 85 kDa size (dimer of SP-A and SP-D protein) were also visible in lanes e of Figure 1A and 1B due to incomplete reduction, respectively. There were no differences in the detectable isoforms of SP-A or SP-D in the BALF samples from infected mice as compared to non-infected control mice (data not shown). The antibodies did not cross-react with Coccidioidal antigens in CDN lysate or CDN filtrate (Fig 2). The amounts of SP-A and SP-D were significantly reduced in BALF samples from C. posadasii infected BALB/c mice when compared to saline injected, non-infected control mice after 10 days of intranasal challenge (p < 0.001) (Fig 3A). No significant changes were observed in the amounts of SP-A and SP-D in BALF samples collected from BALB/c mice, 5 days after intranasal challenge with C. posadasii (data not shown).
Figure 2 Western blot of CDN-lysate and CDN-filtrate for crossreactivity with (A) anti-human SP-A and (b) anti-mouse SP-D antibodies. Lanes (a): 20 μg, (b): 10 μg and (c): 1 μg CDN-filtrate protein. Lanes (d): 20 μg, (e): 10 μg and (f): 1 μg CDN-lysate protein and last lane: 10 ng purified human SP-A protein or recombinant SP-D protein.
Figure 3 SP-A and SP-D levels in BALF samples from (A) C. posadasii infected BALB/c mice (ng/μg protein, % of non-infected control mice, n = 5 of each type) (B) FKS immunized, C. posadasii infected C57BL6 mice (protected mice) (ng/μg protein, % of FKS immunized non-infected mice, n = 5 of each type). The data are shown from one representative experiment of two independent experiments. * p < 0.001 (ANOVA)
The fungal colonies of C. posadasii were recovered from both lung and spleens of infected animals indicating the presence of active infection (Fig 4). Recovery of fungus in the spleen provides evidence of dissemination of C. posadasii to extra-pulmonary organs.
Figure 4 Fungal load in the lung and spleen tissues of C. posadasii infected BALB/c mice (n = 5). The numbers of fungal colonies (CFU) were normalized with organ weight (g).
Lavage SP-A and SP-D levels are unaltered in C. posadasii protected mice
No significant changes were seen in SP-A or SP-D levels in BALF samples of protected (FKS immunized, C. posadasii arthroconidia infected) C57BL6 mice when compared to FKS immunized, non-infected mice (Fig 3B). Also, there was no significant change in total protein content in BALF samples of FKS immunized, C. posadasii arthroconidia infected mice (890.9 μg) versus FKS immunized, non-infected mice (538.4 μg).
SP-A and SP-D bind to Coccidioidal antigens
We further examined the binding of SP-A and SP-D to Coccidioidal antigens (CDN-lysate and CDN-filtrate) coated onto microtiter wells (Fig 5). Both SP-A and SP-D bound to coccidioidal antigens, but not to BSA in a concentration-dependent manner (Fig 5). Binding of SP-A to CDN-lysate and CDN-filtrate antigens was saturable, and maximum SP-A binding was reached between 2.5–5 μg/ml and 5–10 μg/ml, respectively (Fig 5A). Similarly binding of SP-D to CDN-lysate and CDN-filtrate antigens was also saturable, and maximum SP-D binding was reached between 5–10 μg/ml (Fig 5B).
Figure 5 Binding of (A) SP-A and (B) SP-D to Coccidioidal antigens (CDN-lysate and CDN-filtrate). The binding of 1–10 μg/ml purified human SP-A or recombinant SP-D proteins was detected in CDN-lysate or CDN-filtrate or BSA coated wells (0.5 μg/well). Results are from one representative experiment of two independent experiments performed in duplicate. Values are shown as mean+SEM. In some cases, the error bars are smaller than the symbols.
Discussion
In the present study we found that the levels of pulmonary surfactant collectins were altered in the lungs of C. posadasii infected mice, but were intact in lungs of C. posadasii protected mice after immunization with protective FKS vaccine. Furthermore, our results suggest that both SP-A and SP-D bind to Coccidioidal antigens. This is the first study where the amounts of SP-A and SP-D were measured in BALF samples from mice infected with lethal dose of C. posadasii and the binding of pulmonary collectins to Coccidioidal antigens was assessed. Since lung is the first organ of the body that comes into contact with air-borne C. posadasii arthroconidia, we hypothesized that the pulmonary surfactant may play an important role in regulating the immune response against C. posadasii. Among four surfactant proteins, SP-A and SP-D, interact with most of the clinically important fungal pathogens including Pneumocystis carinii [23]Cryptococcus neoformans [24], Aspergillus fumigatus [25] and Candida albicans [26]. SP-D has been shown to bind to C. albicans and directly inhibit growth by aggregation of the organism without involvement of macrophage dependent phagocytosis [27]. On the other hand, surfactant proteins also induce phagocytosis, activation and killing of A. fumigatus conidia and C. neoformans by alveolar macrophages and neutrophils [28,29]. In support of these findings, further evidence comes from a study by Madan et al., that suggests that the introduction of recombinant SP-D improves the lung function and increases the survival rate of mice infected with A. fumigatus [25]. The decrease in amounts of SP-A and SP-D during C. posadasii infection versus the unaltered amounts in the lungs of protected mice and binding of collectins to Coccidioidal antigens indicate that pulmonary collectins may be involved in uptake/phagocytosis of C. posadasii by antigen presenting cells and downstream immune regulation.
Besides changes in amounts of SP-A and SP-D, a decrease in the amount of BALF phospholipids was also observed in C. posadasii infected mice (Table 2). Earlier, Sheehan et al., [29] and Hoffman et al., [30] have reported similar findings of reduced surfactant phospholipid level in BALF samples of rats and humans infected with Pneumocystis carinii [29,30]. To date, however, the information is lacking concerning how surfactant phospholipids may be involved in host defense [31]. Likewise, the mechanisms underlying the decrease of BALF surfactant in murine model of Coccidioidomycosis remain to be defined. We speculate that the reduction in the collectins and phospholipids could be either due to metabolic dysfunction of pulmonary type II epithelial cells during C. posadasii infection or due to their utilization in the binding and uptake of C. posadasii by local antigen presenting cells. The metabolic pathways of pulmonary type II epithelial cells may be affected by secondary inflammatory mediators, such as TNF-α or IL-1β, secreted by inflammatory cells during C. posadasii infection. A variety of host inflammatory mediators and substances such as, cytokines (TNF-α) and growth factors are released during infection and inflammation. These cytokines and growth factors affect the synthesis and secretion of pulmonary surfactant by pulmonary epithelial cells [32,33]. In the present study we found slightly increased levels of SP-D in C. posadasii protected mice, but not of SP-A (Fig 2). We speculate that the difference could be due to diverse mechanisms for regulation of SP-A and SP-D expression. Probably the cytokines and chemokines that are released as a result of FKS vaccination (protective immune response) increase the SP-D expression, but do not affect the SP-A expression. As reported earlier, the expression of SP-A and SP-D is differentially regulated during lung infection [34]. In future, more studies are warranted to understand the mechanism of the alterations in levels of surfactant phospholipids, SP-A and SP-D.
At present, the treatment of C. posadasii infected patients with disseminated disease is not very effective [35]. Treatment with anti-fungal agents is most often related to relapse of the infection and side effects on the body. Earlier, the FKS based immunization has been found protective against Coccidioides infection in murine model of coccidioidomycosis, but failed in humans [35]. More pre-clinical studies on developing different vaccine strategies are underway. Since C. posadasii and C. immitis are highly virulent organisms, cause endemic infection, and pose a risk for bioterrorism, there is an urgent need for discovery of improved therapeutic drugs and regimens or preventive vaccines [3,35].
Conclusions
In future, clearance experiments after in vivo administration of artificial or natural surfactant in C. posadasii infected mice may be useful in determining their therapeutic usefulness. We speculate that the findings from our present study would initiate similar studies to understand the role of pulmonary innate immune components in infectious diseases caused by C. posadasii or other virulent respiratory pathogens.
Authors' Contributions
SA designed and co-ordinated the study, performed assays and statistical analysis and drafted the manuscript. JJC assessed lung pathology. DMM prepared and immunized mice with FKS. All authors read and approved the final manuscript.
Acknowledgements
Authors acknowledge the technical assistance of Adrian Donias and Amy Chein in animal studies. We also thank Dr. Rebecca A. Cox (UTHSCSA, San Antonio, TX), Dr. Richard J. King (UTHSCSA, San Antonio, TX, now retired), Dr. Erika C. Crouch (Washington University in St. Louis, St. Louis, MO) and Dr. Jo Rae Wright (Duke University Medical Center, Durham, NC) for providing us with coccidioidal antigens, SP-A and SP-D antigens and antibodies. This work is supported by research grants from San Antonio Area Foundation from Semp Russ Foundation, and California Health Care Foundation, the Department of Health Services of the State of California, California State University at Bakersfield.
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| 15588319 | PMC543449 | CC BY | 2021-01-04 16:47:23 | no | Respir Res. 2004 Dec 10; 5(1):28 | utf-8 | Respir Res | 2,004 | 10.1186/1465-9921-5-28 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-1-131558829410.1186/1743-422X-1-13MethodologyEffect of oligonucleotide primers in determining viral variability within hosts Bracho Maria Alma [email protected]ía-Robles Inmaculada [email protected]énez Nuria [email protected] Manuela [email protected] Andrés [email protected]ález-Candelas Fernando [email protected] Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, Edifici Instituts, Polígon "La Coma" s/n, Paterna (València) 46980 SPAIN2004 9 12 2004 1 13 13 22 10 2004 9 12 2004 Copyright © 2004 Bracho et al; licensee BioMed Central Ltd.2004Bracho et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Genetic variability in viral populations is usually estimated by means of polymerase chain reaction (PCR) based methods in which the relative abundance of each amplicon is assumed to be proportional to the frequency of the corresponding template in the initial sample. Although bias in template-to-product ratios has been described before, its relevance in describing viral genetic variability at the intrapatient level has not been fully assessed yet.
Results
To investigate the role of oligonucleotide design in estimating viral variability within hosts, genetic diversity in hepatitis C virus (HCV) populations from eight infected patients was characterised by two parallel PCR amplifications performed with two slightly different sets of primers, followed by cloning and sequencing (mean = 89 cloned sequences per patient). Population genetics analyses of viral populations recovered by pairs of amplifications revealed that in seven patients statistically significant differences were detected between populations sampled with different set of primers.
Conclusions
Genetic variability analyses demonstrates that PCR selection due to the choice of primers, differing in their degeneracy degree at some nucleotide positions, can eclipse totally or partially viral variants, hence yielding significant different estimates of viral variability within a single patient and therefore eventually producing quite different qualitative and quantitative descriptions of viral populations within each host.
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Background
One of the most difficult tasks faced by virologists is the documentation and evaluation of the genetic variability of viral populations in infected patients. These analyses are greatly facilitated by the use of the polymerase chain reaction (PCR). PCR based techniques do not always produce a highly specific and homogeneous product. When the template is a complex mixture of homologous sequences the aim of the amplification would be to preserve as much as possible the template-to-product ratios of every sequence in order to obtain a good representation of the diversity present in the initial sample. PCR products are derived from templates by a process involving complex chemical kinetics, and the relative abundance of the different homologous genomes among the final products is often a parameter of interest. This is the case, for instance, in experiments aimed at determining natural diversity in microbial communities [1] or at identifying members of multigene families [2] and it is of special relevance for studies of viral variability within hosts, especially for highly variable RNA viruses.
The precise mechanisms involved in the preferential amplification of some templates from non-homogeneous sources are not fully understood and should be differentiated from those related to stochastic or tube-to-tube variations in amplification efficiency. When dealing with heterogeneous templates, two different processes can alter template-to-product ratios: PCR selection and PCR drift [3]. The former comprises mechanisms that favour the amplification of certain templates leading to their overrepresentation in the final product. Preferential denaturation due to GC content (in overall template and primer), differential efficiency of primer hybridisation or differential DNA polymerase extension rates (due to secondary structures of DNA) can all account for this type of bias. The second type of bias is related to stochastic variation in the early cycles of the reaction and its outcome should therefore be different in replicate PCR experiments. However, in a recent report analysing sampling strategies and repeatability to determine genetic variability in viral populations [4], we did not detect such PCR drift-caused bias.
Given the high levels of variability found in RNA virus populations, primers involved in RT-PCR (retrotranscription followed by PCR) are usually designed as degenerate sequences to ensure that the chance of amplifying the different sequences present in an heterogeneous template will be more uniform and, therefore, all will be present in the amplified product in similar proportions to those in the original template. However, the use of even highly degenerate primers does not preclude the possibility of mismatches occurring between a given primer and some of the sequences present in a heterogeneous template, especially for highly variable regions. This would lead to differential amplification of sequences [5] (i.e. PCR selection, see above). Although unnoticed for the experimenter, if this preferential amplification does indeed occur, conclusions of many evolutionary studies, clinical predictions or even genotyping assessments would be affected.
Hepatitis C virus (HCV) is a positive-sense, single-stranded RNA virus of approximately 9.4 kb, classified in a separate genus (Hepacivirus) of the Flaviviridae family. HCV has been recognised as a major etiological agent of acute and chronic hepatitis, cirrhosis, and hepatocellular carcinoma around the world [6]. HCV isolates can be highly divergent and have been classified into six major genotypes and more than 30 subtypes based on molecular phylogenetic analysis [7]. Moreover, like most RNA virus, HCV circulates in vivo as a highly polymorphic population of genetically closely related variants. This genetic variability may have implications not only for pathogenesis and prevention [8], but also for predicting the therapeutic outcome of HCV infection during interferon therapy [9,10].
Results
Population and phylogenetic analyses
Genetic variability in two different regions of the HCV genome was studied by means of RT-PCR amplification in eight infected patients. A fragment comprising partially E1 and E2 region including HVR1 and HVR2 (hypervariable regions 1 and 2, respectively) was amplified in six infected patients. In the other two patients, part of the NS5A region including the ISDR (interferon-sensitivity determining region) and the variable region 3 (V3) was amplified.
Each fragment was amplified twice from each HCV infected patient with two slightly different sets of primers (Table 1). Differences between primer sets 1 and 2 for both regions are based on degeneracy of some nucleotide positions, with primer set 2 being more degenerate than primer set 1, except for primer 2-Ng2 (Table 1). After cloning and sequencing, about 100 sequences for the E1E2 region and about 50 sequences for the NS5A region were obtained from each patient and set of primers. Therefore, from each patient we obtained two different groups (populations) of sequences corresponding to the two parallel PCR reactions performed with primer sets 1 and 2 respectively.
Table 1 List of primers for the E1E2 and NS5A regions of HCV
Region Primer namea Nucleotide positionb Primer set Sequence 5'-3'c Primer degeneracy
E1E2 1-Eg1 1290–1309 1 CGCATGGCATGGRATATGAT 2
2-Eg1 1290–1309 2 CGCATGGCYTGGGAYATGAT 4
1-Eg2 1300–1321 1 GGRATATGATGATGAACTGGTC 2
2-Eg2 1300–1321 2 GGGATATGATRATGAAYTGGTC 4
1-Ea 1873–1854 1 GGAGTGAAGCARTATACTGG 2
2-Ea 1873–1854 2 GGGGTGAARCARTAYACYGG 16
NS5A 1-Ng1 6715–6739 1 TGGAYGGGGTGCGCCTACATAGGTW 4
2-Ng1 6715–6739 2 TGGACGGGGTGYGMCTRCAYAGGTT 16
1-Ng2 6734–6753 1 TAGGTWYGCSCCCCCYTGCA 16
2-Ng2 6734–6753 2 TAGGTTYGCGCCCCCYTGCA 4
1-Na 7519–7503 1 CCCTCSAGRGGGGGCAT 4
2-Na 7519–7503 2 CCYTCSARGGGRGGCAT 16
ag indicates genomic sense and a indicates antigenomic sense.
bNucleotide positions according to sequence accession no. M62321.
cNucleotides in bold indicate differences between primer sets and underlined nucleotides indicate overlapping positions of nested primers.
Two types of population analysis were carried out (Additional file 1) with the two groups of sequences from each patient: (a) common measures of genetic variability within each group of sequences, and (b) a population genetics test that detects differentiation between groups of sequences (permutation test of Hudson [19]). In addition, for each genomic region, a phylogenetic reconstruction using all detected haplotypes obtained with the two sets of primers was performed in order to visually inspect their distribution along the branches.
Differences between groups of sequences from the same infected patient
HCV genetic variability estimates from sequence data obtained for one of the two regions analysed for the eight infected patients showed a wide range of values (Additional file 1). For the E1E2 region, the number of polymorphic sites (S) detected in a group of sequences from a particular set of primers ranged from 1 (in patient E04) to 68 (in patient E16). Similarly, for region NS5A, with only two patients analysed, S ranged from 4 to 70 for the same set of primers (patients N02 and N07, respectively). Haplotype diversity (HT) also reached both extreme values: in some groups of sequences corresponding to a particular primer set from a single patient (i. e. E10, E16, N07), almost every sequence constituted a different haplotype, resulting in HT ~ 1; whereas in other groups (i. e. E03, E04, E25) very few haplotypes were detected, with HT ~ 0.1 or even lower. When variability was measured taking both the frequencies of haplotypes and their genetic distances a wider variability range was observed: nucleotide diversity (π) of the viral populations estimated for the eight patients analysed differed by up to three orders of magnitude (Additional file 1).
The statistical significance of the observed differences in amount of genetic variation among groups of sequences could not be tested as no appropriate statistical test is currently available (but see [21]). However, the statistical significance of population differentiation can be evaluated between pairs of groups of sequences. In this way, for each patient the statistic and its corresponding P value was estimated using the permutation test [19].
Only for one patient the outcomes from the two parallel PCR amplifications were not significantly different. In patient E25, the use of different primers set seemed not to affect variability measures, although primer set 1 recovered more variability for all measurements than set 2. This is the only patient for which the average number of pairwise nucleotide substitutions (k) between both groups of sequences was lower than k obtained for a single primer set (e. g. primer set 2). Moreover, the main haplotype was similarly sampled by the two sets of primers (48 and 44 sequences respectively, see additional file 1), and therefore no significant statistical genetic differentiation between the two groups of sequences was detected. The distribution of viral sequences in the phylogenetic tree (Fig. 1a) shows that sequence sampling with the two sets of primers can be considered very similar for this patient.
Figure 1 Phylogenetic trees obtained by the neighbour-joining method for the different haplotypes of a) E1E2 region, patients E03, E04, E10, E16, E23 and E25; b) NS5A region, patients N02 and N07. Black dots represent haplotypes obtained with the set of primers 1; white dots represent haplotypes obtained with the set of primers 2, and grey dots are shared haplotypes obtained with both sets of primers. The number next to the dot indicates the number of times this haplotype was detected by a particular primer set. For shared haplotypes two numbers are given, the first one corresponds to primer set 1 and the second to primer set 2. The scale bar represents number of nucleotide substitutions per site (0.02 and 0.01, respectively).
For the remaining patients we found that each set of primers produced a very different collection of viral sequences, leading to significant genetic differentiation between the two groups of sequences obtained with each primer set. However, in patients E10, E23 and N07, the total amount of variability recovered with both sets of primers was similar (Additional file 1), although the distribution of haplotypes detected with each primer set in the phylogenetic tree was not homogeneous (Fig. 1a and 1b). Moreover, population differentiation tests showed that the groups of sequences obtained with different primer sets for these three patients were significantly different. Even for patient E10, for which both groups of sequences were the most diverse of all analysed in this report and were apparently intermixed in the phylogenetic tree, the differentiation test detected significant differences.
In patients E03, E04, E16 and N02 the population differentiation statistic () also showed that the two groups of sequences obtained from each patient were statistically different, with genetic distances (k) between the two groups of sequences from the same patient largely exceeding the genetic distances estimated within a single group of sequences. Moreover, in these four patients, the amount of genetic variability detected also seemed to be deeply affected by the set of primers chosen for PCR (see additional file 1) with one set of primers recovering at least twice as many haplotypes as the other set. It is remarkable that in patients E03 and E04 the most frequently detected (main) haplotype with one set of primers was genetically distant from the main haplotype found with the alternative set, as shown by their relative positions in the phylogenetic tree (Fig. 1a). It is also worth noticing that in patients E16 and E02 rare haplotypes were more abundant in PCR products obtained with primer set 2.
Although we have dealt here with cloned sequences, direct sequencing of PCR products can also be much affected by PCR selection: both consensus sequences obtained from PCR amplification of the same cDNA aliquot from patient E04 with sets of primers 1 and 2 respectively, showed up to 7 nucleotide differences in 472 nucleotides (data not shown).
Discussion
PCR drift (i. e. bias in template-to-product ratios produced by random events occurred in the early cycles of the reaction) and PCR selection (i. e. differential amplification of specific sequences caused by differential annealing of oligonucleotide primers) are two processes that can lead to bias in template-to-product ratio of PCR amplifications [3,22]. In a previous report aimed at studying PCR repeatability on sampling HCV sequences from four infected patients [4] we found no evidence of such PCR drift under our conditions. However a role for PCR selection could not be ruled out. The main contributing factor to PCR selection is usually the differential affinity of primers for template sequences due to differences in the primary or secondary structure of DNA at target sites [3]. PCR assays rely on the efficient hybridization of primers to the target sequence. However, mismatches between the primer and the target molecules can affect duplex stability and may compromise the ability of the system to amplify and detect the target sequences. Numerous factors determine the final effect of mismatches, including primer length, nature and position of mismatches, hybridization temperature, presence of co-solvents (such as DMSO) and concentrations of both primers and monovalent and divalent cations. For instance, Ishii and Fukui [23] showed how using complex templates with different annealing temperatures severely affected the PCR outcome because of the presence of primer mismatch. Therefore, in samples with a heterogeneous composition of templates (such as viral populations in infected patients) the presence of mismatches can introduce differences in amplification efficiencies of the different templates hence leading to template-to-product ratios alterations during PCR. Some of these factors have been experimentally proven to cause this alteration.
Attempts to reduce PCR bias caused by primer-template mismatches usually involve designing degenerate primers. Here we have studied the effect of small differences in primer degeneracy on PCR outcome with heterogeneous templates and their implications and extent on viral genetic variability at the intrapatient level. Our results indicate that template-to-product ratios can be significantly biased in standard PCR amplifications of non-homogeneous templates. By means of RT-PCR sampling of HCV sequences from eight infected patients we have found that sequence sampling from a single source varied considerably when two slightly different sets of primers were used in the PCR amplification: both sets of primers were chosen after inspection of HCV-1 aligned sequences from GenBank, and degenerations were introduced in both sets following only slightly different criteria. The use of one set of primers or another not only gave rise to different collections of sequences, but also to their different distribution along phylogenetic trees in most of viral populations studied. These results indicate that bias in template-to-product ratios can severely distort the results of analysis of variability in virus populations, both quantitatively (i. e. amount of genetic variability) and qualitatively (i. e. particular sequence clusters on a phylogenetic tree).
Fan et al. [5] demonstrated that partially mismatched primers used in RT-PCR preferentially amplified different HVR1 sequences in a HCV virus population, but they pointed to the specific primer set used for the cDNA synthesis in the RT reaction as the main cause for the observed bias. To avoid this possible source of error, here we have used random hexamers to perform reverse transcriptions on viral RNA templates and consequently we have only focussed on the alterations caused by primers during PCR. This kind of PCR bias related to primer preferences to anneal to some viral templates has been previously demonstrated at the level of detection of HCV infections [24], detection of HCV mixed-genotype infections [25], differences in genotype assignment [26], or frequent failure to amplify hypervariable regions (references in [5]). But here we have demonstrated that this bias can be relevant at the population genetics level to the point that two independently obtained groups of sequences from the same patient could be considered as two significantly different viral populations. One consequence of our results is that viral variability estimated by means of PCR sampling of viral sequences will almost surely be an underestimation, and should always be considered as a minimum value. However relative changes in viral variability through time could probably be reliably assessed. Underestimates of variability due to PCR selection could be in agreement with the failure to find correlation between genetic diversity present in viral populations before treatment and treatment outcome [10], although significant changes in relative viral diversity yielded prognosis information. Another consequence is that, as recovered viral sequences after PCR are those more related to the sequences used in primer design, an unknown proportion of the original viral population present in the template source will not even be detected. This is shown in our experiment by the presence in the phylogenetic trees of divergent clusters of sequences from a single patient only amplified by one of the two sets of primers tested (see for instance patients E03, E04, E16, N02 and N07) in figure 1. Since search for particular sequences related to therapy response is a common issue in antiviral resistance studies, this observation is of crucial interest as some sequences of specific or potential interest could remain unnoticed under particular PCR conditions. For example, in HCV the evolution of interferon sensitivity-determining region (ISDR) during IFN therapy is controversial [27]. It cannot be ruled out that discrepant results related to the predictive value of particular viral sequences detected by means of PCR could be partially due to the sampling bias of amplified sequences as those shown in the present report.
The results obtained in this study allow us to strongly suggest that, for PCR-based variability studies, a certain level of primer degeneration, compatible with specific product amplification, would be more than advisable for variability studies in which, as with viral populations, there is no possibility of designing a perfect set of primer pairs that equally amplify all possible templates. For this, it is convenient to align many available related sequences and empirically determine which positions are most polymorphic and therefore susceptible to participate in mismatches.
Conclusions
PCR selection (differential amplification of specific sequences due to differential annealing of oligonucleotides) was detected and attributed to differences in degeneracy at some nucleotide positions in the oligonucleotides involved in amplification. Alterations in the template-to-product ratio during PCR amplification significantly affects viral population descriptions to the extent that two PCR outcomes from the same infected patient can result, after genetic population analyses, in two genetically distinct populations. Two important implications can be derived: first, all estimates of genetic variability parameters should be considered always as a minimum, and second, the search for particular existing genomes, such as drug resistant genomes, can be totally or partially eclipsed by others more susceptible to be annealed by the olinucleotides used in the amplification.
Methods
Viral RNA extraction and amplification
Serum samples from eight patients infected with HCV were chosen for this study. Six patients infected with HCV-1b (E03, E04, E10, E16, E23 and E25) were analysed using two sets of primers that partially amplified the E1E2 region (472 nucleotides) and two patients infected with HCV-1a (N02 and N07) were analysed using two sets of primers that partially amplified the NS5A region (743 nucleotides).
The first step in the design of oligonucleotide primers was to collect a representative variety of HCV-1 sequences from GenBank. From 50 homologous sequences, nucleotide positions for primers were chosen with GeneFisher [11], and two sets of homologous primers were designed for the E1E2 region and the same procedure for the NS5A region. For each region, both sets of homologous primers basically differ in their degree of degeneracy at some polymorphic positions (see table 1).
Viral RNA was extracted from 140 μl of serum using High Pure Viral RNA Kit (Roche). In order to prevent any bias during reverse transcription reactions due to oligonucleotide specificity, all reverse transcription reactions were performed using random hexadeoxynucleotides. Reverse transcriptions (RT) were performed in a 20 μl volume containing 5 μl of eluted RNA, 4 μl of 5x RT buffer, 0.5 mM of each deoxynucleotide, 0.5 μg of random hexamers, 100 U of MMLV reverse transcriptase (Promega), and 20 U of RNasin Ribonuclease Inhibitor (Promega). Reactions were incubated at 37°C for 60 min, followed by 2 min at 95°C.
A first PCR round was then carried out in a 100 μl volume containing 10 μl of the reverse transcription product, 0.2 mM of each dNTP, 400 nM of genomic primer and 400 nM of antigenomic primer and 1.25 units of Pfu DNA polymerase (Promega). The outer set of primers for the E1E2 region were 1-Eg1 (or alternatively 2-Eg1) and 1-Ea (or 2-Ea) (see Table 1). Hemi-nested PCR was carried out to amplify a 472-bp fragment with nested primer 1-Eg2 (or 2-Eg2) and original primer 1-Ea (or 2-Ea). The outer set of primers for the NS5A region were 1-Ng1 (or 2-Ng1) and 1-Na (or 2-Na). Hemi-nested PCR were carried out to amplify a 743-bp fragment with 1-Ng2 (or 2-Ng2) and 1-Na (or 2-Na). All reactions were performed in a Perkin Elmer 2400 thermalcycler according to the following profile: initial denaturation at 94°C for 1 min; 5 cycles at 94°C for 30 s, 55°C 30 s, 72°C 3 min; then 35 cycles at 94°C 30 s, 52°C 30 s, 72°C 3 min, and a final extension at 72°C for 10 min. A single amplified product was observed after electrophoresis on 1.4 % agarose gels stained with ethidium bromide. The same PCR conditions were strictly applied to every primer set in both regions. The 233 newly reported sequences (haplotypes) are deposited in the EMBL nucleotide sequence database under accession numbers AF715552-AF715784.
Cloning and sequencing of viral populations
Amplified products from the second round of PCR for the E1E2 and NS5A regions were purified using High Pure PCR product Purification Kit (Roche) and directly cloned into EcoRV-digested pBluescript II SK (+) phagemid (Stratagene). Recombinant plasmid DNA was purified using the High Pure Plasmid Isolation Kit (Roche). Cloned products were sequenced using vector-based primers KS and SK (Stratagene). Sequencing was carried out using ABI PRISM BigDye Terminator v3.0 Ready Reaction Cycle Sequencing KIT (Applied Biosystems) on an ABI 3700 automated sequencer. Sequences were verified and both strands assembled using the Staden package [12].
Phylogenetic reconstruction and population genetics analysis
Sequences were aligned using CLUSTALX v1.81 [13]. The neighbour-joining algorithm [14] applied on the pairwise nucleotide divergence matrix using Kimura's two parameter model [15] was used to obtain phylogenetic trees using the MEGA program [16].
Polymorphism and genetic differentiation were analysed using DNAsp version 4.0 [17]. Estimated polymorphism parameters included: number of polymorphic sites (S); haplotype diversity (HT) considering as haplotype each different sequence; nucleotide diversity (π) [18]; and average number of pairwise differences between sequences (k). Genetic differentiation between groups of sequences was estimated as the average number of nucleotide substitutions between groups (dxy). The statistical significance of genetic differentiation between groups, as estimated by , was established by the permutation test [19]. The proportion of nucleotide diversity attributable to variation between populations, the fixation index Fst, was calculated using the ARLEQUIN program ver. 2.000 [20].
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
MAB and IG-R co-conceived, designed and coordinated the study, participated in the molecular studies and sequence alignment, interpreted data, oversaw the training of technicians, and co-drafted the manuscript; MAB isolated viral genomes, co-performed population and phylogenetic analyses; NJ and MT-P participated in molecular studies and sequence alignment, interpreted the data and helped draft the manuscript; AM interpreted data and participated in proofreading of the manuscript; FG-C coordinated the study, interpreted data, co-performed population and phylogenetic analyses and participated in proofreading of the manuscript. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Summary of genetic variability and population differentiation of within patient HCV populations based on viral sequences obtained with two alternative primer set. E1E2 region was analysed in patients E03, E04, E10, E16, E23 and E25, and NS5A region in patients N25 and E02
Click here for file
Acknowledgements
This work was supported by the Conselleria de Sanitat, Generalitat Valenciana, and by the Spanish Ministerio de Educación, Cultura y Deporte, Plan Nacional I+D (project 1FD97-2328) and the Ministerio de Ciencia y Tecnología (BMC2001-3096).
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| 15588294 | PMC543450 | CC BY | 2021-01-04 16:38:32 | no | Virol J. 2004 Dec 9; 1:13 | utf-8 | Virol J | 2,004 | 10.1186/1743-422X-1-13 | oa_comm |
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Nutr JNutrition Journal1475-2891BioMed Central London 1475-2891-3-211558828910.1186/1475-2891-3-21ResearchVitamin A deficiency and inflammatory markers among preschool children in the Republic of the Marshall Islands Maqsood Maria [email protected] Barbara [email protected] Mary V [email protected] Neal A [email protected] Michelle O [email protected] Kennar [email protected] Richard D [email protected] Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA2 Mailman School of Public Health, Columbia University, New York, New York, USA3 Department of Family Practice and Community Health, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA4 Ministry of Health and Environment, Republic of the Marshall Islands2004 8 12 2004 3 21 21 9 10 2004 8 12 2004 Copyright © 2004 Maqsood et al; licensee BioMed Central Ltd.2004Maqsood et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The exclusion of individuals with elevated acute phase proteins has been advocated in order to improve prevalence estimates of vitamin A deficiency in surveys, but it is unclear whether this will lead to sampling bias. The purpose of the study was to determine whether the exclusion of individuals with elevated acute phase proteins is associated with sampling bias and to characterize inflammation in children with night blindness.
Methods
In a survey in the Republic of the Marshall Islands involving 281 children, aged 1–5 years, serum retinol, C-reactive protein (CRP), and α1-acid glycoprotein (AGP) were measured.
Results
Of 281 children, 24 (8.5%) had night blindness and 165 (58.7%) had serum retinol <0.70 μmol/L. Of 248 children with AGP and CRP measurements, 123 (49.6%) had elevated acute phase proteins (CRP >5 mg/L and/or AGP >1000 mg/L). Among children with and without night blindness, the proportion with serum retinol <0.70 μmol/L was 79.2% and 56.8% (P = 0.03) and with anemia was 58.3% and 35.7% (P = 0.029), respectively. The proportion of children with serum retinol <0.70 μmol/L was 52.0% after excluding children with elevated acute phase proteins. Among children with and without elevated acute phase proteins, mean age was 2.8 vs 3.2 years (P = 0.016), the proportion of boys was 43.1% vs. 54.3% (P = 0.075), with no hospitalizations in the last year was 11.0% vs 23.6% (P = 0.024), and with anemia was 43.8% vs 31.7% (P = 0.05), respectively.
Conclusions
Exclusion of children with inflammation in this survey of vitamin A deficiency does not improve prevalence estimates for vitamin A deficiency and instead leads to sampling bias for variables such as age, gender, anemia, and hospitalization history.
acute phase responseinflammationretinolvitamin A deficiencyxerophthalmia
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Vitamin A deficiency is a major cause of morbidity and mortality among preschool children in developing countries [1]. Vitamin A, or all-trans retinol, is available as preformed vitamin A in foods such as eggs and dairy products and as provitamin A carotenoids in foods such as dark green leafy vegetables, pumpkin, and papaya. Vitamin A is essential for normal immune function, hematopoiesis, growth, and vision [1]. Among preschool children, risk factors for vitamin A deficiency include age, such as the period that follows weaning when vitamin A intake is low and risk of precipitating infections is high [2,3], recent infections such as diarrheal disease [4], and low socioeconomic status [5]. The syndrome of vitamin A deficiency is characterized by increased susceptibility to infections, anemia, and elevated acute phase proteins [1,6,7].
The identification of populations at risk for vitamin A deficiency is important in order to target groups that would benefit from interventions to improve vitamin A status. Xerophthalmia, such as night blindness and Bitot spots, is used to determine the prevalence of clinical vitamin A deficiency in populations [1]. Other surveys that are conducted to determine whether vitamin A deficiency is a public health problem often rely upon the measurement of serum or plasma retinol concentrations [8]. However, plasma retinol is a negative acute phase reactant that decreases during infections [9], and it has been thought that the measurement of plasma retinol concentrations may not accurately reflect the vitamin A status of populations with a high prevalence of subclinical infections, as some portion of low retinol concentrations may be due to an acute phase response [10]. Recently, Thurnham and colleagues proposed that in surveys that rely upon plasma or serum retinol concentrations to estimate the prevalence of vitamin A deficiency, one method to improve the accuracy of the prevalence estimates is to exclude all individuals with elevated acute phase proteins [11]. The underlying assumption using this method is that subclinical infection is randomly distributed in a population, and that the exclusion of individuals with elevated acute phase proteins will not lead to sampling bias.
We hypothesized that exclusion of individuals with elevated acute phase proteins, and selection of only apparently healthy children would lead to lead to sampling bias in a survey of vitamin A deficiency. A secondary hypothesis was that children with night blindness would have elevated acute phase proteins. Vitamin A deficiency has been recognized as a major public health problem in many areas of the South Pacific region [12], including the Republic of the Marshall Islands [13,14]. In order to address these hypotheses, we characterized serum retinol concentrations, night blindness, and markers of inflammation in a population-based survey among preschool children in the Republic of the Marshall Islands.
Subjects and Methods
A population-based survey, the Republic of the Marshall Islands Vitamin A Deficiency Study, was conducted between November 1994 and March 1995 to assess the prevalence of vitamin A deficiency among children, aged 1–5, in the Republic of the Marshall Islands. The Republic of the Marshall Islands (population 68,000) is a complex of 28 coral atolls and 5 small (non-atoll) islands, with a total of 1136 islands spreading over a very small land area of 134 km2. The sampling strategy for the study was based on the 1988 census of the Republic of the Marshall Islands, which provided data on the average number of children of the target age group within each household, determined by dividing the number of children in a locality by the number of households in the same location. This number was then divided into the number of children to be sampled to obtain the number of households to be visited. Households to be visited were chosen by systematic sampling of every fifth household. When available, the birth dates of the children were ascertained from the children's health cards; otherwise, the birth dates were obtained by asking the parent or guardian. The survey teams consisted of at least one Marshallese-speaking health care worker, a phlebotomist, and a medical doctor.
Of the children in the survey, a systematic subsample of every fifth child was obtained in which a standardized questionnaire was used to collect information on various risk factors such as night blindness, demographics, breastfeeding, visits to the hospital, presence of a vegetable garden, and food consumption. Parents were asked if the child currently had night blindness. A history of night blindness has been shown to be a reliable indicator of vitamin A deficiency [15]. Oral informed consent was obtained from a parent or guardian prior to participation in the survey as considered appropriate by the institutional review board for this setting.
Blood samples were obtained by venipuncture. Hemoglobin was measured using a HemoCue instrument (HemoCue, Inc., Mission Viejo, CA). Venous blood samples were collected in red-top serum separator tubes and immediately wrapped in aluminum foil, continuously shielded from light, and stored at 4°C until centrifugation (200 g × 10 min, room temperature). Aliquots of serum were made, kept in liquid nitrogen or at -70°C, and shipped by overnight air express from the Republic of the Marshall Islands to New York City. Retinol remains stable at -70°C for 15 y or more [16]. Serum retinol was measured using reverse-phase high performance liquid chromatography, as described elsewhere [13]. Serum C-reactive protein (CRP) was determined using a commercial enzyme-linked immunosorbent assay kit (Hemagen Diagnostics, Waltham, MA). Serum α1-acid glycoprotein (AGP) was measured using radial immunodiffusion assay (Bindarid, The Binding Site, Birmingham, UK). Interleukin-6 (IL-6) was analyzed using a high sensitivity commercial enzyme-linked immunosorbent assay (R&D systems, Inc, Minneapolis, MN). Serum retinol, CRP, AGP, and IL-6 values were available for 281, 250, 248, and 176 of 281 pre-school children, respectively. Missing values were due to inadequate volume of sera. Controls provided by the manufacturer were used to measure intra- and inter-assay coefficients of variation (CV) in laboratory analysis. For serum retinol, the within-assay and between-assay CVs were 3% and 8%, respectively. For serum CRP, AGP, and IL-6, the within-assay and between-assay CVs were 4.1 and 5.0, 3.9 and 1.6, and 3.2 and 2.9, respectively. The study protocol was approved by the institutional review board of the Pacific Health Research Institute of Hawaii and the Ministry of Health and Environment of the Republic of the Marshall Islands. The study was conducted in accordance with the Helsinki Declaration.
Groups were compared using Student's t-test or analysis of variance for continuous variables where appropriate, and categorical variables were compared using χ2 or exact tests. Vitamin A deficiency was defined as serum retinol <0.70 μmol/L [1]. Inflammation was defined as CRP >5 mg/L and/or AGP >1000 mg/L [6,7,11]. Anemia was defined as hemoglobin <110 g/L [14]. Nonparametric Wilcoxon rank sum test was used on continuous variables in which the distribution was not normal. The proportion of children with serum retinol <0.70 μmol/L was "adjusted" using two methods: (1) excluding all children with inflammation and then presenting the proportion of children who had serum retinol <0.70 μmol/L for the remaining children without inflammation, and (2) calculating geometric mean serum retinol concentrations in children with and without inflammation, multiplying the serum retinol concentrations in the group with inflammation by a constant that makes the geometric mean serum retinol in the group with inflammation equivalent to the group without inflammation, and then reporting the proportion of children who had serum retinol <0.70 μmol/L [11]. One sample test of proportions was used to compare proportions of children with serum retinol <0.70 μmol/L between "adjusted" and unadjusted groups. Spearman correlation was used to examine correlation between AGP, CRP, IL-6, retinol, and hemoglobin. The level of significance used in this study was P < 0.05.
Results
A total of 919 Marshallese children, age 1–5, participated in the survey, and the systematic subsample of every fifth child involved 281 children from the following atolls (n): Ailuk (16), Arno (26), Enejelar (3), Enewetak (14), Kwajalein (73), Majuro (77), Namu (32), Utrik (25), and Wotje (15). There were 141 boys and 140 girls, and the mean (±standard deviation) age of the children was 2.93 ± 1.38 years. Due to limited sample volume, serum CRP, AGP, and IL-6 were not measured in 31, 33, and 105 pre-school children, respectively. Of the 281 children, there were 24 (8.5%) with night blindness and 165 (58.7%) with serum retinol <0.70 μmol/L. Of 248 children who had both AGP and CRP measured, there were 123 (49.6)% with inflammation (CRP >5 mg/L and/or AGP >1000 mg/L).
The characteristics of children with and without inflammation are shown in Table 1. Children with inflammation were older (P = 0.016), were more likely to have been hospitalized in the last year (P = 0.024), and were more likely to be anemic (P = 0.05) compared with children without inflammation. Mean retinol concentration was lower (P = 0.0005) and the proportion of children with serum retinol <0.70 μmol/L was higher (P = 0.013) among children with inflammation compared to children without inflammation. The findings suggest that children with inflammation were more likely to be girls (P = 0.075) compared to children without inflammation. There were no significant differences in the proportion with night blindness, the mean number of people living in the house with the child, current breastfeeding status, and the presence of a vegetable garden at home between children with and without inflammation.
Table 1 Relationship between inflammation and demographic characteristics of children, age 1–5 years, in the Republic of the Marshall Islands
Inflammation 1
Characteristic 2 No (n = 125) Yes (n = 123) P
Mean age (years) 3.2 (3.0, 3.4) 2.8 (2.5, 3.0) 0.016
Sex (% male) 54.3 43.1 0.075
Night blind (%) 9.5 8.9 0.83
Hospitalized in last year (%)
0 23.6 11.0 0.024
1–2 44.7 43.2
3–4 18.7 31.4
5+ 13.0 14.4
Mean number of people living in house 11.4 (10.1, 12.7) 11.3 (10.1, 12.4) 0.62
Currently breastfeeding (%) 14.3 18.9 0.33
Presence of a vegetable garden at home (%) 32.2 32.8 0.93
Mean hemoglobin (g/L) 119 (108, 112) 109 (106, 110) 0.19
Hemoglobin <110 g/L (%) 31.7 43.8 0.05
Mean retinol (μmol/L) 0.66 (0.61, 0.72) 0.52 (0.47, 0.58) 0.0005
Retinol <0.70 μmol/L (%) 52.0 67.5 0.013
1 Defined as CRP >5 mg/L and/or AGP >1000 mg/L. 2 Geometric mean; 95% CI in parentheses.
Of the 281 children 58.7% had serum retinol <0.70 μmol/L. In contrast, if children with inflammation are excluded as proposed by Thurnham and colleagues [11], the proportion with serum retinol <0.70 μmol/L is 52.0% (Table 1). Alternatively, it has been proposed that serum retinol concentrations are "adjusted using acute phase proteins" based upon retinol values among children with and without inflammation [11]. When this alternative method is used, the proportion of children with serum retinol <0.70 μmol/L is 47% (95% C.I. 0.43–0.60), compared with the unadjusted proportion of 58.7% (P = 0.08).
The characteristics of children with and without night blindness are shown in Table 2. Children with night blindness were slightly older (P = 0.03), had lower serum retinol (P = 0.007), were more likely to have serum retinol <0.70 μmol/L (P = 0.03), and to be anemic (P = 0.029) compared to children without night blindness. The findings were suggestive that there may be a higher proportion of boys than girls who are night blind (P = 0.09), and with lower mean hemoglobin concentrations (P = 0.09). There were no significant differences in geometric mean AGP, CRP, IL-6, or inflammation between children with and without night blindness.
Table 2 Characteristics of children, aged 1–5 years, with and without night blindness in the Republic of the Marshall Islands
Night blindness
Characteristic 1 Yes (n = 24) No (n = 257) P
Mean age (years) 3.5 (3.0, 4.0) 2.9 (2.7, 3.1) 0.03
Sex (% male) 66.7 48.6 0.09
Geometric mean AGP (mg/L) 925 (819, 1044) 924 (881, 970) 0.99
AGP >1000 mg/L (%) 43.5 45.8 0.83
Geometric mean CRP (mg/L) 1.3 (0.2, 11.8) 1.7 (0.1, 324.3) 0.36
CRP >5 mg/L (%) 8.7 23.1 0.11
With inflammation (%) 2 47.8 49.3 0.89
Geometric mean IL-6 (pg/mL) 3.9 (0.9, 14.1) 3.9 (0.7, 16.2) 0.96
Mean retinol (μmol/L) 0.40 (0.04, 1.00) 0.62 (0.02, 1.90) 0.007
Retinol <0.70 μmol/L (%) 79.2 56.8 0.03
Mean hemoglobin (g/L) 106 (101, 110) 109 (108, 110) 0.09
Hemoglobin <110 g/L (%) 58.3 35.7 0.029
1 For continuous variables (95% CI). 2 Inflammation defined as AGP >1000 mg/L and/or CRP >5 mg/L.
Spearman correlations of serum AGP, CRP, IL-6, retinol, and hemoglobin are shown in Table 3. Serum AGP was positively correlated with serum IL-6 and CRP (P < 0.0001 for both). Serum AGP and CRP individually were inversely correlated with serum hemoglobin (P < 0.0001) and retinol (P = 0.0004). IL-6 was positively associated with CRP (P < 0.0001), and inversely associated with retinol (P < 0.0001). Hemoglobin had low correlation with retinol that was of borderline significance (P = 0.09) and low inverse correlation with IL-6 that was also of borderline significance (P = 0.08).
Table 3 Spearman correlation between AGP, CRP, IL-6, retinol, and hemoglobin in preschool children in the Republic of the Marshall Islands
Hemoglobin Retinol IL-6 CRP
AGP -0.19 -0.25 0.48 0.60
P = 0.0026 P < 0.0001 P < 0.0001 P < 0.0001
CRP -0.13 -0.22 0.44
P < 0.0001 P = 0.0004 P < 0.0001
IL-6 -0.12 -0.28
P = 0.08 P < 0.0001
Retinol 0.10
P = 0.09
Discussion
In this population of children from the Republic of the Marshall Islands, more than half had serum retinol concentrations <0.70 μmol/L. According to criteria of the World Health Organization, vitamin A deficiency is considered a public health problem if more than 15% of the population has serum or plasma retinol concentrations <0.70 μmol/L [17]. Vitamin A deficiency is also considered a public health problem if >1% of children less than six years old have night blindness [1], and in this survey, the prevalence of night blindness was more than eight times higher than this criterion. Thus, vitamin A deficiency was certainly a public health problem among children, aged 1–5 years, in the Republic of the Marshall Islands. Since the time of this survey, the Republic of the Marshall Islands has implemented a countrywide vitamin A capsule distribution program.
Nearly half of the children in this study had inflammation, as indicated by elevated AGP and/or CRP. These findings suggest that the prevalence of subclinical infection is high in this population that has a high prevalence of vitamin A deficiency. These findings are consistent with the concept that the syndrome of vitamin A deficiency is associated with depressed immunity and increased infections [6,7]. Children with subclinical vitamin A deficiency may have pathological alterations in T and B cell function and mucosal immunity that make them more susceptible to subclinical infections, such as diarrheal disease [18].
Although the exclusion of individuals with elevated acute phase proteins has been advocated to improve prevalence of vitamin A deficiency in surveys that rely upon plasma or serum retinol concentrations [11], this study shows that exclusion of these individuals leads to sampling bias. The remaining subjects in the sample without inflammation are different from those excluded from the sample of the study, as there was selection bias in regard to age, gender, anemia, and morbidity history. Thus, by excluding those with inflammation, the prevalence estimates of vitamin A deficiency are based upon a biased sample that may be healthier. Subclinical infections are more likely to occur among malnourished children and among children from poorer families [19,20]. Studies among adults suggest that elevated acute phase proteins are more common among those with lower socioeconomic status [21,22]. The present study is limited in that data on maternal education, socioeconomic status, and other demographic indicators was not collected. Similar analyses could be conducted with other large existing data sets to corroborate and characterize the extent of sampling bias in vitamin A surveys when individuals with elevated acute phase proteins are excluded.
The alternative method of having serum retinol concentrations "adjusted using acute phase proteins" [11] involves the same problem of sampling bias, as the group without inflammation that is used for "adjusting" the serum retinol concentrations of the group with inflammation is different as discussed above.
Children with night blindness had significantly lower serum retinol concentrations compared with children without night blindness, a finding that is consistent with previous studies [6,7,15]. The ability to see at night depends on the visual pigment, rhodopsin, in rod photoreceptors of the retina. Synthesis of rhodopsin depends in part upon the availability of circulating retinol. The prevalence of anemia was higher among children with night blindness than children without night blindness. Vitamin A deficiency is associated with anemia, and there may be several mechanisms by which vitamin A deficiency could cause anemia, including impairment of iron metabolism, and immune dysfunction and associated anemia of infection [23]. In the present study, children with night blindness were not more likely to have elevated acute phase proteins than children without night blindness. However, the number of children with night blindness in this study was small, and the study had limited statistical power to address this secondary hypothesis. Other studies among pregnant women in Nepal [6] and preschool children in Indonesia [7] show that individuals with night blindness are more likely to have elevated acute phase proteins.
The relationship between IL-6 and elevated acute phase proteins has not well been characterized in epidemiological studies of vitamin A deficiency. IL-6 is a proinflammatory cytokine that plays a role in the upregulation of CRP [24] and AGP [25]. In the present study, IL-6 concentrations had a moderate correlation with both CRP and AGP. CRP is one component of a first line of innate host defense against infectious diseases [24]. The biological function of AGP has not been well characterized [25]. The inverse correlation of hemoglobin with AGP, CRP, and IL-6 suggests the role of proinflammatory cytokines and inflammation in the anemia of chronic infection [26].
In summary, the method of excluding individuals with elevated acute phase proteins from this survey of vitamin A deficiency resulted in sampling bias and a prevalence estimate that was based upon about half of the original sample. The remaining sample was healthier, older, less likely to have been hospitalized, and with a higher proportion of boys and a lower proportion with anemia. The method of excluding individuals with elevated acute phase proteins may lead to underestimation of the prevalence of vitamin A deficiency. Vitamin A deficiency remains a major problem in many developing countries worldwide, and further studies are needed to develop unbiased epidemiological methods for the estimation of the prevalence of vitamin A deficiency in populations.
Acknowledgements
This study was support in part by the Pacific Health Research Institute, UNICEF, the Fergussen Foundation Hawaii, the Hawaii Community Foundation, and the National Institute of Child Health and Human Development (R01 HD30042) of the National Institutes of Health.
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| 15588289 | PMC543451 | CC BY | 2021-01-04 16:39:29 | no | Nutr J. 2004 Dec 8; 3:21 | utf-8 | Nutr J | 2,004 | 10.1186/1475-2891-3-21 | oa_comm |
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Lipids Health DisLipids in Health and Disease1476-511XBioMed Central London 1476-511X-3-271558830410.1186/1476-511X-3-27ResearchUnusual metabolic characteristics in skeletal muscles of transgenic rabbits for human lipoprotein lipase Gondret Florence [email protected] Sanjay B [email protected] Marie [email protected] Patrick [email protected] Céline [email protected] Louis-Marie [email protected] Jean-François [email protected] INRA, UMR sur le Veau et le Porc, 35590 Saint Gilles, France2 INRA, Unité de Recherche sur les Herbivores, 63122 Saint-Genès Champanelle, France3 INRA, Biologie du Développement et Reproduction, Domaine de Vilvert, 78352 Jouy-en-Josas cedex, France2004 9 12 2004 3 27 27 15 11 2004 9 12 2004 Copyright © 2004 Gondret et al; licensee BioMed Central Ltd.2004Gondret et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The lipoprotein lipase (LPL) hydrolyses circulating triacylglycerol-rich lipoproteins. Thereby, LPL acts as a metabolic gate-keeper for fatty acids partitioning between adipose tissue for storage and skeletal muscle primarily for energy use. Transgenic mice that markedly over-express LPL exclusively in muscle, show increases not only in LPL activity, but also in oxidative enzyme activities and in number of mitochondria, together with an impaired glucose tolerance. However, the role of LPL in intracellular nutrient pathways remains uncertain. To examine differences in muscle nutrient uptake and fatty acid oxidative pattern, transgenic rabbits harboring a DNA fragment of the human LPL gene (hLPL) and their wild-type littermates were compared for two muscles of different metabolic type, and for perirenal fat.
Results
Analyses of skeletal muscles and adipose tissue showed the expression of the hLPL DNA fragment in tissues of the hLPL group only. Unexpectedly, the activity level of LPL in both tissues was similar in the two groups. Nevertheless, mitochondrial fatty acid oxidation rate, measured ex vivo using [1-14C]oleate as substrate, was lower in hLPL rabbits than in wild-type rabbits for the two muscles under study. Both insulin-sensitive glucose transporter GLUT4 and muscle fatty acid binding protein (H-FABP) contents were higher in hLPL rabbits than in wild-type littermates for the pure oxidative semimembranosus proprius muscle, but differences between groups did not reach significance when considering the fast-twitch glycolytic longissimus muscle. Variations in both glucose uptake potential, intra-cytoplasmic binding of fatty acids, and lipid oxidation rate observed in hLPL rabbits compared with their wild-type littermates, were not followed by any modifications in tissue lipid content, body fat, and plasma levels in energy-yielding metabolites.
Conclusions
Expression of intracellular binding proteins for both fatty acids and glucose, and their following oxidation rates in skeletal muscles of hLPL rabbits were not fully consistent with the physiology rules. The modifications observed in muscle metabolic properties might not be directly associated with any LPL-linked pathways, but resulted likely of transgene random insertion into rabbit organism close to any regulatory genes. Our findings enlighten the risks for undesirable phenotypic modifications in micro-injected animals and difficulties of biotechnology in mammals larger than mice.
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Background
The endothelial cell-associated lipoprotein lipase (LPL) works to break down triacylglycerol-rich dietary fats absorbed after a meal, thus generating free fatty acids transported in the blood. Earlier works suggested that LPL acts as a metabolic gate-keeper for fatty acid partitioning between adipose tissue for storage and muscle primarily for energy use [1]. Then, variation of LPL activity among fat depots as well as ratio of adipose tissue to skeletal muscle LPL activity, have been proposed to be linked to the development of regional obesity under certain genetic predisposition [2]. Transgenic mouse lines that highly over-express LPL exclusively in muscles, further evidence a role of LPL in the intracellular fate of nutrients into skeletal muscles. Indeed, induced mutant mice over-expressing human LPL (hLPL) exclusively in muscles have, proportional to the level of LPL transgene expression, increases in LPL activity and free FA concentration in muscle [3], a higher number of metabolic organelles (mitochondria, peroxisomes) in muscles [4], and elevated muscle oxidative enzymes activities [3,4]. Thus, a greater use of lipids for energy production during fasting has been suggested in transgenic hLPL mice [5]. In agreement with the inverse relative rates of fatty acid oxidation and glucose utilization in muscle first proposed by Randle and coworkers [6], mice with over-expression of hLPL specifically in muscles show alterations in muscle glucose metabolism, such as elevated blood glucose levels [7], increased glycogen stores [3] or glucose-6-phosphate content [5], and(or) impaired glucose tolerance [5,8]. Finally, as shown in mice over-expressing a mutant defective hLPL, enhanced lipoprotein uptake into cells may also occur via pathways independent of LPL catalytic activity, resulting in a mitochondriopathy as well as in muscle glycogen accumulation similar to the pattern observed in mice expressing active hLPL [9]. However, another point not studied so far in hLPL transgenic animals is that uptake of nutrients and(or) intra-myocellular trafficking to target organelles are facilitated to a great extent by specific transporters and(or) binding proteins. Convincing data are available for the involvement of both membrane-associated and cytoplasmic fatty acid-binding proteins in fatty acid uptake by skeletal muscles [10,11]. Especially, a permissive action of heart-type fatty acid binding protein (H-FABP), also known as muscle FABP or FABP3, in delivering fatty acids to mitochondrial β-oxidation systems has been shown [12,13]. Facilitated glucose transport across membranes of muscle cells mediated by GLUT4 is usually considered as rate-limiting for glucose utilization by skeletal muscles in laboratory rodents [14]. However, it remains to determine whether LPL effects on muscle oxidative pathways involve modifications in the intracellular binding of nutrients.
Altogether, metabolic studies in muscles of transgenic animals help to understand the biological links between fatty acid uptake, intracellular lipid metabolism, and some metabolic disorders such as diabetes in human beings. However, most data have been established in mice. Potential advantages of rabbit compared with mouse as human disease model [15,16] relate in part to its lipoprotein profile which more closely mimics that of humans. Therefore, this study aimed to characterize the oxidative phenotype of two skeletal muscles in transgenic rabbits harboring a DNA fragment of the human LPL gene. The study revealed that despite lack of differences in tissue LPL activity when compared with their wild-type littermates, transgenic hLPL rabbits displayed modest increases in both H-FABP and GLUT4 contents in a pure oxidative muscle and significant lower mitochondrial fatty acid oxidation rates in two skeletal muscles differing in their fiber type composition. This suggested that a random insertion of hLPL DNA into the rabbit genome resulted into unexpected disruption of target nutrient pathways.
Results
Tissue LPL level
An amplification product corresponding to hLPL fragment was evidenced in adipose tissue, semimembranosus proprius and longissimus muscles of hLPL group, proving the expression of the transgene in tissues of the rabbit organism. On the contrary, no signal was detected in the same tissues of wild-type animals (figure 1). Surprisingly, hLPL rabbits and their wild-type littermates exhibited similar LPL activity for adipose tissue and muscles (table 1).
Figure 1 Expression of human lipoprotein lipase (hLPL) mRNA in transgenic rabbits. The cDNA obtained by reverse transcription (RT) of total RNA extracted from skeletal muscles or perirenal fat and primed by random primers followed by 35 cycles of PCR with hLPL-specific primers, was loaded on 2% agarose gel. Reaction was performed in parallel in the absence of reverse transcriptase (RT-), to ensure for lack of genomic DNA contamination. Typical RT-PCR results are shown for semimembranosus proprius muscle. Lanes 1–4: RT-PCR product in hLPL rabbit; Lane 5: RT- in hLPL rabbit; Lane 6: 100 bp DNA ladder; Lane 7–10: RT-PCR product in wild-type rabbit; Lane 11: RT- in wild-type rabbit. A band at the expected size of 137 bp was detected in hLPL rabbits only.
Table 1 Lipoprotein lipase activity1 in tissues of wild-type and hLPL transgenic rabbits
Tissues Wild-type rabbits hLPL rabbits
Perirenal fat 1305 ± 336 1420 ± 500
SMP muscle 2036 ± 583 1546 ± 397
LL muscle 493 ± 111 396 ± 58
1Activities are presented as mean ± SEM in perirenal fat, semimembranosus proprius (SMP) and longissimus (LL) muscles (nmol free fatty acids released. min-1 per g of tissue). There were no differences among groups (P > 0.10).
Plasma metabolites and tissue lipids
Plasma concentrations in triglycerides, free fatty acids, and glucose, were similar in the two genetic groups (table 2). Moreover, there were no differences between the two groups for lipid contents in muscles and perirenal adipose tissue (table 3), as well as for fat proportion relative to body weight (18.8 ± 1.0 g/kg and 20.6 ± 0.9 g/kg in wild-type and hLPL rabbits, respectively).
Table 2 Plasma triglycerides, free fatty acids and glucose levels1 in wild-type and hLPL transgenic rabbits
Plasma metabolites Wild-type rabbits hLPL rabbits
Triglycerides 0.69 ± 0.13 0.81 ± 0.14
Free fatty acids 0.41 ± 0.10 0.41 ± 0.11
Glucose 9.21 ± 0.60 8.60 ± 0.27
1Concentrations are presented as mean + SEM (mmol/L). There were no differences among groups (P > 0.10).
Table 3 Lipids and intracellular nutrient trafficking1 in perirenal fat and skeletal muscles in wild-type and hLPL rabbits
Wild-type rabbits hLPL rabbits
Perirenal fat
Lipids 677 ± 25 691 ± 31
GLUT4 198 ± 18 248 ± 30
Semimembranosus proprius muscle
Lipids 46.5 ± 4.8 42.9 ± 6.5
H-FABP 222 ± 19 *289 ± 20
GLUT4 96.8 ± 14.9 †131.4 ± 8.5
Longissimus muscle
Lipids 13.1 ± 2.3 11.8 ± 1.3
H-FABP 20.4 ± 4.6 32.0 ± 6.0
GLUT4 76.3 ± 11.8 94.4 ± 12.2
1Data are presented as mean + SEM in perirenal fat, semimembranosus proprius (SMP) and longissimus (LL) muscles. Abbreviation used: au (arbitrary units).
*Difference in heart-fatty acid bind protein content (H-FABP) in hLPL rabbits as compared with wild-type littermates (P < 0.05).
†Difference in insulin-sensitive glucose transporter GLUT4 in hLPL rabbits as compared with wild-type littermates (P < 0.10).
Nutrient oxidative pathways
Muscle content in H-FABP (responsible for cytoplasmic binding of fatty acids in muscle cells), was 30% higher in semimembranosus proprius samples of hLPL rabbits compared with wild-type littermates (table 3). The difference between the two groups (+56%) did not reach significance level in the longissimus muscle. This was probably related to a high intra-assay variability, due to the low expression of H-FABP in low-fat glycolytic muscles. The content in insulin-sensitive glucose transporter GLUT4 (the first step in glucose utilization by tissues) was 36% higher (P = 0.07) in hLPL rabbits than in wild-type animals for semimembranosus proprius muscle, but it did not vary in other sites under study (table 3). The mitochondrial oxidation rates of oleate were reduced by 45% and 41% (P < 0.05) in semimembranosus proprius and longissimus muscles, respectively (figure 2), in hLPL rabbits compared with wild-type littermates. By contrast, oxidation rate in peroxisomes did not vary between groups.
Figure 2 Mitochondrial and peroxisomal oxidation rates of oleate. Oxidation rates were measured in freshly excised samples of semimembranosus proprius and longissimus muscles, using [1-14C]oleate as substrate in the presence (peroxisomal oxidation) or absence (total oxidation) of mitochondrial inhibitors. Mitochondrial oxidation rates were calculated by difference between total and peroxisomal oxidation rates. Values shown are mean ± SEM of oleate oxidation (nmole/min-1 per g of muscle wet weight). The * indicates a significant difference (P < 0.05) in mitochondrial oxidation rate in hLPL rabbits in comparison with their wild-type (WT) littermates.
Discussion
Despite a clear evidence for expression of the hLPL transgene in the tissues under study in the hLPL rabbits only, total LPL (human +native) activity in skeletal muscles or perirenal adipose tissue was similar in hLPL rabbits and in their wild-type littermates. This situation contrasts with the moderately enhanced LPL activity in post-heparin plasma reported in another line of hLPL transgenic rabbits [17], and with the marked elevation of LPL activity observed in adipose tissue [18] or skeletal muscle [3,4,7] of hLPL mice. A preliminary study in the heart of our hLPL trangenic rabbits and their wild-type littermates using polyclonal [19] and monoclonal [20] antibodies which recognize different epitopes of the LPL molecule, did not evidence any significant differences in total LPL protein content between groups with both antibodies (data not shown). Furthermore, plasma triglyceride concentration was currently found similar in hLPL transgenic rabbits and wild-type animals, which is again in favor to a similar content in LPL protein among groups rather than to a catalytically defective hLPL enzyme in transgenic rabbits. Indeed, triglyceridemia is consistently found lower in transgenic animals over-expressing a catalytic active hLPL [7,17] and mutant catalytic defective enzyme [9], although this effect may be less pronounced on some genetic backgrounds [9]. Then, a possible explanation is that failure in hLPL mRNA traduction currently resulted in no hLPL protein, due to lack of regulatory essential elements in the transgene sequence [21]. A second explanation may be that expression of hLPL in transgenic animals led to a down regulation of native LPL.
Surprisingly, despite the lack of difference in LPL activity, many metabolic differences were found between hLPL rabbits and their wild-type littermates in semimembranosus proprius (a muscle composed exclusively of slow-twitch type I fibers), and to a lesser extent in the fast-twitch glycoytic longissimus muscle. However, unlike results in mice, our findings reported a lower muscle fatty acid oxidation rates in hLPL rabbits. Others have observed that over-expression of hLPL gene specifically in skeletal muscle of transgenic mice rather led to a dose-dependent increase in oxidative enzymes and to a proliferation of the oxidative specialized organelles [3,4].
According to the well-known fatty acid-glucose cycle in skeletal muscles [6], decreased fat oxidation is generally associated with increased glucose utilization. In accordance with this rule, the muscle content in insulin-sensitive glucose transporter GLUT4, i.e., the first step of glucose utilization in skeletal muscle [14], was currently found higher in the pure oxidative muscle of hLPL rabbits compared with their wild-type littermates. However, possibly enhanced utilization of glucose was not followed by any variations in blood glucose level. Either elevated blood level [7] or similar plasma concentration in glucose [3] have been observed in hLPL mice compared with wild-type animals.
Finally, the reason for a higher content of intra-cytoplasmic fatty acid binding proteins (H-FABP) in semimembranosus proprius muscle of our hLPL rabbits compared with wild-type animals remains largely unknown. Indeed, a preferential involvement of H-FABP in delivering intracellular fatty acids to sites of oxidation has been widely suggested [22]. Here, fatty acid oxidation rate was decreased in hLPL rabbits compared with wild-type littermates, but muscle lipid content and body fat did not vary among groups. One hypothesis may be that fatty acids bound to H-FABP within cell cytoplasm would by-pass any muscle metabolic pathways. If true, fatty acids would be immediately re-exported into the blood circulation and subsequently oxidized in the liver, presumably to prevent muscle from toxicity due to increased fatty acids entry. In vitro and ex vivo findings indeed recently suggest that non-adipose tissue, such as cardiomyocytes, can re-export fatty acids when influx exceeds oxidation rate [23]. However, there is no clear evidence for such a mechanism in our hLPL rabbits, since plasma concentration of free fatty acids was found similar in the two genetic groups. Various results are reported in the literature data on free fatty acids concentration in serum of hLPL transgenic mammals, with either similar, increased, or decreased levels [3,4] depending of mice strain and level of hLPL over-expression.
Altogether, the various metabolic disruptions in skeletal muscles of hLPL rabbits are in favor to a random integration of the micro-injected hLPL DNA within or close to endogenous genes. In transgenic mice, estimates of the frequency of these insertional mutations range from 7 to 20% [24]. This may have resulted in a loss of function of neighboring genes, aberrant expression patterns, and therefore in unexpected phenotypes. However, genes coding for LPL, H-FABP, GLUT4, and oxidative pathway (e.g. carnitine palmitoyl-transferase I) are not clustered on the same chromosome in the human genome [25] and likely, although not available, in the rabbit map. Therefore, if any, the integration site of the foreign DNA must have conflicted with regulatory elements of any molecular factors able to modify whole nutrient metabolic cascade.
Conclusions
During the last 15 years, transgenesis has been extended from mice to larger mammals, with the aim of benefiting human health. Transgenic rabbits for LPL gene may offer useful models to test the relationships between uptake of nutrients, intracellular trafficking and subsequent metabolic fate in a species sharing a lipoprotein profile closely similar to that in Human. However, we currently reported alterations of nutrient bindings and oxidative metabolism in skeletal muscles of hLPL rabbits, despite the lack of difference in tissue LPL activity between transgenic rabbits and their wild-type littermates. It is thus suggested that hLPL phenotype emerged from insertional mutation of hLPL DNA within or close to endogenous genes. This study underlined the risk of unpredictable phenotypic properties in micro-injected transgenic rabbits, and thereby the difficulty of animal biotechnology in mammals larger than mice. Nevertheless, transgenic rabbits remain useful tools for understanding the relative importance of the various metabolic pathways involved in the control of tissue lipid content, especially when the genetic map now under progress will be available in the rabbit.
Methods
Rabbits
The Genetic Committee of the French Ministry of Agriculture approved the experiment. Rabbits were reared and killed in accordance with the French regulations for humane care and use of animals in research. New-Zealand White rabbit does were super-ovulated by injections of porcine-follicle-stimulating hormone, as previously described [26], and were mated to males of the same genetic background. Embryos were collected 17 hours later. The human LPL (hLPL) fragment of genomic DNA, inserted into a 90-kilobase P1 phagemid together with regulatory elements, was kindly provided by N. Duverger (Aventis, Evry, France). The expression of hLPL fragment in the host organism was governed by the P1 phagemid promoter. DNA solution was injected into the male pronuclei, and the injected embryos were transferred to the pseudo-pregnant females (INRA, Laboratoire de Biologie Cellulaire, Jouy-en-Josas, France). Genomic DNA was extracted from ear biopsy in the offspring [27]. Presence or absence of hLPL DNA was screened by PCR using 5'-CCCTTTTTCCTGTCTTTTT-3' as sense and 5'-AGTGCTTGAGACTGTC-TCCTAA-3' as anti-sense primers. These primers framed a fragment of 201 bp of the human LPL gene spanning intron 9 and exon 10.
Two transgenic littermate male founders were cross-bred with 20 females of a standard New-Zealand White line (A-1067, INRA, France) at the INRA experimental unit (Le Magneraud, Surgères, France), to provide F1 animals for analysis. Pup genotypes were determined at the hLPL locus by PCR from tail tip DNA at the age of 4 weeks, using the primers described above. Control PCR amplification of the actin gene was performed in parallel to ensure DNA quality.
After weaning (5 weeks), young rabbits were housed collectively by genotype (8 animals per cage), under a controlled light/dark cycle (16/8 h). They were offered free access to water and to a standard rabbit pelleted diet (16.5% crude protein, 16.4% cellulose, 2.8% fat, 8.3% ash, and 3790 kcal/kg gross energy). At 10 weeks of age, pairs of hLPL rabbits and wild-type littermates of similar body weight (2400 g ± 53, n = 6 in each genotype) were selected within litters, and bled in the fed state.
Analysis of plasma metabolites
Enzymatic methods adapted to a Cobas Mira multi-analyzer apparatus (ABX, Montpellier, France), were used to determine levels of triglycerides (kit PAP 150, BioMérieux, Marcy l'Etoile, France), free fatty acids (kit Wako NEFA-C kit, Richmond, VA, USA) and glucose (kit PAP 1200, BioMérieux, Marcy l'Etoile, France) in rabbit plasma collected at the time of the death.
Tissue preparation
Portions of perirenal fat, semimembranosus proprius (SMP) as a muscle composed solely of slow-twitch oxidative fibers, and longissimus (LL) muscle representing predominantly fast-twitch glycolytic fibers, were stored at -70°C until RNA analysis and biochemical measurements. About 300 mg of each muscle was homogenized immediately after sacrifice in an appropriated buffer for measurements of ex vivo oxidation rates, as described previously [28,29].
RNA analysis for expression of the transgene
Total RNA from 600 mg of tissues was extracted by acid guanidium thiocyanate-phenol chloroform method [30], and was reverse transcribed into cDNA using pd(N)6 random primers (Amersham Biosciences, Orsay, France). Nested PCR (Quiagen, Courtaboeuf, France) was carried out (35 cycles) using hLPL cDNA specific primers, as follows. 5'-TTCTGTGAAGAATGAAGTGG-3' as sense and 5'-AGTGCTTGACA-CTGTCTCCTAA-3' as anti-sense primers framed a 137 bp fragment in the exon 10 of hLPL gene. PCR products were loaded on 2% agarose gel. The amplified fragment was picked up and sequenced (ESGS Cybergene, Evry, France). In each sample, the absence of genomic DNA contamination was checked by performing RT-PCR reaction without reverse transcriptase.
Lipoprotein lipase activity
Lipoprotein lipase (LPL) activity was assessed after homogenization of the tissues in a buffer composed of ammonia-HCl (25 mM) pH 8.2, containing EDTA (5 mM), Triton-X-100 (8 g/l), sodium dodecyl sulfate (0.10 g/l), heparin (5000 IU/l) and peptidase inhibitors. Insoluble material was discarded by centrifugation at 20000 × g for 20 min at 4°C. As previously described [31], rat serum was used as activator, and Intralipid® (Pharmacia, Stockholm, Sweden), into which [3H] triolein has been incorporated, was used as the substrate. Liberated [3H]-free fatty acids were quantified by liquid scintillation.
Biochemical analyses
Tissue lipid content was determined after chloroform/methanol extraction [32]. Muscle content in H-FABP was determined by ELISA analysis on cytosolic protein preparations [33] using a rat polyclonal antibody [34]. Taken into account yields of proteins in cytosolic fractions, results were converted into arbitrary densitometric (DO) units per g tissue wet weight. Insulin-sensitive glucose transporter GLUT4 content was investigated by Western-blot analysis [35] using a polyclonal antibody raised against a synthetic peptide of the C-terminal part of GLUT4, on tissue preparations obtained for LPL activity determination. Results were converted into arbitrary densitometric (DO) per g tissue wet weight, after taking into account yields of proteins in tissue preparations.
Rates of fatty acid oxidation
Oxidation rate of oleic acid was determined in freshly excised muscles as described earlier for rat and bovine muscles [28], with minor modifications to take into account rabbit specificity [29]. Briefly, samples were minced with scissors and homogenized at a tissue concentration of 60 mg/mL in 0.25 M sucrose, 2 mM EDTA, and 10 mM Tris-HCl ice-cold buffer (pH = 7.4), using a glass-glass homogenizer. A tracer amount of [1-14C]oleic acid bound to defatted albumin in a 5:1 molar ratio was used as substrate. Oleate oxidation was measured using L-carnitine and other cofactors, in the absence (total oxidation rate) or presence (peroxisomal oxidation) of mitochondrial inhibitors of the respiratory chain (i.e., 75.6 μM antimycin A, and 10 μM rotenone, SIGMA, St-Louis, MO). The difference between total oxidation and peroxisomal oxidation was considered to be mitochondrial oxidation. All assays were performed in triplicates.
Statistics
The Kruskal-Wallis non-parametric test was used to analyze differences between groups (SAS Inst, Cary NC, NY, USA). All data are presented as mean ± SEM.
Authors' contributions
FG, JFH and PH conceived of the study, participated in its design and co-ordination. FG, SBJ and JFH carried out biochemical analyses. MD carried out pup genotyping. CV and LMH carried out micro-injection and provide transgenic breeder animals. FG, JFH and MD drafted the manuscript. All authors read and approved the final manuscript.
Acknowledgements
This work was supported in part by a grant of the Animal Nutrition and Breeding department of INRA (France). We thank Nicolas Duverger (Aventis, France) for the kind gift of hLPL transgene construction.
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| 15588304 | PMC543452 | CC BY | 2021-01-04 16:39:18 | no | Lipids Health Dis. 2004 Dec 9; 3:27 | utf-8 | Lipids Health Dis | 2,004 | 10.1186/1476-511X-3-27 | oa_comm |
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Cardiovasc UltrasoundCardiovascular Ultrasound1476-7120BioMed Central London 1476-7120-2-271558830510.1186/1476-7120-2-27ResearchAblation lesions in Koch's triangle assessed by three-dimensional myocardial contrast echocardiography Szili-Torok Tamas [email protected] Geert-Jan [email protected] Marcoen [email protected] Andrew [email protected] Folkert Ten [email protected] Jos [email protected] Luc [email protected] Department of Cardiology, Thoraxcentre, Erasmus MC, Rotterdam, The Netherlands2004 9 12 2004 2 27 27 1 11 2004 9 12 2004 Copyright © 2004 Szili-Torok et al; licensee BioMed Central Ltd.2004Szili-Torok et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Myocardial contrast echocardiography (MCE) allows visualization of radiofrequency (RF) ablation lesions in the left ventricle in an animal model. Aim: To test whether MCE allows visualization of RF and cryo ablation lesions in the human right atrium using three-dimensional echocardiography.
Methods
18 patients underwent catheter ablation of a supraventricular tachycardia and were included in this prospective single-blind study. Twelve patients were ablated inside Koch's triangle and 6, who served as controls, outside this area. Three-dimensional echocardiography of Koch's triangle was performed before and after the ablation procedure in all patients, using respiration and ECG gated pullback of a 9 MHz ICE transducer, with and without continuous intravenous echocontrast infusion (SonoVue, Bracco). Two independent observers analyzed the data off-line.
Results
MCE identified ablation lesions as a low contrast area within the normal atrial myocardial tissue. Craters on the endocardial surface were seen in 10 (83%) patients after ablation. Lesions were identified in 11 out of 12 patients (92%). None of the control patients were recognized as having been ablated. The confidence score of the independent echo reviewer tended to be higher when the number of applications increased.
Conclusions
1. MCE allows direct visualization of ablation lesions in the human atrial myocardium. 2. Both RF and cryo energy lesions can be identified using MCE.
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Introduction
Catheter ablation is a curative treatment for most patients with arrhythmias. In some patients, the results are still suboptimal[1] because of inadequate lesion formation during ablation. Therefore, in these patients direct visualization of ablation lesions may have significant impact on the outcome of the ablation procedures. Direct visualization can also provide additional information for both the development and testing of new dedicated ablation tools. Intracardiac echocardiography (ICE) has been extensively investigated for this purpose [2-6], but the reported results are disappointing[5,7]. Recently, myocardial contrast echocardiography (MCE) has been tested for visualization of ablation lesions in animals in the left ventricle during continuous intracoronary echocontrast infusion[8]. The aim of the present study was to assess the potential use of MCE to demonstrate ablation lesions in human atrial myocardial tissue with continuous venous echocontrast administration.
Methods
Patients and study protocol
18 patients were included into this study. The clinical characteristics of the patients are shown in Table 1. All patients underwent EP study and subsequent ablation procedures for supraventricular tachycardia. The Ethics Committee of Erasmus MC, Rotterdam, The Netherlands approved this study. Written, informed consent was obtained. Regardless of the final diagnosis, Koch's triangle was visualized with ICE in all patients at baseline without echocontrast and immediately thereafter, during continuous echocontrast infusion (SonoVue, Bracco). After the ablation procedure, which was either inside or outside Koch's triangle, the ICE procedure was repeated in six patients without echocontrast and in all patients using echocontrast. All ICE procedures were performed using a respiration and ECG gated and triggered pullback technique allowing three dimensional (3D) reconstruction of Koch's triangle. All 2D recordings were analyzed off-line by two independent echocardiographers and they provided confidence scores using a 1–10 grade scale. They were not aware that 6 patients were not ablated in Koch's triangle. This protocol implies that this was a prospective single blind study. 3D reconstruction of the ablation lesions was performed in patients where ablation lesions were seen.
Table 1 Clinical characteristics and procedural outcome of the study patients
Overall group
Number of patients (n) 18
Gender (F/M) 10/8
Age (years ± SD) 49.3 ± 15.7
AVNRT (n) 12
AP (n) 6
Successful ablation (n) 17
Procedure time (min ± SD) 177.1 ± 68.9
Fluoroscopy time (min ± SD) 38.8 ± 27.5
RF/cryo applications (n) 4.2 ± 5.2
n = number, F = female, M = male, AVNRT = atrio-ventricular nodal reentry tachycardia, AP = accessory pathway, min = minutes, RF = radiofrequency, cryo = cryothermy, SD = standard deviation
Electrophysiology testing and ablation
Standard electrophysiology (EP) and ablation procedures were undertaken using quadripolar electrode catheters in the high right atrium, to record the His bundle electrogram, in the right ventricle and a decapolar diagnostic catheter was inserted into the coronary sinus (CS). The initial portion of the EP procedure was directed at determining the presence of dual AV nodal physiology or accessory pathways, measuring the conduction properties and refractory periods of the fast and slow AV nodal pathways (if present), and determining the mechanism of the paroxysmal SVT. Mapping was performed and after the target site was identified, ablation was applied. Cryothermy and radiofrequency energy were used alternately during the study period.
Myocardial contrast echocardiography
MCE was performed using SonoVue (Bracco), which is a second generation contrast agent made of microbubbles stabilized by phospholipids and containing sulphur hexafluoride. The mean bubble diameter is 2.5 μm and more than 90% of the bubbles are smaller than 8 μm. The blood level curve shows a distribution half-life of about 1 minute and an elimination half-life of about 6 minutes[9]. In this study we administered SonoVue by continuous intravenous infusion through the cubital vein at a rate of 100 ml/hour. Gain settings were not changed throughout the study.
Intracardiac echocardiography (ICE)
The ClearView™ system (CardioVascular Imaging Systems Inc, Fremont, CA) was used with an 8F sheath-based ICE imaging catheter that incorporates a 9 MHz beveled single-element transducer rotating at 1800 rpm (model 9900, EP Technologies, Boston Scientific Corp., San Jose, CA, USA).
ECG- and respiration-gated image acquisition and 3-D image processing
A custom-designed ECG- and respiratory-gated pullback device and a 3D-ultrasound workstation (EchoScan, TomTec GmbH, Munich, Germany) were used to acquire and process the ICE images using a technique described elsewhere[10]. The pullback device is controlled by the 3D workstation and uses a stepping motor to move the catheter stepwise and linearly through the right atrium. The workstation receives video input from the ICE system and an ECG- and respiration-signal (impedance measurement) from the patient. Prior to the acquisition run, the range of RR- and breathing-intervals are measured to define the upper- and lower-limits. The workstation starts acquisition of 2D images after detecting the peak of the R-wave and in the same phase of respiration, at a speed of 25 images/ sec (image interval 40 ms). After acquiring one cardiac cycle, the workstation stores the images, and the catheter is then pulled back by a 0.5-mm axial increment. This process is repeated until the inferior vena cava (IVC) is reached. The acquisition time is much shortened when all cardiac cycles are of the same length, therefore, the right ventricular apex is paced at 100 bpm. In accordance with their timing in the cardiac cycle, all images are formatted in volumetric data sets (256*256*256 pixels/each 8 bits). During post-processing, several algorithms are applied to reduce noise, enhance edges, and reduce spatial artifacts (ROSA filter).
Statistical analysis
Continuous variables are expressed as mean ± standard deviation. Correlation analysis between the confidence scores and number of ablation lesions were performed using Pearson's test. The level of significance was set at a p value of 0.05.
Results
Ablation results (Table 1)
Of the 18 patients undergoing catheter ablation of supraventricular arrhythmias 12 had AVNRT tachycardia. 6 out of these 12 patients were ablated using cryothermy. All but one patient were successfully ablated (one patient with AVNRT). The number of applications in Koch's triangle was 6 ± 4.9, ranging from 1 to 15 applications. The fluoroscopy and procedure times were 45.7 ± 30.8 min and 196 ± 80.2 min, respectively.
Myocardial contrast echocardiography (Table 2)
MCE identified ablation lesions as a low contrast area within the normal atrial myocardial tissue (Figure 1). Lesions were identified in 11 out of 12 patients (91%) ablated in Koch's triangle. In only one patient with a single radiofrequency application, was the lesion not recognized. None of the control patients were recognized as having been ablated. The average confidence scores of the independent echo reviewers were 8.5 ± 2.4 and 8.1 ± 2.4, respectively. Both reviewer's confidence scores ranged from 3–10. The confidence score of the independent echo reviewer tended to be higher when the number of applications increased (Reviewer 1: r = 0.697, p = 0.025; Reviewer 2: r = 0.748, p = 0.013). Craters on the endocardial surface were seen in all 12 patients after ablation, by both echo reviewers (Figures 1 and 2).
Table 2 Results of myocardial contrast echocardiography in patients ablated in Koch's triangle
Reviewer 1 Reviewer 2
Pt. No Ablation energy Lesion before ablation Lesion after ablation Crater after ablation Lesion before ablation Lesion after ablation Crater after ablation
NC C NC C NC C NC C
1. RF - - NA + + - - NA + +
2. Cryo - - NA + + - - NA + +
3. RF - - NA - + - - NA - +
4. Cryo - - NA + + - - NA + +
5. RF - - NA + + - - NA + +
6. Cryo - - NA + + - - NA + +
7. RF - - NA + + - - NA + +
8. Cryo - - NA + + - - NA + +
9. RF - - - + + - - - + +
10. Cryo - - - + + - - - + +
11. RF - - - + + - - - + +
12. Cryo - - - + + - - - + +
RF = radiofrequency, Cryo = cryothermy, No = number, NC = no contrast (without contrast), C = with contrast, NA = not applicable, Pt = patient
Figure 1 Two-dimensional intracardiac echocardiography images showing part of Koch's triangle between the tricuspid valve and the ostium of the coronary sinus under four different conditions. A: Native 2D horizontal cross-sectional echocardiography image before ablation. B: The same region before ablation with use of echocontrast. C: The same region after radiofrequency energy ablation without echocontrast infusion. A crater as an indirect sign of the ablation lesion (arrow) can be seen on the endocardial surface at the atrial side adjacent to the tricuspid valve. D: The same region after radiofrequency energy ablation and during echocontrast infusion. The ablation lesion (arrow) is visualized as a low contrast area within the atrial myocardial tissue. A crater can be seen on the atrial side adjacent to the tricuspid valve. In both C and D situations (post-ablation) there is significant swelling of the ablated region compared with pre-ablation situations (A and B). ICE = central artifact of the intracardiac echocardiography catheter, TV = tricuspid valve, RA = right atrium, CSos = ostium of the coronary sinus
Figure 2 Three-dimensional reconstruction of Koch's triangle: "En face" view of a radiofrequency ablation lesion (arrow). The crater on the right atrial endocardial surface is also well visualized directly to the right. RA = right atrium, TV = tricuspid valve, SUP = superior, INF = inferior
3D reconstruction of the lesion
Koch's triangle was successfully reconstructed in 3D in all patients in whom the ablation lesion was previously identified. Lesions could be easily found by 3D echocardiography. An "en face" view of the lesion could be reconstructed in all of these patients (Figure 2).
Discussion
This study demonstrates that MCE allows visualization of ablation lesions in the human right atrial myocardium during continuous venous echocontrast infusion. This potentially opens a new avenue for objective, and easily accessible evaluation of ablation lesions. Since transmural lesion formation is critical for successful ablation[11], the MCE method may have a significant impact on the outcome of ablation procedures.
The role of echocardiography in assessment of ablation lesions
Crater formation and tissue changes as increased echo-density have been detected using ICE immediately after RF ablation[12,13]. However, the lesions are not always seen after ablation and indirect signs are being searched for such as changes in local wall thickness[3,4]. The magnitude of the observed changes showed a certain level of correlation with the lesion size. These indirect signs may indicate appropriate lesion formation, but there is an obvious need for direct visualization of the lesions. MCE offers this potential and has allowed visualization of ablation lesions in animals in the left ventricle during continuous intracoronary echocontrast perfusion[8]. The investigators demonstrated high accuracy and reliability in visualizing tissue damage.
Human use does not seem to be practical in this way and neither was safety assessed. In the present study we used continuous peripheral venous echocontrast infusion and we screened lesions in Koch's triangle in humans. We showed in atrial myocardial tissue that the lesions can be visualized and continuous venous infusion provides sufficient differences in echo contrast intensity to directly visualize ablation lesions after focal ablation. In this study we used cryothermy as well as radiofrequency energy for creating lesions in Koch's triangle. We do not have a sufficient number of patients for volumetric comparison of ablation lesions using this method, but it seems that both types of ablation lesion can be reliably visualized using this technique.
Limitations of the study
This single blind controlled study allowed two independent reviewers to examine the 2D ICE recordings. Although in none of the control patients a lesion in the area of interest was recognized, there was one patient with a single ablation lesion who was not identified by the two independent reviewers. This may suggest that the sensitivity of the technique is still suboptimal. One possible reason is that the concentration of the echocontrast infusion was titrated too low. This is also reflected in the results of the confidence scores. These showed a linear correlation with the number of applications. Furthermore, a correlative study to gross inspection and histopathology would be advantageous. Obviously, this cannot be done in humans.
The role of 3D reconstruction and future clinical implications
3D reconstruction and a creation of a volumetric data set allow visualization of an "en face" view of the ablation lesion. Without 3D reconstruction determination of the depth and the shape of the ablation lesion was not possible. Therefore the technique could be used to assess whether the ablation lesion was transmural. Furthermore, with 3D reconstruction, the volume of the lesion can be determined. This could potentially be a major asset for clinical electrophysiology since on line 3D echocardiography would allow real-time assessment of ablation lesions. Most importantly, the appropriateness of such lesions could be judged during linear ablations and the continuity of the lines could be checked.
In conclusion, MCE is a safe and promising new method to detect ablation lesion in the human atrial tissue.
==== Refs
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| 15588305 | PMC543453 | CC BY | 2021-01-04 16:38:29 | no | Cardiovasc Ultrasound. 2004 Dec 9; 2:27 | utf-8 | Cardiovasc Ultrasound | 2,004 | 10.1186/1476-7120-2-27 | oa_comm |
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Proteome SciProteome Science1477-5956BioMed Central London 1477-5956-2-81559834610.1186/1477-5956-2-8ResearchExpressional patterns of chaperones in ten human tumor cell lines Myung Jae-Kyung [email protected] Leila [email protected] Maureen [email protected] Irene [email protected] Gert [email protected] Department of Pediatrics, Medical University of Vienna, Vienna, Austria2004 14 12 2004 2 8 8 5 8 2004 14 12 2004 Copyright © 2004 Myung et al; licensee BioMed Central Ltd.2004Myung et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Chaperones (CH) play an important role in tumor biology but no systematic work on expressional patterns has been reported so far. The aim of the study was therefore to present an analytical method for the concomitant determination of several CH in human tumor cell lines, to generate expressional patterns in the individual cell lines and to search for tumor and non-tumor cell line specific CH expression.
Human tumor cell lines of neuroblastoma, colorectal and adenocarcinoma of the ovary, osteosarcoma, rhabdomyosarcoma, malignant melanoma, lung, cervical and breast cancer, promyelocytic leukaemia were homogenised, proteins were separated on two-dimensional gel electrophoresis with in-gel digestion of proteins and MALDI-TOF/TOF analysis was carried out for the identification of CH.
Results
A series of CH was identified including the main CH groups as HSP90/HATPas_C, HSP70, Cpn60_TCP1, DnaJ, Thioredoxin, TPR, Pro_isomerase, HSP20, ERP29_C, KE2, Prefoldin, DUF704, BAG, GrpE and DcpS.
Conclusions
The ten individual tumor cell lines showed different expression patterns, which are important for the design of CH studies in tumor cell lines. The results can serve as a reference map and form the basis of a concomitant determination of CH by a protein chemical rather than an immunochemical method, independent of antibody availability or specificity.
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Background
Chaperones (CH) and heat shock proteins (HSPs) play important roles in tumor biology and still are holding centre stage. The heat shock response was discovered in 1962 by Ritossa [1], who reported that elevated temperature led to the appearance of a new 'puffing' pattern in the salivary gland polytene chromosomes of Drosophila busckii. Since then, efforts from a large number of investigators have shown that the heat shock response is ubiquitous and highly conserved. It is observed in all organisms from bacteria to plants and animals. CH form an essential defense mechanism for protection of cells from a variety of harmful conditions, including temperature elevation or heat shock, decrease in pH, hypersalinity, alcohols, heavy metals, oxidative stress, inhibitors of energy metabolism, fever or inflammation [2,3]. This broad spectrum of functions gave rise to the term 'molecular chaperone' an entity that acts to assist other proteins' folding and maturation in the cell. However, not all HSPs are CH and not all CH are HSPs [4].
Genetic studies showed that most HSPs are essential to life. They are believed to play an indispensable role in the conformational maturation of a nascent polypeptide chain in prokaryotic and eukaryotic cells. Traditionally, HSPs are grouped into five major families according to molecular weights. They were designated HSP90 (heat shock protein of apparent molecular weight 90 kDa), HSP70 (70-kDa HSPs), HSP60 (60-kDa HSPs), HSP40 or DnaJ (40-kDa HSPs), and the small heat shock proteins (sHSPs) [5-7].
The connection of HSPs with tumor immunity was discovered in the 1980s [8-10]. It was found that structurally unaltered HSPs which are purified from tumor cells could immunize animals to generate tumor-specific immunity whereas corresponding preparations from normal tissues did not. Many recent interesting observations have been made with regards to CH's ability to regulate tumor biology. HSP70 and other CH are known to be determinants of cell death and cell transformation processes. Elevated expression of HSP70 and HSP90 in tumor cells was detected in several cases [11,12]. Recently, it has been recognised that HSPs regulate apoptosis. HSP27 and HSP70 are antiapoptotic, while HSP60 and HSP10 are proapoptotic. The ability of HSPs to protect cells from stressful stimuli suggests that these proteins play a role in tumorigenicity, with the fact that cells or tissues from various tumors have been shown to express unusually high levels of one or more HSPs. Experimental models support the role of HSPs in tumorigenesis since HSP27 and HSP70 have been shown to increase the tumorigenic potential of rodent cells in syngeneic hosts [13,14].
The contribution of HSPs to tumorigenesis may be attributed to their pleiotropic activities as molecular chaperones that provide the cancer cell with an opportunity to alter protein activities, in particular components of the cell cycle machinery, kinases and other proteins implicated in tumor progression. HSP70 chaperone activity may also influence tumorigenesis by regulating the activity of proteins that are involved in the cell cycle machinery [15].
Clinically, in a number of cancers such as leukaemia, breast cancer and endometrial cancer, an increased level of HSP27, relative to its level in non-transformed cells has been detected [16]. In addition, increased expression of HSP70 has also been reported in high-grade malignant tumors such as breast and endometrial cancer, osteosarcoma and renal cell tumors [17-19]. HSP70 levels correlate with malignancy in osteosarcoma and renal cell tumors; its expression is paradoxically associated with improved prognosis [18,20]. HSP90 and HSP60 are also over-expressed in breast tumors, lung cancer, leukaemias and Hodgkin's disease [19,21-23]. The molecular basis for over-expression of HSPs in tumors is not completely understood and may have different etiologies. For example, overexpression may be due to the suboptimal cellular environment in poorly vascularised hypoxic tumors or to growth conditions within the solid tumor [24].
Many studies have focused on the critical role of chaperones in protein folding, their relevance in protein conformational diseases and tumorigenesis. There is, however, no systematic information available on their expressional pattern in individual tumor cell lines in a wide range of tumors.
This study addresses the question of differential chaperone expression studied in ten different tumor and three normal cell lines using 2-DE and matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-TOF/TOF) allowing concomitant determination of many CH at the protein chemical level rather than by immunochemical methods, independent of antibody availability and specificity.
Results
Chaperone proteins were taken from the list of all identified proteins from ten tumor and three normal cell lines using a predetermined list of expected CH based upon our own experiments, databases and literature. Identified CH proteins made up approx. 12% of all identified proteins in all cell lines studied. All major housekeeping proteins (cytoskeleton and metabolic) expected to occur in any cell lines were present in all cell lineages studied (data not shown).
A series of chaperone proteins with different expression patterns in ten different tumor and three normal cell lines using 2-DE and MALDI-MS were identified and listed (see Additional file 1 and 2) in Table 1 and 1-1. Chaperone proteins were classified according to their domains. Most proteins have similar pI values and molecular weights with theoretical value. The observed pI was represented in Table 2 (see Additional file 3) with the total score and the number of peptides matched. The expressional patterns of each tumor and normal cell line are shown in Figure 1,2,3,4,5,6,7,8,9,10,11,12,13.
Figure 1 Saos-2 cell Colloidal Coomassie Blue stained 2D gels representing protein maps of Saos-2 cell line: Swiss prot accession numbers are used to identify proteins.
Figure 2 SK-N-SH cell Colloidal Coomassie Blue stained 2D gels representing protein maps of SK-N-SH cell line: Swiss prot accession numbers are used to identify proteins.
Figure 3 HCT116 cell Colloidal Coomassie Blue stained 2D gels representing protein maps of HCT 116 cell line: Swiss prot accession numbers are used to identify proteins
Figure 4 CaOv-3 cell Colloidal Coomassie Blue stained 2D gels representing protein maps of CaOv-3 cell line: Swiss prot accession numbers are used to identify proteins
Figure 5 A549 cell Colloidal Coomassie Blue stained 2D gels representing protein maps of A549 cell line: Swiss prot accession numbers are used to identify proteins
Figure 6 HL-6 cell Colloidal Coomassie Blue stained 2D gels representing protein maps of HL-6 cell line: Swiss prot accession numbers are used to identify proteins
Figure 7 A-375 cell Colloidal Coomassie Blue stained 2D gels representing protein maps of A-375 cell line: Swiss prot accession numbers are used to identify proteins
Figure 8 A-673 cell Colloidal Coomassie Blue stained 2D gels representing protein maps of A-673 cell line: Swiss prot accession numbers are used to identify proteins
Figure 9 MCF-7 cell Colloidal Coomassie Blue stained 2D gels representing protein maps of MCF-7 cell line: Swiss prot accession numbers are used to identify proteins
Figure 10 Hela cell Colloidal Coomassie Blue stained 2D gels representing protein maps of Hela cell line: Swiss prot accession numbers are used to identify proteins
Figure 11 HK-2 cell Colloidal Coomassie Blue stained 2D gels representing protein maps of Kidney HK-2 cell line: Swiss prot accession numbers are used to identify proteins
Figure 12 Lymphocyte 3610 cell Colloidal Coomassie Blue stained 2D gels representing protein maps of Lymphocyte 3610 cell line: Swiss prot accession numbers are used to identify proteins
Figure 13 Hs 545 SK cell Colloidal Coomassie Blue stained 2D gels representing protein maps of Fibroblast Hs 545 SK cell line: Swiss prot accession numbers are used to identify proteins
Proteins with HATPase_C and HSP90 domains
Prominent members of the HSP90 family of proteins are heat shock protein 90-alpha (HSP90α), heat shock protein-beta (HSP90β) and endoplasmin (GRP94) [25]. The two HSP90 isoforms are essential for the viability of eukaryotic cells. They are rather abundant constitutively, making up 1–2% of cytosolic proteins, and can be further stimulated in their expression level by stress. HSP90α and HSP90β were expressed in HCT116 and Hela cells. In addition, HSP90α was also detected in A-673, MCF-7 and A549 cell lines and HSP90β was in Saos-2, SK-N-SH, HL-60 and A375 cell lines, respectively. The isoform of HSP90β (Acc.No.Q9NTK6) was also detected in A-673 cells and is represented in Figure 8.
All cell lines except adenocarcinoma cells (CaOv-3) expressed endoplasmin (GRP94) protein that functions in the endoplasmic reticulum [26] and the protein similar to tumor rejection antigen is observed in the osteosarcoma (Saos-2) cell line represented in Figure 1.
Heat shock protein 75 kDa is named tumor necrosis factor type 1 receptor associated protein or TRAP1. The 2.4-kilobase TRAP-1 mRNA was variably expressed in skeletal muscle, liver, heart, brain, kidney, pancreas, lung, and placenta. TRAP-1 mRNA was also detected in each of eight different transformed cell lines [27]. At the protein level, it was expressed in ovary (CaOv-3), lung (A549), skin (A-375), rhabdomyosarcoma (A-673), breast cancer (MCF-7) and cervix carcinoma (Hela) cells.
Proteins with HSP 70 domain
The HSP70 family constitutes the most conserved and best studied class of HSPs. Human cells contain several HSP70 family members including stress inducible HSP70, constitutively expressed HSC70, mitochondrial HSP75, and GRP78 localised in the endoplasmic reticulum [28]. HSP70 has been shown to increase the tumorigenicity of cancer cells in rodent models [13]. All ten tumor cells expressed HSC70 proteins and the isoform of HSC70 was also observed in promyeloblasts (HL-60).
The 105-kDa protein shows high similarity with HSP90 on peptide mapping with trypsin digestion. Except for the molecular mass, the physicochemical properties of the 105-kDa protein are similar to those of HSP90, although it has a HSP70 domain. This is detected in the brain, but not in the liver, lung, spleen, kidney, ovary, or uterus, in contrast to the wide distribution of HSP90 [29]. In case of tumor cells, it was observed in adenocarcinoma (CaOv-3), rhabdomyosarcoma (A-673), breast cancer (MCF-7) and cervix carcinoma (Hela) cells and is shown in Figure 4,8,9 and 10.
HSP70 kDa protein 1(HSP70-1) stabilises preexistent proteins against aggregation and mediate the folding of newly translated polypeptides in the cytosol as well as within organelles. HSP70-1 in mitochondria and endoplasmic reticulum plays an additional role by providing a driving force for protein translocation. They are involved in signal transduction pathways in cooperation with HSP90 and participate in all these processes through their ability to recognise non-native conformations of other proteins. Eight cell lines except SK-N-SH and CaOv-3 cells expressed HSP70-1.
Heat shock 70 kDa protein 4 was observed in SK-N-SH, HL-60 and A-673 cells and is represented in Figure 2,6 and 8.
Stress-70 protein (GRP75) was proposed to be implicated in the control of cell proliferation and cellular aging [30] and was observed in eight cell lines except MCF-7 and Hela cells.
Heat shock-related 70 kDa protein 2 and 150 kDa oxygen-regulated protein were detected in SK-N-SH and MCF-7 cells.
The best characterised GRP is a 78 kDa protein known as GRP78, which is identical to BiP, the immunoglobulin heavy chain binding protein [31]. Since GRP78 shares similar function and 60% amino acid identity with HSP70 [32], it is also categorised within the HSP70 multi-gene family [33]. GRP78 / BiP protein were expressed in all ten tumor cells.
Proteins with Cpn60_TCP1 domain
Proteins with a Cpn60_TCP1 domain are involved in chaperonins, which belong to the 55–64 kDa family of HSP or stress proteins [34]. Mammalian HSP60, also called chaperonin, is mostly contained within the mitochondrial matrix, although it has also been detected in extramitochondrial sites. HSP60 participates in the folding of mitochondrial proteins and facilitates proteolytic degradation of misfolded or denatured proteins in an ATP-dependent manner. The chaperone function of HSP60 is regulated by HSP10, which binds to HSP60 and regulates substrate binding and ATPase activity [7]. In this study, all cells except MCF-7 expressed 60-kDa heat shock protein.
The chaperonin-containing T-complex polypeptide has many subunits. Among these, alpha, beta, gamma, epsilon and zeta units as well as the isoform of epsilon subunit were shown in 2-DE gels of tumor cell lines. T-complex protein 1, zeta subunit was detected in all ten tumor cell lines.
Proteins with DnaJ and DnaJ_C domains
Among numerous co-chaperones for HSC70 [35], the DnaJ family is an essential group.. The human DnaJ (HSP40) family [36] is a noncanonical member of DnaJ, which lacks the zinc-finger domain. DnaJ homolog subfamily A member 1 and 2 were detected in A549 and SK-N-SH cell lines and DnaJ homolog subfamily B member 11 was found in SK-N-SH, A549 and Hela cells (Figure 2,5 and 10).
Proteins with a thioredoxin domain
Protein disulfide isomerase, protein disulfide A3 and A6 containing 2 thioredoxin domains are members of the protein disulfide isomerase family and rearrange both intra-chain and inter-chain disulfide bonds in proteins to form the native structures. Protein disulfide isomerase was expressed in CaOv-3, A549 and HL-60 cells. Protein disulfide isomerase A3 was detected in nine cells except Hela cell and protein disulfide isomerase A6 was observed in HL-60, A-375, A-673 and Hela cell lines.
Proteins with a TPR domain
Stress-induced-phosphoprotein 1 showing 9 TPR domains was expressed in all cell lines except Saos-2 cell. Hsc70-interacting protein with 3 TPR domains was detected in HL60 and A-375 cell. FK506-binding protein 4 has 3 TPR and 2FKBP_C domains. This protein was observed in SK-N-SH, A549, HL-60, A-375 and MCF-7 cells.
Proteins with other domains
Heat shock 27 kDa protein (HSP27) is involved in the stabilisation of cytoskeletal proteins and in the protective mechanisms against oxidative stress by abolishing the burst of intracellular reactive oxygen species (ROS) [37]. HSP27 is the only protein detected among small heat-shock proteins in SK-N-SH, HCT, A549, HL-60, MCF-7 and Hela cell.
Peptidyl-prolyl-cis-trans isomerase A contains a Pro_isomerase domain and accelerates folding of proteins. It was observed in SK-N-SH, A549, HL-60, A-375 and A-673 cell lines.
ERP29_C has been recently characterised as a novel 29 kDa endoplasmic reticulum protein that is widely expressed in rat tissues. It plays an important role in the processing of secretory proteins within the ER [38]. SK-N-SH, A549, A-375, MCF-7 and Hela cells expressed ERP29 protein.
Prefoldin subunit 2 has a KE2 domain but profoldin subunit 3 has a prefoldin domain. Two of them were expressed in Hela cells and profoldin subunit 2 was also detected in SK-H-SH cell. The protein DUF704 is an activator of 90 kDa heat shock protein ATPase homolog 1. This protein was observed in bone marrow neuroblastoma (SK-N-SH) and Hela cells.
BAG-1 binds the ATPase domains of HSP70 and Hsc70, modulating their chaperone activity and functioning as a competitive antagonist of the co-chaperone Hip. The human BAG-1, BAG-2, and BAG-3 proteins bind with high affinity to the ATPase domain of Hsc70 and inhibit its chaperone activity. All these proteins contain a conserved 45-amino acid region near their C termini (the BAG domain) that binds Hsc70/HSP70, but they differ widely in their N-terminus [39]. This protein was observed in A549 and Hela cells.
GrpE protein homolog1 with GrpE domain and HSPC015 with DcpS domain were observed in A-673 and HL-60 cell exclusively.
Major differences between the ten tumor and the three normal cell lines were observed: 21 CH were observed in tumor cell lines only and vice versa, two CH were expressed in three normal cell lines exclusively (see Additional files 1 and 2).
Discussion
The main outcome of the study is the generation of tumor cell specific patterns of chaperone and heat shock protein expression. The results form the basis for designing chaperone protein expression studies needed to evaluate the role of these structures in tumor biology by providing an analytical tool for the concomitant determination of CH, unambiguously identifying CH by a protein chemical rather than an immunochemical technique, independent of antibody availability and specificity.
It must be mentioned that only high abundance CH have been detected by this method and that the generated individual maps show relative abundance proteins only. A series of relatively abundant CH were presented by more than one spot indicating the presence of splicing variants or posttranslational modifications including glycosylation, phosphorylation, methylation, oxidation, truncation, to name a few and the molecular diversity is currently subject of detailed studies using advanced proteomic tools as MS-MS sequencing and Q-TOF instrumentation. Carbamylation during sample preparation may have been contributing to electrophoretic shifts as well
Methodologically, identification of proteins by MS-MS is a sound approach and the major (inherent) problem with the interpretation may be at the cell culture level: the standard protocols for cultivating the individual cell lines according to the supplier were followed, but the conditions for the individual cell cultures vary.
Different antibiotics or fungizides used may well lead to differences in CH expression and indeed, the use of geldanamycin has been already reported to induce heat shock expression in brain tissue [40]. Other tentative confounding factors as e.g. cell cycle differences, differences in growth and proliferation have to be taken into account, but it was the aim of the study to examine CH expression in cell lines in the absence of stressors or metabolic derangement, warranted by the use of standard protocols, wherefrom toxic effects are expected. Moreover, a list of non-tumor cell lines has been studied for CH expression already using this principle as well as comparable analytical technique and this may allow some comparison between tumor and non-tumor cell line CH profiles. Herein, we tested three more normal cell lines and observed a large number of differentially expressed CH between tumor and normal cell lineages. We are aware of the fact that the already reported CH expression patterns of five cell lines [41] and the three normal cell lines are not sufficient to find out differences between normal and tumor cell CH expression in general. We used herein cell types widely used in life sciences as e.g. fibroblasts, lymphocytes and kidney cells, that are representing well-characterised normal cell lines. It may be impossible to show specific differences between normal and tumor cell lines as the stem cells from which tumor cell lines originate are not generally known.
Conclusions
Basically, differences between individual CH expression patterns may be due to different functional roles in individual cells and the presence of specific proteins in the individual cell lineages: there are CH to specifically protect individual proteins as e.g. specific chaperoning of tubulin beta 1 by the TCP complex [42] and of procollagen by HSP47 [43]. Differential CH expression may reflect or lead to tumor biological characteristics including dignity and carcinogenesis, and the method given herein provides the possibility and option to test these characteristics in a high-throughput performance. The specific nature of cell line patterns is given by the observation that only heat shock cognate 71 kDa protein and TCP zeta subunit protein were expressed non-specifically in all ten cell lines studied and previous protein expression profiles of CH in tumor cells or tissues are hereby extended and confirmed. Absence of a CH in an individual cell line could be explained by poor resolution in an individual gel but gel quality was checked by comparing general patterns and therefore this explanation is rather unlikely
The main focuses in CH protein research maybe now to investigate detection of more CHs, probably by prefractionation into different compartments [44], to characterise the splice variants expressed at the protein level and to evaluate post-translational modifications that may be responsible for a significant part of multiple expression forms.
Methods
Cell culture
Ten different tumor cell lines were purchased from American Type Culture Collection (ATCC). The cell lines and their ATCC numbers are given in Table 3 (see Additional file 4).
SK-N-SH (bone marrow neuroblastoma) and Hela (cervix carcinoma) cell lines were grown in Minimum Essential Medium (Eagle) with 2 mM L-glutamine and Earle's Basic Salt Solution (BSS) adjusted to contain 1.5 g/L sodium bicarbonate, 0.1 mM non-essential amino acids, and 1 mM sodium pyruvate, with 10% fetal bovine serum (FBS). The same conditions were used to culture the MCF-7 cell line except for supplementing 10% FBS with 0.01 mg/ml bovine insulin. HCT 116 (colorectal carcinoma) and Saos-2 (osteosarcoma) cell lines were cultured in McCoy's 5a medium with 90% 1.5 mM L-glutamine and 10% FBS. Ham's F12K medium with 2 mM L-glutamine adjusted to contain 1.5 g/L sodium bicarbonate and 10% FBS were used for the A549 (lung carcinoma) cell culture. HL-60 (acute promyelocytic leukaemia) cells were cultured with Iscove's modified Dulbecco's medium with 4 mM L-glutamine adjusted to contain 80% 1.5 g/L sodium bicarbonate and 20% FBS. A-673 (rhabdomyosarcoma), Caov-3 (ovarial adenocarcinoma) and A-375 (malignant melanoma) cell lines were cultured in DMEM with 4 mM L-glutamine adjusted to contain 1.5 g/L sodium bicarbonate and 4.5 g/L glucose with 10% FBS.
Three normal cell lines, fibroblasts, lymphocytes and kidney cells were used:
Fibroblasts, Hs 545 SK ATCC CRL-7318 (Manassas, VA), were obtained from human skin and grown in monolayers in Dulbecco's modified Eagle's medium (DMEM) containing 4 mM L-glutamine adjusted to contain 1.5 g/L sodium bicarbonate, 4.5 g/L glucose, with 10% fetal bovine serum (FBS), Penicillin and Streptomycin (GIBCO BRL) according to standard techniques Lymphocyte cell line 3610 is a spontaneously EBV transformed cell line from a patient with osteosarcoma and was obtained from the St. Anna Kinderspital-Forschungsinstitut (Vienna, Austria). The cell line was established from peripheral heparinized blood by a density gradient centrifugation using Ficoll-Paque (AMERSHAM BIOSCIENCE, Uppsala, Sweden) and grown in RPMI 1640 with 10% FBS, 70 μM gentamicin sulfate and 2 mM glutamine at a density of 2 × 106 cells per ml in 96 well plates.
HK-2 cells (Human Kidney 2) are proximal tubular epithelial cells derived from normal kidney and were purchased from ATCC CRL-2190. Cells were cultured in Keratinocyte-Serum Free Medium (GIBCO-BRL 17005–042) with 5 ng/ml recombinant epidermal growth factor (positive for alkaline phosphatase, gamma glutamyltranspeptidase, leucine aminopeptidase, acid phosphatase, cytokeratin, alpha 3 beta 1 integrin, fibronectin; negative for factor VIII-related antigen, 6.19 antigen and CALLA endopeptidase) and 0.05 mg/ml bovine pituitary extract.
All cell cultures were maintained in a humified atmosphere of 5% (v/v) CO2 in air at 37°C and logarithmically growing cells were harvested by trypsinisation.
Sample preparation
Harvested cells were washed three times with 10 mL PBS (phosphate buffered saline) (Gibco BRL, Gaithersburg, MD, USA) and centrifuged for 10 min at 800 g at room temperature. The supernatant was discarded and the pellet was suspended in 1 ml of sample buffer consisting of 40 mM Tris, 7 M urea (Merck, Darmstadt, Germany), 2 M thiourea (Sigma, St. Louis, MO, USA), 4% CHAPS (3-[(3-cholamidopropyl) dimethylammonio]-1-propane-sulfonate) (Sigma, St. Louis, MO, USA), 65 mM 1,4-dithioerythritol (Merck, Germany), 1 mM EDTA (ethylenediaminetetraacetic acid) (Merck, Germany), protease inhibitors complete (Roche, Basel, Switzerland) and 1 mM phenylmethylsulfonyl fluoride (PMSF). The suspension was sonicated for approximately 30 sec in an ice bath. After homogenisation samples were left at room temperature for 1 h and centrifuged at 14,000 rpm for 1 h. The supernatant was transferred into Ultrafree-4 centrifugal filter units (Millipore, Bedford, MA), for desalting and concentrating proteins. Protein content of the supernatant was quantified by the Bradford protein assay system [45]. The standard curve was generated using bovine serum albumin and absorbance was measured at 595 nm.
Two-dimensional gel electrophoresis (2-DE)
Samples prepared from each cell line were subjected to 2-DE as described elsewhere [46]. 1 mg protein was applied on immobilised pH 3–10 nonlinear gradient strips in sample cups at their basic and acidic ends. Focusing was started at 200 V and the voltage was gradually increased to 5000 V at a rate of 3 V/min and then kept constant for a further 24 h (approximately 180,000 Vhs totally). After the first dimension strips (18 cm) were equilibrated for 15 min in a buffer containing 6 M urea, 20% glycerol, 2% SDS, 2% DTT and then for 15 min in the same buffer containing 2.5% iodoacetamide instead of DTT. After equilibration, strips were loaded on 9–16% gradient sodium dodecylsulfate polyacrylamide gels for second-dimensional separation. Gels (180 × 200 × 1.5 mm) were run at 40 mA per gel. Immediately after the second dimension run, gels were fixed for 18 h in 50% methanol, containing 10% acetic acid, the gels were stained with Colloidal Coomassie Blue (Novex, San Diego, CA) for 12 h on a rocking shaker. Molecular masses were determined by running standard protein markers (Biorad Laboratories, Hercules, CA) covering the range 10–250 kDa. pI values were used as given by the supplier of the immobilised pH gradient strips (Amersham Bioscience, Uppsala, Sweden). Excess of dye was washed out from the gels with distilled water and gels were scanned with an ImageScanner (Amersham Bioscience). Electronic images of the gels were recorded using Adobe Photoshop and Microsoft Power Point softwares.
Matrix-assisted laser desorption ionisation mass spectrometry (MALDI-MS)
Spots visualised by Colloidal Coomassie Blue staining were excised with a spot picker (PROTEINEER sp™, Bruker Daltonics, Germany), placed into 96-well microtiter plates and in-gel digestion and sample preparation for MALDI analysis were performed by an automated procedure (PROTEINEER dp™, Bruker Daltonics) [44,47]. Briefly, all visible spots were excised and washed with 10 mM ammonium bicarbonate and 50% acetonitrile in 10 mM ammonium bicarbonate. After washing, gel plugs were shrunk by addition of acetonitrile and dried by blowing out the liquid through the pierced well bottom. The dried gel pieces were reswollen with 40 ng/μl trypsin (Roche Diagnostics, Penzberg, Germany) in enzyme buffer (consisting of 5 mM Octyl β-D-glucopyranoside (OGP) and 10 mM ammonium bicarbonate) and incubated for 4 h at 30°C. Peptide extraction was performed with 10 μl of 1% TFA in 5 mM OGP. Extracted peptides were directly applied onto a target (AnchorChip™, Bruker Daltonics) that was load with α-cyano-4-hydroxy-cinnamic acid (CHCA) (Bruker Daltonics) matrix thinlayer. The mass spectrometer used in this work was an Ultraflex™ TOF/TOF (Bruker Daltonics) operated in the reflector for MALDI-TOF peptide mass fingerprint (PMF) or LIFT mode for MALDI-TOF/TOF with a fully automated mode using the FlexControl™ software. An accelerating voltage of 25 kV was used for PMF. Calibration of the instrument was performed externally with [M+H]+ ions of angiotensin I, angiotensin II, substance P, bombesin, and adrenocorticotropic hormones (clip 1–17 and clip 18–39). Each spectrum was produced by accumulating data from 200 consecutive laser shots. Those samples which were analysed by PMF from MALDI-TOF were additionally analysed using LIFT-TOF/TOF MS/MS from the same target. A maximum of three precursor ions per sample were chosen for MS/MS analysis. In the TOF1 stage, all ions were accelerated to 8 kV under conditions promoting metastable fragmentation. After selection of jointly migrating parent and fragment ions in a timed ion gate, ions were lifted by 19 kV to high potential energy in the LIFT cell. After further acceleration of the fragment ions in the second ion source, their masses could be simultaneously analysed in the reflector with high sensitivity. PMF and LIFT spectra were interpreted with the Mascot software (Matrix Science Ltd, London, UK). Database searches, through Mascot, using combined PMF and MS/MS datasets were performed via BioTools 2.2 software (Bruker). A mass tolerance of 100 ppm and 1 missing cleavage site for PMF and MS/MS tolerance of 0.5 Da and 1 missing cleavage site for MS/MS search were allowed and oxidation of methionine residues was considered. The probability score calculated by the software was used as criterion for correct identification.
The algorithm used for determining the probability of a false positive match with a given mass spectrum is described elsewhere [48].
List of abbreviations
CH chaperone
HSP heat shock protein
2-DE two dimensional gel electrophoresis
MALDI-MS matrix-assisted laser desorption ionisation mass spectrometry
OGP octyl-β-D-glucopyranoside
CHCA α-cyano-4-hydroxy-cinnamic acid
PMF peptide mass fingerprint
GRP glucose regulated protein
TCP t-complex protein
Competing interests
The author(s) declare that they have no competing interests.
Authors contributions
JKM did data mining and contributed to the preparation of the manuscript. LAS performed protein extraction, two-dimensional electrophoresis and data handling. MFC carried out MALDI-TOF-TOF analyses. IS developed methodology for studying proteins from cell lines. GL initiated and planned the study developing the concept, supervised 2-DE, mass spectrometry, creating data and the manuscript. All authors have read and approved the final manuscript.
Supplementary Material
Additional File 1
Table 1. Identified proteins in different human tumor cell lines: Saos-2, SK-N-SH, HCT 116, CaOv-3, A549, HL-60, A-375, A-673, MCF-7 and Hela.
Click here for file
Additional File 2
Table 1-1. Identified proteins in different normal cell lines: Kidney, Lymphocyte, Fibroblast
Click here for file
Additional File 3
Table 2. Theoretical molecular weight, theoretical pI, observed pI, total score and peptide matched of molecular chaperones in tumor cell lines
Click here for file
Additional File 4
Table 3. The list of tumor cell lines
Click here for file
Acknowledgements
We are highly indebted to the Red Bull Company, Salzburg, Austria, for generous financial assistance and for support from the Verein zur Durchführung der wissenschaftlichen Forschung auf dem Gebiet der Neonatologie und Kinderintensivmedizin "Unser Kind" of the study. We kindly acknowledge technical contribution by Kiseok Lee, MSc.
The contribution of the Österreichische Nationalbank (Project No: 9187) funding L.A. is highly appreciated.
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| 15598346 | PMC543454 | CC BY | 2021-01-04 16:37:14 | no | Proteome Sci. 2004 Dec 14; 2:8 | utf-8 | Proteome Sci | 2,004 | 10.1186/1477-5956-2-8 | oa_comm |
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Int J Health GeogrInternational Journal of Health Geographics1476-072XBioMed Central London 1476-072X-3-301558830210.1186/1476-072X-3-30ResearchUnpacking analyses relying on area-based data: are the assumptions supportable? Glover John [email protected] Diana [email protected] Sarah [email protected] Public Health Information Development Unit, The University of Adelaide, South Australia, Australia2 Health Information Centre, Department of Health, Royal Street, East Perth, Western Australia, Australia2004 9 12 2004 3 30 30 7 10 2004 9 12 2004 Copyright © 2004 Glover et al; licensee BioMed Central Ltd.2004Glover et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
In the absence in the major Australian administrative health record collections of a direct measure of the socioeconomic status of the individual about whom the event is recorded, analysis of the association between the health status, use of health services and socioeconomic status of the population relies an area-based measure of socioeconomic status.
This paper explores the reliability of the area of address (at the levels typically available in administrative data collections) as a proxy measure for socioeconomic disadvantage. The Western Australian Data Linkage System was used to show the extent to which hospital inpatient separation rates for residents of Perth vary by socioeconomic status of area of residence, when calculated at various levels of aggregation of area, from smallest (Census Collection District) to largest (postcode areas and Statistical Local Areas). Results are also provided of the reliability, over time, of the address as a measure of socioeconomic status.
Results
There is a strong association between the socioeconomic status of the usual address of hospital inpatients at the smallest level in Perth, and weaker associations when the data are aggregated to larger areas. The analysis also shows that a higher proportion of people from the most disadvantaged areas are admitted to hospital than from the most well-off areas (13% more), and that these areas have more separations overall (47% more), as a result of larger numbers of multiple admissions.
Of people admitted to hospital more than once in a five year period, four out of five had not moved address by the time of their second episode. Of those who moved, the most movement was within, or between, areas of similar socioeconomic status, with people from the most well off areas being the least likely to have moved.
Conclusion
Postcode level and SLA level data provide a reliable, although understated, indication of socioeconomic disadvantage of area. The majority of Perth residents admitted to hospital in Western Australia had the same address when admitted again within five years. Of those who moved address, the majority had moved within, or between, areas of similar socioeconomic status.
Access to data about individuals from the Western Australian Data Linkage System shows that more people from disadvantaged areas are admitted to a hospital, and that they have more episodes of hospitalisation. Were data to be available across Australia on a similar basis, it would be possible to undertake research of greater policy-relevance than is currently possible with the existing separations-based national database.
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Background
The majority of work in Australia describing the association between the health status, use of health services and socioeconomic status of the population uses an area-based measure of socioeconomic status. It is necessary to use a proxy measure (the socioeconomic status of the population in the area) because there is no direct measure in the major administrative health record collections of the socioeconomic status of the individual about whom the event is recorded.
However, the application of an area-based measure requires a number of assumptions, including that people who move do so between, or within, geographic areas of similar socioeconomic status; and that the (often large) areas used in these analyses provide a reliable indication of the socioeconomic status and health service utilisation of the individuals in the area about whom the event is recorded. Area level socioeconomic status can also be considered as an independent predictor. For example, an individual with low socioeconomic status in an area of higher socioeconomic status is more likely to have better health outcomes than their counterpart in an area of lower socioeconomic status [1,2]. This aspect is not addressed in this paper.
In relation to this latter point, Hyndman et al [3] found that "Misclassification of individuals to SES groups based on the basis of postcode caused an underestimation of the true relationship between SES and health-related measures. A reduction of this misclassification by using smaller spatial areas, such as CD or census enumeration districts, will provide improved validity in estimating the true relationship." A reduction in strength of correlation with increasing size of area is consistent with the results of this paper. In a study of hospitalisations in Michigan, USA, Hofer et al [4] found that 'the impact of socioeconomic characteristics on hospitalization rates is consistent when measured by individual or community-level measures'. This is an encouraging finding for those limited to using area-based data.
Another limitation of the majority of Australian health-related datasets is that they record events (eg., hospital inpatient separations, services by general medical practitioners), rather than individuals.
The analysis in this report uses the Western Australian Data Linkage System to explore the reliability of area data as a proxy for socioeconomic disadvantage when analysed for the relatively large geographic units often used in health-related research: it also addresses the limitations of using data about events, rather than individuals. It does this by examining the extent to which hospital inpatient separation rates vary, both overall and by socioeconomic status of area of residence, when calculated at various levels of aggregation, from Census Collection District (CD) – the smallest area level for which a measure of socioeconomic status is available – to the larger units of postcode and Statistical Local Area (SLA). Methods applied include the calculation of correlation coefficients and examination of hospital separation rates by quintile of socioeconomic disadvantage of area, separately for events and individuals.
The report also examines the reliability of the socioeconomic status of the address over time, by examining the extent of change in socioeconomic status of area of residence for individuals with repeat hospital episodes over a five year period.
The analysis shows that aggregating data to larger area reduces the gap between the index scores for the most disadvantaged and least disadvantaged areas, with the greatest impact on the scores for the most disadvantaged areas. This results in an understatement of the extent of disadvantage in the most disadvantaged areas, as well as an understatement in inequality between the most well off and the poorest areas.
Results
Individuals
Over the five years from 1994 to 1998, a total of 358 948 residents of Perth were admitted to a hospital in Western Australia on one or more occasion, an average of 71 750 individuals admitted per annum. Just over half (53.6%) the individuals admitted were females; 46.4% were males.
The rate of individuals admitted was 16.4% higher for females (247.6 separations per 1000 population) than for males, (212.7 separations per 1000 population) (Table 1). As can be seen in Figure 1, the rates of males and females admitted vary notably by age. For females, the highest rate is in the 30 to 34 year age group (with a further three of the five highest female rates between ages 20 and 39 years), with the second highest rate in the 80 years and over age group. The highest male rate, in the 80 years and over age group, is substantially above the next highest male rates, in the 50 to 69 year age groups.
Table 1 Perth residents admitted to hospital, by age and sex, at first admission, 1994–98
Rate per 1000
Age Males Females Persons
0–4 185.3 147.2 166.8
5–9 207.1 168.4 188.3
10–14 163.5 135.5 149.9
15–19 201.0 244.4 222.4
20–24 204.3 288.2 245.6
25–29 207.8 315.7 261.4
30–34 212.9 328.1 270.9
35–39 214.3 282.5 248.8
40–44 213.4 250.4 232.3
45–49 214.3 242.4 228.2
50–54 243.6 270.2 256.5
55–59 242.6 254.7 248.6
60–64 252.7 257.5 255.2
65–69 240.5 241.4 241.0
70–74 232.2 237.9 235.3
75–79 237.5 254.8 247.6
80+ 291.7 283.5 286.2
Total 212.7 247.6 230.3
Figure 1 Perth residents admitted to hospital, by age and sex at first admission, 1994–98. Perth population is at 30 June 1996. Per cent shown is of males and females separately, not for persons.
A total of 358 768 Perth residents had one admission to a Western Australian hospital over the five years from 1994 to 1998, with a further 298 805 people admitted on two or more occasions (Table 2). The number of people with two or more admissions in any period is higher in the earlier years, as the more time that passes the greater the opportunity for a second admission. That is, those with a first admission in 1994 have had more time to record a second admission than have those with a first admission in 1995: thus the greater number with two or more admissions in 1994.
Table 2 Perth residents admitted to hospital, by number of admissions and year of separation, 1994–98
Year Individuals
One admission Two or more admissions Total
1994 71 566 118 039 189 605
1995 68 400 75 830 144 230
1996 68 989 52 577 121 566
1997 71 917 34 497 106 414
1998 77 896 17 862 95 758
Total 358 768 298 805 657 573
Just over half (54.6%) those admitted to hospital had one admission over this period, and more than one third (36.0%) had between two and four admissions, together comprising 90.6% of those admitted (Table 3).
Table 3 Residents of Perth admitted to hospital, 1994–1998, by number of admissions per person
Admissions per person Number Per cent
1 358 769 54.6
2–4 236 611 36.0
5–9 46 377 7.1
10+ 15 821 2.4
Total 657 578 100.0
Females accounted for just over half (53.6%) of those admitted once, compared with 59.7% of those admitted more than once. For males, the proportions were 46.4% and 40.3%, respectively.
Separations
There were 1 665 308 separations of Perth residents from Western Australian hospitals, an average of 2.53 separations per person admitted over the five years from 1994 to 1998. Over half (55.1%) of the separations were of females and 44.9% were of males.
Figure 2 shows the profiles of males and females, by age, for both individuals admitted (as in Figure 1) and separations. For males, the proportion of individuals admitted is highest at ages 20 to 49 years, dropping away at younger and older ages, with the latter exhibiting a particularly marked drop. Total separations for males are generally highest at older ages (the highest at ages 70 to 74 years), reflecting the higher number of separations per person. The notable exception is the high proportion of separations in the 0 to 4 year age group. The profile of the proportion of females admitted is similar to that for males, although it is somewhat distended at ages 20 to 39 years. The proportion of separations of females at ages 25 to 54 years closely follows that for females (individuals) admitted.
Figure 2 Perth residents admitted to hospital and total separations, by age and sex, 1994–98. Perth population is at 30 June 1996. Per cent shown is of males and females separately, not for persons.
The main differences in the profiles of male and female separations are evident at the youngest ages (higher proportions of males), from ages 20 to 44 years (higher proportions of females) and from 50 to 79 years (higher proportions of males). The ages at which the highest rates of admissions of individuals and of multiple admissions (the gap between the separations and admitted profiles) occur are clearly visible in the chart.
Unlike the rates for individuals admitted (Table 1, above), the highest rates for separations of both males and females occur in the oldest age groups (Table 4). The five highest rates for both males and females are in the age groups 60 to 64 years and over, with male rates higher (and often substantially so) than female rates. Also of note is the high rate of separations for females at ages 30 to 34 years (1,672.2 admissions per 1000 population): this is the sixth highest rate for females, and is more than twice the rate for males at the same age (729.5 separations per 1000 population).
Table 4 Separations of Perth residents, by age and sex, 1994–98
Rate per 1000
Age Males Females Persons
0–4 989.4 697.9 847.6
5–9 513.2 379.4 448.0
10–14 375.3 310.5 343.8
15–19 443.7 693.4 567.2
20–24 505.2 1 151.9 823.5
25–29 630.1 1 562.6 1 093.4
30–34 729.5 1 672.2 1 203.9
35–39 747.8 1 411.6 1 083.2
40–44 780.6 1 175.8 982.2
45–49 947.6 1 253.0 1 099.0
50–54 1 289.3 1 528.1 1 405.5
55–59 1 686.8 1 666.8 1 676.9
60–64 2 210.8 1 957.6 2 082.6
65–69 2 859.5 2 356.6 2 598.8
70–74 3 991.2 2 661.3 3 268.6
75–79 4 723.2 2 979.7 3 706.7
80+ 4 823.9 3 086.5 3 667.4
Total 1 099.0 1 345.0 1 223.0
Discussion
Effect of aggregation of areas on disadvantage scores
As noted, the majority of the analysis by socioeconomic status undertaken in the health sector in Australia is area based, and uses the postcode or SLA as the unit of analysis. This raises the question of the extent to which area based analyses at the postcode or SLA level provide a reliable indication of the socioeconomic status and health service utilisation of the individuals admitted. This report explores the reliability of postcode or SLA level data by examining the extent to which rates of individuals admitted and separations vary when calculated at various levels of aggregation (CD, postcode and SLA). Ideally, the comparison would be between the socioeconomic status of individuals and of areas; however, the smallest area level for which a measure of socioeconomic status is available is the CD.
Variation in the minimum and maximum Index of Relative Socio-economic Disadvantage (IRSD) scores when calculated at the CD, postcode and SLA level is striking and clearly shows the value of the smaller unit in area based analyses (Table 5). The range at the CD level is from a minimum index score of 532 to a maximum index score of 1221, a differential of 2.3 times. When individuals and separations are analysed by postcode, the range in the IRSD scores is narrower, from 863 to 1168 (a differential of 1.4). At the SLA level it is slightly lower again (a differential of 1.3). The effect of aggregation to the larger areas is most noticeable in the minimum IRSD score, increasing the minimum score by 70.5% from the CD level to the SLA level. At the other end of the scale, the maximum score varies little, dropping by 4.0%. That is, the greatest loss in specificity in the IRSD score is in the most disadvantaged areas.
Table 5 Range of IRSD scores for area of address of individuals and separations
Variable Median for individuals Minimum Maximum Ratio: Maximum/minimum
for separations
Collection District (1) 1012 532 1221 2.30
Postcode (2) 1015 863 1168 1.35
Statistical Local Area (3) 1017 907 1174 1.29
Ratio of IRSD scores in area (3) to area (1) 1.00 1.70 0.96 ..
Thus, the use of larger area aggregates reduces the gap between the index scores for the most disadvantaged and least disadvantaged areas (thus lessening the extent of inequality between these areas), with the greatest impact on the scores for the most disadvantaged areas (thus understating the extent of inequality in these areas). Notably, the difference between the maximum and minimum scores, and the absolute level of the scores, is much less marked between the postcode and SLA.
There was a strong association between the IRSD scores for CDs and those for postcode of usual address at the first admission (a Spearman correlation coefficient of 0.74). The correlations were between CDs grouped to quintiles and postcodes grouped to quintiles, ranked by the IRSD, and not between individual CDs and postcodes. A weaker association was found between the quintiles for CDs and those for SLAs (0.64 for people with one separation and 0.63 for people with more than one separation) (Table 6). There was little difference in correlation coefficients for those who had moved address. Similar Spearman correlation coefficients were calculated for raw IRSD scores.
Table 6 Spearman correlation coefficients between IRSD of address for individuals (at first discharge) and area level
Variable Area level of first discharge
CD Postcode SLA
Individuals:
one separation 1.00 0.74 0.64
more than one separation 1.00 0.74 0.63
more than one separation & moved address 1.00 0.73 0.62
Effect of aggregation of areas on separation rates
Data at the CD level for the five years from 1994 to 1998 show a variation in rates of individuals admitted from 51 442 admissions per 100 000 population in the most advantaged areas to 58 130 admissions per 100 000 population in the most disadvantaged areas (Table 7). This is a differential of 13%. The differential in separation rates is substantially higher, at 47%, reflecting multiple admissions.
Table 7 Residents of Perth admitted to hospital, 1994–1998, by socioeconomic disadvantage of area for selected area levels
Quintile Individuals admitted Separations
CD Postcode SLA CD Postcode SLA
Number
Q1: Least disadvantaged 126 615 123 380 138 127 294 130 303 131 340 294
Q2 130 907 123 465 114 244 294 307 326 652 279 537
Q3 133 073 126 770 142 107 316 066 328 999 363 908
Q4 124 279 128 863 123 199 327 228 328 630 313 879
Q5: Most disadvantaged 142 704 155 100 139 901 433 577 377 896 367 690
Total 657 578 657 578 657 578 1 665 308 1 665 308 1 665 308
Rate (per 100 000 population)
Q1: Least disadvantaged 51 442 48 247 51 950 119 813 120 567 127 986
Q2 53 343 48 239 52 235 120 582 129 945 127 810
Q3 53 889 48 789 52 656 127 995 126 618 134 841
Q4 50 919 51 263 52 564 133 342 128 400 133 920
Q5: Most disadvantaged 58 130 61 691 58 491 176 157 147 734 153 728
Total 53 547 53 547 53 547 135 607 135 607 135 607
Rate ratio: Ratio of rate in Q5 rate in Q1 1.13*** 1.28*** 1.13*** 1.47*** 1.23*** 1.20***
The extent of any inequality is shown by the rate ratio, which expresses the ratio of the rate in Quintile 5 to the rate in Quintile 1; rate ratios indicating differing significantly from 1.0 are shown with * p < 0.05; ** p < 0.01; *** p < 0.001.
When data are aggregated to postcode area or SLA, the differentials in separation rates between Quintile 5 and Quintile 1 areas are smaller (differentials of 1.23 and 1.20, respectively) than at the CD level (a differential of 1.47) (Table 7). In the case of postcodes, this is largely because of the lower separation rate in Quintile 5 areas (likely to be a result of the process of aggregating CDs), whereas for SLAs it is a combination of a lower separation rate in Quintile 5 areas and a higher rate in Quintile 1 areas (likely to be a result of the aggregation process, exacerbated by the variable size of SLAs – see section titled 'Methods, Area' under 'Methods.' The differential in rates of individuals admitted is the same for data at the SLA and CD level, but higher for postcode areas. These results again reflect the difficulty inherent in producing groups of approximately equal populations.
While just over half (54.6%) those admitted to hospital had one separation over this period, the proportion varied from 56.3% in Quintile 1 to 51.9% in Quintile 5 (Table 8). This is as expected, with people from the most disadvantaged areas representing a smaller proportion of those with one separation and a larger proportion with more than one separation.
Table 8 Number of separations per individual, by socioeconomic disadvantage of area, Perth residents, 1994–1998
Separations per person Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Total
Number
1 69 485 69 118 69 960 69 709 80 497 358 769
2–4 43 274 43 776 45 566 46 747 57 244 236 607
5–9 7 907 7 902 8 449 9 220 12 899 46 377
10+ 2 714 2 668 2 793 3 187 4 459 15 821
Total 123 380 123 465 126 770 128 863 155 100 657 578
Per cent
1 56.3 56.0 55.2 54.1 51.9 54.6
2–4 35.1 35.5 35.9 36.3 36.9 36.0
5–9 6.4 6.4 6.7 7.2 8.3 7.1
10+ 2.2 2.2 2.2 2.5 2.9 2.4
Total 100.0 100.0 100.0 100.0 100.0 100.0
There is a substantial difference in the proportion of the population in Quintiles 5 and 1 having two or more separations (a difference of 38.8%, from a rate of 30 389 separations per 100 000 persons in Quintile 5 to 21 897 separations per 100 000 persons in Quintile 1): the differential for people having one separation is lower, although still notable at 16.1% (a rate of 32 790 separations per 100 000 persons in Quintile 5 and 28 231 separations per 100 000 persons in Quintile 1) (Table 9).
Table 9 Separations per individual, by socioeconomic disadvantage of area, Perth residents, 1994–1998
Separations per person Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Total Ratio of rates in Q5/Q1
Per cent
1 19.4 19.3 19.5 19.4 22.4 100.0 ..
2+ 18.0 18.2 19.0 19.8 25.0 100.0 ..
Total 18.8 18.8 19.3 19.6 23.6 100.0 ..
Rate per 100 000 population
1 28 231 28 165 28 331 28 561 32 790 29 215 1.16***
2+ 21 897 22 146 23 006 24 236 30 389 24 332 1.39***
Total 50 128 50 311 51 337 52 797 63 180 53 547 1.26***
Average admissions per person with two or more admissions
Number 4.2 4.1 4.3 4.4 4.7 4.4 1.12***
The extent of any inequality is shown by the rate ratio, which expresses the ratio of the rate in Quintile 5 to the rate in Quintile 1; rate ratios differing significantly from 1.0 are shown with * p < 0.05; ** p < 0.01; *** p < 0.001.
The average number of admissions per person for people admitted to hospital on more than one occasion over the five years to 1998 was 4.4; this varied from 4.2 separations per person admitted in the least disadvantaged areas to 4.7 in the most disadvantaged areas.
Reliability over time of address as a proxy for socioeconomic status
Studies using the address of usual residence as a proxy for socioeconomic status require two important assumptions. They are that:
• people who move do so within, or between, areas of similar socioeconomic status; and that
• the areas used in an area based analysis (which can vary in size and are quite often large) provide a reliable indication as to the socioeconomic status and use of health services of the individuals in the area.
Data from the 1996 Census show that 53.5% of Perth's population at the 1996 Census reported that they had a different address to that at the previous Census, five years earlier [5]. Data were not available to compare the IRSD of the first and last SLA of address of the Perth population who moved. However, almost one quarter (24.0%) of Perth residents who moved between the 1991 and 1996 Censuses moved to an address within the same SLA. That is, some 59.3% of the population were in the same SLA after five years (either moved within the SLA, or did not move). This is an encouraging statistic for area based analyses.
Similarly, almost four out of five people admitted to hospital more than once in a five year period had not moved (out of the CD of their address at the first separation) by the time of their second separation. For example, of the 298 809 people admitted to a Perth hospital more than once over the five year period 1994 to 1998, over three quarters (78.6%, 64 075 people) had the same address at the time of the second separation. People were recorded as having 'moved' if the CD of their address changed between the first and last separation over the period from 1994 to 1998. Movement to a different address within a CD was not included.
The following table illustrates, for people with multiple admissions, the extent of movement by socioeconomic status. For this part of the analysis, the CD of first and last separation have been allocated to quintiles of socioeconomic disadvantage of area, to provide a comparison of the extent of movement between different levels of socioeconomic status. The construction of the quintiles is described in the section titled 'Methods, Measurement of socioeconomic status' under 'Methods.'
Table 10 shows, for people who moved to an address in another CD, that:
Table 10 Residents of Perth admitted to hospital more than once, 1994–1998, who changed address, by socioeconomic disadvantage of area
CD of first separation CD of last separation (%) Total
Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Total Number
Quintile 1 40.2 22.8 16.4 15.9 4.7 100.0 9 537
Quintile 2 21.5 24.4 22.9 23.6 7.5 100.0 10 551
Quintile 3 12.7 20.3 24.1 32.5 10.5 100.0 11 730
Quintile 4 7.8 14.6 22.0 40.3 15.3 100.0 13 298
Quintile 5 4.6 9.2 15.0 40.7 30.5 100.0 18 875
Total 14.8 16.9 19.6 32.6 16.0 100.0 63,991
• people from the most well off areas are less likely to have moved to areas of greatly different socioeconomic status (ie, changed quintiles) than are those from the most disadvantaged areas – 40.2% of people in the most advantaged areas (Quintile 1) remained there, despite moving from the CD of their first separation. The proportion in the most disadvantaged (Quintile 5) areas was a lower 30.5%;
• while there is movement right across the socioeconomic profile, most movement is between adjacent quintiles. For example, of the 18 875 people who lived in the most disadvantaged areas at their first separation (and moved before a subsequent admission), 71.2% had moved to a CD in the same or next ranked quintile (Quintiles 5 or 4), with just 4.6% moving to the most advantaged areas. Similarly, of the 9 537 people in the most well off areas at their first separation, 63.0% had moved to a CD in the same or next ranked quintile (Quintiles 1 or 2), with a similarly low proportion (4.7%) moving to the most disadvantaged areas;
• the most substantial movement between quintiles was of people moving from an address rated as Quintile 5 to one rated as Quintile 4 (40.7%); this was marginally higher than the proportions moving within Quintiles 4 or 1 (40.3% and 40.2%, respectively).
There is a strong association between the quintile of socioeconomic disadvantage of area at the first and the last discharge when analysed by CD (a correlation coefficient of 0.88) or SLA (a correlation coefficient of 0.89) of usual address (Table 11). This supports the earlier finding that people admitted to hospital who had moved between episodes, moved to or within areas of similar socioeconomic status. The weaker correlations between CD and SLA (see table) highlight the loss in specificity of the index score when aggregated to the (larger) SLA level.
Table 11 Correlation coefficients between quintile of socioeconomic disadvantage of area of address of first and last separation, 1994–98
Area of address CD of SLA of
first separation last separation first separation last separation
CD of first separation 1.00 0.88 0.66 0.60
CD of last separation 0.88 1.00 0.60 0.65
SLA of first separation 0.66 0.60 1.00 0.89
SLA of last separation 0.60 0.65 0.89 1.00
Table includes people admitted more than once, who had moved from the CD of their address at their first separation. Area of address shown at various levels of aggregation of areas.
Conclusions
The analysis shows that, for Perth residents admitted to hospital, the use of larger area aggregates reduces the gap between the index scores for the most disadvantaged and least disadvantaged areas, thus understating the extent of inequality between these areas. The greatest impact of aggregation of areas is on the scores for the most disadvantaged areas. This results in an understatement of the extent of disadvantage in the most disadvantaged areas, as well as an understatement in the extent of inequality between the most well off and the poorest areas.
Further, the analysis shows that a more people from the most disadvantaged areas are admitted to hospital than from the most well-off areas (13% more), and that these people have more separations overall (47% more), as a result of larger numbers of multiple admissions.
As regards the extent of movement, four out of five people admitted to hospital more than once in a five year period had not moved (out of the CD of their address at the first separation) by the time of their second separation. In addition:
• people from the most well off areas are less likely to have moved to areas of greatly different socioeconomic status than are those from the most disadvantaged areas;
• while there is movement right across the socioeconomic profile, most movement out of a quintile is to areas in adjacent quintiles; and
• the most substantial movement between quintiles was of people moving from an address rated as Quintile 5 to an address rated as Quintile 4, although this was only marginally higher than the proportions moving within Quintiles 4 or 1.
In summary, postcode level and SLA level data provide a reliable indication of socioeconomic disadvantage of area, when compared with CD-level data. That is, the association between rates of total separations and individuals admitted and socioeconomic disadvantage of area evident at the smallest area level (CD) is also evident in the higher level area aggregates of postcode and SLA.
It is reasonable to assume that similar relationships exist in other Australian cities, as well as in other health-related activity (eg. visits to general medical practitioners).
Given the widespread use in Australia of area based analyses at the postcode and SLA level, and the limitations of CDs an area level for the analysis of most health datasets, it is important to know that such analyses provide a reliable indication of the direction and underlying strength of the influence of socioeconomic factors in hospital admissions rates.
This is not to imply that the postcode or SLA are the ideal areal unit for analysis, nor that data for Collection District would be. The ideal population size for area-level analysis is likely to vary dependent on the number of cases in the dataset under analysis. For datasets with a large number of cases per capita (eg. services by general medical practitioners) the number will be smaller than those with a small number of cases per capita (eg. deaths), even with aggregation of data over a number of years. May SLAs have much larger populations than are necessary to produce reliable results; and the populations of most CDs are too small (see Table 12). HealthWIZ [6], the National Social Health Database, comprising among the most widely available small area datasets in Australia, seeks to provide health service use and health status data for areas with populations of approximately 10 000. This is a useful benchmark.
Table 12 Number of areas and average population for CDs, postcodes and SLAs in Perth, 1996
Area Number Population
In smallest In largest Average
CD 2,297 15 1 861 535
Postal area 105 42 49 551 11 780
SLA 37 876 103 736 33 631
It is also clear that data as to socioeconomic position at the smallest area level possible or, more importantly, of individuals, would also be of value. Were data to be available across Australia on a similar basis to that from the Western Australian Data Linkage System, it would be possible to undertake research of greater policy-relevance than is currently possible with the existing separations-based national database. Such moves are under consideration in several Australian States.
Further, linking data (eg, using probabilistic linkage) for individuals in the Western Australian Data Linkage System to the Australian Bureau of Statistics Population Census has the potential to add considerable value to such analyses. For example, it would be possible to examine an individual's characteristics of education, occupation, labour force status, housing tenure etc., and to more directly examine the relationships between the number of individuals admitted and total separations and these important socioeconomic variables. Linkage to death registration data would also be valuable in understanding more about outcomes related to socioeconomic status. This latter example is a possibility under recently announced plans for the ABS to test the linking of 2006 Census of Housing and Population data to other datasets, such as deaths registrations, held under their Act. This is similar to the approach elsewhere, including New Zealand [7]. It is to be hoped that such arrangements can be put in place in Australia in the near future.
Methods
Terminology
The report addresses differences in the number of individuals admitted and the number of separations they incurred. These are described as 'individuals', or individuals admitted' and separations (the total number of separations, where an individual may have had one or more episodes of hospitalisation over the period of the analysis). 'Separation' is the term describing a completed hospital episode: it is defined in the section titled 'Glossary, Separation' under 'Glossary.'
Data sources
Details of all separations to public and private hospitals in Western Australia for the five years from 1994 to 1998 were extracted from the Western Australian Hospital Morbidity Database (HMDS). Any separation records thought to belong to the same person had previously been linked together within the Data Linkage System, permitting analyses to be performed for both separations and individual persons. The population used in calculating rates is the 1996 Census population.
The analysis has been limited to separations of residents of Perth, but includes separations occurring at any public acute or private hospitals in Western Australia.
Area
Areas used in the analysis are the Census Collection District (CD), postcode and Statistical Local Area (SLA). See Glossary for definitions of CD, postal area and SLA.
The HMDS includes address details for each separation from a hospital in Western Australia since 1993. These addresses have been linked to a Western Australian street address database to assign northing and easting points (geo-codes). These points were then assigned to the appropriate 1991 or 1996 CD using the ABS CData96 mapping tool. The postcode and SLA of the address were then determined by allocation of CDs to postcode or SLA. The boundaries for CDs and SLAs are consistent. However, boundaries for CDs and postcodes are not, so CDs were allocated to postcodes on a 'best fit' basis (see Glossary).
Consequently, comparisons can be made between results for CDs and postcode areas, CDs and SLAs and postcode areas and SLAs. This is particularly important, as much of the area analysis undertaken in the health sector in Australia uses the postcode or the SLA, as a majority of data are only available at these area levels, and it is widely accepted that the larger the area, the less homogenous the population is likely to be.
There were 2 297 CDs in Perth at the 1996 Census, with 105 postcodes and 37 SLAs. The average population size at each of these area levels is shown in Table 12; these data emphasise the variation in size of the areas at each area level.
Measurement of socioeconomic status
In the absence of any direct measure of socioeconomic status in the hospital inpatient data, the socioeconomic status of the area of the address of the individual admitted is used as a proxy measure. The Index of Relative Socio-Economic Disadvantage (IRSD) is the measure used to provide the socioeconomic status of the area of the address. The IRSD is one of five Socio-Economic Indexes for Areas (SEIFA) produced by the Australian Bureau of Statistics (ABS) from data collected at the 1996 Population Census. It is calculated at the CD level and can be produced for other area levels. The postcode and SLA level index scores in this report are the population weighted average of the IRSD scores for the CDs in the postcode or SLA. This calculation is undertaken for all CDs in the postcode or SLA, not just those for which hospital episodes were recorded.
Each area level (CD, postcode or SLA) was allocated to one of five groups (quintiles). For example, for SLAs, Quintile 1 comprises the SLAs with the highest IRSD scores (most advantaged areas), and Quintile 5 comprises the SLAs with the lowest IRSD score (most disadvantaged areas): each quintile comprises approximately 20% of the Perth population. This process does not provide an exact allocation of population, so the resultant populations are only 'approximately' equal, and the larger the areal units being allocated, the less likely they are to be equal. As shown in Table 13, when areas were ranked by their IRSD score at the CD level and then grouped to produce quintiles, the resultant populations were relatively close to the ideal population in each quintile of 245 607 (one fifth of 1 288 036). The quintiles based on postcode areas had rather 'lumpier' populations (greater variation around the one fifth figure of 254 859 per quintile – and a higher total of 1 274 297, due to boundary differences between CDs and postcodes. The quintiles based on SLAs were the most variable. For example, the SLA of Wanneroo – South West (with a population of 103 176) had a score marginally below the cut-off score between Quintile 1 and Quintile 2. However, the inclusion of Wanneroo – South West in Quintile 2 resulted in populations in Quintile 1 and 2 of 161 707 and 321 889, respectively. Moving Wanneroo – South West to Quintile 1 left a population of 218 713 in Quintile 2 and increased that in Quintile 1 to 265 883. While these populations are substantially different from the ideal population, they are the best that can be achieved.
Table 13 Population of quintiles at various area levels, 1996
Quintile CD Postcode SLA
1 246 131 255 726 265 883
2 245 406 255 942 218 713
3 246 937 259 835 269 879
4 244 072 251 378 234 378
5 245 490 251 416 239 183
Total 1 228 036 1 274 297 1 228 036
Analysis
Three (different) IRSD scores were added to each hospital separation record, based on the CD, postcode or SLA that had been previously assigned to the address on that record. It should be noted that these IRSD scores were actually the average score for the particular CD, postcode or SLA as calculated from 1996 Census data. Quintile ranks for each aggregation level were also applied using population weighting as described above.
For analyses involving multiple admissions, the IRSD value used was that for the first separation in the five-year period. These 'first' separations were isolated using the internal links between separation records for the same person and the separation date. Of course, many of these 'first' separations could have been preceded by separations occurring before 1994.
Rates are crude rates, per 100 000 population. Ideally the data would have been standardised (by the indirect method). However, access to the source data were limited and to requested tables, and standardisation was not an option.
As the data were from a complete enumeration (all admissions to hospital), confidence intervals were only calculated for measures of difference (in this case, rate ratios).
The Spearman Rank Correlation has been used in the analysis to indicate the degree of correlation between pairs of variables.
Glossary
CD
The Collection District (CD) is the smallest area level in the Australian Bureau of Statistics' statistical geography and is primarily an area used in the five yearly population census.
Index of Relative Socio-Economic Disadvantage
The Index of Relative Socio-Economic Disadvantage (IRSD) is one of five Socio-Economic Indexes for Areas produced by the Australian Bureau of Statistics at recent population censuses. Produced using Principal Components Analysis, it summarises information available from variables related to education, occupation, income, family structure, race (the proportion of Indigenous people), ethnicity (poor proficiency in use of the English language) and housing. The variables are expressed as percentages of the relevant population. The IRSD is available at the Census Collection District level and was then be calculated for postcodes and SLAs by weighting the CD level scores by their population. The IRSD is calculated to show the relativity of areas to the Australian average for the particular set of variables which comprise it; this average score is set at 1000. Scores below 1000 indicate areas with relative disadvantaged populations under this measure, and scores above 1000 indicate areas with relatively advantaged populations. The IRSD scores at the Census Collection District (CD) level have been grouped to postal area, an area developed by ABS for the presentation of population counts and other Census data from the five-yearly population censuses to approximate postcode areas, as the ABS does not collect the postcode at the Census.
Separation
The term describing a completed hospital episode is a 'separation'. At the time of admission to hospital, the age, sex, address of usual residence and other personal details of the patient are recorded. At the end of the episode, at the time of separation from hospital, details of the episode itself are recorded, including the date, time and method of separation (discharge, death or transfer of a patient to another care setting eg. hospital, nursing home). Consequently, hospital inpatient data collections are based on separations.
Postal area
The postal area is an area developed by ABS for the presentation of population counts and other Census data from the five-yearly population censuses. It approximates postcode areas, as the ABS does not collect the Australia Post postcode at the Census. Postal areas comprise Census Collection Districts (CDs) grouped to approximate postcode areas. Where a CD does not fit entirely within a postcode area, it is allocated to the postcode area into which the population largely falls. Where a CD covers more than one postcode area, the total CD population is allocated to one postcode.
The IRSD scores at the Census Collection District (CD) level have been allocated to postal areas as described in the section titled 'Methods, Index of Relative Socio-Economic Disadvantage' under 'Methods.'. Similarly, the postal area of each separation was approximated from the CD of the address.
The term postcode, rather than postal area, is used in the text, for ease of reading.
Postcode
See postal area, above.
Quintile of socioeconomic disadvantage of area
See section titled 'Methods, Measurement of socioeconomic status' under 'Methods.'
SLA
An SLA in Perth is generally equivalent to a local government area, with additional codes allocated to local government areas split for statistical purposes (mainly local government areas with large populations, split to form SLAs with smaller populations).
Acknowledgements
The hospital inpatient data on which this analysis was based were drawn from the Western Australian Data Linkage System. The Western Australian Data Linkage System was established in 1995 with three year funding from the Western Australian Lotteries Commission. The system is now jointly funded, managed and staffed by the Department of Health (WA), the University of WA; the Institute for Child Health Research and Curtin University of Technology.
Its aim is to link unit records from core Department of Health data collections and other relevant data collections, for the purpose of providing linked data to support health planning, purchasing, evaluation and research.
The data extraction and analysis for this report was undertaken by Diana Rosman, Manager, Data Linkage Unit, Health Information Centre, Department of Health (Western Australia).
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| 15588302 | PMC543455 | CC BY | 2021-01-04 16:39:01 | no | Int J Health Geogr. 2004 Dec 9; 3:30 | utf-8 | Int J Health Geogr | 2,004 | 10.1186/1476-072X-3-30 | oa_comm |
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-4-351545391610.1186/1471-2148-4-35Research ArticleLong branch attraction, taxon sampling, and the earliest angiosperms: Amborella or monocots? Stefanović Saša [email protected] Danny W [email protected] Jeffrey D [email protected] Department of Biology, Indiana University, Bloomington, IN 47405, USA2 Department of Biology, University of Toronto at Mississauga, Mississauga ON, L5L 1C6, Canada2004 28 9 2004 4 35 35 6 8 2004 28 9 2004 Copyright © 2004 Stefanović et al; licensee BioMed Central Ltd.2004Stefanović et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Numerous studies, using in aggregate some 28 genes, have achieved a consensus in recognizing three groups of plants, including Amborella, as comprising the basal-most grade of all other angiosperms. A major exception is the recent study by Goremykin et al. (2003; Mol. Biol. Evol. 20:1499–1505), whose analyses of 61 genes from 13 sequenced chloroplast genomes of land plants nearly always found 100% support for monocots as the deepest angiosperms relative to Amborella, Calycanthus, and eudicots. We hypothesized that this conflict reflects a misrooting of angiosperms resulting from inadequate taxon sampling, inappropriate phylogenetic methodology, and rapid evolution in the grass lineage used to represent monocots.
Results
We used two main approaches to test this hypothesis. First, we sequenced a large number of chloroplast genes from the monocot Acorus and added these plus previously sequenced Acorus genes to the Goremykin et al. (2003) dataset in order to explore the effects of altered monocot sampling under the same analytical conditions used in their study. With Acorus alone representing monocots, strongly supported Amborella-sister trees were obtained in all maximum likelihood and parsimony analyses, and in some distance-based analyses. Trees with both Acorus and grasses gave either a well-supported Amborella-sister topology or else a highly unlikely topology with 100% support for grasses-sister and paraphyly of monocots (i.e., Acorus sister to "dicots" rather than to grasses). Second, we reanalyzed the Goremykin et al. (2003) dataset focusing on methods designed to account for rate heterogeneity. These analyses supported an Amborella-sister hypothesis, with bootstrap support values often conflicting strongly with cognate analyses performed without allowing for rate heterogeneity. In addition, we carried out a limited set of analyses that included the chloroplast genome of Nymphaea, whose position as a basal angiosperm was also, and very recently, challenged.
Conclusions
These analyses show that Amborella (or Amborella plus Nymphaea), but not monocots, is the sister group of all other angiosperms among this limited set of taxa and that the grasses-sister topology is a long-branch-attraction artifact leading to incorrect rooting of angiosperms. These results highlight the danger of having lots of characters but too few and, especially, molecularly divergent taxa, a situation long recognized as potentially producing strongly misleading molecular trees. They also emphasize the importance in phylogenetic analysis of using appropriate evolutionary models.
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Background
A correct understanding of relationships among the "earliest" lineages of angiosperms is important if we wish to elucidate the causes and consequences of their origin, to understand patterns and tempos of character evolution in the earliest lineages, and to decipher subsequent patterns of diversification. [We sometimes use "earliest", "deepest", "basal", etc. as a convenient shorthand to refer to plants hypothesized to belong to lineages that result from the first or one of the first evolutionary branchings within angiosperm evolution. We do not mean to imply that any extant plants (e.g., Amborella) are themselves the "earliest" angiosperms, but rather that they belong to the lineage of angiosperms that resulted from the first evolutionary split in angiosperm evolution. When the term "sister" is used to refer to a phylogenetic placement it refers to the sister group to the rest of the angiosperms unless otherwise specified.] A breakthrough in the seemingly intractable problem of identifying the earliest lineages of angiosperms occurred in 1999 and 2000, when each of many multigene studies identified the same three groups as the earliest branching angiosperms [1-9]. Most of these studies, as well as most subsequent analyses [10-17] have converged on the placement of the monotypic genus Amborella, a vessel-less shrub with unisexual flowers endemic to New Caledonia, as the sister-group to all living angiosperms (Fig. 1, Table 1), with the next two divergences within angiosperms corresponding to the water lilies (Nymphaeaceae) and then the Austrobaileyales. This grade leads toward the well-supported remainder of the flowering plants, also known as core angiosperms [18] (Fig. 1). The monophyly of each of the five lineages of core angiosperms is well established, but relationships among them are unclear (Fig. 1).
Figure 1 Current consensus hypothesis of angiosperm relationships. Tree topology is based on [42, 91] and references in Table 1. Small asterisks indicate the general phylogenetic position of the ten angiosperms (generic names shown for all but the three grasses) examined by Goremykin et al. [19]. The large asterisk indicates the addition in this study of the early-arising monocot Acorus to the Goremykin et al. [19] dataset. The height of the triangles reflects the relative number of species in eudicots (~175,000 species), monocots (~70,000), and magnoliids (~9,000) as estimated by Judd et al. [18] and Walter Judd (personal communication). The other five angiosperm groups shown contain only between 1 and ~100 species.
Table 1 Comparison of recent studiesa that identify the sister lineages of angiosperms.
Study reference No. of genes (genomesb) No. of angiosperms No. of nucleotides Amborella sister to the rest of angiospermsc Basal vs. core angiospermsc Monophyly of monocotsc
[4] 5 (c, m, n) 97 8,733 + 90 + 97 + 99/98
[3] 5 (c, m, n) 45 6,564 + 94d + 99d + 98d
[6] 3 (c, n) 553 4,733 + 65e + 71e + 95e
[1] 2 (n) 26 2,208 + 92/83f + 86 + 100
[2] 2 (n) 52 2,606 + 88/57f + 68 + 87
[8] 6 (c, m, n) 33 8,911 - n/ag + 99 + 100
[9] 17 (c) 18 14,244 + 69 + 94 + 53
[11] 1 (c) 38 4,707 + 99 + 100 + 100
[14] 1 (c) 361 1,749 + 86 + 89 + 99
aNot included are several other studies also supportive of Amborella-sister, but which are largely duplicative of the above [5, 7, 31], or whose structure does not match sufficiently with the structure of this table [10, 12, 13], or which have extremely limited sampling (6 taxa) within angiosperms [15].
bc = chloroplast; m = mitochondrial; n = nuclear
cIndicated relationship recovered (+) or not recovered (-); parsimony BS values shown unless otherwise specified. See Fig. 1 for definition of indicated relationships.
dOnly BS values derived from ML analysis are shown.
eJackknife support values.
fBootstrap values were inferred from separate phyA and phyC treatments; other BS values in this study were derived from concatenated phyA and phyC sequences.
gn/a – not applicable. This study found Amborella+Nymphaea as sister to all other angiosperms (see Discussion).
In sharp contrast stands the study of Goremykin et al. [19], in which the Amborella chloroplast genome was sequenced and in which 61 protein genes shared among 13 land plants (including 10 angiosperms) were analyzed. In 31 of 33 phylogenetic analyses this study found that "Amborella is not the basal angiosperm and not even the deepest branching among dicots" ([19] Abstract). Instead, these results indicate, with 100% BS in most analyses, that the first split within angiosperm evolution occurred between monocots and dicots. Goremykin et al. [19] imply that the earlier studies are in error with respect to the placement of Amborella because these "studies were based on a limited number of characters derived from only a few genes" and used "unmasked sequences of chloroplast genes [i.e., with all three codon positions included] with high substitution rates at their synonymous sites" (p. 1503).
Thus, we are faced with a major paradox. On the one hand, many different studies, employing in aggregate 28 different genes (19 chloroplast, five mitochondrial, and four nuclear; Table 1), consistently and strongly place the branch leading to Amborella deeper in angiosperm evolution than the branch leading to the monocots, whereas a study that employed twice as many genes found the opposite result, also with strong support. It is critical to resolve this paradox, for the groups and issues involved are such important ones in angiosperm phylogeny.
One notable difference between the two sets of studies concerns taxon sampling, which can be critical in phylogenetic analysis [20-24]. Even though sampling strategies in the Amborella-deep studies listed in Table 1 varied substantially, ranging from 18 to 553 species of angiosperms and from 2,208 to 14,244 nucleotides (NT) of aligned data, a commonality was their relatively broad taxon sampling. Most of these studies attempted to represent the diversity of living angiosperms by including critical species identified by prior morphological [25-28] and single-gene molecular analyses [29-31]. Even the listed study with the fewest taxa [9] was based on exemplar species, compiled by the Green Plant Phylogeny Research Coordination Group and chosen to represent most of the major putatively basal lineages suggested by a large body of previously accumulated results. In contrast, the Goremykin et al. [19] study included only 10 angiosperms. Five of these belong to a single derived group (eudicots) and three are grasses (the only monocots sampled), leaving Amborella and Calycanthus (the only sampled member of the other three lineages of core angiosperms) as the other two angiosperms sampled (Fig. 1). It is known that grasses have accelerated substitution rates in all three genomes [9,32-35], especially the chloroplast genome, making them a poor representative for such a large and diverse group as monocots.
Relevant here is that the grasses-sister topology obtained by Goremykin et al. [19] (see their Fig. 3, which also corresponds to our Fig. 3A) shows one long branch, leading to grasses, connecting to another long branch, separating angiosperms from the outgroups. When the outgroups are removed and the Goremykin et al. [19] tree is taken as an unrooted network, it becomes apparent that there is no difference between their ingroup topology and those of studies that obtained the Amborella-sister rooting. In other words, given the taxonomic sampling of Goremykin et al. [19], their grasses-sister topology differs from the canonical Amborella-sister topology only with respect to where the outgroup branch attaches [36], either to grasses or to Amborella (see Discussion and Fig. 8 for an elaboration of this point).
These observations led us to suspect that the grasses-sister topology is an artifact stemming from long branch attraction (LBA), a phenomenon known [37-39] to give strongly supported, but spurious results under precisely the set of conditions operative in the Goremykin et al. [19] study. These are 1) inadequate taxon sampling, 2) large amounts of data per taxon, 3) two known long branches (the grass branch and the outgroup branch) separated by short internodes, and 4) phylogenetic analyses that do not account for rate heterogeneity.
The current study was undertaken to test whether the grasses-sister topology is indeed an LBA artifact. We hypothesize that, by analyzing the Goremykin et al. [19] dataset with a focus on rate heterogeneity and taxon sampling of monocots, the Amborella-sister topology will be recovered instead. In addition, we carried out a similar, but much more limited set of analyses in response to a follow-up paper by Goremykin et al. [40] that appeared while this manuscript was in the final stages of preparation and which similarly challenged the position of Nymphaea as a basal angiosperm.
Results
Addition of Acorus
We gathered new sequence data for an additional monocot representative, Acorus, and added it to the 13 taxa, 61 gene first- and second-position alignment matrix of Goremykin et al. [19] to give a 14 taxa, 61 gene first- and second-position alignment matrix. Acorus was chosen for two reasons. First, it is well supported as the sister to all other monocots [41-43]. Thus, Acorus plus grasses represent monocot diversity about as well as any two groups of monocots. Second, unlike grasses, its chloroplast genome does not appear to have evolved at unusually high rates [9,44]. The Acorus dataset consisted of 40 protein gene sequences, 22 newly determined in this study and 18 from preexisting databases. This corresponds to 65.6% (40/61) of the genes and 71.4% (32,072/44,937) of the nucleotide characters analyzed by Goremykin et al. [19].
A number of initial analyses were conducted in parallel on the "full" Acorus matrix, containing data for all 61 genes and including gaps where data for Acorus were not available, and a "truncated" matrix, containing only those 40 genes where Acorus sequences were available. Inspection of the resulting trees revealed no topological incongruences and no significant change in bootstrap support (BS) between the full and truncated analyses [see Additional files 1 and 2]. The results presented hereafter for Acorus are based on the full matrix dataset. This allows us to include all available relevant data, allowing the fullest and most direct comparisons to the Goremykin et al. [19] analyses.
Representative results of either adding Acorus to the Goremykin et al. [19] matrix or substituting it for grasses are shown in Fig. 2. Using Acorus instead of grasses to represent monocots has a major effect on the results. This is especially dramatic for equal-weighted maximum parsimony (MP) analyses of both nucleotides and amino acids, where there is a shift from 100% BS for monocots-sister when only grasses are used to represent monocots (Figs. 2A and 2D) to 100% and 93% support for Amborella-sister when Acorus is used instead (Figs. 2B and 2E). The same topological shift is seen with maximum likelihood (ML) using equal rates across sites (cf. Figs. 2G and 2H), although the swing in BS values is less pronounced (61% for grasses-sister vs. 100% for Amborella-sister). Transversion parsimony (RY-coding) of the original dataset (Fig. 2J) gives the Amborella-sister topology, but with poor support (56%). Substituting Acorus for grasses improves the support for Amborella-sister to 100% (Fig. 2K).
Figure 2 The effect of changing sampling of monocots as a function ofphylogenetic method. Analysis of the 61-gene data matrix using: Rows A-C, DNA parsimony; D-F, protein parsimony; G-I DNA ML HKY85 with no rate categories; J-L, RY-coded DNA parsimony. The first column of trees is with the Goremykin et al. [19] taxon sampling (grasses, but not Acorus), the second is with Acorus but not grasses, and the third is with both grasses and Acorus. All analyses used the first- and second-position matrix, either with or without the addition of Acorus as explained in Methods. Trees J-L use the same matrices, but with the nucleotides RY-coded.
Figure 3 Neighbor joining analyses using different evolutionary models and/or taxon sampling. Distance matrices were calculated from the first- and second-position matrix of Goremykin et al. [19] using (A) the K2P model, (B) the ML HKY85 model with four gamma-distributed rate categories and parameters estimated from the corresponding ML analysis, and (C) the K2P model with Acorus added to the first- and second-position matrix as described in Methods.
Inclusion of both grasses and Acorus produced two very different topologies, depending on the method used. On the one hand, standard MP, with both nucleotides (Fig. 2C) and amino acids (Fig. 2F), gives a grasses-sister topology in which monocots are paraphyletic with 100% BS (i.e., there is 100% support for Acorus as the sister to "dicots" to the exclusion of grasses). On the other hand, equal-rates ML (Fig. 2I) and transversion parsimony (Fig. 2L) give an Amborella-sister topology, with moderate (79%) to strong (98%) support, in which monocots are monophyletic with equivalent support.
To make the results more directly comparable to the Goremykin et al. study [19] and to investigate the performance of various distance-based models, we tested many different neighbor joining (NJ) models. We did this also because, of all MP, ML and NJ methods initially investigated, the only approaches that failed to give the Amborella-sister topology when Acorus was substituted for grasses were the NJ methods without a ML model. When the PAUP* [45] distance is set to any of 12 settings (Mean, P, JC [46], F81 [47], TajNei [48], K2P [49], F84 [50], HKY85 [51], K3P [52], TamNei [53], GTR [54,55] or LogDet [56,57]), Amborella, Calycanthus, and Acorus form a monophyletic group with 100% BS. Importantly, however, this same grouping is obtained, with all 12 distance settings, even when grasses are included, such that, as in equal-weighted parsimony analyses (Figs. 2C and 2F), grasses are sister to all other angiosperms and monocots are not monophyletic (Fig. 3C and analyses not shown).
Finally, it should be noted that ML and NJ methods using models (see next section) that give Amborella-sister when only grasses represent monocots, continue to do so, but with higher BS, when Acorus is added, either with or without grasses [see Additional files 1 and 2].
Site-to-site rate heterogeneity
If the lineage leading to Amborella is sister to the rest of angiosperms, as the analyses with Acorus strongly indicate, why do so many of the Goremykin et al. [19] analyses support the grasses-sister topology? We explored this question by conducting analyses using a broad range of models and methods as applied to their data matrix (i.e., with only grasses representing monocots).
We first compared the relative likelihood of the grasses-sister and Amborella-sister topologies using ML with all 56 combinations of the 14 substitution models and four rate-heterogeneity conditions specified by the MODELBLOCK script provided by MODELTEST [58]. The four rate-heterogeneity conditions are 1) equal rates across sites, 2) estimated percentage of invariant sites, 3) four gamma-distributed rate categories and 4) a combination of invariant sites and gamma-rate categories. With equal rates across sites, the grasses-sister topology received the higher likelihood for all 14 substitution models (Table 2). For the least complex, Jukes-Cantor [46] model (a single substitution rate with equal base frequencies), all four rate-heterogeneity conditions preferred the grasses-sister topology. In a more complex model (F81), which uses estimated base frequencies, the Amborella-sister topology was preferred when either invariant sites or gamma rate categories were used but not when they were used in combination. For the other 12 models, the Amborella-sister topology was preferred for all three conditions that allowed for rate heterogeneity across sites (Table 2).
Table 2 The 56 MODELTEST models and the grasses- or Amborella-sister topology that received the higher likelihood.
Model equal +I +G +I +G
JC grasses grasses grasses grasses
F81 grasses Amborella Amborella grasses
K80 grasses Amborella Amborella Amborella
HKY grasses Amborella Amborella Amborella
TrNef grasses Amborella Amborella Amborella
TrN grasses Amborella Amborella Amborella
K81 grasses Amborella Amborella Amborella
K81uf grasses Amborella Amborella Amborella
TIMef grasses Amborella Amborella Amborella
TIM grasses Amborella Amborella Amborella
TVMef grasses Amborella Amborella Amborella
TVM grasses Amborella Amborella Amborella
SYM grasses Amborella Amborella Amborella
GTR grasses Amborella Amborella Amborella
The four rate-heterogeneity conditions used in these MODELTEST analyses are: 1) "equal" = equal rates across sites; 2) "+I" = estimated percentage of invariant sites; 3) "+G" = four gamma-distributed rate categories; and 4) "+I+G" = combination of invariant sites and 4 gamma-rate categories.
These results held when the parameters estimated on one topology (either Amborella- or grasses- sister) were used to calculate the likelihood of the other topology (the topology used had only a minor effect on the values of the parameter estimates). For both topologies, the model chosen by MODELTEST using either the hierarchical likelihood ratio tests or the Akaike information criterion was the 5-substitution-type-transversion (TVM) + I + G model, where the probability of going between A and G is equal to that of C and T. With this model, using parameter estimates from either topology, a heuristic search found the Amborella-sister topology with 98% BS, and the SH-test [59] showed the grasses-sister topology to be significantly worse at the 5% level (p = 0.04).
These MODELTEST analyses identified site-to-site rate heterogeneity, accounted for using either gamma-distributed rates or invariant sites, as a critical analytical parameter. We therefore explored this in greater detail using one particular substitution model, the HKY85 model [51]. We chose the moderately complex and commonly used HKY85 substitution model with empirical base frequencies over the TVM model to help speed up the calculation of bootstrap replicates. A ML-HKY85 analysis with equal rates and an estimated transition:transversion (Ti/Tv) ratio of 1.485 gives the same, grasses-sister topology (Fig. 4A) as found by Goremykin et al. [19] (see Fig. 2G, which is equivalent topologically to their Fig. 3), albeit with low BS (61%) for grasses-sister. In contrast, a tree built using four rate categories, with the gamma shape parameter (α = 0.31) estimated from the Goremykin et al. [19] matrix and topology, gives 96% BS for Amborella-sister (Fig. 4B). Although we present here only the commonly used, four-rate-category model, a two-rate-category model gives the same qualitative results in all cases analyzed [see Additional file 3].
Figure 4 Maximum likelihood analyses using different evolutionary models. Trees A-C were calculated using the first- and second-position Goremykin et al. [19] matrix. Tree D was calculated using all three codon positions. All trees were built using ML with the HKY85 model and the following treatments of rate heterogeneity: A. No rate categories. B. Four gamma-distributed rate categories. C. Estimated proportion of invariant sites (no gamma rate categories). D. No rate categories (all three positions). Parameters were estimated separately for each analysis as described in Methods.
To assess the stability of the topology to changes in the α parameter, we scanned the range α = [0.01–20.0], with the number of rate categories fixed at four. The same, Amborella-sister topology obtained using the estimated α (0.31) was also recovered over a wide range of α values (α = 0.01–9.0; Fig. 5A). The BS for Amborella-sister and the SH-test p-value [59] of the Amborella-sister over the grasses-sister topology both improve as α decreases to the estimated value and continue to improve as α approaches zero (Fig. 5A). As α approaches infinity, the rate categories approach the same value (i.e., equal rates) [60]. Accordingly, the BS and p-value curves in Fig. 5 approach the values of the equal-rates trees.
Figure 5 Bootstrap support and the SH-test p-value for the Amborella-sister or grasses-sister topologies as a function of (A) the gamma distribution α parameter value or (B) the proportion of invariable sites. The left vertical line in A and right line in B indicate the rate-heterogeneity parameter estimated from the data. The right vertical line in A and left line in B indicate the boundary where the topology of the best tree transitions between Amborella-sister and grasses-sister. All analyses were performed using the 61-gene first- and second-position matrix of Goremykin et al. [19] and the ML HKY85 model with the α parameter or proportion of invariant sites indicated on the X-axis. The transition-transversion parameter was estimated for each specified rate-heterogeneity parameter. p(Δ|LAmb-Lgrasses|) signifies the SH-test p-value for the difference between the likelihood scores of the two topologies. Bootstrap searches and SH-tests were performed as described in Methods.
We performed a similar analysis with the proportion of invariant sites (Plnvar option in PAUP). Using the estimated PInvar = 0.58 without gamma-distributed rate categories, we obtained the Amborella-sister topology (Fig. 4C) with 97% BS. As with α, the Amborella-sister topology was stable over a wide range of PInvar [0.09 <= PInvar <= 0.995 (Fig. 5B)]. The BS and the SH-test p-value for Amborella-sister improve as PInvar increases (Fig. 5B). The SH-test for Amborella-sister is significant at the 5% level using the estimated value of PInvar and remains significant as PInvar increases.
The BS for a sister-group relationship of Amborella and Calycanthus is identical (within the variance expected for BS values) with that for grasses-sister across the entire range of both α and PInvar values, while both of these BS values always equal 100 minus the BS value for Amborella-sister (Figs. 5A and 5B). This is exactly as expected (see Discussion) if the only difference between the grasses-sister/Amborella+Calycanthus topology and the Amborella-sister topology is where the outgroup branch roots within angiosperms. Put another way, almost all of the BS replicates were one of these two topologies.
There are 20,071 (out of 30,017; 66.9%) constant sites in theGoremykin et al. [19] matrix. When these constant sites are removed, the highest HKY85 ML tree (using equal rates) places Amborella-sister with 98% BS and with p = 0.03 for the SH-test relative to grasses-sister [see Additional file 4, Fig. A]. Furthermore, NJ analysis with the equal-rate ML model also obtains Amborella-sister (with 100% BS) when constant sites are removed [see Additional file 4, Fig. B]. This is another way of allowing the rates to increase since the rates of the sites that are changing are not constrained by the constant sites. This allows the ML model to work with a more homogenous set of rates and reduces the need for using rate categories. Removing these constant sites allows the ML model to simulate the actual evolutionary process of sites that are changing more accurately than when imposing a proportion of invariant sites because there is no invariant site weighting of the sites that are changing. As a consequence of the faster rate with constant sites excluded, the branch lengths of the resulting trees are ~2.6 times longer than when constant sites are included.
We further explored the NJ method using ML models of evolution to compute distances and with constant sites included. We were able to precisely reproduce the grasses-sister result (Fig. 3 from Goremykin et al. [19]) with NJ and the K2P model(Fig. 3A). NJ using a distance matrix calculated based on ML and using parameters estimated with the HKY85 model with equal rates alsogives grasses-sister with 100% BS. However, distances calculated using the ML HKY85 model and estimated proportion of invariant sites puts Amborella-sister with low BS of 58% [see Additional file 5], while distances derived from the ML HKY85 model with four gamma-distributed rate categories estimated gives Amborella-sister with stronger support (89%; Fig. 3B).
Third codon positions
In order to most directly assess the Goremykin et al. [19] analyses, which used only first and second codon position, the above analyses were restricted to first and second codon positions. In addition, however, most of the above analyses were also carried out with a dataset that includes all three codon positions. The resulting trees provide similar if not higher support for Amborella-sister than those obtained with just first and second positions. For example, using all three positions, the gamma rates ML tree analogous to Fig. 4B gives 100% BS for Amborella-sister, and the ML distance based NJ tree analogous to Fig. 3B gives 99% BS for Amborella-sister (trees available upon request). The most noteworthy shift towards stronger support involves ML analysis with equal rates, where inclusion of third positions changes the topology, from grasses-sister (with 61% BS; Fig. 4A) to Amborella-sister (and with 100% support; Fig. 4D). We also conducted a few analyses of third positions only (again using the set of taxa analyzed by Goremykin et al. [19]). These too recovered Amborella-sister, with 100% BS using ML with either equal rates or gamma-distributed rates [see Additional file 6].
Individual gene analyses
By taking rate heterogeneity into account or improving taxon sampling, we have shown that the concatenated genes dataset supports the Amborella-sister hypothesis, strongly so in most analyses. To explore the effects of phylogenetic methods and taxon sampling on individual gene analyses, we analyzed each of the 61 genes in the Goremykin et al. [19] dataset individually (Fig. 6). These much smaller subsets of data are, as expected, more sensitive than the concatenated dataset to the model of DNA evolution, taxon sampling, and inclusion/exclusion of third positions. Without appropriately taking these factors into account some genes give topologies that conflict with the current consensus view of plant phylogeny. With all three positions and using ML with four gamma-distributed rate categories, the highest likelihood tree in 29 of 61 genes is the Amborella-sister topology and only five genes give grasses-sister (Fig. 6A). The highest scoring trees for the remaining genes (most of which are short) place a wide variety of groups as sister, in nearly all cases with low BS (data not shown). Bootstrap support values and the number of trees having Amborella sister increase with gene length (Fig. 6A). When MP is used on the same datasets the opposite pattern is observed. Here, the grasses are sister in 27 of 61 trees, whereas Amborella is sister with only 12 genes (Fig. 6B). Excluding third positions results in the same trend in terms of MP versus ML, but the support values are much lower and the number of highly unlikely topologies is much higher (see Additional file 7).
Figure 6 Support for Amborella-sister or grasses-sister from the 61 chloroplast genes analyzed individually. A. ML HKY85 analyses with four gamma-distributed rate categories. Parameter estimates were calculated individually for each gene in a manner analogous to that performed on the concatenated dataset. B. MP analyses. All three codon positions are included in all analyses shown in both figures. Solid red lines correspond to Amborella-sister and dashed blue lines to grasses-sister topologies.
The single gene trees also illustrate the effect of taxon sampling. When Acorus is added and all three positions are used in ML analyses with four rate categories, none of the gene trees find monocots sister, whereas exactly half of the 40 genes put Amborella sister [see Additional file 8, top figure]. When the third position is excluded, 12 genes put Amborella sister and BS levels drop significantly, while still no genes put monocots sister [see Additional file 8, bottom figure]. Very similar results are obtained when the grasses are removed [see Additional file 9]. In contrast to the parsimony results without Acorus (where grasses-sister is the favored topology; Fig. 6B), when Acorus is added and parsimony is used (with all three positions), only two genes put monocots sister (and both with low, 13 and 34%, BS), whereas 11 of 40 genes put Amborella sister [see Additional file 10, top figure]. With Acorus added and grasses removed, 21 genes place Amborella sister and 1 places Acorus sister [see Additional file 10, bottom figure].
Addition of Nymphaea
While this manuscript was in the final stages of preparation, the chloroplast genome sequence of Nymphaea alba became available (released to EMBL database on July 13, 2004). This sequence was generated as part of a very recent study, also by Goremykin et al. [40], in which it was added, as the only new sequence, to the same data matrix as analyzed in their earlier study [19] and subjected to a similar set of phylogenetic analyses. Under these conditions, the grasses-sister topology was again recovered (and with 100% support) in nearly all analyses, with Nymphaea and Amborella recovered as sister taxa (also with 100% support). In their abstract, Goremykin et al. [40] present these findings as supporting their prior conclusion [19] that monocots are sister to the rest of angiosperms. However, their Discussion presents a more nuanced treatment than before, concluding that "we may be some ways from being confident of identifying the most basal angiosperms. Clearly the sequencing of genomes for more closely related outgroups and putatively basal angiosperms will be important for overcoming potential problems of model misspecification and long-branch attraction."
We carried out a limited set of analyses of the 14-taxa Goremykin et al. [40] data matrix. We did so because of time constraints and because it became immediately clear from our relatively few analyses with Nymphaea that our main results and conclusions were entirely unchanged by its inclusion/exclusion. Using the Goremykin et al. [40] methods, we also recovered the same, grasses-sister trees they reported (data not shown). However, when using analytical conditions described in the preceding sections, we never found grasses-sister (Fig. 7). Instead, grasses were grouped with the other core angiosperms with strong BS (86–100%). Interestingly, contrary to most published studies (see Background and Table 1), Amborella alone did not emerge as sister to all other angiosperms in any of these analyses. Most commonly (Figs. 7B,7C,7D), Amborella and Nymphaea together comprised the sister lineage to other angiosperms (with 66–100% BS), whereas an equal-rates ML analysis found Nymphaea deepest (albeit with low, 47% BS) and Amborella next deepest (Fig. 7A).
Figure 7 Inclusion of Nymphaea in analyses that account for rate heterogeneity. A. ML HKY85 with no rate categories (cf. Fig. 4A). B. ML HYK85 with four gamma-distributed rate categories (cf. Fig. 4B). C. ML with estimated proportion of invariant sites (no gamma rate categories; cf. Fig. 4C). D. NJ using a ML HKY85 model with four gamma-distributed rate categories to calculate distances (cf. Fig. 3B). All analyses used first- and second-positions only.
Discussion
The grasses-sister topology is an LBA artifact
That long branch attraction can be a serious problem in phylogenetic inference has long been known to the systematics community, ever since this phenomenon was first explored by Felsenstein [37]. Felsenstein described conditions of unequal evolutionary rates under which phylogenetic inference will result not only in an incorrect topology, but will converge asymptotically to the wrong phylogeny with increasing confidence as more data are added, ultimately producing 100% support for the wrong tree (hence, be positively misleading). Hendy and Penny [39] showed that this phenomenon can occur for parsimony even under equal evolutionary rates if taxa are insufficiently sampled along a branch, while Lockhart et al. [61] showed that an ML equal-rates model can incorrectly join long branches when there is rate heterogeneity across sites. In the case of DNA sequence data, due to the limited number of character states, taxa with the greatest sequence divergence are expected to be "attracted" to each other by chance alone if long and short branches are sufficiently different in length. With large amounts of data, this can result in spurious, yet strongly supported, relationships.
We used two complementary approaches to test the hypothesis that the grasses-sister topology favored in the study of Goremykin et al. [19] is caused by spurious attraction of the long branches leading to angiosperms and to grasses. Both approaches were designed to make the most direct comparisons possible to their dataset and phylogenetic methodology. First, and most importantly, we found that – even in the absence of corrections for rate heterogeneity – addition of just one more monocot to their dataset produced trees strongly supportive of 1) the Amborella-sister topology and 2) the idea that the grasses-sister topology is a consequence of LBA causing a misrooting of angiosperms. When the monocot Acorus was directly substituted for grasses, strong support for Amborella-sister was obtained (Fig. 2). This even occurred under analytical conditions that give strong support for grasses-sister when Acorus is not included. When Acorus and grasses were both included, two alternative, seemingly radically different topologies were obtained. Reconciliation of these topologies gets to the heart of the phylogenetic issues at hand. For as Fig. 8 shows, these two topologies are actually entirely congruent with respect to relationships among the various angiosperms, differing only in where the outgroup branch attaches within angiosperms [62], i.e., on the branches leading either to Amborella or to grasses (also see Fig. 5 and its treatment in Results).
Figure 8 Competing hypotheses for the rooting of angiosperms showing the same underlying angiosperm topology when outgroups are excluded. A. Rooting within monocots (Mono), on the branch between grasses and all other angiosperms (see Fig. 2C, whose BS values are shown here, and also Fig. 2F; also see Goremykin et al. [19]). B. Unrooted network, with arrow showing alternative rootings as in A and C. C. Canonical rooting on the branch between Amborella and the rest of angiosperms (see Fig. 2I, whose BS values are shown here, and also Fig. 2L). We emphasize that 100% BS was obtained for Amborella-sister and for monocot monophyly (compared to 79% and 78% in C) using ML methods that allow for site-to-site rate heterogeneity (e.g., Additional files 1–3).
The Amborella-sister topology is in agreement with the many diverse phylogenetic studies summarized in Table 1 and in Background, except for that of Goremykin et al. [19]. With Acorus included (Figs. 2I and 2L), it also shows monocots as monophyletic, consistent with a large body of evidence [7,35,41-43,63], and depicts faster chloroplast DNA evolution on the monocot lineage leading to grasses than in the Acorus lineage, also consistent with a substantial body of evidence (e.g. [9,44]). Conversely, the grasses-sister topology (Figs. 2C and 2F) is consistent only with the Goremykin et al. [19] results, fails to recover monophyly of monocots [has them either paraphyletic (Figs. 2C and 2F) or even polyphyletic (Fig. 3C), and always with 100% support], and fails to portray the known rapid evolution of chloroplast DNA in the lineages leading to grasses. All this leads us to conclude that the grasses-sister topology is almost certainly an artifact, most likely due to LBA between the long branches leading to grasses and to angiosperms.
Second, we reanalyzed the same dataset used by Goremykin et al. [19] and found that methods that account for rate heterogeneity across sites [61,64-67] put Amborella sister, usually with high BS (Figs. 2J, 3B, 4B, 4C, and 5; also see most Additional files). This was true for all 14 MODELTEST substitution models (Table 2) except for the simplest, JC model. When rates vary between sites, as with the chloroplast dataset under consideration, it is usually appropriate to model the evolutionary process to reflect this. The evolutionary models explored here point to LBA as the cause of the controversial grasses-sister topology and demonstrate that even with conservative corrections for rate heterogeneity, Amborella moves to the sister position within angiosperms (e.g., Figs. 5A and 5B).
In summary, our two principal approaches for reassessing the results and analyses of Goremykin et al. [19] lead to what we regard as compelling evidence for two major conclusions. First, Amborella, not grasses, is the sister angiosperm among this set of taxa. Second, any tendency for angiosperms to root on grasses is an LBA artifact stemming from the confluence of limited taxon sampling, rapid evolution in grasses, a long branch between the outgroups and angiosperms, and rate heterogeneity across sites. Furthermore, we point out that while our manuscript was nearly finished, two independent papers appeared [68,69] that also challenged Goremykin et al. [19] and reached similar conclusions to our study. Both studies are complementary to ours, because instead of taking the Goremykin et al. [19] 61-gene chloroplast dataset as the starting point, as we did, they used a 3-gene dataset (the same two chloroplast genes and one nuclear gene) plus the Goremykin et al. [19] set of taxa as the starting point for a variety of taxon-sampling experiments. In addition, an important forthcoming study [70] which added five new chloroplast genome sequences to the dataset of Goremykin et al. [19], found "strong support" for the Amborella-sister topology. That four entirely independent studies, using a variety of taxon sets, character sets, and analytical approaches, all lead to such similar results and conclusions makes it all the more likely that the grasses-sister topology is indeed a phylogenetic artifact.
Is Amborella or Amborella+Nymphaeaceae sister to the rest of angiosperms?
Although our results reject grasses/monocots as the sister to all other angiosperms, support for Amborella as the first branch of angiosperm evolution must necessarily be qualified given the very limited sampling of whole chloroplast genomes (besides Amborella, only monocots, Calycanthus, and eudicots; see Fig. 1). There is still uncertainty as to the exact placement of Amborella relative to the other two deepest lineages of angiosperms, especially Nymphaeaceae [8,9], although the overall weight of published evidence currently favors Amborella as the deepest angiosperm (see [10,12] and references in Table 1). This uncertainty is heightened by our limited analyses that included Nymphaea and used methods that account for rate heterogeneity. These analyses never recovered an Amborella-sister topology. Instead, they most commonly found a sister clade comprising both Amborella and Nymphaea (Figs. 7B,7C,7D), or even found Nymphaea alone to be the sister-most angiosperm (Fig. 7A). Likewise, in the one analysis reported by Goremykin et al. [40] in which Amborella and Nymphaea were found sister to the other angiosperms these two taxa clustered as sisters rather than forming a basal grade.
Clearly, then, the question of which group is sister to the rest of extant angiosperms should be regarded as unsettled and in need of further exploration, using much more data (such as whole chloroplast genomes from a large number of diverse angiosperms, as well as more mitochondrial and/or nuclear data) and better analytical methodologies as they become available. At the same time, we must face up to two serious limitations arising from extinction. First, Amborella trichopoda is the only known species in the entire Amborellaceae/Amborellales, i.e., it is the only taxon available whose DNA can be used to represent a lineage of ca. 150 million years in age arising at or near the base of angiosperms. Second, the stem branch leading to angiosperms is long in length and years [9,62] (also approaching 150 million years) and thus represents a long-branch attractor, with the potential to spuriously attract other branches besides that leading to grasses. LBA between outgroup and ingroups is particularly insidious, because, as illustrated in Fig. 2 (C and F vs. I and L), it tends to mask the long nature of the ingroup branches. Amborella does not show any evidence of having a long branch in published analyses with more extensive taxon sampling. It is nonetheless difficult to rule out (but see [10]) the possibility that Amborella may be only near-sister among angiosperms (e.g., part of a Nymphaeaceae/Amborella clade that itself is the earliest branch of angiosperms; as suggested by Barkman et al. [8] and some of our analyses), with its generally sister position representing only a slight topological distortion (nearest neighbor interchange) caused by attraction to the long outgroup branch. For that matter, we point out (also see [71]) that the long branch leading to angiosperms also makes it difficult to rule out the possibility that the monophyletic-gymnosperm topologies recovered by multigene analyses (e.g., [35,72-74]) might result from LBA between angiosperms and the outgroup branch leading to seed plants.
General implications
Many of our analyses, including all but one of the 61-gene concatenate analyses shown, included only first and second codon positions. This is because Goremykin et al. [19] chose to exclude third codon positions from their analyses, and because we wanted to make the most direct comparisons possible to their analyses. Third positions were excluded because most of the 61 chloroplast genes were claimed to be "very divergent" at synonymous sites (Ks for most genes between Pinus and angiosperms was between 0.50 and 1.50 substitutions/site), which they felt could lead to "misleading" phylogenetic results. However, because our analyses with all three positions or only third positions gave such similar results to those using only first and second positions, we believe that for this particular dataset third positions are not contributing "excessive" homoplasy and leading to spurious affiliations. This conclusion is consistent with a considerable body of literature dealing with the phylogenetic utility of third positions in organellar genes [75-80], while simulations have shown that "saturated" data can be very reliable, provided that taxon sampling is sufficiently high [21,24]. Caution is nonetheless well advised in situations involving relatively sparse taxon sampling (some of which may be unavoidable, i.e., where extinction has been significant) and/or greater divergences than in this study. For example, chloroplast third positions are problematic in analyses across all of algal/plant evolution (e.g., [81]), and even appear to be problematic at the relatively shallow level of seed plant phylogeny [35,73,82].
Our findings, and those of others [68-70,83], highlight the potential danger of phylogenetic analyses that employ lots of genes, but too few and/or the wrong taxa. Adequate taxon sampling is in a sense even more important here than with single or few-gene trees, because of the potential for even subtle systematic bias in a particular lineage's evolution to generate strongly supported misleading trees. Equally, if not more importantly, our results emphasize the crucial importance of using phylogenetic methods that best model the underlying molecular evolutionary processes, especially by accounting for site-to-site rate variation.
Methods
Sequencing chloroplast genes from Acorus
We used long PCR to generate full-length or partial sequences from Acorus gramineus Soland. (a voucher specimen is deposited at the IND herbarium) for 22 of the 61 chloroplast genes analyzed by Goremykin et al. [19]. Long PCRs were conducted using the AccuTaq™ LA DNA Polymerase (Sigma, Atlanta, GA, USA), following instructions provided by the manufacturer. Initially, sets of primers designed by Graham and Olmstead [9], which cover a large portion of the chloroplast genome (psbC-D and psbE-J operons; from rpl2 to 3'-rps12 gene), as well as the primers described in [84-87] for the rbcL, atpB, trnL-F, and trnE-D region, respectively, were used for amplifications and/or sequencing. For the most part, however, based on the initial sequences, a number of sequencing primers were designed and used for chromosome walking with long PCR products. Primer sequences are available upon request from SS. PCR products were separated by electrophoresis using 0.8% agarose gels, visualized with ethidium-bromide, and cleaned using Qiagen columns (Valencia, CA, USA). Cleaned products were then directly sequenced using the BigDye™ Terminator cycle sequencing kit (PE Applied Biosystem, Foster City, CA, USA) on an ABI 3100 DNA automated sequencer (PE Applied Biosystem, Foster City, CA, USA). Sequence data were edited and assembled using Sequencher™ 4.1 (Gene Codes Corporation, Ann Arbor, MI, USA). The Acorus sequences for these 22 chloroplast genes (atpA, atpE, clpP, cemA, lhbA, 3'-petB, petD, petG, petL, psaB, psaI, rpl20, rpoA, rpoB, rpoC1, rpoC2, rps2, rps14, rps18, rps19, ycf3, ycf4) are deposited in GenBank (accession numbers AY757810-AY757831). These were combined for phylogenetic analyses with full-length or partial Acorus sequences already available in GenBank for 18 other chloroplast genes [AF123843 (psbB, psbT, psbN, psbH), AF123771 (rps7, 3'-rps12), AF123828 (psbE, psbF, psbL), AF123813 (psbD, psbC), AF123785 (rpl2), D28866 (rbcL), X84107 (rps4), U96631 (psbA), AB040155 (matK), AF197616 (atpB), and AJ344261 (psaA)]. The 40 Acorus genes used here come from two closely related species – A. calamus (14 genes) and A. gramineus (26 genes) – and correspond to 65.6% (40/61) of the genes and 71.4% (32,072/44,937) of the nucleotide characters analyzed by Goremykin et al. [19].
Alignment
For all first and second codon position analyses, the data matrix provided by V. Goremykin was used without modification. For analyses that included Acorus, the Acorus genes were individually aligned with the individually extracted gene alignments from the Goremykin et al. [19] dataset using CLUSTALW [88], and the resulting gene alignments were concatenated to regenerate a matrix identical to the original except for the extra row containing Acorus. Using the same procedure, Acorus was also added to the amino acid matrix provided by V. Goremykin. The relevant 61 chloroplast genes of Nymphaea [40] were likewise added to both alignments.
We also constructed a new matrix consisting of all three codon positions by extracting genes from 13 sequenced chloroplast genomes of land plants (GenBank numbers: AP002983, AP000423, AJ271079, Z00044, AJ400848, AJ506156, AJ428413, X86563, AB042240, X15901, D17510, AP004638, X04465), aligning them, and hand editing apparent mistakes. The first and second position version of this matrix was nearly identical to the Goremykin et al. [19] matrix, except for a few minor differences (the overall length was slightly shorter due to removal of terminal extensions that either were created by single taxon indels or where multiple extending genes were nonhomologous). All phylogenetic trees resulting from this first and second position matrix and the Goremykin et al. [19] matrix were identical in topology and nearly identical in BS values. All alignments used in this study are available in Nexus format upon request of DWR.
Phylogenetic analyses
Phylogenetic analyses were performed in PAUP* 4.0b10 [45]. Unless specified, all nucleotide-based trees were built using only first- and second-codon positions. For ML analyses, parameters were initially estimated using an equal-weighted parsimony tree. A ML tree was then built, and parameters were re-estimated using this tree if it differed from the parsimony tree. This iteration was continued until the last two topologies converged (the final ML topology was almost always equal to the one in which the ML parameters were estimated from the parsimony topology). For all ML analyses we also calculated a NJ tree using distances calculated from the ML model being tested. For DNA and protein parsimony the default PAUP* 4.0b10 [45] step matrices were used.
Bootstrap support [89] was estimated with 100 replicates using parameters estimated from the final topology. Thus the methodology cited for a particular tree refers to the model used for the bootstrap replicates. For parsimony and ML searches the heuristic algorithm was used with simple and as-is stepwise addition, respectively; tree bisection-reconnection swapping; and no limit on the number of trees saved in memory. Unless specified, the default PAUP* settings were used in all analyses. An automated script (available upon request from DWR) was used to run the analyses. Detailed log files and trees of each analysis were saved and are available upon request from DWR. Most analyses were performed on two 3 GHz Linux machines. Treetool [90] was used for viewing and printing trees.
The Shimodaira-Hasegawa (SH) test [59] was performed using the "lscores" command of PAUP* with the options SHTest = RELL and BootReps = 10000. ML parameters being tested were estimated on each topology to calculate its own log likelihood except where otherwise specified.
Abbreviations
BS – bootstrap support; LBA – long branch attraction; ML – maximum likelihood; MP – maximum parsimony; NJ – neighbor joining; Ti/Tv – transition:transversion; NT – nucleotides; Plnvar – proportion of invariant sites
Authors' contributions
SS generated the new sequences (from Acorus) used in this study and conceived and drafted the first and last figures. DWR carried out the phylogenetic analyses and made all other figures. All three authors contributed to the overall design of the study, drafted parts of the manuscript, and read and approved the final manuscript.
Supplementary Material
Additional File 1
Trees from truncated matrix with Acorus. These first- and second-position trees show that the results are essentially the same when positions that have Acorus data missing are removed. The first row using the ML HKY85 model is with four rate categories and parameters estimated as described in Methods. The third row uses the ML model parameters calculated as in the first row to calculate a distance matrix that was used for NJ analyses. For comparison the corresponding bootstrap values for Amborella sister to the angiosperms in the full matrix, going across each row, are 1. (99 vs. 100, 100 vs. 100), 2. (NA but same topology and similar BS, 100 vs. 100), 3. (86 vs. 88, 84 vs. 90).
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Additional File 2
Trees from truncated RY-coded matrix with Acorus included. This are the same analyses as in Additional file 1 except the DNA is RY-coded. For comparison, the corresponding BS values for the Amborella sister relationship in the full matrix, along each row, are: 1. (100 vs. 100, 100 vs. 100), 2 (98 vs. 100, 100 vs. 100), 3. (100 vs. 100, 100 vs. 100).
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Additional File 3
Comparison of gamma-distributed rates with two versus four rate categories. This figure shows that using two rate categories gives essentially the same results as using four rate categories with this dataset. The dataset is the first- and second-position, 61-gene matrix with grasses, Acorus, or both used to represent monocots. The ML HKY85 model was used and parameters were estimated as described in Methods.
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Additional File 4
Trees when constant sites are removed from the first- and second-position matrix of Goremykin et al. [19]. A. ML HKY85 and equal rates. B. NJ with distances calculated using an ML HKY85 model and equal rates.
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Additional File 5
NJ analysis using ML proportion of invariant distances. Distances were calculated using the ML HKY85 model, the estimated proportion of invariant sites, and the first- and second-position matrix of Goremykin et al. [19].
Click here for file
Additional File 6
ML trees using third positions only. A. HKY85 model with equal rates. B. HKY85 model with four gamma-distributed rates.
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Additional File 7
Sister group to the rest of angiosperms found in individual gene analyses using first- and second-position data without Acorus Top, ML HKY85 with four gamma-distributed rates. Bottom, Parsimony analysis.
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Additional File 8
Sister group to the rest of angiosperms found in individual gene analyses using the ML HKY85 model with four gamma-distributed rates and Acorus added. Top, all three positions. Bottom, first and second positions.
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Additional File 9
Sister group to the rest of angiosperms found in individual gene analyses using the ML HKY85 model with four gamma-distributed rates with Acorus added and grasses removed. Top, all three positions. Bottom, first and second positions.
Click here for file
Additional File 10
Sister group to the rest of angiosperms found in individual gene analyses using parsimony on all three positions. Top, Acorus added. Bottom, Acorus added and grasses excluded.
Click here for file
Acknowledgments
We thank Ulfar Bergthorsson, Eric Knox, and Richard Olmstead for useful comments on earlier versions of the manuscript, and Vadim Goremykin for providing the 61-gene data matrices that were the starting point for this study. Funding for this work came from National Institutes of Health grant GM-35087 to J.D.P.
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| 15453916 | PMC543456 | CC BY | 2021-01-04 16:29:01 | no | BMC Evol Biol. 2004 Sep 28; 4:35 | utf-8 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-35 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-981561056410.1186/1471-2164-5-98Research ArticleEvaluation of the chicken transcriptome by SAGE of B cells and the DT40 cell line Wahl Matthias B [email protected] Randolph B [email protected] Andrzej M [email protected] Hiroshi [email protected] Eduardo [email protected] Nina [email protected] Christian [email protected] Manuel [email protected] Manuela [email protected] Yan-Dong [email protected] Volkmar [email protected] Jean-Marie [email protected] Institute of Molecular Radiobiology, GSF, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany2 Laboratory of Systems Biology Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5a, 02-106 Warszawa, Poland3 Research Group of Biomedical Informatics, IMIM/Universidad Pompeu Fabra/Centre de Regulacio Genomica, E08003 Barcelona, Spain4 Institute of Biomathematics, GSF, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany5 Stowers Institute for Medical Research, 1000 E. 50th Street, Kansas City, MO 64110, USA2004 21 12 2004 5 98 98 28 10 2004 21 12 2004 Copyright © 2004 Wahl et al; licensee BioMed Central Ltd.2004Wahl et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The understanding of whole genome sequences in higher eukaryotes depends to a large degree on the reliable definition of transcription units including exon/intron structures, translated open reading frames (ORFs) and flanking untranslated regions. The best currently available chicken transcript catalog is the Ensembl build based on the mappings of a relatively small number of full length cDNAs and ESTs to the genome as well as genome sequence derived in silico gene predictions.
Results
We use Long Serial Analysis of Gene Expression (LongSAGE) in bursal lymphocytes and the DT40 cell line to verify the quality and completeness of the annotated transcripts. 53.6% of the more than 38,000 unique SAGE tags (unitags) match to full length bursal cDNAs, the Ensembl transcript build or the genome sequence. The majority of all matching unitags show single matches to the genome, but no matches to the genome derived Ensembl transcript build. Nevertheless, most of these tags map close to the 3' boundaries of annotated Ensembl transcripts.
Conclusions
These results suggests that rather few genes are missing in the current Ensembl chicken transcript build, but that the 3' ends of many transcripts may not have been accurately predicted. The tags with no match in the transcript sequences can now be used to improve gene predictions, pinpoint the genomic location of entirely missed transcripts and optimize the accuracy of gene finder software.
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Background
The definition of transcription units within a finished genome sequence in higher eukaryotes is challenging and relies on genome mapping of cDNAs and ESTs backed up by theoretical gene finder algorithms. An increasing number of gene sequences from model organisms have made the prediction of well conserved ORFs easier, but less conserved coding and untranslated regions are difficult to detect. The best way to unambiguously define transcription units is full length cDNAs, but large scale projects are expensive in terms of labor and costs. One also needs to bear in mind that some cDNAs elude detection, because unusual secondary structure or toxicity inhibits reverse transcription or cloning.
SAGE investigates the transcription profile of a given cell sample by large scale sequencing of short cDNA tags derived from the bulk mRNA [1,2]. Whereas tag mapping to the cDNA and genome databases of the organism indicates the type of expressed genes, the prevalence of individual tags within the library reflects their relative levels of expression. Since SAGE tags are only short sequences, they can be collected more easily in higher numbers than ESTs and full length cDNA sequences. The potential of SAGE to discover new or better define already known transcription units is particularly advantageous in situations where the entire genome sequence of an organism has been determined, but gene predictions based on theoretical algorithms and the mapping of a relatively small number of EST and cDNA sequences remain tentative. LongSAGE generates longer tags of 21 bases as compared to the classical SAGE protocol and is therefore better suited for the unambiguous assignments of tag to genome sequences [3,4].
Cellular and molecular features of early B cell development [5] and lymphoma formation [6,7] have been extensively studied in the chicken. Gene expression signatures of primary bursal B cells, pre-neoplastic and neoplastic lymphoma cells were collected by microarray hybridizations in a first attempt to identify genes up- or down-regulated during myc-induced B cell lymphoma development [8].
The whole chicken genome including a genome scale transcript build from Ensembl [9] and a collection of bursal full-length cDNAs [10] have recently been released. We describe here the mapping of large collections of SAGE tags from bursal lymphocytes and DT40 to these reference datasets to evaluate the quality of the transcript build. Furthermore, the transcription profiles of bursal cells and DT40 as defined by this first SAGE analysis in the chicken should lead to a better understanding of B cell transformation and facilitate the selection of candidate genes for disruption in DT40 [11].
Results and Discussion
Generation of SAGE tag libraries and SAGE tags collections
Two SAGE libraries, named busage and dt40sage, were made from the bursa of Fabricius and DT40 cells using the LongSAGE technique which generates tags of 21 nucleotides in length and therefore decreases the likelihood of ambiguous matches [3,4]. Of the 129,568 tags collected, about equal numbers were derived from the busage and the dt40sage libraries respectively (Table 1). In total 38,212 unitags were derived from the SAGE tags of both libraries. The library from bursal cells and the DT40 cell line seem to be similar with regard to the number of extracted unitags and the average counts of matching SAGE tags. Underlying a standard binomial model, one would expect to find a special Unitag among the busage tags or dt40 tags with a probability of 95% at least once if the relative abundance of this unitag among all busage tags or dt40 tags is at least 4.55 * 10-5 or 4.69 * 10-5 respectively.
Table 1 SAGE and unitag collections
SAGE tags Unitags Average frequency of matching SAGE tags within library Average count of SAGE tag per unitag
Busage 65,798 24,064 2.73 4.63
Dt40sage 63,770 21,308 2.99 4.97
Total 129,568 38,212 3.39
Tag to gene assignment using bursal cDNAs, the Ensembl transcript build and the genome sequence
Successful mapping of SAGE tags to reference sequences is influenced by the quality of the sequences, the complexity of the reference sequence datasets and the prevalence of polymorphisms within the tag sequences. It was therefore decided to first search for matches within a bursal cDNA collection which represents the best possible reference dataset, as it was derived from the same tissue and genetic background as the busage library. Subsequently, unitags were mapped to the Ensembl transcript build and finally the chicken genome sequence. Unitags found in a previous dataset were not searched for any more in the next. To facilitate the searches, candidate tags starting with the CATG tetra-nucleotide were extracted from each reference dataset prior to analysis.
As expected the highest rate of matching to total candidate tags was found for the bursal cDNA collection (3,030 of 26,044 candidate tags matched unitags), followed by the Ensembl transcript build (2,934 of 208,048) and the genome (14,505 of 9,091,924) (Table 2). Some unitags mapped more than once within a dataset making an unambiguous assignment difficult. In comparison to the complexity of the dataset, multiple hits occurred more frequently in the bursal cDNA (33/26,044; 0.0012%) and Ensembl dataset (637/208,048, 0.0031%) than in the genome (1,003/9,091,924; 0.0001%). This can be explained if these transcript collections are not completely normalized or if there is bias for certain sequence motifs within gene transcripts. Manual analysis of the bursal cDNAs revealed that most of the multiple unitag matches were due to alternative processing of transcripts originating from the same locus (data not shown). A relatively large fraction of unitags (17,743/38,212; 46.4%) did not match to any reference dataset. It is currently impossible to analyze this in more detail, but sequencing errors, polymorphisms and positions of tags on exon/exon boundaries are likely to explain the missed hits [12]. Non-matching unitags have a significantly lower average count of SAGE tags than matching unitags (2.1 versus 4.65) suggesting that they either over-represent lowly expressed genes or are artifacts of the SAGE technique.
Table 2 Unitag mapping to reference datasets
Dataset matches of unitag Unitags Candidate tags Average count of SAGE tags per unitag
Bursal cDNA 3,030 26,044 6.89
1 2,997 6.90
> 1 33 5.15
Ensembl transcript build 2,934 208,048 9.93
1 2,275 10.60
> 1 659 7.63
Genome 14,505 9,091,924 2.84
1 13,427 2.83
> 1 1,078 2.91
Total matching 20,469 4.45
Non-matching 17,743 2.17
SAGE tags are expected at the position of the NlaIII site closest to the polyA tail of the transcript, but alternative transcript processing as well as incomplete NlaIII digestion or internal priming can produce upstream tags. Indeed, when the positions of the matching candidate tags were analyzed for bursal cDNA transcripts, about 40% of the tags matched to non-last positions (data not shown).
Mapping of tags to the genome
Most interesting from the perspective of gene discovery are the 13,427 unitags without transcript match, but with a single match in the genome (Table 3). When the positions of these tags within the genome were correlated with the positions of the Ensembl transcripts, 1,637 fell within annotated transcript boundaries indicating that they are located on missed or incomplete exons. To see whether the remaining tags were located in the neighborhood of already identified transcripts, the numbers of tags falling within regions of defined length upstream and downstream of the Ensembl transcripts were determined. Indeed many tags map very close to annotated transcripts with a strong preference for the region downstream of the transcript, as would be expected, if the tag matches the missed 3' end of an annotated Ensembl gene. Since not all tags are derived from the most 3' transcript position, the tags matching immediately upstream of transcripts might indicate missed 5' exons. Some of the tags mapped close to upstream and downstream transcripts (12 at the 500 base distance limit), perhaps indicating that these transcripts belong together. At a distance limit of 5000 bases, 7,169 tags mapped into the neighborhood of annotated transcripts; 5,627 downstream, 669 upstream and 1,061 both upstream and downstream. When the distance limit was extended to 10,000 bases, the number of downstream matching tags was only marginally increased to 5,627 whereas the number of dual positioned tags more than doubled to 2,101. This indicates that at distances over 5000 bases the tag assignment to the neighboring transcripts is becoming increasingly ambiguous, and the tags might in fact correspond to entirely missed genes.
Table 3 Locations of unitags having a single match in genome but no transcript match
Unitags Bases searched next to annotated Ensembl transcripts Matching unitags Matches only downstream of Ensembl transcripts Matches only upstream of Ensembl transcripts Matches upstream and downstream of Ensembl transcripts
Total 13,427
Within Ensembl transcript boundaries 1,637
Outside Ensembl transcript boundaries 11,177 100 409 362 46 1
200 732 668 64 2
500 1,651 1,496 143 12
1,000 2,896 2,553 262 81
5,000 7,169 5,439 669 1,061
10,000 8,639 5,627 911 2,101
Relationship of genome mapping unitags to Ensembl transcripts
To further investigate those unitags mapping close to the 5' boundary of Ensembl transcripts or within transcript boundaries to the genome, the bursal EST database [11] was searched for ESTs matching the tags in the sense strand orientation. These ESTs were then aligned to the chicken genome sequence and the neighboring Ensembl gene predictions. As many ESTs linked the SAGE tags to the Ensembl transcripts, this provided independent experimental evidence that these tags are indeed derived from non-annotated parts of these transcripts (Table 4).
Table 4 Analysis of unitags mapping 5' of or within Ensembl transcript boundaries. #
Unitag Ensembl ID BLAST result ## Supporting bursal EST Unitag relationship to Ensembl transcript ###
Unitags mapping 5'
CATGCTGCTCGCACGAGCCCT ENSGALT00000002525.1 Q9W7P7 riken1_17l12r1 Upstream 5' exon
CATGGCGGGGTTCCCGGGGCA ENSGALT00000005092.1 PEF protein with a long N-terminal hydrophobic domain riken1_18i20r1 Upstream 5' exon (EST supports two additional 5' exons)
CATGCTCCTGCTGCTGGCTGG ENSGALT00000009521.1 LAC_CHICK dkfz426_24a5r1 Upstream 5' exon
CATGAGGCACCTCCTGTTGGC ENSGALT00000001476.1 GR78_CHICK riken1_25c14r1 5' upstream/Exon1 (EST supports one additional 5' exon)
CATGGCCGCCCAAGGAGAGCC ENSGALT00000004055.1 RAN_CHICK riken1_25b20r1 5' upstream/Exon1 (EST supports one additional 5' exon)
Unitags mapping within transcript boundaries
CATGTACTGGTTGTCTGTTTT ENSGALT00000025884 HG14_CHICK dkfz426_13h16r1 Intron 4–5
CATGCATAGAGGCTTTATTGC ENSGALT00000021336 Aldo-keto reductase family 1 member dkfz426_3h12r1 Intron 8–9
CATGTTGGGACTCACCACTCT ENSGALT00000000504 No description dkfz426_13d22r1 Intron 5–6/Exon6
CATGGTCACCCTAGTAAATAG ENSGALT00000009677 Protein kinase C, beta type dkfz426_38f16r1 Intron 14–15
CATGTAAAGTGTTAGCTGTAC ENSGALT00000006857 ITF2_CHICK dkfz426_14i24r1 Intron 8–9
CATGTTACCTGCAACCTGCTG ENSGALT00000021577 Centromeric protein E dkfz426_17a21r1 Intron 28–29
CATGGGATATACTGAAAATCT ENSGALT00000009956 T-cell activation leucine repeat-rich protein dkfz426_41d20r1 Intron 1–2
CATGGGCTGGTTGGTTTTTGT ENSGALT00000028428 No description dkfz426_43g3r1 Intron 2–3
CATGGTCAAGTACAACTCTTA ENSGALT00000022583 Bcl-2-associated transcription factor dkfz426_12n7r1 Intron 8–9
# Only a few representative examples are shown
## BLAST results are abbreviated
### Unitag aligns within an intron or exon or lies across an intron/exon or upstream sequence/exon boundary
To confirm that the distribution of the tags downstream of Ensembl transcripts is statistically significant, their positions were compared to the positions of simulated tags generated by randomly selecting 21 bp sequences in the genome beginning with the 'CATG' tetra-nucleotide. This comparison shows that the real tags map closer to the 3' end of the Ensembl predicted coding sequences (CDS) than the simulated tags providing strong evidence that most of the closely positioned tags are indeed related to the predicted transcripts (Figure 2).
Figure 2 Mappings of SAGE unitags downstream of Ensembl transcripts compared to simulated genomic tags. The number of tags falling within windows of 10 bp is plotted on the y-axis whereas the distance from the 3' end of the nearest predicted Ensembl transcript is plotted on the x-axis. Sage unitags coordinates are indicated by crosses and randomly selected tag coordinates by diamonds.
If one summarizes the unitag to transcript mappings, 5,964 unitags map directly to transcripts, 1,637 map to not annotated sequences within the limit of the Ensembl transcripts and 7,169 map to within 5,000 bases of annotated transcript boundaries (Table 5). This leaves about 20% (4,621 out of 19,391 total) bona fide unitags unaccounted for which might be taken as an estimate for the percentage of genes present in the released genome sequence, but absent from the Ensembl transcript collection. Nevertheless, one needs to bear in mind that this calculation includes a number of uncertainties. It is for example possible that the 5000 base limit is too large, since only 5% of 3' UTR sequences in the human transcriptome are reported to be over 2,000 bps according to NCBI's AceView database or that both SAGE and the gene predictions have missed a substantial number of lowly expressed transcripts. In these cases, the estimate of the percentages of missed genes would increase.
Table 5 Unitag mapping to transcripts
Unitag Match to annotated transcript Match to genome within boundaries of annotated transcript Match next to annotated transcript using 5000 base cut-off Match distant from annotated transcript
Total 38,212
Without match 17,743
With only multiple genome matches 1,078
With match to annotated transcripts or single genome match 19,391 5,964 1,637 7,169 4,621
Significant gene expression differences between bursal cells and the DT40
One of the goals of this SAGE analysis was the identification of differentially expressed transcripts between the two libraries and the significance of count differences for the busage and the dt40sage tags were calculated for each unitag. In total 629 unitags showed p values below 0.01 suggesting that the corresponding transcripts are differentially expressed in bursal cells and DT40. In contrast to this, the false discovery rate (FDR) controlling procedure of Benjamin & Hochberg would admit the first 229 genes at an FDR of 5% [13]. Twenty-five of the most significant unitags mapping to bursal cDNAs are listed in Table 6.
Table 6 List of genes differentially expressed in bursal cells and DT40
Unitag Busage DT40sage Significance Sequence ID# Best BLAST result##
CATGGCAGGGGGCGGAAACCT 4 45 2.83E-10 riken1_2o24 (AAH61765) Hypothetical protein
CATGGTGAGCCAAGGTGTTGT 24 82 2.06E-9 riken1_4m1 (AAH69219) Cold inducible RNA-binding protein
CATGCAGAAATAAGCTTCTCC 45 109 4.09E-8 riken1_7b15 (Q7ZUR6) Similar to muscle-specific beta 1 integrin binding protein
CATGAGCGGGGGCAGCACTTG 118 203 5.75E-7 riken1_25p23 (Q90YW7) Ribosomal protein L4
CATGCTGGAAGAAAGAATAAC 46 114 1.92E-8 riken1_32c11 (Q9YGQ1) Peptide elongation factor 1-beta
CATGCGCTCTCCTTTTAAAAG 9 41 2.67E-6 riken1_15l3 (CAA31409) Chinese hamster asparagine synthetase
CATGGATGGCCAGCAAGTGTT 29 4 1.17E-5 riken1_4k19 (P13796) L-plastin (Lymphocyte cytosolic protein 1)
CATGTCCGTGGCATCCTTTGA 0 16 1.18E-5 riken1_24e23 (Q8BGQ8) Heterogeneous nuclear ribonucleoprotein K
CATGGCTTTGGAATATTTGAC 25 3 2.90E-5 riken1_2f9 (AAH46152) Selenoprotein P precursor
CATGGAGTCCATAACACGGCG 21 2 6.88E-5 riken1_34m12 (Q96CJ1) Testosterone regulated apoptosis inducer and tumor suppressor
CATGCAAAGTGCCCTTGGCTT 17 1 1.46E-4 riken1_10g19 (P30281) G1/S-specific cyclin D3
CATGTAAGCCAATTCTGAACC 19 1 4.09E-5 riken1_33a18 (Q8JHJ4) TNF family B cell activation factor
CATGTTGTACACACGGGCACT 11 0 5.79E-4 riken1_5g12 (Q90YB0) FEN-1 nuclease
CATGTGCCCGTGACCCCCATC 2 16 6.12E-4 riken1_4n15 (Q13200) 26S proteasome non-ATPase regulatory subunit 2
CATGTCGTGCTCTGTGCCTCC 5 26 9.28E-5 riken1_2i9 (Q90W60) XNop56 protein
CATGCTTTCTGCTTTGACTTT 21 4 9.42E-4 riken1_12p16 (P22794) Ecotropic viral integration site 2A protein
CATGTTTGTGCATAGCTGTCC 5 28 1.17E-5 riken1_30e3 (Q91XC8) Similar to death-associated protein
CATGGCCGGGCGCCCCACCAG 0 15 2.41E-5 riken1_15i13 (Q99P44) Leucine aminopeptidase
CATGGGACCAACAAATAAAGC 19 4 0.0027 riken1_4o10 (P97440) Histone RNA hairpin-binding protein
CATGAAAATGTACTGTGCTAA 2 13 0.0036 riken1_20p3 (P34022) Ran-specific GTPase-activating protein
CATGTATACAGAACTGCTGGA 8 0 0.0044 riken1_2i24 (Q9UMR2) ATP-dependent RNA helicase DDX19
CATGGCCAAATTAGAGGAGTG 1 10 0.0051 riken1_32c11 (Q9YGQ1) Peptide elongation factor 1-beta
CATGCTACGCTGTGTCTGCCA 11 1 0.0062 riken1_2m14 (AAQ20009) Heterogeneous nuclear ribonucleoprotein H1-like protein
CATGCTCTCCGGTGGTACAAT 0 7 0.0070 riken1_32c11 (Q9YGQ1) Peptide elongation factor 1-beta
CATGTTGATTCCTATGCTAAA 7 0 0.0087 riken1_3a6 (Q9H165) B-cell lymphoma/leukemia 11A
# only unitags matching bursal cDNAs are listed
## BLAST results are abbreviated
To verify the validity of the SAGE data, semi-quantitative PCR was performed using primers close to the tags for 27 transcripts (Figure 3). This confirmed the expression pattern suggested by SAGE tag counts in the majority (21 out of 27) of the cases. Certainly, these PCR results could not be explained by the statistical variation in the SAGE data alone (FDR below 5% vs. FDR of 22% indicated by PCR). Although more analysis is needed to find out which differentially expressed genes are related to differences in the behavior of bursal B cells and DT40, the freely available SAGE repository will be a good resource to select candidates for more detailed investigations.
Figure 3 Confirmation of differential gene expression using semi-quantitative PCR. Primers derived from reference genes for SAGE tags were used for the amplification of cDNA from bursal cells and DT40 employing different cycle numbers as indicated on top of the lanes. Based on the SAGE tag counts, the reference genes were classified as likely to be equally expressed (left part), higher expressed in bursal cells (middle part) or higher expressed in DT40 (right part). The size of the expected PCR product is indicated by a bar adjacent to the gel image. The numbers of tags found for the busage and dt40sage libraries as well as the calculated significance for differential expression are indicated in brackets under the gene names.
Conclusions
The mapping of the SAGE tags to the recently released cDNA collections and the chicken genome has been useful to assess the completeness and accuracy of the current transcript catalog. On the positive side, it appears that the transcript build may have missed only a low percentage of genes, since relatively few tags map to genome regions far away from annotated transcription units. On the downside, fewer than 6,000 of over 19,000 tags with matches to reference sequences could be mapped to transcripts. The majority of the tags missed in transcripts are positioned downstream of annotated transcripts with a minority mapping upstream or within the genomic boundaries of transcripts. The most straightforward explanation for this is that many transcripts in the current version of the chicken transcriptome do not accurately reflect the 3' and the 5' ends of transcripts. This proposition is independently supported by the comparisons of the bursal full length cDNAs to the Ensembl transcript build which detected discrepancies to Ensembl annotated transcripts for approximately 50% of the cDNAs [10]. Another explanation for at least part of the missing transcript matches is variability in poly-adenylation and splicing, which seems to account for substantial variety in the human transcriptome [12].
Accurate definitions of the transcribed parts of the chicken genome is highly desirable not only to ascertain the correct ORFs, but also to identify transcription and translational control sequences often located in 5' and 3' untranslated regions. It should be interesting to use the genomic positions of the missed transcript tags in combination with current gene finder algorithms to improve transcript coverage. Many of the missed tags are close to already annotated exons facilitating this task. It should also be possible to use promising tag sequences to screen cDNA libraries for clones whose sequence will identify missed genes or exons. The riken1 bursal cDNA library is of excellent quality and should be suitable for this purpose.
Although the presented SAGE data provides valuable information about the expression levels of many genes in bursal cells and the DT40 cell line, the full potential of SAGE for gene expression profiling could not be exploited due to the difficulties in tag to gene assignment. Nevertheless, this first SAGE analysis in the chicken lays the basis for further studies. SAGE has the advantage that data from different experiments and laboratories are easily comparable as the tag sequences serve as a common standard. Accumulation of additional data will increasingly facilitate the interpretation of results because bona fide tags will be distinguished from artifacts by being replicated and even polymorphic tags will eventually be defined and assigned to their corresponding transcripts.
Methods
LongSAGE library construction
Total RNA from bursal tissue of chicken 20 day old CB-inbred chicks and from DT40 Cre1 cells [14] was extracted using TRIzol reagent (Invitrogen) according to the manufacturer's instructions. PolyA RNA was isolated using the mRNA DIRECT kit from Dynal . The RNA bound to oligo(dT)25 magnetic beads was immediately used for the construction of a LongSAGE library [1,3] following a modified protocol as described previously [15]. High fidelity PfuUltra (Stratagene) polymerase was used for the PCR amplification step. The SAGE libraries from bursal tissue and DT40 were named busage and dt40sage respectively. For each library, distinct Linker/Primer combinations were used to exclude accidental amplification of ditags from the other library.
Sequencing of SAGE library clone inserts
The pZero-1 (Invitrogen) plasmids containing SAGE ditags as multimeric inserts were transformed into E. coli. Zeocin resistant colonies transformed by the plasmids were grown at low density on agar plates, picked and directly suspended in 50 microliters of H2O. This suspension was heated at 95°C for 10 minutes and stored at -20°C until further processing. The PCR amplification used primers from the plasmid backbone, M13 forward and reverse. Sequencing was performed using the Big Dye v3.1 ready reaction mix (Applied Biosystems) and a nested primer (SSP2) from the plasmid poly-linker. Reactions were analyzed on an ABI 3730 DNA Analyzer (Applied Biosystems). The raw sequencing files were processed as described previously [16].
Ditag, tag and unitag definition
The library insert sequences were searched for ditags in which the flanking CATG tetra-nucleotides are separated by a spacer sequence of more than 31 and less than 37 bases. Ditags of identical sequence were entered only once for each library to avoid the possibility of entering PCR amplification artifacts. The ditags were then divided into two SAGE tags of 21 bases including the CATG tetra-nucleotides. The combined SAGE tag collections of both libraries were normalized to generate a collection of unitags possessing unique tag sequences. A low number of tags (197 of 129,568 total tags) were found to be identical to the sequences of the linker tags used for the library construction and therefore were removed. Care was taken to minimize the possibility of tag sequence errors by using a high fidelity polymerase for the PCR amplification step of the library construction and by rejecting any ditag sequences which contained even a single ambiguous base call or a PHRED score lower than 10. It is possible that some unitags are due to sequencing errors, but these artificial tags are unlikely to match transcript or genome sequences.
Tag-to-gene mapping
To map the unitags to reference sequences, candidate tags were extracted from i) full length bursal cDNA sequences [10], ii) the Ensembl transcript build and iii) the chicken chromosome sequences . Candidate tags in the transcript datasets were extracted only in the sense orientation whereas both strands of the chromosome sequences were searched. The SAGE tags, unitags and candidate tags together with relevant information concerning their positions and frequencies were entered into tables of a relational database to facilitate further analysis.
Unitag matches were sequentially searched for in the bursal cDNA collection, the Ensembl transcript build and the Genome. Once a match had been identified, that tag fell out of the remaining search process and only matches of identical sequences were accepted. To relate the position of matching unitags in the genome sequence to the Ensembl transcripts, the chromosome coordinates of the Ensembl transcripts and their orientation were extracted from their headers. The database table structure, all tabulated entries as well as the FOUNTAIN software [17] used for the analysis is freely available for download under and .
Calculation of the significance of SAGE count differences
To evaluate the significance of SAGE tag count differences between the libraries for each unitag, we used Fishers exact test [18] since it is most easy to use, has exact size and does not require specifying hyper-parameters like for a Bayesian approach. As usual, no method to account for multiple testing was used, so p-values were just used as a convenient tool to rank the unitags.
Semi-quantitative PCR
cDNA was synthesized from bursal tissue and DT40 Cre1 cell line using the SuperScript Preamplification System (Invitrogen). Primers were designed to amplify a region of a few hundred base pairs encompassing the SAGE unitag sequence of the reference transcript. PCR amplification was performed using the Expand Long Template PCR System (Roche) under the following conditions: 2 min initial incubation at 93°C; 20, 25, 30 and 35 cycles consisting of 10 sec at 93°C, 30 sec at 65°C and 5 min at 68°C with 20 sec elongation per cycle.
Authors' contributions
JMB and MBW conceived the project. MBW, RBC, HA and JMB participated in the design of the study and its coordination. MBW constructed the libraries. MBW, RBC, NH, CJ, MS, MC and YDW performed clone management, sequencing and data analysis. HA performed confirmation PCR analysis. MBW, RBC, AMK, EE, VL and JMB performed bioinformatics and statistical analysis. AMK and JMB programmed the FOUNTAIN software package to include the SAGE analysis modules. MBW, RBC, AMK, HA, VL and JMB helped draft the manuscript. All authors read and approved the final manuscript.
Figure 1 Outline of SAGE tag production and reference gene assignment.
Acknowledgments
This work was supported by the EU grants 'Genetics in a cell line' and 'Mechanisms of gene integration'. We would like to thank Kenji Imai for stimulating discussion.
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| 15610564 | PMC543457 | CC BY | 2021-01-04 16:32:43 | no | BMC Genomics. 2004 Dec 21; 5:98 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-98 | oa_comm |
==== Front
BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-981561056410.1186/1471-2164-5-98Research ArticleEvaluation of the chicken transcriptome by SAGE of B cells and the DT40 cell line Wahl Matthias B [email protected] Randolph B [email protected] Andrzej M [email protected] Hiroshi [email protected] Eduardo [email protected] Nina [email protected] Christian [email protected] Manuel [email protected] Manuela [email protected] Yan-Dong [email protected] Volkmar [email protected] Jean-Marie [email protected] Institute of Molecular Radiobiology, GSF, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany2 Laboratory of Systems Biology Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5a, 02-106 Warszawa, Poland3 Research Group of Biomedical Informatics, IMIM/Universidad Pompeu Fabra/Centre de Regulacio Genomica, E08003 Barcelona, Spain4 Institute of Biomathematics, GSF, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany5 Stowers Institute for Medical Research, 1000 E. 50th Street, Kansas City, MO 64110, USA2004 21 12 2004 5 98 98 28 10 2004 21 12 2004 Copyright © 2004 Wahl et al; licensee BioMed Central Ltd.2004Wahl et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The understanding of whole genome sequences in higher eukaryotes depends to a large degree on the reliable definition of transcription units including exon/intron structures, translated open reading frames (ORFs) and flanking untranslated regions. The best currently available chicken transcript catalog is the Ensembl build based on the mappings of a relatively small number of full length cDNAs and ESTs to the genome as well as genome sequence derived in silico gene predictions.
Results
We use Long Serial Analysis of Gene Expression (LongSAGE) in bursal lymphocytes and the DT40 cell line to verify the quality and completeness of the annotated transcripts. 53.6% of the more than 38,000 unique SAGE tags (unitags) match to full length bursal cDNAs, the Ensembl transcript build or the genome sequence. The majority of all matching unitags show single matches to the genome, but no matches to the genome derived Ensembl transcript build. Nevertheless, most of these tags map close to the 3' boundaries of annotated Ensembl transcripts.
Conclusions
These results suggests that rather few genes are missing in the current Ensembl chicken transcript build, but that the 3' ends of many transcripts may not have been accurately predicted. The tags with no match in the transcript sequences can now be used to improve gene predictions, pinpoint the genomic location of entirely missed transcripts and optimize the accuracy of gene finder software.
==== Body
Background
The definition of transcription units within a finished genome sequence in higher eukaryotes is challenging and relies on genome mapping of cDNAs and ESTs backed up by theoretical gene finder algorithms. An increasing number of gene sequences from model organisms have made the prediction of well conserved ORFs easier, but less conserved coding and untranslated regions are difficult to detect. The best way to unambiguously define transcription units is full length cDNAs, but large scale projects are expensive in terms of labor and costs. One also needs to bear in mind that some cDNAs elude detection, because unusual secondary structure or toxicity inhibits reverse transcription or cloning.
SAGE investigates the transcription profile of a given cell sample by large scale sequencing of short cDNA tags derived from the bulk mRNA [1,2]. Whereas tag mapping to the cDNA and genome databases of the organism indicates the type of expressed genes, the prevalence of individual tags within the library reflects their relative levels of expression. Since SAGE tags are only short sequences, they can be collected more easily in higher numbers than ESTs and full length cDNA sequences. The potential of SAGE to discover new or better define already known transcription units is particularly advantageous in situations where the entire genome sequence of an organism has been determined, but gene predictions based on theoretical algorithms and the mapping of a relatively small number of EST and cDNA sequences remain tentative. LongSAGE generates longer tags of 21 bases as compared to the classical SAGE protocol and is therefore better suited for the unambiguous assignments of tag to genome sequences [3,4].
Cellular and molecular features of early B cell development [5] and lymphoma formation [6,7] have been extensively studied in the chicken. Gene expression signatures of primary bursal B cells, pre-neoplastic and neoplastic lymphoma cells were collected by microarray hybridizations in a first attempt to identify genes up- or down-regulated during myc-induced B cell lymphoma development [8].
The whole chicken genome including a genome scale transcript build from Ensembl [9] and a collection of bursal full-length cDNAs [10] have recently been released. We describe here the mapping of large collections of SAGE tags from bursal lymphocytes and DT40 to these reference datasets to evaluate the quality of the transcript build. Furthermore, the transcription profiles of bursal cells and DT40 as defined by this first SAGE analysis in the chicken should lead to a better understanding of B cell transformation and facilitate the selection of candidate genes for disruption in DT40 [11].
Results and Discussion
Generation of SAGE tag libraries and SAGE tags collections
Two SAGE libraries, named busage and dt40sage, were made from the bursa of Fabricius and DT40 cells using the LongSAGE technique which generates tags of 21 nucleotides in length and therefore decreases the likelihood of ambiguous matches [3,4]. Of the 129,568 tags collected, about equal numbers were derived from the busage and the dt40sage libraries respectively (Table 1). In total 38,212 unitags were derived from the SAGE tags of both libraries. The library from bursal cells and the DT40 cell line seem to be similar with regard to the number of extracted unitags and the average counts of matching SAGE tags. Underlying a standard binomial model, one would expect to find a special Unitag among the busage tags or dt40 tags with a probability of 95% at least once if the relative abundance of this unitag among all busage tags or dt40 tags is at least 4.55 * 10-5 or 4.69 * 10-5 respectively.
Table 1 SAGE and unitag collections
SAGE tags Unitags Average frequency of matching SAGE tags within library Average count of SAGE tag per unitag
Busage 65,798 24,064 2.73 4.63
Dt40sage 63,770 21,308 2.99 4.97
Total 129,568 38,212 3.39
Tag to gene assignment using bursal cDNAs, the Ensembl transcript build and the genome sequence
Successful mapping of SAGE tags to reference sequences is influenced by the quality of the sequences, the complexity of the reference sequence datasets and the prevalence of polymorphisms within the tag sequences. It was therefore decided to first search for matches within a bursal cDNA collection which represents the best possible reference dataset, as it was derived from the same tissue and genetic background as the busage library. Subsequently, unitags were mapped to the Ensembl transcript build and finally the chicken genome sequence. Unitags found in a previous dataset were not searched for any more in the next. To facilitate the searches, candidate tags starting with the CATG tetra-nucleotide were extracted from each reference dataset prior to analysis.
As expected the highest rate of matching to total candidate tags was found for the bursal cDNA collection (3,030 of 26,044 candidate tags matched unitags), followed by the Ensembl transcript build (2,934 of 208,048) and the genome (14,505 of 9,091,924) (Table 2). Some unitags mapped more than once within a dataset making an unambiguous assignment difficult. In comparison to the complexity of the dataset, multiple hits occurred more frequently in the bursal cDNA (33/26,044; 0.0012%) and Ensembl dataset (637/208,048, 0.0031%) than in the genome (1,003/9,091,924; 0.0001%). This can be explained if these transcript collections are not completely normalized or if there is bias for certain sequence motifs within gene transcripts. Manual analysis of the bursal cDNAs revealed that most of the multiple unitag matches were due to alternative processing of transcripts originating from the same locus (data not shown). A relatively large fraction of unitags (17,743/38,212; 46.4%) did not match to any reference dataset. It is currently impossible to analyze this in more detail, but sequencing errors, polymorphisms and positions of tags on exon/exon boundaries are likely to explain the missed hits [12]. Non-matching unitags have a significantly lower average count of SAGE tags than matching unitags (2.1 versus 4.65) suggesting that they either over-represent lowly expressed genes or are artifacts of the SAGE technique.
Table 2 Unitag mapping to reference datasets
Dataset matches of unitag Unitags Candidate tags Average count of SAGE tags per unitag
Bursal cDNA 3,030 26,044 6.89
1 2,997 6.90
> 1 33 5.15
Ensembl transcript build 2,934 208,048 9.93
1 2,275 10.60
> 1 659 7.63
Genome 14,505 9,091,924 2.84
1 13,427 2.83
> 1 1,078 2.91
Total matching 20,469 4.45
Non-matching 17,743 2.17
SAGE tags are expected at the position of the NlaIII site closest to the polyA tail of the transcript, but alternative transcript processing as well as incomplete NlaIII digestion or internal priming can produce upstream tags. Indeed, when the positions of the matching candidate tags were analyzed for bursal cDNA transcripts, about 40% of the tags matched to non-last positions (data not shown).
Mapping of tags to the genome
Most interesting from the perspective of gene discovery are the 13,427 unitags without transcript match, but with a single match in the genome (Table 3). When the positions of these tags within the genome were correlated with the positions of the Ensembl transcripts, 1,637 fell within annotated transcript boundaries indicating that they are located on missed or incomplete exons. To see whether the remaining tags were located in the neighborhood of already identified transcripts, the numbers of tags falling within regions of defined length upstream and downstream of the Ensembl transcripts were determined. Indeed many tags map very close to annotated transcripts with a strong preference for the region downstream of the transcript, as would be expected, if the tag matches the missed 3' end of an annotated Ensembl gene. Since not all tags are derived from the most 3' transcript position, the tags matching immediately upstream of transcripts might indicate missed 5' exons. Some of the tags mapped close to upstream and downstream transcripts (12 at the 500 base distance limit), perhaps indicating that these transcripts belong together. At a distance limit of 5000 bases, 7,169 tags mapped into the neighborhood of annotated transcripts; 5,627 downstream, 669 upstream and 1,061 both upstream and downstream. When the distance limit was extended to 10,000 bases, the number of downstream matching tags was only marginally increased to 5,627 whereas the number of dual positioned tags more than doubled to 2,101. This indicates that at distances over 5000 bases the tag assignment to the neighboring transcripts is becoming increasingly ambiguous, and the tags might in fact correspond to entirely missed genes.
Table 3 Locations of unitags having a single match in genome but no transcript match
Unitags Bases searched next to annotated Ensembl transcripts Matching unitags Matches only downstream of Ensembl transcripts Matches only upstream of Ensembl transcripts Matches upstream and downstream of Ensembl transcripts
Total 13,427
Within Ensembl transcript boundaries 1,637
Outside Ensembl transcript boundaries 11,177 100 409 362 46 1
200 732 668 64 2
500 1,651 1,496 143 12
1,000 2,896 2,553 262 81
5,000 7,169 5,439 669 1,061
10,000 8,639 5,627 911 2,101
Relationship of genome mapping unitags to Ensembl transcripts
To further investigate those unitags mapping close to the 5' boundary of Ensembl transcripts or within transcript boundaries to the genome, the bursal EST database [11] was searched for ESTs matching the tags in the sense strand orientation. These ESTs were then aligned to the chicken genome sequence and the neighboring Ensembl gene predictions. As many ESTs linked the SAGE tags to the Ensembl transcripts, this provided independent experimental evidence that these tags are indeed derived from non-annotated parts of these transcripts (Table 4).
Table 4 Analysis of unitags mapping 5' of or within Ensembl transcript boundaries. #
Unitag Ensembl ID BLAST result ## Supporting bursal EST Unitag relationship to Ensembl transcript ###
Unitags mapping 5'
CATGCTGCTCGCACGAGCCCT ENSGALT00000002525.1 Q9W7P7 riken1_17l12r1 Upstream 5' exon
CATGGCGGGGTTCCCGGGGCA ENSGALT00000005092.1 PEF protein with a long N-terminal hydrophobic domain riken1_18i20r1 Upstream 5' exon (EST supports two additional 5' exons)
CATGCTCCTGCTGCTGGCTGG ENSGALT00000009521.1 LAC_CHICK dkfz426_24a5r1 Upstream 5' exon
CATGAGGCACCTCCTGTTGGC ENSGALT00000001476.1 GR78_CHICK riken1_25c14r1 5' upstream/Exon1 (EST supports one additional 5' exon)
CATGGCCGCCCAAGGAGAGCC ENSGALT00000004055.1 RAN_CHICK riken1_25b20r1 5' upstream/Exon1 (EST supports one additional 5' exon)
Unitags mapping within transcript boundaries
CATGTACTGGTTGTCTGTTTT ENSGALT00000025884 HG14_CHICK dkfz426_13h16r1 Intron 4–5
CATGCATAGAGGCTTTATTGC ENSGALT00000021336 Aldo-keto reductase family 1 member dkfz426_3h12r1 Intron 8–9
CATGTTGGGACTCACCACTCT ENSGALT00000000504 No description dkfz426_13d22r1 Intron 5–6/Exon6
CATGGTCACCCTAGTAAATAG ENSGALT00000009677 Protein kinase C, beta type dkfz426_38f16r1 Intron 14–15
CATGTAAAGTGTTAGCTGTAC ENSGALT00000006857 ITF2_CHICK dkfz426_14i24r1 Intron 8–9
CATGTTACCTGCAACCTGCTG ENSGALT00000021577 Centromeric protein E dkfz426_17a21r1 Intron 28–29
CATGGGATATACTGAAAATCT ENSGALT00000009956 T-cell activation leucine repeat-rich protein dkfz426_41d20r1 Intron 1–2
CATGGGCTGGTTGGTTTTTGT ENSGALT00000028428 No description dkfz426_43g3r1 Intron 2–3
CATGGTCAAGTACAACTCTTA ENSGALT00000022583 Bcl-2-associated transcription factor dkfz426_12n7r1 Intron 8–9
# Only a few representative examples are shown
## BLAST results are abbreviated
### Unitag aligns within an intron or exon or lies across an intron/exon or upstream sequence/exon boundary
To confirm that the distribution of the tags downstream of Ensembl transcripts is statistically significant, their positions were compared to the positions of simulated tags generated by randomly selecting 21 bp sequences in the genome beginning with the 'CATG' tetra-nucleotide. This comparison shows that the real tags map closer to the 3' end of the Ensembl predicted coding sequences (CDS) than the simulated tags providing strong evidence that most of the closely positioned tags are indeed related to the predicted transcripts (Figure 2).
Figure 2 Mappings of SAGE unitags downstream of Ensembl transcripts compared to simulated genomic tags. The number of tags falling within windows of 10 bp is plotted on the y-axis whereas the distance from the 3' end of the nearest predicted Ensembl transcript is plotted on the x-axis. Sage unitags coordinates are indicated by crosses and randomly selected tag coordinates by diamonds.
If one summarizes the unitag to transcript mappings, 5,964 unitags map directly to transcripts, 1,637 map to not annotated sequences within the limit of the Ensembl transcripts and 7,169 map to within 5,000 bases of annotated transcript boundaries (Table 5). This leaves about 20% (4,621 out of 19,391 total) bona fide unitags unaccounted for which might be taken as an estimate for the percentage of genes present in the released genome sequence, but absent from the Ensembl transcript collection. Nevertheless, one needs to bear in mind that this calculation includes a number of uncertainties. It is for example possible that the 5000 base limit is too large, since only 5% of 3' UTR sequences in the human transcriptome are reported to be over 2,000 bps according to NCBI's AceView database or that both SAGE and the gene predictions have missed a substantial number of lowly expressed transcripts. In these cases, the estimate of the percentages of missed genes would increase.
Table 5 Unitag mapping to transcripts
Unitag Match to annotated transcript Match to genome within boundaries of annotated transcript Match next to annotated transcript using 5000 base cut-off Match distant from annotated transcript
Total 38,212
Without match 17,743
With only multiple genome matches 1,078
With match to annotated transcripts or single genome match 19,391 5,964 1,637 7,169 4,621
Significant gene expression differences between bursal cells and the DT40
One of the goals of this SAGE analysis was the identification of differentially expressed transcripts between the two libraries and the significance of count differences for the busage and the dt40sage tags were calculated for each unitag. In total 629 unitags showed p values below 0.01 suggesting that the corresponding transcripts are differentially expressed in bursal cells and DT40. In contrast to this, the false discovery rate (FDR) controlling procedure of Benjamin & Hochberg would admit the first 229 genes at an FDR of 5% [13]. Twenty-five of the most significant unitags mapping to bursal cDNAs are listed in Table 6.
Table 6 List of genes differentially expressed in bursal cells and DT40
Unitag Busage DT40sage Significance Sequence ID# Best BLAST result##
CATGGCAGGGGGCGGAAACCT 4 45 2.83E-10 riken1_2o24 (AAH61765) Hypothetical protein
CATGGTGAGCCAAGGTGTTGT 24 82 2.06E-9 riken1_4m1 (AAH69219) Cold inducible RNA-binding protein
CATGCAGAAATAAGCTTCTCC 45 109 4.09E-8 riken1_7b15 (Q7ZUR6) Similar to muscle-specific beta 1 integrin binding protein
CATGAGCGGGGGCAGCACTTG 118 203 5.75E-7 riken1_25p23 (Q90YW7) Ribosomal protein L4
CATGCTGGAAGAAAGAATAAC 46 114 1.92E-8 riken1_32c11 (Q9YGQ1) Peptide elongation factor 1-beta
CATGCGCTCTCCTTTTAAAAG 9 41 2.67E-6 riken1_15l3 (CAA31409) Chinese hamster asparagine synthetase
CATGGATGGCCAGCAAGTGTT 29 4 1.17E-5 riken1_4k19 (P13796) L-plastin (Lymphocyte cytosolic protein 1)
CATGTCCGTGGCATCCTTTGA 0 16 1.18E-5 riken1_24e23 (Q8BGQ8) Heterogeneous nuclear ribonucleoprotein K
CATGGCTTTGGAATATTTGAC 25 3 2.90E-5 riken1_2f9 (AAH46152) Selenoprotein P precursor
CATGGAGTCCATAACACGGCG 21 2 6.88E-5 riken1_34m12 (Q96CJ1) Testosterone regulated apoptosis inducer and tumor suppressor
CATGCAAAGTGCCCTTGGCTT 17 1 1.46E-4 riken1_10g19 (P30281) G1/S-specific cyclin D3
CATGTAAGCCAATTCTGAACC 19 1 4.09E-5 riken1_33a18 (Q8JHJ4) TNF family B cell activation factor
CATGTTGTACACACGGGCACT 11 0 5.79E-4 riken1_5g12 (Q90YB0) FEN-1 nuclease
CATGTGCCCGTGACCCCCATC 2 16 6.12E-4 riken1_4n15 (Q13200) 26S proteasome non-ATPase regulatory subunit 2
CATGTCGTGCTCTGTGCCTCC 5 26 9.28E-5 riken1_2i9 (Q90W60) XNop56 protein
CATGCTTTCTGCTTTGACTTT 21 4 9.42E-4 riken1_12p16 (P22794) Ecotropic viral integration site 2A protein
CATGTTTGTGCATAGCTGTCC 5 28 1.17E-5 riken1_30e3 (Q91XC8) Similar to death-associated protein
CATGGCCGGGCGCCCCACCAG 0 15 2.41E-5 riken1_15i13 (Q99P44) Leucine aminopeptidase
CATGGGACCAACAAATAAAGC 19 4 0.0027 riken1_4o10 (P97440) Histone RNA hairpin-binding protein
CATGAAAATGTACTGTGCTAA 2 13 0.0036 riken1_20p3 (P34022) Ran-specific GTPase-activating protein
CATGTATACAGAACTGCTGGA 8 0 0.0044 riken1_2i24 (Q9UMR2) ATP-dependent RNA helicase DDX19
CATGGCCAAATTAGAGGAGTG 1 10 0.0051 riken1_32c11 (Q9YGQ1) Peptide elongation factor 1-beta
CATGCTACGCTGTGTCTGCCA 11 1 0.0062 riken1_2m14 (AAQ20009) Heterogeneous nuclear ribonucleoprotein H1-like protein
CATGCTCTCCGGTGGTACAAT 0 7 0.0070 riken1_32c11 (Q9YGQ1) Peptide elongation factor 1-beta
CATGTTGATTCCTATGCTAAA 7 0 0.0087 riken1_3a6 (Q9H165) B-cell lymphoma/leukemia 11A
# only unitags matching bursal cDNAs are listed
## BLAST results are abbreviated
To verify the validity of the SAGE data, semi-quantitative PCR was performed using primers close to the tags for 27 transcripts (Figure 3). This confirmed the expression pattern suggested by SAGE tag counts in the majority (21 out of 27) of the cases. Certainly, these PCR results could not be explained by the statistical variation in the SAGE data alone (FDR below 5% vs. FDR of 22% indicated by PCR). Although more analysis is needed to find out which differentially expressed genes are related to differences in the behavior of bursal B cells and DT40, the freely available SAGE repository will be a good resource to select candidates for more detailed investigations.
Figure 3 Confirmation of differential gene expression using semi-quantitative PCR. Primers derived from reference genes for SAGE tags were used for the amplification of cDNA from bursal cells and DT40 employing different cycle numbers as indicated on top of the lanes. Based on the SAGE tag counts, the reference genes were classified as likely to be equally expressed (left part), higher expressed in bursal cells (middle part) or higher expressed in DT40 (right part). The size of the expected PCR product is indicated by a bar adjacent to the gel image. The numbers of tags found for the busage and dt40sage libraries as well as the calculated significance for differential expression are indicated in brackets under the gene names.
Conclusions
The mapping of the SAGE tags to the recently released cDNA collections and the chicken genome has been useful to assess the completeness and accuracy of the current transcript catalog. On the positive side, it appears that the transcript build may have missed only a low percentage of genes, since relatively few tags map to genome regions far away from annotated transcription units. On the downside, fewer than 6,000 of over 19,000 tags with matches to reference sequences could be mapped to transcripts. The majority of the tags missed in transcripts are positioned downstream of annotated transcripts with a minority mapping upstream or within the genomic boundaries of transcripts. The most straightforward explanation for this is that many transcripts in the current version of the chicken transcriptome do not accurately reflect the 3' and the 5' ends of transcripts. This proposition is independently supported by the comparisons of the bursal full length cDNAs to the Ensembl transcript build which detected discrepancies to Ensembl annotated transcripts for approximately 50% of the cDNAs [10]. Another explanation for at least part of the missing transcript matches is variability in poly-adenylation and splicing, which seems to account for substantial variety in the human transcriptome [12].
Accurate definitions of the transcribed parts of the chicken genome is highly desirable not only to ascertain the correct ORFs, but also to identify transcription and translational control sequences often located in 5' and 3' untranslated regions. It should be interesting to use the genomic positions of the missed transcript tags in combination with current gene finder algorithms to improve transcript coverage. Many of the missed tags are close to already annotated exons facilitating this task. It should also be possible to use promising tag sequences to screen cDNA libraries for clones whose sequence will identify missed genes or exons. The riken1 bursal cDNA library is of excellent quality and should be suitable for this purpose.
Although the presented SAGE data provides valuable information about the expression levels of many genes in bursal cells and the DT40 cell line, the full potential of SAGE for gene expression profiling could not be exploited due to the difficulties in tag to gene assignment. Nevertheless, this first SAGE analysis in the chicken lays the basis for further studies. SAGE has the advantage that data from different experiments and laboratories are easily comparable as the tag sequences serve as a common standard. Accumulation of additional data will increasingly facilitate the interpretation of results because bona fide tags will be distinguished from artifacts by being replicated and even polymorphic tags will eventually be defined and assigned to their corresponding transcripts.
Methods
LongSAGE library construction
Total RNA from bursal tissue of chicken 20 day old CB-inbred chicks and from DT40 Cre1 cells [14] was extracted using TRIzol reagent (Invitrogen) according to the manufacturer's instructions. PolyA RNA was isolated using the mRNA DIRECT kit from Dynal . The RNA bound to oligo(dT)25 magnetic beads was immediately used for the construction of a LongSAGE library [1,3] following a modified protocol as described previously [15]. High fidelity PfuUltra (Stratagene) polymerase was used for the PCR amplification step. The SAGE libraries from bursal tissue and DT40 were named busage and dt40sage respectively. For each library, distinct Linker/Primer combinations were used to exclude accidental amplification of ditags from the other library.
Sequencing of SAGE library clone inserts
The pZero-1 (Invitrogen) plasmids containing SAGE ditags as multimeric inserts were transformed into E. coli. Zeocin resistant colonies transformed by the plasmids were grown at low density on agar plates, picked and directly suspended in 50 microliters of H2O. This suspension was heated at 95°C for 10 minutes and stored at -20°C until further processing. The PCR amplification used primers from the plasmid backbone, M13 forward and reverse. Sequencing was performed using the Big Dye v3.1 ready reaction mix (Applied Biosystems) and a nested primer (SSP2) from the plasmid poly-linker. Reactions were analyzed on an ABI 3730 DNA Analyzer (Applied Biosystems). The raw sequencing files were processed as described previously [16].
Ditag, tag and unitag definition
The library insert sequences were searched for ditags in which the flanking CATG tetra-nucleotides are separated by a spacer sequence of more than 31 and less than 37 bases. Ditags of identical sequence were entered only once for each library to avoid the possibility of entering PCR amplification artifacts. The ditags were then divided into two SAGE tags of 21 bases including the CATG tetra-nucleotides. The combined SAGE tag collections of both libraries were normalized to generate a collection of unitags possessing unique tag sequences. A low number of tags (197 of 129,568 total tags) were found to be identical to the sequences of the linker tags used for the library construction and therefore were removed. Care was taken to minimize the possibility of tag sequence errors by using a high fidelity polymerase for the PCR amplification step of the library construction and by rejecting any ditag sequences which contained even a single ambiguous base call or a PHRED score lower than 10. It is possible that some unitags are due to sequencing errors, but these artificial tags are unlikely to match transcript or genome sequences.
Tag-to-gene mapping
To map the unitags to reference sequences, candidate tags were extracted from i) full length bursal cDNA sequences [10], ii) the Ensembl transcript build and iii) the chicken chromosome sequences . Candidate tags in the transcript datasets were extracted only in the sense orientation whereas both strands of the chromosome sequences were searched. The SAGE tags, unitags and candidate tags together with relevant information concerning their positions and frequencies were entered into tables of a relational database to facilitate further analysis.
Unitag matches were sequentially searched for in the bursal cDNA collection, the Ensembl transcript build and the Genome. Once a match had been identified, that tag fell out of the remaining search process and only matches of identical sequences were accepted. To relate the position of matching unitags in the genome sequence to the Ensembl transcripts, the chromosome coordinates of the Ensembl transcripts and their orientation were extracted from their headers. The database table structure, all tabulated entries as well as the FOUNTAIN software [17] used for the analysis is freely available for download under and .
Calculation of the significance of SAGE count differences
To evaluate the significance of SAGE tag count differences between the libraries for each unitag, we used Fishers exact test [18] since it is most easy to use, has exact size and does not require specifying hyper-parameters like for a Bayesian approach. As usual, no method to account for multiple testing was used, so p-values were just used as a convenient tool to rank the unitags.
Semi-quantitative PCR
cDNA was synthesized from bursal tissue and DT40 Cre1 cell line using the SuperScript Preamplification System (Invitrogen). Primers were designed to amplify a region of a few hundred base pairs encompassing the SAGE unitag sequence of the reference transcript. PCR amplification was performed using the Expand Long Template PCR System (Roche) under the following conditions: 2 min initial incubation at 93°C; 20, 25, 30 and 35 cycles consisting of 10 sec at 93°C, 30 sec at 65°C and 5 min at 68°C with 20 sec elongation per cycle.
Authors' contributions
JMB and MBW conceived the project. MBW, RBC, HA and JMB participated in the design of the study and its coordination. MBW constructed the libraries. MBW, RBC, NH, CJ, MS, MC and YDW performed clone management, sequencing and data analysis. HA performed confirmation PCR analysis. MBW, RBC, AMK, EE, VL and JMB performed bioinformatics and statistical analysis. AMK and JMB programmed the FOUNTAIN software package to include the SAGE analysis modules. MBW, RBC, AMK, HA, VL and JMB helped draft the manuscript. All authors read and approved the final manuscript.
Figure 1 Outline of SAGE tag production and reference gene assignment.
Acknowledgments
This work was supported by the EU grants 'Genetics in a cell line' and 'Mechanisms of gene integration'. We would like to thank Kenji Imai for stimulating discussion.
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| 15588298 | PMC543458 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Dec 9; 5:193 | latin-1 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-193 | oa_comm |
==== Front
Ann Gen Hosp PsychiatryAnnals of General Hospital Psychiatry1475-2832BioMed Central London 1475-2832-3-151559834910.1186/1475-2832-3-15Primary ResearchRelationship among Dexamethasone Suppression Test, personality disorders and stressful life events in clinical subtypes of major depression: An exploratory study Fountoulakis KN [email protected] A [email protected] F [email protected] M [email protected] A [email protected] G [email protected] Lab of Psychophysiology, 3rd Department of Psychiatry, Aristotle University of Thesssaloniki, Greece2 Lab of Clin Neurophysiology, 1st Department of Neurology Aristotle University of Thesssaloniki, Greece3 Lab of Biochemistry, Aristotle University of Thesssaloniki, Greece2004 14 12 2004 3 15 15 27 11 2004 14 12 2004 Copyright © 2004 Fountoulakis et al; licensee BioMed Central Ltd.2004Fountoulakis et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The present study aimed to investigate the relationship between dexamethasone suppression test, personality disorder, stressful life events and depression.
Material
Fifty patients (15 males and 35 females) aged 41.0 ± 11.4 years, suffering from Major Depression according to DSM-IV criteria entered the study.
Method
Diagnosis was obtained with the aid of the SCAN v 2.0 and the IPDE. Psychometric assessment included the HDRS, HAS, the Newcastle Scale (version 1965 and 1971), the Diagnostic Melancholia Scale, the Personality Deviance Scale and the GAF scale. The 1 mg DST was used.
Statistical Analysis
Included MANOVA, ANOVA with LSD post hoc test and chi-square test.
Results
Sixteen (32%) patients were non-suppressors. Eight patients without Personality Disorder (PD) (23.5%), and 5 of those with PD of cluster B (50%) were non-suppressors. Atypical patients were the subtype with the highest rate of non-suppression (42.85%). No difference between suppressors and non-suppressors was detected in any of the scales.
Discussion
The results of the current study suggest that pathological DST is not a core feature of major depression. They also suggest that there are more than one subtypes of depression, concerning the response to stress. It seems that the majority of depressed patients (50%) does not experience high levels of stress either in terms of self reported experience or neuroendocrine function. The rest of patients however, either experience high levels of stress, or manifest its somatic analogue (DST non-suppression) or have a very low threshold of stress tolerance, which makes them to behave in a hostile way.
Depressionstressful life eventsstresspersonality disordersDexamethasone suppression test.
==== Body
Background
Life events and environmental stressful factors may relate to the development of depression [1-4]. However, biological theories suggest that the cause of depression rely on a biochemical disturbance of the functioning of the central nervous system (CNS).
The Dexamethasone Suppression Test (DST) [5] is the most known and worldwide used biological marker, its results suggest that a disorder of the HPA axis is present in at least some depressed patients [6]. DST non-suppression is of unknown aetiology, and as a test is not specific to any disease. Rather it constitutes an endocrin expression of stress. Basically, DST is reported to assess norepinephrine function. Topographically, it assesses the function of the hypothalamus and indirectly of the structures, which project to it. However, it is also supposed to be the result of an increased serotonin (5-HT) or Ach activity, or of a disturbance of the feedback to the hippocampus [7] and the hypothalamus. A debate still holds, whether some forms of depression are characterized by hypercortisolaimia or early escape from HPA tests. Possibly, DST non-suppression and hypercortisolemia are two different things [8].
The present study aimed to investigate the relationship between dexamethasone suppression test, personality disorder (PD), stressful life events and clinical manifestations of major depression. The hypothesis to test was that subtypes of depression could be identified on the basis of the presence of personality disorder (which constitutes an abnormal interpretation and response to environmental stimuli), the presence of abnormal DST results and/or hypercortisolemia (which both constitute an idiosyncratic neuroendocrine response to stress) and the presence or not of stressful life events (which trigger the above behavioral and neuroendocrine responses).
The presence or not of Personality Disorder, and the response to the DST are both characteristics of the patient. Life events reflect the impact of the environment on the patient. So, life events provoke responses from the side of the patient, which are largely determined by Personality and DST response. Thus, four groups of patients can be identified and studied, according to the combination of the co-existence of DST non-suppression and personality disorder.
Material
Fifty (50) major depressive patients (15 males and 35 females) aged 41.0 ± 11.4 (range 21–60) years [9,10], took part in the study. All provided written informed consent. Fourteen of them fulfilled criteria for atypical features, 16 for melancholic features (according to DSM-IV) and 32 for somatic syndrome (according to ICD-10). Nine patients did not fulfil criteria for any specific syndrome according either classification system.
Patients were in- or outpatients of the 3rd department of psychiatry, Aristotle University of Thessaloniki, Greece. They constituted consecutive cases that fulfilled the inclusion criteria and no systemic bias exists.
The SCAN v 2.0 [11] was used for the diagnosis of depression and its subtypes and the IPDE [12-14] was used for the diagnosis of personality disorders.
Seventeen patients (34%) suffered from a personality disorder (PD). Ten of them (20%) had a cluster B PD. Concerning depressive subtypes, 5 (out of 16) melancholics (26.32%), 7 (out of 14) atypicals (50%), 9 (out of 32) patients with somatic syndrome (28.13%), and 3 (out of 9) 'undifferentiated' patients (33.33%), fulfilled criteria for PD (note: patients with PD are not 5 + 7 + 9 + 3 = 24, but only 17 as mentioned above, because there is ovelapping between depressive syndromes). No patient suffered from a paranoid, schizotypal, antisocial, dissocial, narcissistic, and avoidant PD, although individual criteria were met. No criteria belonging to the schizotypal or antisocial PDs were met.
No patient fulfilled criteria for catatonic or psychotic features or for seasonal affective disorder. No patient fulfilled criteria for another DSM-IV axis-I disorder, excepting generalized anxiety disorder (N = 10) and panic disorder (N = 7). Another 5 patients had both generalized anxiety disorder and panic disorder (totally 22 patients that is 44% had some anxiety disorder).
The present study did not include a normal controls group, since the aim of the study was to compare depressive subtypes between each other.
Method
Laboratory Testing included blood and biochemical testing, test for pregnancy, T3, T4, TSH, B12 and folic acid.
The Psychometric Assessment included the Hamilton Depression Rating Scale (HDRS), the Hamilton Anxiety Scale (HAS), the 1965 and 1971 Newcastle Depression Diagnostic Scale (1965 and 1971-NDDS) and the Diagnostic Melancholia Scale (DMS) [15] and the General Assessment of Functioning Scale (GAF) [16]. An attempt was made to assess the direction of aggression of the depressed patients, with the use of the Personality Deviance Scale (PDS) [17]. This was done mainly because the direction of aggression is considered to be a core feature for the etiopathogenesis of depression according to psychodynamic theories, but also is related to personality traits.
The PDS consists from the following subscales:
a. Extrapunitive Scale (ES) which consists of 1. HT: Hostile Thoughts and 2. DO: Denigratory Attitudes Toward other People. All these scales and subscales are scored in such a way that high scores denote lack of the characteristic.
b. Intropunitive Scale (IS), which consists of 1. LSC: Lack of Self-Confidence and DEP: Overdependency on Others. All these scales and subscales are scored in such a way that high scores denote presence of the characteristic.
c. Dominance Scale (DS) which consists of 1. MIN: Domineering Social Attitude and 2. HA: Uninhibited Hostile Acts. The MIN is scored in such a way that high scores denotes presence of the characteristic, while HA has opposite properties.
Data concerning personal and family history and stressful life events
a. age of onset b. presence of a recent suicide attempt c. history of such attempts d. The questionnaire of Holmes [18] was used to search for stressful life events during the last 6 months before the onset of the symptomatology.
The 1 mg Dexamethasone Suppression Test (DST) protocol demands the administration of 1 mg dexamethasone per os at 23.00 of the first day, and determination of cortisol serum levels simultaneously and the next day at 16.00 and 23.00. Cortisol levels expressed in μg/dl were measured with Luminance Immunoassay (intra-essay reliability: 4.9%; inter-essay: 7.5%). Non-suppression cut-off level: 5 μg/dl.
Statistical Analysis
Multiple Analysis of Variance (MANOVA) was performed with DST (suppression vs. non suppression) and Personality Disorder (present vs. absent) as factors. The dependent variables list included: Age, Age of Onset, Number of previous episodes, Number of DSM-IV Criteria, Number of atypical features, Number of melancholic features, GAF, NDDS 1965, NDDS 1971, Endogenous axis of DMS, Reactive axis of DMS, Number of stressful life events, HDRS-17, HDRS-21, HDRS Depressive index, HDRS Anxiety index, HDRS Sleep index, HDRS non-specific index, HAS, HAS Somatic subscale, HAS Psychic subscale, PDS-Hostile Thoughts Scale, PDS-Denigratory Attitude Scale, PDS-Extrapunitive Scale, PDS-Low Self Confidence Scale, PDS-Overdependency by others Scale, PDS-Intropunitive Scale, PDS-Domineering Social Attitude Scale, PDS-Uninhibited Hostile Acts Scale and PDS-Dominance Scale.
Afterwards, Analysis of Variance (ANOVA) with Least Significance Difference (LSD) test as post-hoc test was performed.
Finally, Chi-square test was performed. PD and DST were independently placed in cross-tabulation with the presence or absence of Recent Suicide Attempt, History of Suicide Attempt, Generalized Anxiety or Panic Disorder, Melancholic Features, Atypical Features, Somatic Syndrome, 'Undifferentiated' symptomatology, Full and sustained remission, With Relapsing circumscribed episodes, Chronic Depression without full remission, Presence of Stressful life events, Family history of any mental disorder, Family history of depression in 1st degree relatives, and Family history of depression in 2nd degree relatives.
Results
Women were twice as many as men (70% versus 30%), which is not uncommon [19] and reflects the higher prevalence of depression observed in women.
Sixteen out of 50 depressed patients (32%) were DST non-suppressors (NS).
Eight out of 17 (47.05%) depressed patients with PD were also NS.
When the patients with a coexistent personality disorder (PD) were excluded, then 8 out of 33 (24.24%) patients left, were NS.
When only cluster b PDs were excluded, the respected percentage of NS climbs to 27.5% (11 out of 40).
Fifty percent of Cluster b PD patients were NS (5 S and 5 NS).
Six out of 14 (42.85%) atypical patients were NS, and this percentage makes this subtype the one with the highest NS percentage.
No one of Chi-square tests revealed any significant findings (at p > 0.01).
MANOVA results were significant both for Personality Disorder (p < 0.001) and for DST (P < 0.001) (table 1).
Table 1 2-way MANOVA results. Both Personality disorders and DST results and their interaction produce significant results.
Wilks' Lambda Rao's R df 1 df 2 p-level
Factors:
1-Personality Disorder (present vs. absent) and
2-DST results (suppressors vs. non-suppressors)
1 0.02 18.26 30 12 0.000
2 0.02 20.99 30 12 0.000
12 0.01 28.42 30 12 0.000
ANOVA testing, separately for each dependent variable, revealed significant findings concerning the number of episodes, and HT, DO and HA subscales of the PDS. When PD was used as the sole factor variable, significant findings were found concerning the endogenous axis of DMS and the HDRS depressive index. The interaction of PD and DST produced significant findings concerning age, age of onset, number of atypical features, number of stressful life events, and the DO subscale of the PDS (table 2). Post-hoc comparisons for DST showed that NS were more endogenous (1971-NDDS and DMS endogenous axis) but with lower HDRS depressive index (p < 0.05). Post-hoc comparisons for PD characteristics showed that patients without PD had more previous episodes and less hostile thoughts (HT) and less uninhibited hostile acts (HA) (p < 0.05). The post-hoc results for the groups defined by the interaction of PD with DST are shown in table 3. A graphical representation of these results is shown in figures 1 and 2.
Table 2 ANOVA results for each dependent variable separately (only significant results are shown.
df Effect MS Effect df Error MS Error F p-level
Factors:
1-Personality Disorder (present vs. absent) and
2-DST results (suppressors vs. non-suppressors)
Dependent variable: age
1 1 93.29 46.00 103.25 0.90 0.347
2 1 80.23 46.00 103.25 0.78 0.383
12 1 935.13 46.00 103.25 9.06 0.004
Dependent variable: endogenous axis of DMS
1 1 9.08 46.00 8.26 1.10 0.300
2 1 78.71 46.00 8.26 9.53 0.003
12 1 21.10 46.00 8.26 2.55 0.117
Dependent variable: age of onset
1 1 71.51 46.00 117.92 0.61 0.440
2 1 82.59 46.00 117.92 0.70 0.407
12 1 750.95 46.00 117.92 6.37 0.015
Dependent variable: number of episodes
1 1 17.46 46.00 2.11 8.28 0.006
2 1 0.48 46.00 2.11 0.23 0.637
12 1 0.31 46.00 2.11 0.15 0.703
Dependent variable: number of atypical features
1 1 0.81 46.00 0.75 1.09 0.302
2 1 0.59 46.00 0.75 0.79 0.377
12 1 4.35 46.00 0.75 5.82 0.020
Dependent variable: number of stressful life events
1 1 10.45 46.00 3.27 3.20 0.080
2 1 4.87 46.00 3.27 1.49 0.229
12 1 19.51 46.00 3.27 5.97 0.018
Dependent variable: HDRS Depressive Index
1 1 1.47 46.00 7.04 0.21 0.650
2 1 44.23 46.00 7.04 6.29 0.016
12 1 4.01 46.00 7.04 0.57 0.454
Dependent variable: PDS HT subscale
1 1 76.28 41.00 9.74 7.83 0.008
2 1 4.23 41.00 9.74 0.43 0.514
12 1 10.51 41.00 9.74 1.08 0.305
Dependent variable: PDS DO subscale
1 1 44.95 41.00 10.11 4.44 0.041
2 1 10.27 41.00 10.11 1.02 0.319
12 1 40.50 41.00 10.11 4.01 0.052
Dependent variable: PDS HA subscale
1 1 97.48 41.00 13.12 7.43 0.009
2 1 7.91 41.00 13.12 0.60 0.442
12 1 30.77 41.00 13.12 2.35 0.133
Table 3 Post-hoc comparison between the four diagnostic groups determined by DST results and the presence of personality disorder concerning the continuous variables (Least Significance Difference-LSD Test).
Group A Group B Group C Group D
N = 25 (50%) N = 8 (16%) N = 9 (18%) N = 8 (16%) p p p p p p
Mean SD Mean SD Mean SD Mean SD A/B A/C A/D B/C B/D C/D
Age 44.90 9.55 34.00 10.89 33.78 8.96 40.57 11.63 0.005 0.002 0.168 0.964 0.241 0.173
Age of Onset 33.33 11.24 29.00 10.74 23.44 7.13 35.00 13.14 0.217 0.009 0.967 0.223 0.313 0.028
Number of Episodes 1.52 1.89 1.88 1.55 0.33 0.71 0.43 0.53 0.575 0.068 0.092 0.017 0.021 0.893
Number of atypical features 0.71 0.85 1.63 1.06 1.67 1.00 1.14 0.38 0.019 0.010 0.102 0.935 0.375 0.298
DMS Endogenous axis 4.33 2.29 5.88 1.89 2.11 2.52 6.57 4.28 0.217 0.032 0.155 0.004 0.754 0.018
Number of Life Events reported 2.05 0.97 2.50 2.39 4.22 2.77 2.14 1.77 0.260 0.001 0.529 0.193 0.720 0.082
HDRS depressed index 11.43 2.38 8.50 2.14 10.22 3.87 8.86 2.79 0.005 0.350 0.014 0.282 0.837 0.378
HT 19.24 2.36 19.63 2.56 17.44 3.88 15.71 4.50 0.703 0.129 0.012 0.197 0.045 0.422
DO 13.00 3.16 9.88 3.44 13.11 2.57 14.14 3.63 0.028 0.927 0.431 0.043 0.036 0.515
HA 18.86 3.61 19.75 3.28 17.44 4.90 14.71 1.25 0.548 0.385 0.007 0.279 0.002 0.175
DST baseline cortisol value (day 1, 23:00) 3.85 2.79 7.71 10.28 3.79 1.71 5.43 4.37 0.123 0.724 0.568 0.275 0.491 0.474
DST cortisol level at day 2, 16:00 1.40 1.13 6.81 7.91 1.34 0.98 4.84 5.32 0.002 0.973 0.001 0.057 0.584 0.047
DST cortisol level at day 2, 23:00 1.25 1.45 8.04 5.19 1.36 0.71 5.13 1.40 0.000 0.769 0.000 0.002 0.212 0.000
Group A: DST suppressors, no PD
Group B: DST non-suppressors, no PD
Group C: DST suppressors, with PD
Group D: DST non-suppressors, with PD
Figure 1 Histogram of the Distribution of Frequencies of Depressive Subtypes in the Four Groups
Figure 2 Characteristics of the four groups (white arrows in dark background indicate that the characteristic takes its largest or lower value in the respective group in comparison to all 4.
DST suppressors without PD were older, with more severe depressed mood and less atypical features (50% of patients, figure 2, group A).
DST non-suppressors without PD were hypercortisolemic, with less severe depressed mood and denigratory attitude towards others (16% of patients, figure 2, group B).
DST suppressors with PD were younger, with younger age of onset, more atypical features and less endogeneity and more stressful life events (18% of patients, figure 2, group C).
DST non-suppressors with PD had older age of onset, high endogeneity and high levels of expressed hostility (16% of patients, figure 2, group D).
Discussion
The current study reports that personality disorders (PD) in depressed patients is 2.5–3 times higher in comparison to the general population. Half (47.05%) of these PD patients were also DST non-suppressors (NS). Atypical patients was the depressive subtype with the highest frequency of both personality psychopathology and DST NS.
Figure 2 represents a graphical image of the intercorrelations between personality disorder, DST results and clinical manifestations. It seems that there is a circular relationship between PD, DST, age at interview, age of onset, number of episodes, reactivity to environment, hostility and depressed mood.
DST results seem to be a severity marker rather than directly related to symptomatology. In patients without PD, DST NS (group B in figure 2) may relate to milder depressed mood, higher denigratory attitude and hostility, higher number of previous episodes and hypercortisolemia. In patients with PD, non suppression (group D in figure 2) was related to 'endogenous quality' of depression, and higher levels of hostility. These patients (group B) are highly hostile and perform uninhibited hostile acts, however simultaneously have lower denigratory attitude and hostile thoughts (possibly the hostility is impulsive) and older age of onset.
Half of depressed patients belonged to the A group (suppressors without PD), and were characterized by the absence of atypical features. One could say that they represent a more 'formal' group of depressed patients. The rest of patients were equally distributed in the three groups (B, C and D). Groups B and C may represent two distinct types of vulnerability to stress (hypercortisolemia, DST non suppression and PD), while group D seems to represent a more severe form of depression, with an 'autonomous' hostility independent from the environment. This severe type could be considered to be the product of the accumulation of both vulnerabilities that characterize groups B and C, with the addition of a very low threshold for the tolerance of stress.
Nearly 4–10% of normal persons are reported to be DST-NS. The reason for this is unknown, however it has been suggested that it is due to an underlying mood disorder or family history of affective disorder. Another explanation suggests that DST reflects in fact the degree of psychological pressure or discomfort of the subject and not a specific vulnerability or characteristic of depression. It seems that non-suppression is gradually increasing along a continuum, which has mourning outpatients on the one pole (13% NS) and severe psychotic melancholic inpatients with psychotic features and suicidal ideation on the opposite one (64% NS) [20]. In this frame, the percentage of non-suppression reported in the current study (32%) is not in contrast with the international literature, since most of patients were out-patients and 16 of them (32%) were melancholics. An important finding is the 42.85% rate of non-suppression in atypical patients. This is reported for the first time in the international literature.
DST NS and hypercortisolemia may constitute two separate entities. For example, a patient may have baseline cortisol equal to 6 μg/dl, second cortisol value equal to 2.5 μg/dl and third cortisol value equal to 5.5 μg/dl and thus is classified as NS, but is not hypercorisolemic. On the contrary, a patient with baseline cortisol value equal to 10 μg/dl, second value equal to 4 μg/dl and third also equal to 4 μg/dl, is classified as NS, but is hypercorisolaimic.
Kirschbaum et al [21] reported that it is possible, some normal control subjects do not manifest the hypercorisolaimic response to stressful life events when these events are repeated (habituation). They also divided responses in high and low-cortisol responses. They related the first group with low self-confidence, increased depressed mood and higher number of symptoms, and the second group with lower extraversion. Joyce et al [22] suggested that the hypercortisolaimic response is related to a tendency for dependence and extravagance. These are generally in accord with the findings of the present study.
In contrast to what is widely accepted, NS is appeared to be closer to the atypical subtype. There are no direct reports in the international literature on this matter. However, the results of the study of Kocsis et al [23], in essence are in accord with the current study.
Rothschild et al [24] related DST NS with increased dopamine (DA) activity. Atypical patients, on the other hand, when compared with melancholics, reported more stressful life events, relatively higher levels of anxiety and shorter brain potentials [25]. While it is not possible to interpret what is the cause and what is the effect, it is interesting that there are papers in the international literature suggesting that conditions of internal conflict increase DA activity and lead to the appearance of displacement activities, which in turn serve the lowering of the level of arousal and stabilize the system [26]. Increased appetite, food intake and weight gain (atypical features) could be attributed to such a displacement activity. From the opposite point of view, the exhaustion of DA storage is reported to increase vulnerability to stress, because the already hyperfunctioning neurons (DST non-suppression) fail to respond properly [27]. According to Tazi et al [26], behavioral analogues of the defensive mechanism of displacement seem to suppress this procedure and in this way contribute to the better copying with stressful situations.
Conclusion
Although the study sample of the current study is relatively small, the results suggest that there are more than one subtypes of depression, concerning the response to stress. The majority of depressed patients (50%) seems not to experience high levels of stress both in terms of self reported experience and neuroendocrine function. The rest of patients however, experience high levels of stress, either internally or have the somatic analogue of it (DST non-suppression) or have a very low threshold of stress tolerance, which makes them to behave in a hostile way.
Competing interests
The authors declare that they have no competing interests.
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| 15598349 | PMC543459 | CC BY | 2021-01-04 16:36:21 | no | Ann Gen Hosp Psychiatry. 2004 Dec 14; 3:15 | utf-8 | Ann Gen Hosp Psychiatry | 2,004 | 10.1186/1475-2832-3-15 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-2001560359110.1186/1471-2105-5-200Research ArticleAnalysis of superfamily specific profile-profile recognition accuracy Casbon James A [email protected] Mansoor AS [email protected] Bioinformatics Group, Centre for Infectious Disease, Institute of Cell and Molecular Science, Queen Mary's School of Medicine and Dentistry, University of London, 32 Newark St, London E1 2AA, UK2004 16 12 2004 5 200 200 9 8 2004 16 12 2004 Copyright © 2004 Casbon and Saqi; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Annotation of sequences that share little similarity to sequences of known function remains a major obstacle in genome annotation. Some of the best methods of detecting remote relationships between protein sequences are based on matching sequence profiles. We analyse the superfamily specific performance of sequence profile-profile matching. Our benchmark consists of a set of 16 protein superfamilies that are highly diverse at the sequence level. We relate the performance to the number of sequences in the profiles, the profile diversity and the extent of structural conservation in the superfamily.
Results
The performance varies greatly between superfamilies with the truncated receiver operating characteristic, ROC10, varying from 0.95 down to 0.01. These large differences persist even when the profiles are trimmed to approximately the same level of diversity.
Conclusions
Although the number of sequences in the profile (profile width) and degree of sequence variation within positions in the profile (profile diversity) contribute to accurate detection there are other superfamily specific factors.
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Background
Currently some of the best methods for detecting relationships between protein sequences below the so-called twilight zone of sequence similarity are offered by iterative search algorithms such as PSI-BLAST [1] which, in effect, compare sequences to a profile. More recently profile-profile matching protocols [2-5] have been shown to offer considerable benefits over sequence-profile matching.
Here, we examine how the performance of remote homolog detection by profile-profile methods varies between particular superfamilies. Since superfamilies are believed to constitute sets of remote homologs, detection of same-superfamily relationships is an important task for bioinformatics, and with the increasing number of structures becoming available, improvement in this area will help build a complete structural map of sequence space. In this paper, we use a set of superfamilies that are very sequence diverse to benchmark profile-profile methods. By sequence diverse, we mean that the superfamily has many domains that show no detectable sequence similarity to each other; this lack of detectable sequence similarity means this set is a difficult benchmark for remote homolog detection methods.
Previous work has shown that the performance of profile-profile methods is chiefly determined by the width and diversity of the profiles. By profile width, we mean the number of sequences in the profile, defined in contrast to profile length and by diversity we mean the degree of sequence variation within positions in the profile. In particular, Panchenko suggested that there may be an optimum level of profile diversity [6], whilst Grishin suggested that the inclusion of as many related sequences as possible gives maximum performance [7].
We examine the performance of profile-profile matching with regard to specific superfamilies with both the full profiles generated from a PSI-BLAST search, and with profiles that are trimmed to similar width and diversity. Significant differences in recognition performance exist between superfamilies for both the full and trimmed profiles. This suggests that performance of profile-profile matching is not simply a function of profile width and diversity. We examine how the performance relates to the structural diversity of superfamilies and find that structurally conserved superfamilies are recognised more successfully than structurally diverse superfamilies.
Results
Width and diversity of profiles
Table 1 shows the width and diversity for the full and trimmed profiles. The table shows average profile width in for each superfamily in the dataset before and after trimming (as detailed in the Methods section). The table also shows average Neff (defined as the total number of different amino acids in a given column of a profile [1,6,7]) across all non-gapped columns for each profile in the superfamily. The full profiles show considerable variation in both size and diversity of the profiles. The trimmed profiles, however, are much more similar in both width and diversity, with values of Neff consistently around three.
Superfamily specific performance of remote homolog detection
Figure 1 shows the value of the performance measure ROC10 (see Methods for definition) for each superfamily. The figure shows that there is a large variation in performance with respect to superfamily for both the full profiles and the trimmed profiles.
For the full profiles, the alpha/beta-Hydrolases, Cytochrome c and S-adenosyl superfamilies perform well, all having with ROC10 values ≥ 0.7, the fibronectin, thioredoxin-like, (trans)glycosidases, immunoglobulin and FAD/NAD(P)-binding have ROC10 > 0.2 and the remaining 8 superfamilies all perform poorly, having a performance less than 0.1.
After trimming, although performance is reduced, the overall pattern of performance still remains. All the well recognised superfamilies (with the exception of the (trans)glycosidases and thioredoxin-like) still show ROC10 values greater than 0.2, while the rest are still less than 0.1.
The fact that the performance varies greatly between superfamilies despite the trimming of the profiles indicates that the profile generation is not the only limiting step in the performance of profile-profile methods. One might have thought that, for instance, the bad recognition of 4-helical cytokines is due to the small number of homologs drawn from the profile-building stage. Whilst this still may be true, it is not necessarily true: the Cytochrome c superfamily still shows a ROC10 of 0.7 when using trimmed profiles despite having, on average, less than 20 sequences in the profile.
Structural diversity
Figure 2 shows the average root mean square deviation (RMSD) across each superfamily in our dataset. As can be seen, there is a large range in the degree of structural diversity across the dataset: some superfamilies are highly structurally conserved showing a narrow range of small RMSDs whilst other show large mean RMSDs with large deviations from the mean. For example, the FAD-NAD(P)-binding SCOP super-family contains 21 domains in the astral_10 data set, and despite the low sequence identity there is high structural conservation with an average RMSd of 1.47Å. Furthermore, the range of RMSDs within this super-family is very small, generally within 0.5-2Å. By comparison, super-families such as the P-loop containing nucleotide triphosphate hydrolases, the (Trans)Glycosidases and the Viral-coat and capsid proteins are very structurally diverse, having high average RMSds with the distribution of RMSds generally higher than 1.5Å, and with a long tail.
Relation between structural diversity, sequence conservation and recognition performance
Figure 3 shows a scatter of mean RMSD against ROC10 for each superfamily. The figure shows a correlation between the mean RMSD of each super-family and its ROC10 value. The figure shows that superfamilies with a mean RMSD of less than 2 Å tend to be well recognised by profile-profile methods, whilst the structurally diverse superfamilies are not.
It may be the case that despite the absence of any discernible global sequence similarity within our dataset some local patterns of conservation do exist. These patterns may be present more strongly in some superfamilies than in others. In order to examine this possibility we constructed multiple structure based sequence alignments for each of the 16 superfamilies and then looked down the columns of the multiple sequence alignments to examine the extent of conservation at each position (see Methods section).
Figure 4 shows a plot of performance (ROC10) versus conservation. Apart from the cytochrome c superfamily (an outlier with a high ROC10 of 0.7 despite a conservation score of 0.2 because the superfamily has a conserved CxxCH motif that facilitates detection), the well performing superfamilies (the alpha beta hydrolases, immunoglobulins, FAD/NAD(P)-binding and fibronectin with ROC10 values for the trimmed profiles of at least 0.25) have conservation measures of greater than 0.25. This suggests that some superfamilies although highly sequence diverse, may retain some patterns of conservation that facilitate recognition. Further investigation of the functional implications of this variation would be a next step.
Figure 5 shows a plot of mean RMSD versus performance (ROC10). The P-loop and Viral coat superfamilies have low conservation scores and and large structural diversity reflected by high RMSD values. In contrast, the fibronectin and immunoglobulin superfamilies have higher conservation values (both around 0.28) and lower RMSDs (around 1.5Å). However the figure does not show any clear correlation between conservation and RMSD.
Discussion
Our results suggest that profile profile methods can detect remotely related sequences for some superfamilies significantly better than for others. In our dataset the sequence identity between domains in all the superfamilies is low (not greater than 10% as defined by the ASTRAL). Although the mean width and diversity of the profiles varies across the superfamilies this does not appear to be the only factor contributing to the differences in detection.
The effect of the trimming varied depending on superfamily. For the best performing profile (alpha/beta hydrolases) the trimming reduced the performance by about 50% (from 0.95 to 0.43) but the effect on the rank was small dropping from first place to second. Similarly the trimming impacted significantly on the performance of the S-adenosyl methyl transferases with ROC10 dropping from 0.70 to 0.22. However trimming had no effect on performance for the FAD/NAD(P)-binding superfamily, and only resulted in a small reduction in performance for the immunoglobulins and the cytochrome c superfamilies. Importantly the membership of the top ranking superfamilies in terms of performance did not change after trimming.
Although the overall level of sequence similarity within our dataset is low (not more than 10% identity) the different superfamilies exhibit different levels of conservation at positions within the multiple structure based alignments. These conserved positions may facilitate recognition. The extent to which they constrain the structures leading to less diverse alignments is unclear. We recognise also that our measure of conservation and also the use of RMSD as a measure of structural diversity both have their shortcomings. It would be interesting to identify and extract a conserved core and represent structural profiles as combination of core profiles separated by regions of variable length.
Conclusions
There exist large superfamily specific differences in the performance of profile profile matching for the detection of remote sequence relationships. Some superfamilies can be detected far more successfully than others. The width and diversity of the profiles are important factors in successful recognition. However these are not the only factors that contribute to these superfamily specific differences.
Methods
Dataset
We took release 1.63 of ASTRAL [8] which provides a filtered version of the SCOP database [9] where no two sequences have a pairwise sequence identity of over 10%. From this, we chose the sequence diverse superfamilies by selecting all superfamilies with more than 20 domains. This resulted in a dataset of 543 domains which only show a random (not greater than 10%) level of sequence similarity. The particular superfamilies used and a summary of their properties is shown in Table 2. Superfamily is a readable description of the superfamily, sunid is the SCOP unique identifier, families is the number of families in superfamily, domains is the number of domains in superfamily, length shows the average length of the domains in the superfamily and RMSD shows average RMSD between members of superfamily.
Profile generation
For each domain of each of the 16 superfamilies we executed a five round PSI-BLAST [1] run against the protein non redundant protein database nr (dated 5/2/04). We used the "-m6" option to output a multiple alignment and the "e 0.05" to only include hits with e-values less than 0.05 in the alignment. Positions in the multiple alignment that correspond to gaps in the query are removed. We use the resulting multiple alignment as the profile for the query domain.
To produce trimmed profiles, we take the full profile and remove the bottom sequence (corresponding to the most remote homolog) until a stopping criterion is reached. The stopping criterion is based on Neff, a statistic previously used for this task [1,6,7]. Neff is defined as the total number of different amino acids in a given column of a profile. Our stopping criterion was that Neff must be less than 8 in all non-gapped positions in profile, where non-gapped positions are defined as those with a gap content of less than half.
Profile-profile matching
We use the program COMPASS [2] to perform the profile profile matching. COMPASS performs a local alignment of a query profile to each member of a database of profiles. COMPASS uses a generalisation of PSI-BLAST profile-sequence scoring to score similarities between profiles and estimate the statistical significance of the score of the local alignment.
Assessing performance
To assess the performance of profile-profile matching, each domain of each of the 16 superfamilies was used as a query and its sequence profile was matched against a library of sequence profiles representing the dataset. A profile database was then created using the 543 profiles. When matching the profile of domain i of superfamily j, (), the sequence profile corresponding to was not included in the sequence profile library. This procedure was carried out twice: firstly with the full profiles, and the again with the trimmed profiles.
We use ROC10 as a statistic that describes the performance of the profiles for a particular super-family. ROCn is defined as , where T is the total number of true hits possible and ti is the number of true positives with a score better than the ith false hit. Variance in the ROC10 statistic was calculated using the method given in [10].
Structural diversity of superfamilies
To evaluate the structural diversity within each superfamily, each member of a superfamily was structurally compared to every other member. For all the domains in a superfamily we perform pairwise structural alignments using the program SAP [11] to all other domains. Since these domains do not share more than about 10% sequence identity, we would expect that they effectively capture the extent of structural variation within the superfamily. We obtain an average measure of structural similarity (root mean square deviation, RMSD) for each of the 16 superfamilies.
Structure based multiple alignments
To create a structure based multiple alignment of a superfamily, we first made all pairwise structural comparisons between all pairs within a superfamily using SAP [11,12]. We then created a T-Coffee [13] library for each pairwise comparison, where the score between two equivalenced residues is i and j at positions xi, xj in the superposition, is defined to be ((1 + RMSD)(1 + |xi - xj|))-1. A detailed explanation and analysis of this method is given in [14].
Conservation measure
We used the Taylor Venn diagram [15] to assign residues in a column of the multiple alignment to a given set. The sets are overlapping and they group together amino acids at differing levels of detail (eg the hydrophobic set includes aromatic [FYWH] as a subset). However, we adopted a fairly general measure of conservation and marked a position (column) as conserved if 80% of the residues at that position could be assigned to any one set. The conservation measure for a superfamily was the number of conserved positions divided by the average length of domains in our dataset belonging to that superfamily. Only those columns that contained at least 80% of positions ungapped were considered.
Authors' contributions
JAC carried out the benchmarking and wrote the necessary code and helped to prepare the manuscript. MASS conceived of the study, provided input into the design and refined the manuscript. All authors read and approved the final manuscript.
Acknowledgements
JAC wishes to acknowledge the financial support from the Special Trustees of the Royal London Hospital
Figures and Tables
Figure 1 ROC10 values for each superfamily in the dataset for full and trimmed profiles.
Figure 2 Mean RMSD values for superfamilies in the dataset. Error bars show one standard deviation.
Figure 3 ROC10 of the trimmed profiles versus average pairwise RMSD. Error bars show one standard deviation.
Figure 4 ROC10 of the trimmed profiles versus conservation for superfamilies in our dataset.
Figure 5 Mean RMSD of the trimmed profiles versus conservation for superfamilies in our dataset.
Table 1 Profile width and Neff for dataset
Profile Width Neff
Superfamily Full Trimmed Full Trimmed
(Trans)glycosidases 410.4 23.93 13.11 3.21
4-helical cytokines 85.71 43.57 4.3 2.86
alpha/beta-Hydrolases 509.43 22.32 16.32 3.65
Cytochrome c 413.62 18.86 12.64 3.7
E Set domains 182.73 33.27 7.99 3.16
FAD/NAD(P)-binding 616.52 20.57 15.33 3.68
Fibronectin type 1661.67 24.83 11.44 3.55
Homeodomain-like 255.21 39.33 7 3.34
Immunoglobulin 1614.7 69.04 11.33 3.65
NAD(P)-binding 463.14 29.55 12.32 3.27
Nucleic acid-binding 224.09 23.57 8.21 3.11
P-loop 483.03 26.44 11.64 2.92
S-adenosyl 472.42 22.08 14.88 3.22
Thioredoxin-like 471.72 25.28 12.61 3.58
Viral coat 265.28 35.93 6.11 2.96
Winged helix 206.94 24.81 8.11 3.13
Table 2 Properties of the dataset
Superfamily sunid Families Domains Length RMSD
(Trans)glycosidases 51445 9 30 385.2 2.64
4-helical cytokines 47266 3 21 146.76 3.12
alpha/beta-Hydrolases 53474 22 29 302.28 1.87
Cytochrome c 46626 8 21 116.14 1.6
E Set domains 81296 17 33 120.21 2.49
FAD/NAD(P)-binding 51905 5 21 244.1 1.47
Fibronectin type 49265 1 24 103.42 1.52
Homeodomain-like 46689 10 24 72.17 2.43
Immunoglobulin 48726 4 47 103.23 1.63
NAD(P)-binding 51735 10 49 202.67 1.89
Nucleic acid-binding 50249 10 44 120.86 2.05
P-loop 52540 18 70 257.64 3.99
S-adenosyl 53335 20 24 255.92 1.92
Thioredoxin-like 52833 12 29 121.72 1.88
Viral coat 49611 4 29 271.07 3.43
Winged helix 46785 35 48 92.65 2.33
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Sadreyev R Grishin N COMPASS: a tool for comparison of multiple protein alignments with assessment of statistical significance J Mol Biol 2003 326 317 36 12547212 10.1016/S0022-2836(02)01371-2
Sadreyev R Baker D Grishin N Profile-profile comparisons by COMPASS predict intricate homologies between protein families Protein Sci 2003 12 2262 72 14500884 10.1110/ps.03197403
Tang C Xie L Koh I Posy S Alexov E Honig B On the role of structural information in remote homology detection and sequence alignment: new methods using hybrid sequence profiles J Mol Biol 2003 334 1043 62 14643665 10.1016/j.jmb.2003.10.025
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Panchenko A Finding weak similarities between proteins by sequence profile comparison Nucleic Acids Res 2003 31 683 9 12527777 10.1093/nar/gkg154
Sadreyev R Grishin N Quality of alignment comparison by COMPASS improves with inclusion of diverse confident homologs Bioinformatics 2004 20 818 28 14751996 10.1093/bioinformatics/btg485
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Murzin A Brenner S Hubbard T Chothia C SCOP: a structural classification of proteins database for the investigation of sequences and structures J Mol Biol 1995 247 536 40 7723011 10.1006/jmbi.1995.0159
Schaffer A Aravind L Madden T Shavirin S Spouge J Wolf Y Koonin E Altschul S Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements Nucleic Acids Res 2001 29 2994 3005 11452024 10.1093/nar/29.14.2994
Taylor W Orengo C Protein structure alignment J Mol Biol 1989 208 1 22 2769748 10.1016/0022-2836(89)90084-3
Taylor W Protein structure comparison using SAP Methods Mol Biol 2000 143 19 32 11084900
Notredame C Higgins D Heringa J T-Coffee: A novel method for fast and accurate multiple sequence alignment J Mol Biol 2000 302 205 17 10964570 10.1006/jmbi.2000.4042
Casbon J Saqi M S4: Structure-based Sequence-alignments of Scop Superfamilies To appear in Nucleic Acids Research Database Issue 2005
Taylor W The classification of amino acid conservation J Theor Biol 1986 119 205 18 3461222
| 15603591 | PMC543460 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Dec 16; 5:200 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-200 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1564778010.1371/journal.pmed.0020016Research ArticleHIV/AIDSSexual HealthInfectious DiseasesHIV Infection/AIDSMedicine in Developing CountriesIntegrating HIV Prevention and Treatment: From Slogans to Impact Integrating HIV Prevention and TreatmentSalomon Joshua A
1
*Hogan Daniel R
1
Stover John
2
Stanecki Karen A
3
Walker Neff
3
4
Ghys Peter D
3
Schwartländer Bernhard
5
1Harvard Center for Population and Development Studies, Department of Population and International HealthHarvard School of Public Health, Boston, MassachusettsUnited States of America2The Futures Group International, GlastonburyConnecticutUnited States of America3Joint United Nations Programme on HIV/AIDSGenevaSwitzerland4United Nations Children's Fund, New YorkNew YorkUnited States of America5Global Fund to Fight AIDS, Tuberculosis, and MalariaGenevaSwitzerlandLange Joep Academic EditorUniversity of Amsterdamthe Netherlands
Competing Interests: The authors have declared that no competing interests exist.
Author Contributions: JAS and BS led the design of the study. JS, KAS, NW, and PDG were primarily responsible for developing baseline projections of HIV/AIDS. JAS and DRH were primarily responsible for analysis of prevention and treatment scenarios. All authors participated in interpretation of the results and writing and revision of the report.
* To whom correspondence should be addressed. E-mail: [email protected] 2005 11 1 2005 2 1 e1623 8 2004 26 11 2004 Copyright: © 2005 Salomon et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Modeling the HIV Epidemic in Africa
Background
Through major efforts to reduce costs and expand access to antiretroviral therapy worldwide, widespread delivery of effective treatment to people living with HIV/AIDS is now conceivable even in severely resource-constrained settings. However, the potential epidemiologic impact of treatment in the context of a broader strategy for HIV/AIDS control has not yet been examined. In this paper, we quantify the opportunities and potential risks of large-scale treatment roll-out.
Methods and Findings
We used an epidemiologic model of HIV/AIDS, calibrated to sub-Saharan Africa, to investigate a range of possible positive and negative health outcomes under alternative scenarios that reflect varying implementation of prevention and treatment. In baseline projections, reflecting “business as usual,” the numbers of new infections and AIDS deaths are expected to continue rising. In two scenarios representing treatment-centered strategies, with different assumptions about the impact of treatment on transmissibility and behavior, the change in the total number of new infections through 2020 ranges from a 10% increase to a 6% reduction, while the number of AIDS deaths through 2020 declines by 9% to 13%. A prevention-centered strategy provides greater reductions in incidence (36%) and mortality reductions similar to those of the treatment-centered scenarios by 2020, but more modest mortality benefits over the next 5 to 10 years. If treatment enhances prevention in a combined response, the expected benefits are substantial—29 million averted infections (55%) and 10 million averted deaths (27%) through the year 2020. However, if a narrow focus on treatment scale-up leads to reduced effectiveness of prevention efforts, the benefits of a combined response are considerably smaller—9 million averted infections (17%) and 6 million averted deaths (16%). Combining treatment with effective prevention efforts could reduce the resource needs for treatment dramatically in the long term. In the various scenarios the numbers of people being treated in 2020 ranges from 9.2 million in a treatment-only scenario with mixed effects, to 4.2 million in a combined response scenario with positive treatment–prevention synergies.
Conclusions
These analyses demonstrate the importance of integrating expanded care activities with prevention activities if there are to be long-term reductions in the number of new HIV infections and significant declines in AIDS mortality. Treatment can enable more effective prevention, and prevention makes treatment affordable. Sustained progress in the global fight against HIV/AIDS will be attained only through a comprehensive response.
Combining the two approaches of prevention and treatment of HIV/AIDS could avert more than 29 million new HIV infections by 2020
==== Body
Introduction
In June 2001, heads of state and government convened a United Nations Special Session on HIV/AIDS and adopted unanimously the “Declaration of Commitment on HIV/AIDS” [1]. In preparation for that session, Schwartländer et al. published an estimate of the resource needs for an expanded global response to the epidemic, which called for around US$10 billion for the fight against HIV/AIDS in 2005 [2]. In 2002, on the occasion of the 14th International AIDS conference in Barcelona, Spain, Stover et al. showed that such an immediate and expanded response in low- and middle-income countries could reverse the course of the HIV/AIDS epidemic and avert nearly 30 million infections through 2010 [3]. Today, more resources are available for the fight against HIV than ever before, but global efforts to confront the epidemic continue to disappoint. Worldwide in 2004, more people were living with HIV and more people died of AIDS than in any previous year [4]. In sub-Saharan Africa, home to two-thirds of all people living with HIV/AIDS, and three out of four people dying from AIDS [4], only one in 50 persons with advanced disease had access to life-saving medicines at the beginning of 2004 [5].
The theme of the 15th International AIDS Conference in Bangkok last summer was timely and relevant. “Access for All” calls for extending to all of those in need both sufficient resources and a set of proven interventions to prevent new infections and save lives through effective treatment. Recent developments in HIV treatment, with simple combination therapies priced at less than US$150 per year—unthinkable just a short time ago—were a major driver of discussions during the conference. Widespread access to effective antiretroviral therapy (ART) for people living with HIV/AIDS is now conceivable even in countries with severely limited resources.
The World Health Organization and its partners in the Joint United Nations Programme on HIV/AIDS have defined an ambitious “3 by 5” target of 3 million people on ART—half of those in most urgent need—by the end of 2005. The potential epidemiologic impact of large-scale roll-out of treatment programs, however, remains uncertain. Experience to date is limited, and comes mostly from Western countries and Brazil. While declines in AIDS mortality in the industrialized world have been impressive [6,7,8], many of these success stories have been accompanied by a resurgence in HIV incidence due to increasing risk behavior as emphasis shifted from prevention to treatment in the 1990s [9,10,11]. Will the extension of ART to millions who suffer from AIDS in developing countries be the long-awaited breakthrough in the response to HIV, or will the emphasis on treatment detract from prevention efforts, and thus hamper AIDS control in the medium and long term? The experience in high-income countries underscores the potential perils of failing to adapt prevention strategies to an environment in which life-saving treatment becomes available on a large scale; however, more favorable outcomes in some settings [12,13] indicate that rising risk behavior is not an inevitable outcome of increased treatment access.
In our previous analysis of the potential benefits of a comprehensive package of preventive interventions [3], we noted that these prevention effects would be achieved only in the presence of wide-scale treatment and political support. The two intervening years have seen a dramatic rise in both momentum and financial resources for ART scale-up, but the potential epidemiologic impact of treatment in the context of a broader strategy for HIV/AIDS control has not yet been examined. In this paper, we quantify the opportunities and potential risks of large-scale treatment roll-out. The results of this analysis will be informative for all regions and countries, independent of the level and stage of the epidemic. However, since three of four deaths from AIDS occur in sub-Saharan Africa, successes and failures in rolling out treatments immediately will have the most dramatic effects in this region. We therefore focus our analyses and discussions in this paper on the HIV epidemics in sub-Saharan Africa.
Methods
Projections of HIV Epidemics in Sub-Saharan Africa
Baseline projections of HIV epidemics in sub-Saharan Africa have been developed by the Joint United Nations Programme on HIV/AIDS and the World Health Organization based on the most current data available, and in collaboration with epidemiologic experts and analysts within the countries assessed [4]. These “business as usual” forecasts from 2004 to 2020 are characterized by the absence of behavioral change or ART scale-up in future years. Combined with the natural dynamics of the epidemic, these assumptions result in a relatively stable HIV prevalence rate.
To simulate the effects of prevention and treatment on HIV/AIDS incidence, prevalence, and mortality, we first adapted the analytic approach used in the previously described Goals model [3] to allow explicit modeling of treatment effects, and calibrated the model to the baseline projections for three African regions (East, West/Central, and Southern) (see Protocol S1 for more details). In line with the predominant epidemiologic pattern in sub-Saharan Africa of HIV spreading through heterosexual contact, the model divides the sexually active population into five different interacting risk groups: single men, single women, married men, married women, and female sex workers. In sub-Saharan Africa HIV is transmitted via other modes at comparatively low levels, and these modes were therefore not considered in our analyses.
The model includes underlying regional demography, acquisition of HIV and other sexually transmitted infections (STIs), progression from HIV to AIDS, and progression from AIDS to death. Annual risks of HIV infection in each risk group depend on the number of partnerships, the number of sex acts per partnership, HIV prevalence among partners and condom use. These risks are magnified by the presence of other STIs [14] and also vary as a function of the time since infection, with the highest risks during acute infection, followed by lower levels that persist until viral loads rise with the onset of clinical AIDS [15,16,17].
The regional models were calibrated as follows: first, plausible ranges were specified for model parameters governing sexual behavior and biological factors (e.g., transmission risks and cofactor effects of other STIs) based on review of published studies and survey results; second, multiple simulations were undertaken by sampling values from each of the ranges and recalculating the model for each set of sampled parameter values; third, model fit was assessed by comparing modeled prevalence for adult males and females separately to baseline projections through 2020; and fourth, the best-fitting parameter set in each regional model was selected for the purpose of scenario analysis (see Protocol S1).
Alternative Scenarios for Prevention and Treatment
Potential impacts of prevention efforts at a given coverage level were based on previously published estimates [3] for a comprehensive package of 12 interventions that included mass media campaigns, voluntary counseling and testing, peer counseling for sex workers, school-, youth- and workplace-based programs, condom promotion and distribution, treatment for STIs, and prevention of mother-to-child transmission. The comprehensive package described by Stover and colleagues also included interventions such as harm reduction for injecting drug users and peer outreach for men who have sex with men, which we have not modeled for sub-Saharan Africa. Impacts were captured in terms of changes in condom use, sexual partnerships, treatment-seeking for STIs, and age at first sex. The impacts of treatment included increased survival by a median of 3 y, reductions in transmission probabilities given contact with an infected partner, and behavior change.
We examined a range of alternative scenarios based on various levels and effectiveness of prevention interventions, with and without successful attainment of the 3 by 5 treatment target for sub-Saharan Africa:
Baseline (“business as usual”).
Risk behaviors are maintained at current levels, and no treatment scale-up occurs. This is simply the baseline scenario that produces a relatively stable prevalence rate over the duration of the projection, with the number of people living with HIV and the number of new infections rising slowly over time because of population growth.
Treatment-centered response.
In two alternative scenarios, the 3 by 5 target of 50% coverage of those in need of treatment by the end of 2005 is attained, and scale-up continues to reach 80% ART coverage of those in need by 2010, maintained at 80% thereafter. In an “optimal ART effects” scenario, we assumed that treatment reduces transmissibility by 99%, and that those under treatment have 50% lower annual partnership numbers and two times higher condom use than other adults. With a response that focuses primarily on treatment, it is assumed that behavior in the general community of infected and uninfected adults is unchanged from the baseline. In an alternative “mixed ART effects” scenario, less optimistic assumptions were made: that treatment reduces transmissibility only to the same levels as in asymptomatic infected individuals (two-thirds reduction from no treatment), and that behavior in treated patients is the same as in other adults. To capture the possibility of behavioral disinhibition in response to treatment availability, we assumed that condom use declines by 10% in both treated patients and the general community, with other behaviors unchanged. The potential for disinhibition is suggested primarily by experience in some developed countries, where condom use increased dramatically in the populations at highest risk prior to the introduction of ART but then declined; the likelihood and magnitude of reductions in condom use in sub-Saharan Africa, where such prevention-induced changes generally are much less prominent today, might be questioned. We therefore considered in sensitivity analyses a variant of this scenario that excludes disinhibition but preserves all other assumptions.
Prevention-centered response.
In the absence of wide availability of treatment, reflecting weaker political and social support for HIV control efforts, we modeled a scenario in which the comprehensive prevention package described previously [3] has only partial effectiveness at the population level, and no ART scale-up occurs. As evidence about the magnitude of treatment–prevention interactions remains limited, we considered a reduction of 50% from the full impact as a base case and examined a range of reductions from 25% to 75% in sensitivity analyses.
Combined response.
We examined two scenarios combining treatment and prevention efforts, reflecting either optimistic or pessimistic possibilities. In the optimistic scenario, treatment strengthens prevention efforts. ART coverage is the same as in the two treatment-centered scenarios, with optimal assumptions about treatment impact on transmissibility and patient behavior. It is assumed that widespread availability of treatment enables the full impact of prevention efforts to be attained as described by Stover et al. [3]. In a more pessimistic scenario, an emphasis on treatment leads to less effective implementation of prevention. This scenario includes the mixed assumptions about ART effects (excluding disinhibition in the general community), and assumes only 25% attainment of the maximum potential impact of prevention efforts.
Additional scenarios could include pessimistic assumptions about limited ART scale-up levels and timing, emergence of large-scale drug resistance resulting from low adherence, or other possible unintended outcomes of wider treatment. Certainly, large-scale treatment efforts will demand close monitoring of adverse effects. However, experience with treatment programs in developing countries has been encouraging thus far, with reported adherence levels that are at least as high as those in developed countries [18,19].
Results
In the baseline projections for sub-Saharan Africa, the annual number of new adult HIV infections rises from 2.4 to 3.7 million between 2004 and 2020, and adult AIDS mortality rises from 1.8 to 2.6 million (Figure 1). If scale-up of ART reaches the 3 by 5 target and eventually expands to 80% coverage, without any behavior change in the broader community (treatment-centered response/optimal ART effects), the annual number of new infections could be reduced by up to 6% compared to baseline by 2020. Mortality would initially decline by 33% but long-term trends would converge toward the baseline. We note that total annual death numbers indicate broad trends in mortality but mask more subtle health gains in the form of years added to individuals' lives.
Figure 1 HIV Incidence and AIDS Mortality among Adults in Sub-Saharan Africa, 2003–2020, under Different Intervention Scenarios
(A) HIV incidence.
(B) AIDS mortality.
With less optimistic assumptions (treatment-centered response/mixed ART effects), the number of new infections rises, to 4.3 million per year by 2020 (a 14% increase); mortality trends are similar to the optimistic scenario in the short term, but worse in the long term, even compared to the baseline. Excluding the assumption of reduced condom use through disinhibition from the treatment-centered/mixed effects scenario has minimal effect on the results, lowering the number of new infections in 2020 by only 2% compared to the scenario that includes disinhibition.
A prevention-centered response would have greater impact on the number of new infections, lowering annual incidence by more than half by 2020. The long-term mortality trend is more favorable in the prevention-centered scenario than in the treatment-centered scenario because of reduced incidence, but prevention would produce negligible mortality benefits in the near- and mid-term future in comparison to strategies that include ART. Alternative assumptions regarding overall effectiveness in a prevention-centered response produce results that scale as expected, with reductions in annual incidence of 34% to 64% and reductions in annual mortality of 20% to 42% by 2020.
If treatment and effective prevention are scaled up jointly in a combined response, the benefits in terms of both infections and deaths averted could be substantially higher. In an optimistic scenario in which treatment programs support expanded prevention, the annual number of new infections would be 74% lower and annual mortality would be 47% lower by 2020, compared to baseline. It is worth noting that the long-term decline in AIDS deaths is driven more by prevention of new infections than by direct survivorship benefits from ART. In a pessimistic scenario in which a more narrow treatment focus limits effective prevention, the overall benefits are much more modest, with 26% and 16% reductions, respectively, in new infections and mortality by 2020 compared to the baseline.
Prevalence rises by 7% in the optimal and by 27% in the mixed treatment-centered scenarios by 2020, as longer survival for treated patients offsets reductions in new infections through reduced transmissibility (and risk reductions among treated patients in the more optimistic scenario) (Figure S1). In scenarios that include prevention efforts, prevalence declines by 41% in the prevention-centered scenario, by 53% in the optimistic combined response, and by 6% in the pessimistic combined response by 2020.
The total number of infections averted through a combined response would be 29 million over the period 2004 to 2020 if treatment enhances prevention, a benefit that is ten times greater than that of a strategy which focuses on treatment only, even with optimal assumptions, and 51% greater than that of a strategy which focuses on (less effective) prevention alone (Table 1). If a treatment focus limits the effectiveness of prevention, on the other hand, the total number of averted infections between 2004 and 2020 would be 9 million. Similarly, the benefits of a combined response in terms of mortality reductions are considerably higher under optimistic circumstances than the benefits of either treatment only or prevention only, with 10.1 million deaths averted (27%) through 2020 when treatment enhances prevention, compared to 5.0 million (13%) in the optimal-effects treatment only scenario, 3.5 million (9%) in the mixed-effects treatment only scenario, and 4.8 million (13%) with prevention only. Under more pessimistic assumptions about treatment–prevention interactions, the combined response would avert 5.8 million deaths (16%). Table 1 also reports total benefits of the various strategies over the shorter term, in which the ranking of alternatives is similar with regard to the total number of infections averted, but mortality reductions are attributable almost exclusively to treatment.
Table 1 Total New Adult Infections and Deaths in Sub-Saharan Africa, 2004–2010 and 2004–2020, under Different Intervention Scenarios
Combining treatment with prevention efforts will reduce the resource needs for treatment substantially in the long term (Figure 2). In the various scenarios the numbers of people being treated in 2020 ranges from 9.2 million in the treatment only (mixed effects) scenario, to 4.2 million in the optimistic combined response scenario.
Figure 2 Number of Persons on ART in Sub-Saharan Africa, 2004–2020, under Various Scenarios
Discussion
In this paper, we have examined the potential epidemiologic impact of global HIV/AIDS control efforts under a range of alternative scenarios reflecting varying implementation of strategies for prevention and treatment. Although we focus in particular on population health outcomes and epidemiologic trends, we recognize that there are numerous other social, economic, and individual health effects of interventions including ART that are beyond the scope of this analysis. We also restrict our focus in this paper to sub-Saharan Africa, where the overwhelming majority of people living with and dying from HIV/AIDS reside; however, our findings have broader applicability and more general implications in the worldwide fight against HIV/AIDS, which we highlight here.
The Potential for Treatment to Enhance Prevention Must Be Exploited
Effective prevention requires more than having sufficient funds to offer information and services. It also requires an environment that encourages people to internalize messages about risky behavior and to adopt actual behavior change, and allows people to utilize services such as testing and counseling without fear of stigma or discrimination. Stoneburner and Low-Beer have argued that the supportive social and political environment in Uganda allowed people to discuss AIDS with family members and close friends, which led to greater behavior change than in Kenya or Zambia where most people received information from mass media only [20]. People have little reason to seek HIV testing when a positive result brings only negative consequences, whereas widespread availability of treatment provides a major incentive for people to learn their serostatus.
Involving communities and family members in the delivery of treatment—for example, as treatment monitors—offers unique entry points for effective prevention activities and a lever for population-wide behavior change. Experience with community roll-out of treatment programs has shown, for example, that uptake of voluntary counseling and testing increased by 300% in one year of roll-out in Haiti, and by a factor of 12 in Khayelitsha, South Africa, after treatment introduction [21,22]. A study targeting nine commuter sites in South Africa found the highest levels of condom use, willingness to use a female condom, and willingness to have an HIV test in Khayelitsha, a difference that may be attributed largely to the availability of ART and comprehensive AIDS care [23]. If increased uptake of voluntary counseling and testing is indicative of broader prevention effectiveness where ART is available, we estimate that over 50% more new infections and more than twice as many deaths could be avoided through a combined response compared to prevention alone. In contrast, if a narrow treatment scale-up leads to reduced effectiveness of prevention, short-term mortality reductions will come at the expense of longer-term progress in stemming the tide of the epidemic.
During most of the past 15 years, efforts to address the AIDS epidemic in sub-Saharan Africa have focused on prevention. There have been successes in some countries, but overall these efforts have not achieved their goals. The advent of vastly expanding treatment programs in the coming years, if opportunities to capitalize on broadened political support and community mobilization can be seized, offers the potential to enhance prevention effectiveness and avert many new infections and deaths.
Only Effective Prevention Will Make Treatment Affordable in the Long Run
While prevention programs are unlikely to achieve full impact in the absence of treatment, so too is the impact of treatment programs reduced if vigorous prevention efforts are absent. Without effective prevention, the number of people requiring care and treatment will grow each year. As more and more people are kept alive with ART the treatment burden will become enormous unless effective prevention reduces the number of people becoming newly infected.
Without effective prevention programs, we project that the number of people receiving treatment will grow to 6.3 million by 2010 and up to 9.2 million by 2020 in Africa alone to achieve 80% coverage of those in urgent need. Meeting this need would require a tremendous increase in financing, human capacity and infrastructure that might not be attainable.
If effective prevention programs are combined with treatment programs, the same level of 80% ART coverage would be achieved by treating 5.8 million in 2010 and 4.2 million in 2020. In other words, the same goal could be attained at a far lower treatment cost and with a much greater chance of sustainability.
A Successful Global Response Cannot Rely on Either Prevention or Treatment Alone
Over the long term, it is effective prevention that will reduce the burden of illness due to AIDS and the number of people in need of ART. The lessons learned in the industrialized world have to be taken on board. The availability of treatment and the shift in focus away from very effective prevention programs has led to increases in unsafe sexual behavior, STIs, and HIV transmission in some settings [9,10,11]. There is no doubt that effective therapy can extend and improve the quality of life for those who are treated, but it also must be integrated into a comprehensive community response to HIV so that it can enhance the effectiveness of prevention efforts. Long-term, sustained progress in the fight against AIDS demands more than an exclusive focus on either prevention or treatment alone. Prevention makes treatment affordable, and treatment can make prevention more effective.
Countries in sub-Saharan Africa are faced with the most devastating epidemic of our times. We now have the unique opportunity to derive the maximum impact from available resources. The results from our analyses show how potential synergies between prevention and treatment could be translated into considerable health benefits at the population level. But synergies do not mean simply that prevention and treatment are pursued in parallel. When whole communities become involved in the scale-up of treatment access—as will be necessary to achieve the ambitious treatment targets defined by the 3 by 5 campaign—crucial opportunities can be created for increasing their involvement in prevention activities. Only if interactions with patients, family, and community members occasioned by the provision of treatment are also used to reinforce prevention, and only if prevention workers have an opportunity to refer those in need to care and treatment, will we move at last from slogans to impact.
Supporting Information
Figure S1 Adults Living with HIV/AIDS in Sub-Saharan Africa, 2003–2020, under Different Intervention Scenarios
(80 KB EPS).
Click here for additional data file.
Protocol S1 Technical Appendix
(172 KB PDF).
Click here for additional data file.
Patient Summary
Background
Infections from HIV continue to increase, especially in sub-Saharan Africa. The World Health Organization has a plan to get more than 3 million people on treatment by 2005 (the “3 by 5” initiative); however, the overall effect of this plan on the population's health is uncertain, and will depend on the balance between treatment and prevention efforts.
What Did the Researchers Do?
They tried to predict the number of new infections and deaths each year in sub-Saharan Africa from now until 2020 depending on whether control efforts focused on prevention, treatment, or both. What they found was that by far the most effective way of decreasing new infections and deaths was to combine the two approaches, and that by doing so more than 29 million new infections and 10 million deaths might be prevented compared with continuing at current levels of prevention and care.
Why Is This Information Important, and Who Will Use It?
Despite the huge amounts of money directed at HIV/AIDS, because the problem is so vast, the resources are not enough. Hence it is important to target these resources effectively. Policy makers around the world could use information like this to decide where best to direct attention and funding to combat HIV/AIDS.
Where Can I Find More Information?
Joint United Nations Programme on HIV/AIDS, AIDS epidemic update, December 2004: http://www.unaids.org/wad2004/report.html
World Heath Organization, 3 by 5 Initiative: http://www.who.int/3by5/
Global HIV Prevention Working Group: http://www.kff.org/hivaids/hivghpwgpackage.cfm
JAS and DRH were supported by a grant from the National Institute on Aging (AG17625). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Citation: Salomon JA, Hogan DR, Stover J, Stanecki KA, Walker N, et al. (2005) Integrating HIV prevention and treatment: From slogans to impact. PLoS Med 2(1): e16.
Abbreviations
ARTantiretroviral therapy
STIsexually transmitted infection
==== Refs
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| 15647780 | PMC543462 | CC BY | 2021-01-05 10:38:11 | no | PLoS Med. 2005 Jan 11; 2(1):e16 | utf-8 | PLoS Med | 2,005 | 10.1371/journal.pmed.0020016 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1564778110.1371/journal.pmed.0020018Research ArticleNeurosciencePharmacology/Drug DiscoveryGeriatricsNeurology/NeurosurgeryNeurologyDementiaPharmacology and ToxicologyModulation of Statin-Activated Shedding of Alzheimer APP Ectodomain by ROCK ROCK Regulation of APP SheddingPedrini Steve
1
Carter Troy L
1
Prendergast George
2
Petanceska Suzana
3
Ehrlich Michelle E
1
Gandy Sam
1
2
*1Farber Institute for Neurosciences, Thomas Jefferson UniversityPhiladelphia, PennsylvaniaUnited States of America2Lankenau Institute for Medical Research, WynnewoodPennsylvaniaUnited States of America3Nathan S. Kline Institute for Psychiatric Research, Department of PsychiatryNew York University School of Medicine, Orangeburg, New YorkUnited States of AmericaWinblad Bengt Academic EditorKarolinska InstituteSweden
Competing Interests: The authors have declared that no competing interests exist.
Author Contributions: SP, TC, GP, SP, MEE, and SG designed the study, analyzed the data, and contributed to writing the paper.
*To whom correspondence should be addressed. E-mail: [email protected] 2005 11 1 2005 2 1 e1828 9 2004 30 11 2004 Copyright: © 2005 Pedrini et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
How Statins May Protect against Alzheimer Disease
Background
Statins are widely used cholesterol-lowering drugs that act by inhibiting HMGCoA reductase, the rate-limiting enzyme in cholesterol biosynthesis. Recent evidence suggests that statin use may be associated with a decreased risk for Alzheimer disease, although the mechanisms underlying this apparent risk reduction are poorly understood. One popular hypothesis for statin action is related to the drugs' ability to activate α-secretase-type shedding of the α-secretase-cleaved soluble Alzheimer amyloid precursor protein ectodomain (sAPPα). Statins also inhibit the isoprenoid pathway, thereby modulating the activities of the Rho family of small GTPases—Rho A, B, and C—as well as the activities of Rac and cdc42. Rho proteins, in turn, exert many of their effects via Rho-associated protein kinases (ROCKs). Several cell-surface molecules are substrates for activated α-secretase-type ectodomain shedding, and regulation of shedding typically occurs via activation of protein kinase C or extracellular-signal-regulated protein kinases, or via inactivation of protein phosphatase 1 or 2A. However, the possibility that these enzymes play a role in statin-stimulated shedding has been excluded, leading us to investigate whether the Rho/ROCK1 protein phosphorylation pathway might be involved.
Methods and Findings
We found that both atorvastatin and simvastatin stimulated sAPPα shedding from a neuroblastoma cell line via a subcellular mechanism apparently located upstream of endocytosis. A farnesyl transferase inhibitor also increased sAPPα shedding, as did a dominant negative form of ROCK1. Most conclusively, a constitutively active ROCK1 molecule inhibited statin-stimulated sAPPα shedding.
Conclusion
Together, these data suggest that statins exert their effects on shedding of sAPPα from cultured cells, at least in part, by modulation of the isoprenoid pathway and ROCK1.
The cholesterol-lowering statins seem to have a protective effect against Alzheimer disease. In cultured cells, statins affect cleavage of the amyloid precursor protein via the ROCK kinase. This could explain some of their beneficial effects
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Introduction
Alzheimer disease is the leading cause of dementia among the elderly and is characterized by accumulation of extracellular and vascular amyloid in the brain [1]. Amyloid deposits are composed of the amyloid-β peptide (Aβ), a 4-kDa peptide released during “amyloidogenic” proteolytic processing of the Alzheimer Aβ precursor protein (APP) [2]. APP can also be cleaved by the nonamyloidogenic α-secretases, a disintegrin and metalloproteinase 10 (ADAM-10) and ADAM-17 [3], in a reaction that is believed to occur primarily on the plasma membrane [4] and is known as “ectodomain shedding.” α-Secretase-type ectodomain shedding divides the Aβ domain of APP, thereby generating α-secretase-cleaved soluble APP ectodomain (sAPPα) [4]. This reaction can be stimulated by activation of protein kinase C (PKC) or extracellular-signal-regulated protein kinases (ERKs) [5,6,7] or by inactivation of protein phosphatase 1 or 2A [5].
Reports from retrospective analyses suggest that the statin class of cholesterol-lowering HMGCoA reductase inhibitors may lower the risk for Alzheimer disease by as much as 70% [8,9,10,11]. Studies in wild-type guinea pigs and in plaque-forming transgenic mice have demonstrated that chronic statin treatment can attenuate cerebral amyloidosis [12,13], suggesting that statins may exert their risk-reducing effects, at least in part, by modulating APP metabolism. In cell culture, lovastatin and simvastatin decrease the release of Aβ by rat hippocampal neurons [12,14] while activating α-secretase-type ectodomain shedding [15,16]. However, the molecular mechanisms by which statins modulate ectodomain shedding remain to be elucidated [17,18].
Statin effects on APP metabolism are, to some extent, attributable to cholesterol lowering, but statin actions on APP may also involve cholesterol-independent actions [19]. Reduction in synthesis of mevalonate leads to decreased generation of a number of isoprenoid lipid derivatives. Isoprenoids, such as farnesyl pyrophosphate and geranylgeranyl pyrophosphate, are 15- or 20-carbon lipid moieties. Through the action of farnesyl transferases and type I geranylgeranyl transferases, isoprenoids are attached to the amino acid sequence Cys-Ala-Ala-Xaa (“CAAX”) at the C-terminus of the Rho family of GTPases [20]. These posttranslational lipid modifications are essential for attachment of the GTPases to the cytosolic face of intracellular vesicles and/or to the cytosolic leaflet of the plasma membrane, thereby specifying subcellular targets for GTPase action(s).
Some members of the Rho GTPase family exert their actions through modulation of protein kinase activities. One of the best characterized is Rho-associated protein kinase 1 (ROCK1, also called ROKβ). ROCK1 is a serine/threonine kinase with an apparent mass of 160 kDa that can be activated by either RhoA or RhoB [21,22,23]. Structurally, the ROCK1 N-terminus contains the protein kinase domain, while the C-terminus has both a Rho-binding domain and a pleckstrin homology domain, either of which can modulate protein–protein interactions. In the inactive state, the Rho-binding domain and the pleckstrin homology domain form an autoinhibitory loop by binding and blocking the kinase domain at the N-terminus of the molecule. Activation of ROCK1 occurs when a Rho protein binds to the Rho-binding domain, causing a conformational change that opens the kinase domain for the phosphorylation of downstream effectors [23]. Once activated, ROCK1 phosphorylates several substrates, including myosin light chain phosphatase, LIM kinases (Lin11, Isl1, and Mec3), and ezrin-radixin-moesin proteins [24,25,26,27]. ROCK1 has recently been implicated in modifying the site of substrate cleavage by APP γ-secretase [28], perhaps acting via ROCK1-dependent phosphorylation of a component of the γ-secretase enzyme complex.
In the current study, we demonstrate that activation of sAPPα shedding from cultured cells by atorvastatin or simvastatin involves isoprenoid-mediated protein phosphorylation. Treatment of cells with a farnesyl transferase inhibitor or expression of a dominant negative (DN) ROCK1 molecule led to enhanced sAPPα shedding, supporting the notion that shedding is modulated by the isoprenoid pathway. Transfection with the cDNA for a constitutively active (CA) ROCK1 molecule led to inhibition of statin-activated sAPPα shedding. These results raise the possibility that the apparent beneficial effect of statins in the prevention of Alzheimer disease could be, at least in part, mediated by isoprenoid modulation of APP metabolism.
Methods
Reagents
The APP C-terminal specific polyclonal antibody 369 [29] was used to detect full-length APP and its C-terminal fragments. Monoclonal antibody 6E10 against residues 1–16 of human Aβ (Signet, Dedham, Massachusetts, United States) was used to detect human holoAPP or sAPPα. Anti-ROCK1 antibody was purchased from Chemicon (Temecula, California, United States). Streptavidin-antibody HRP-conjugated C-Myc antibody 9E10, mevalonic acid, arachidonic acid, and phenylarsine oxide were purchased from Sigma (St. Louis, Missouri, United States). Atorvastatin was obtained from Pfizer (Groton, Connecticut, United States), and simvastatin was obtained from LKT Labs (St. Paul, Minnesota, United States). N2 supplement was obtained from Gibco (Carlsbad, California, United States). Sulfo-NHS-LC-Biotin was purchased from Pierce (Rockford, Illinois, United States). CA and DN Myc-tagged ROCK1 vectors were generated as previously described [30,31] and were generous gifts from Liqun Luo (Stanford University). Fugene 6 was purchased from Roche (Basel, Switzerland). Farnesyl transferase inhibitor 1 (FTI-1) was obtained from Biomol (Plymouth Meeting, Pennsylvania, United States). Tumor necrosis factor α (TNFα) protease inhibitor 2 and Y-27632 were purchased from Calbiochem (San Diego, California, United States). Protein concentration assay kit was purchased from Biorad (Hercules, California, United States). LIVE/DEAD Viability/Cytotoxicity Assay Kits and Amplex Red Cholesterol Assay Kits were purchased from Molecular Probes (Eugene, Oregon, United States).
Culture Methods and Sample Preparation
N2a mouse neuroblastoma cells stably transfected with the Swedish mutant form of APP (SweAPP N2a cells; APP695, 595–596 KM/NL) (gift from G. Thinakaran and S. Sisodia, University of Chicago, Chicago, Illinois, United States) were maintained in DMEM, 10% FBS, and 200 μg/ml G418 in the presence of penicillin and streptomycin [32]. For the 24 h prior to pharmacological treatments, the culture media were changed to N2-supplemented lipid-free medium. In some studies, cells were transfected in N2-supplemented FBS-free medium 48 h before pharmacological treatments. Transfections were carried out using the Fugene reagent, according to the manufacturer's instructions. All treatments were performed in the presence of 1 μM mevalonic acid, unless otherwise specified.
Cells were lysed in 1% Triton-X/PBS buffer containing 1 X complete proteinase inhibitor cocktail (Roche), sonicated twice for 30 s, and centrifuged at 5,000g for 5 min. Protein concentration in the supernatant was determined using the Biorad Protein Assay kit, following the manufacturer's instructions. For the detection of holoAPP and sAPPα, samples were separated in 7.5% polyacrylamide gels, transferred to nitrocellulose, and the proteins detected with either 369 (1:3,000 for holoAPP and C-terminal fragments) or 6E10 (1:1,000 for holoAPP or sAPPα), followed by incubation of the transfers with appropriate secondary anti-rabbit or anti-mouse antibodies. For the detection of transfected ROCK1 proteins, samples were immunoprecipitated with 2 μg of anti-Myc antibody, separated in 5% polyacrylamide gels, transferred, and the proteins detected with anti-ROCK1 antibody (1:1,000 dilution).
Cell-Surface Biotinylation
Cells were plated in a 100-mm dish at a concentration of 5 × 106 cells/dish. After treatment, media were harvested, and sAPPα levels were evaluated by immunoblotting as described above. Cells were washed twice in PBS and then incubated with Sulfo-NHS-LC-Biotin for 30 min at 4 oC. Biotinylation reactions were terminated by one wash in Tris followed by two washes in PBS. Cells were lysed in 1% Triton-X/PBS buffer containing protease inhibitor cocktail as indicated above. Lysates were immunoprecipitated with 3 μl of whole 369 antibody serum and 30 μl of protein A beads. After washing twice with 1% Triton/PBS, and then twice with PBS, samples were boiled in sample buffer for 3 min, separated in a 7.5% polyacrylamide gel, and transferred to nitrocellulose. The biotinylated proteins were detected using streptavidin HRP polymer (1:10,000 dilution).
Viability/Cytotoxicity Assays
Cells were plated in an eight-well slide at a concentration of 1 × 104 cells/well. After treatments as indicated, LIVE/DEAD assays were performed following the manufacturer's instructions (Molecular Probes).
Cholesterol Assays
Cholesterol levels in cell lysates were measured using Amplex Red following the manufacturer's instructions (Molecular Probes). We have previously demonstrated that standard doses of either simvastatin or atorvastatin reduce cholesterol levels in N2a cells by 65%–67% [16].
Quantification and Statistical Analysis
Quantification of protein bands was performed using the UVP Bioimaging System, and statistical analysis was performed on paired observations using the Student's t test.
Results
Atorvastatin Activates sAPPα Shedding at a Subcellular Site Upstream of Endocytosis from the Plasma Membrane
We confirmed our previous observation [16] that atorvastatin produces an increase in sAPPα shedding that is dose-dependent, reaching a maximum effect at 5 μM. The increase in sAPPα shedding is accompanied by a corresponding increase in levels of the nonamyloidogenic APP α-C-terminal fragment (C83; data not shown).
In order to refine our localization of the subcellular target of statin action, we evaluated the steady-state levels of cell-surface APP (csAPP) in the absence or presence of statins. After drug treatments, cells were subjected to the surface biotinylation protocol. Cells and media were harvested, and levels of sAPPα, holoAPP, and csAPP were measured. Treatment with atorvastatin increased csAPP by approximately 1.6-fold, similar to the effect of the drug on holoAPP (Figure 1), while sAPPα shedding was increased by approximately 7-fold (p < 0.05). Since csAPP levels were only slightly raised in the same statin-treated cells in which sAPPα shedding was dramatically increased, we interpret this disparity to indicate that the effector of statin-stimulated shedding is probably intrinsic to the plasma membrane. In other studies, the plasma membrane has been proposed to be, or to contain, the statin target. For example, statins have been proposed to cause co-localization of α-secretase and APP within lipid rafts [15,33]; statins might also induce modification of the structure and activity of a protein in the plasma membrane α-secretase complex, perhaps in an action similar to how statins bind and “lock” cell-surface integrins [34].
Figure 1 Atorvastatin Activates sAPPα Shedding Out of Proportion to Its Effect on holoAPP or csAPP
(A) SweAPP N2a cells were treated with atorvastatin (Atv) for 24 h and then surface biotinylation was performed as described in Methods. Evaluation of csAPP was performed by immunoprecipitation–immunoblot after surface biotinylation, while holoAPP and sAPPα were evaluated by immunoblot as described in Methods. C, control.
(B) Graphic representation of data. Y-axis shows effect of treatment (in arbitrary units) divided by effect of untreated control (in arbitrary units); n = 3 independent experiments; *, p < 0.05; **, p < 0.01; Student's t test).
A pulse-chase protocol was also used to study post-transcriptional regulation of APP metabolsm by statins (data not shown). This protocol avoids any confound that might arise because of altered APP transcription. Pulse-chase studies were performed using a 10-min pulse with [35S]methionine followed by various chase times from 0 to 120 min. Typical maturation and half-life of mature cellular holoAPP were observed, as was subsequent release of sAPPα [5,29]. In the presence of either atorvastatin or simvastatin, the time course of maturation and release perfectly paralleled that observed in the absence of either drug, except that the fractional content of cellular mature holoAPP was approximately 2-fold greater in the presence of drug (i.e., at 15 or 75 min chase, mature APP in the presence of statin was approximately 310% of the level of immature APP at t = 0 versus a control [vehicle treatment] of 150% of the level of immature APP at t = 0; also, at t = 30 min, the relative percent values for drug versus vehicle were 380% and 200%, respectively). Fold increases in released sAPPα in the same experiments were approximately 3- to 4-fold (2.0 arbitrary units versus 5.5–8.0 arbitrary units at 120 min for atorvastatin and simvastatin, respectively). Secretory maturation toxicity is one possible mechanism for elevated levels of intracellular mature holoAPP and causes retarded conversion of mature holoAPP to sAPPα. This pattern was not observed following statin treatment, excluding maturation toxicity as a mechanism underlying the altered levels of mature cellular holoAPP.
Instead, the pattern that we observed raises the possibility that statins, presumably via isoprenoids (given the reversibility with mevalonate), as discussed in the next section, may alter sorting of cellular holoAPP, diverting holoproteins away from terminal degradation in the endosomal/lysosomal pathway and into the constitutive secretory pathway that generates sAPPα. However, the fold effect on reduced intracellular turnover in the endosomal/lysosomal pathway (or sorting out of the endosomal/lysosomal pathway and into the constitutive secretory/shedding pathway) is apparently insufficient to explain the fold effect on sAPPα generation (2-fold for the former, vs 3- to 4-fold for the latter), indicating a contribution from a downstream site in the processing pathway. When these results are taken together with independent work on regulated shedding of transforming growth factor α (TGFα) [35,36], a parsimonious explanation is that an important target for activation of ectodomain shedding is probably located at the plasma membrane or downstream of APP residence at the plasma membrane.
The identification of the regulatory components of the ectodomain shedding machinery have been long-sought in other studies employing phorbol esters to stimulate shedding of sAPPα or TGFα [4,35,36,37]. Munc-13 has recently been implicated as a phorbol target in regulated shedding [38]. In our opinion, this molecule is rather unlikely to play a major role in shedding regulation, given the specificity of Munc-13 effects for phorbols and the generalization of the regulated shedding phenomenon to include activation by protein phosphatase inhibitors and neurotransmitters. Neither of these would be predicted to act via the phorbol-binding C1 domain of Munc-13.
Sisodia and colleagues [39] demonstrated that arrest of APP endocytosis from the plasma membrane by deletion of its NPXY clathrin-coated vesicle targeting sequence [40,41] can dramatically stimulate sAPPα shedding, presumably by extending the half-life of co-localized α-secretase and APP on the plasma membrane. In order to exclude the possible contribution of altered endocytosis to statin-stimulated shedding, we evaluated the effect of phenylarsine oxide (PO), an inhibitor of endocytosis, on statin-stimulated shedding (Figure 2). Treatment with either atorvastatin, simvastatin, or PO alone increased sAPPα shedding, as expected. Co-treatment of cells with PO plus either atorvastatin or simvastatin caused stimulation of sAPPα shedding to levels greater than the maximal levels of shedding achievable with inhibition of endocytosis using PO alone or with maximal doses of either statin alone (p < 0.05). The additivity of statin- and PO-stimulated shedding is consistent with the hypothesis that statins act at or near the plasma membrane, prior to internalization of csAPP. Under all circumstances, stimulated sAPPα shedding was completely blocked using TNFα protease inhibitor 2, a standard α-secretase/metalloproteinase inhibitor (Figure 2). We interpret this as an indication that the statin-induced α-cleavage of APP is probably mediated by one of the molecules usually associated with the phenomenon, i.e., ADAM-10 or ADAM-17 [16].
Figure 2 Simultaneous Treatment of SweAPP N2a Cells with Statins and an Inhibitor of Endocytosis (PO) Yields More sAPPα Shedding Than Does Treatment with Either Statins or PO Alone
(A) SweAPP N2a cells were treated for 24 h with atorvastatin (Atv) or simvastatin (Sim) as indicated. Media were then replaced and cells were treated for an additional 20 min with atorvastatin, simvastatin, TNFα protease inhibitor, PO, or combinations, as indicated. Evaluation of sAPPα and holoAPP was performed by Western blot as described in Methods. C, control.
(B) Graphic representation of data. Y-axis shows effect of treatment (in arbitrary units) divided by effect of untreated control (in arbitrary units); n = 3 independent experiments; *, p < 0.05 versus control; **, p < 0.01 versus C; #, p < 0.05 versus atorvastatin alone; ##, p < 0.05 versus simvastatin alone; Student's t test).
Compounds that Modulate Isoprenoid Levels Activate sAPPα Shedding
As discussed above, there is an established relationship between statins and isoprenoid-modulated protein phosphorylation. We therefore tested the effects of FTI-1 on statin-stimulated sAPPα shedding. FTI-1 increased the shedding of sAPPα, but the combination of a statin plus FTI-1 increased sAPPα shedding to levels greater than those achievable by using either compound alone (Figure 3; p < 0.05). In the same experiment, levels of holoAPP were modestly increased but, again, to an extent insufficient to account for the increase in shed sAPPα (Figure 3).
Figure 3 Simultaneous Treatment of SweAPP N2a Cells with a Statin and FTI-1 Causes Greater sAPPα Shedding Than Either Drug Alone
(A) SweAPP N2a cells were treated for 24 h with atorvastatin (Atv, 5 μM), simvastatin (Sim, 1 μM), FTI-1 (5 μM), or a combination of FTI-1 plus a statin. Levels of sAPPα (top panel) or holoAPP (bottom panel) were evaluated as described in Methods. C, control.
(B) Graphic representation of data. Y-axis shows effect of treatment (in arbitrary units) divided by effect of untreated control (in arbitrary units); n = 3 independent experiments; *, p < 0.05 versus control; **, p < 0.05 versus atorvastatin alone; ***, p < 0.05 versus simvastatin alone; Student's t test).
To test whether statin-activated shedding might be attributable to a metabolite downstream of HMGCoA reductase, cells were treated with 1 μM simvastatin, 5 μM FTI-1, and a series of concentrations of mevalonic acid. Since FTI-1 acts downstream of HMGCoA reductase, the stimulatory effect of FTI-1 on sAPPα shedding would not be predicted to be modified by mevalonate supplementation. Low doses (<1 μM) of mevalonic acid did not affect statin-induced sAPPα shedding, but complete inhibition of statin-activated sAPPα shedding was achieved with higher doses of mevalonic acid (100 μM). As predicted, the shedding observed following treatment with FTI-1 was not inhibited by any of the concentrations of mevalonic acid tested (Figure 4). These data are consistent with a role for isoprenoids in statin control of APP metabolism in cultured cells.
Figure 4 Mevalonic Acid Reverses Statin-Induced, but Not FTI-1-Induced, sAPPα Shedding
SweAPP N2a cells were treated for 24 h with simvastatin (Sim, 1 μM), FTI-1 (5 μM), mevalonic acid (0–100 μM), or combinations as indicated. Levels of sAPPα were evaluated by western blot as described in Methods. This figure is representative of the results of two independent experiments. C, control.
Expression of ROCK-Related Molecules Modulates sAPPα Shedding in a Bidirectional Manner
Since many isoprenoid-mediated Rho effects converge on ROCKs, we next transfected N2a cells with cDNAs encoding either green fluorescent protein (GFP) (control), CA ROCK1, or DN ROCK1 (Figure 5). Simvastatin caused a typical activation of sAPPα shedding from GFP-transfected cells. When CA ROCK1 was introduced, however, shedding of sAPPα from both untreated and simvastatin-treated cells was diminished (Figure 5; p < 0.05 versus GFP control). Conversely, DN ROCK1 alone activated shedding of sAPPα. Cellular levels of holoAPP were not affected by transfection (Figure 5; p < 0.05 versus GFP control). In studies aimed at independent confirmation of the involvement of ROCK activation in sAPPα shedding, we treated SweAPP N2a cells with arachidonic acid, an activator of ROCK. As shown in Figure 6, arachidonic acid reduced the shedding of sAPPα without altering levels of holoAPP. Based on this series of results, we concluded that both basal and activated sAPPα shedding from cultured cells are controlled by ROCK activity.
Figure 5 Structure and Expression of ROCK cDNAs, and Their Effect on Basal and Statin-Stimulated sAPPα Shedding
(A) Graphic representation of the ROCK1 constructs. Myc, Myc tag; KD, kinase domain; PH domain, pleckstrin homology domain; RBD, Rho-binding domain.
(B) SweAPP N2a cells were transfected with GFP, CA ROCK1, or DN ROCK1 for 48 h. Cells were lysed and levels of expressed ROCK1 protein evaluated by immunoprecipitation–immunoblot as described in Methods.
(C) Model for ROCK activity modulation by Rho.
(D) SweAPP N2a cells were transfected for 48 h with control (GFP), CA ROCK1, or DN ROCK1 cDNAs. At the end of this incubation, cells were treated for an additional 24 h with simvastatin (Sim, 1 μM). sAPPα and holoAPP were evaluated by immunoblot as described in Methods.
(E) Graphic representation of data. Y-axis shows effect of treatment (in arbitrary units) divided by effect of untreated control (in arbitrary units); n = 3 independent experiments; *, p < 0.05 versus GFP; Student's t test). C, control.
Figure 6 Arachidonic Acid Inhibits Basal sAPPα Shedding but Has No Effect on holoAPP Levels
(A) SweAPP cells were treated for 24 h with arachidonic acid (5 or 50 μM, represented by AA5 and AA50, respectively). Levels of sAPPα were evaluated by immunoblot as described in Methods. C, control.
(B) Graphic representation of data. Y-axis shows effect of treatment (in arbitrary units) divided by effect of untreated control (in arbitrary units); n = 6 independent experiments; *, p < 0.05 versus control; Student's t test.
In some experiments, cells were treated with Y-27632 (10 nM to 50 μM), a compound that can inhibit ROCKs. Y-27632 showed no effect on basal sAPPα release and blocked statin-activated sAPPα shedding (data not shown). This result was unexpected in light of the effects of DN ROCK1. Given the internally consistent actions of DN ROCK1 and CA ROCK1, as well as the results employing either FTI-1 or arachidonate, we concluded that the Y-27632 result might be due to inhibition by Y-27632 of protein kinases other than ROCK1 [23]. The possibility was also considered that cytotoxicity of Y-27632 for the central vacuolar pathway might explain the disparity between the effects of DN ROCK1 and those of Y-27632, but neither impairment of intracellular APP maturation nor increased apoptosis as measured by LIVE/DEAD assay were apparent following Y-27632 treatment (data not shown). Ultimately, we were unable to document any explanation for the disparate results of Y-27632 and DN ROCK1.
Discussion
The isoprenoid pathway involves lipid modification of various members of the Rho family of small GTPases by the addition of either farnesyl or geranylgeranyl moieties [20,42]. Isoprenylation serves to target the GTPases to the proper organelle membrane, where their actions often relate to cytoskeletal dynamics and/or vesicle trafficking [20,42]. ROCKs are important downstream targets of Rho (Figure 7), catalyzing the phosphorylation of effector phosphoprotein substrates [23]. The foregoing data indicate that statin-induced activation of APP shedding in cultured cells involves the Rho/ROCK pathway. More specifically, the data indicate that ROCK1 activation blocks the effects of statins on APP ectodomain shedding, while ROCK1 blockade alone can mimic the effect of statins on APP shedding. By extension, these data predict that application of statins to neurons might directly or indirectly inhibit ROCK1 activity. Evaluation of this possibility will be the subject of future investigation.
Figure 7 Isoprenoid Pathway and Sites of Action of Compounds Used in This Study
FPP, farnesyl pyrophosphate; GGPP, geranylgeranyl pyrophosphate.
The first evidence that APP might be a substrate for ectodomain shedding was provided by Weidemann et al. [43] who identified sAPPα in the cerebrospinal fluid and blood. This aspect of APP metabolism bears resemblance to the proteolytic signal transduction pathways involved in processing pro-TGFα [35,36] and Notch [44]. In the case of Notch, the process is set in motion by the binding of a ligand to the Notch ectodomain, triggering its release (shedding). For APP, intracellular signal transduction appears to be more important [2,29,45]. In early studies, the existence of the shed ectodomain of APP was used to deduce the existence of the proteolytic activity designated α-secretase, which has the unusual specificity of cleaving its substrates at a proscribed distance from the extracellular leaflet of the plasma membrane [4,39]. Ultimately, the integral cell-surface metalloproteinases ADAM-10 and ADAM-17/TACE were found to underlie α-secretase-type ectodomain shedding [3,46].
Why is APP a substrate for ectodomain shedding? To answer this question requires contemplation of the physiological function of APP. APP is a type 1 integral protein that is subjected to a host of post-translational processing events, including N- and O-glycosylation, tyrosyl sulfation, phosphorylation, and proteolysis [39,43,47,48]. Most (60%–80%) newly synthesized APP is subjected to terminal intracellular degradation that generates no discrete fragments [5]. A smaller fraction of APP molecules (approximately 20% in PC12 cells under basal conditions [5]) undergoes ectodomain shedding catalyzed by either the α-secretase (nonamyloidogenic) or β-secretase (potentially amyloiodgenic) pathway. When PKC is activated, the stoichiometry of shed sAPPα rises from 2 mol shed per 10 mol synthesized to 4 mol shed per mole synthesized. Most of this shedding is catalyzed by the α-secretase pathway, but a trace amount (<5% [49]) is catalyzed by β-secretase/β-site APP cleaving enzyme [50]. sAPPα and sAPPβ differ by the inclusion in sAPPα of the first 16 residues of Aβ. Unlike sAPPα, which is generated at the plasma membrane, most sAPPβ is probably generated by cleavage within the trans-Golgi network and endocytic pathway vesicles. HoloAPP levels are likely limiting at one or more sorting steps in the late secretory pathway, since activated sAPPα shedding is apparently accompanied by diminished generation of sAPPβ [51].
What is the function of shed sAPPα? Again, from other molecules, we know that shedding can serve important cellular functions by releasing diffusible ligands from their membrane-bound precursors (e.g., TGFα and TNFα) or by terminating intercellular signaling (e.g., Notch). A popular model holds that sAPPα may function as a neurotrophic and/or neuroprotective factor, and may promote neurite outgrowth [52]. More recent evidence suggests that released APP derivatives modulate efficacy of neurotransmission at the synapse [53]. Targeted deletion of APP has not revealed a striking phenotype [54], presumably because of functional redundancy supplied by APP-like proteins [55]. Mice with double and triple null mutations in various combinations of APP, APP-like protein 1, and APP-like protein 2 are now being created, in search of evidence for a definitive function for APP.
Cao and Sudhof [56] have recently discovered that the APP C-terminal fragment generated by α- or β-secretase is itself cleaved to release Aβ and an APP intracellular domain (AICD) that diffuses into the nucleus, possibly acting there as a transcription factor. The pathway leading to AICD must be initiated by ectodomain shedding: holoAPP cannot directly give rise to AICD. Therefore, one important function for α- and/or β-secretase processing of APP may be the eventual generation of AICD. Our results suggest that Rho/ROCK signaling provides modulation of basal and stimulated α-secretase activity. It will now be important to dissect pathways upstream of Rho/ROCK signaling in order to identify the intracellular and intercellular events that participate in Rho/ROCK regulation of α-secretase under physiological and pathological conditions.
The potential role of cholesterol in α-secretase-mediated shedding was discovered by Bodovitz and Klein [57] who used β-cyclodextrin to lower cellular cholesterol. Kojro et al. [15] confirmed this observation, using not only β-cyclodextrin but also lovastatin to lower cellular cholesterol. These investigators proposed that elevated ADAM-10 activity and protein levels contributed to these effects. These basic observations dovetailed with emerging epidemiological evidence that administration of statins might lead to a diminished incidence of Alzheimer disease [8,9,10,11]. Despite this, however, the association of statins and cholesterol levels with activated α-secretase-mediated shedding of the APP ectodomain was unexpected and not readily explicable by existing knowledge regarding regulation of α-secretase activity. The best characterized regulation of α-secretase processing typically involves protein phosphorylation via PKC [5,29] or ERKs [7] or protein dephosphorylation by protein phosphatase 1 or 2A [29]. We recently excluded the possibility that either PKC or ERK plays a role in statin-activated shedding [16], raising the possibility that other protein phosphorylation signaling pathways might link statins and/or cholesterol to α-secretase activation.
Maillet et al. [58] implicated the Rho pathway in modulation of α-secretase activity while dissecting the activated shedding process that accompanies serotonergic signal transduction. These investigators discovered that Rap1 acts through Rac to modulate α-secretase processing of APP. Soon thereafter, ROCK1 was discovered by Zhou et al. [28] to modulate a downstream processing step in APP metabolism that involves presenilin/γ-secretase-mediated proteolysis of APP C-terminal fragments C99 and C83. These investigators discovered that activation of ROCK1 may account for how nonsteroidal anti-inflammatory drugs specify the scissile bond within the APP transmembrane domain that is cleaved by presenilin/γ-secretase to generate the C-terminus of Aβ. Based on these reports, we asked whether the Rho/ROCK pathway might play a role in controlling shedding of sAPPα following statin application.
CA ROCK1 and DN ROCK1 molecules yielded direct and complementary evidence that ROCK1 was indeed a candidate for modulation of statin-activated α-secretase action. Further, we were able to demonstrate that α-secretase activity could be modulated by molecules further upstream in the isoprenoid pathway (see Figure 7). FTI-1, an inhibitor of farnesyl transferase also known as L-744,832 [59], mimicked and potentiated statin-activated shedding, presumably by blocking transfer of isoprenoid moieties to a Rho protein by farnesyl transferase, and thereby decreasing Rho activity. However, FTI-1 treatment can also increase the level of geranylgeranylated isoforms of certain Rho proteins, e.g., the inhibitory geranylgeranylated RhoB protein [60]. In further support of a role for isoprenoids, we were able to demonstrate that supplementation of cells with mevalonate abolished statin-activated shedding (see Figure 4). Statins block HMGCoA reductase generation of mevalonate from 3-hydroxy-methyl-glutarate (see Figure 7). Therefore, the addition of mevalonate would be predicted to antagonize statin action via the isoprenoid pathway, by relieving statin-induced mevalonate deficiency. As predicted by this model, we observed that statin-activated shedding was abolished by adding mevalonate. Taken together, these results suggest the existence of a reciprocal relationship between isoprenoid-mediated Rho/ROCK signaling and sAPPα shedding, i.e., activation of ROCK1 blocks basal and stimulated shedding while ROCK1 inhibition apparently relieves a tonic negative influence exerted on shedding by ROCK1 activity.
As in PKC- and ERK-activated shedding, the ROCK1 substrate effector molecule or molecules that regulate proteolysis by ADAMs remain to be identified. The cytoplasmic domains of both APP and ADAM-17 have been evaluated as candidates for important targets of protein phosphorylation during the regulated shedding process, but neither “substrate activation” nor “enzyme activation” appears to explain the phenomenon, i.e., phosphorylation of neither APP nor ADAM-17 dramatically increases the efficiency of α-secretion [61,62], indicating that activation is more indirect.
Our data using statins and PO localize the mediator of statin-activated shedding to the plasma membrane, upstream of endocytosis, as appears to be the case for PKC-activated shedding [36,63,64,65]. Similar conclusions were drawn by Bosenberg and colleagues [36] who used streptolysin-porated cells and N-ethylmaleimide to demonstrate that reconstitution of activated shedding of TGFα from CHO cells does not require membrane trafficking and apparently occurs on the plasma membrane. These results suggest that a tightly membrane-associated regulatory subunit of the α-secretase complex is likely to be the key phosphoprotein that mediates α-secretase activity as a function of its state of phosphorylation by PKC and perhaps also ERK and ROCK1. The molecular identity of this phosphoprotein remains unknown.
α-Secretase activation is a potential therapeutic strategy for modifying cerebral amyloidosis in Alzheimer disease [66]. This proposal is supported by recent evidence that either genetic modification of ADAM-10/α-secretase activity [67] or administration of bryostatin, a PKC activator [68], can modulate levels of brain Aβ in plaque-forming transgenic mice. α-Secretase activation may explain how statins lower the risk for Alzheimer disease [69], since atorvastatin diminishes Aβ burden in plaque-forming transgenic mice [13]. If α-secretase stimulation is to be truly viable as a human clinical intervention, it will be essential to assess the possibility that enhanced APP ectodomain shedding might incur mechanism-based toxicity (analogous to the concerns currently surrounding γ-secretase inhibitors). Along this line, extension of this work to other shed proteins will be important to determine the impact of enhanced shedding via ADAM proteinases on other substrates of those proteinases, including Notch, pro-TGFα, pro-TNFα, and CD44 [3].
Preliminary results from a pilot proof-of-concept using atorvastatin in a human clinical treatment trial are consistent with the proposed beneficial effects of this class of compounds [70]. Since atorvastatin has low blood–brain barrier permeability [71], this beneficial effect, if attributable to Aβ lowering, must be due to altered Aβ metabolism in the periphery. Reduction in levels of free Aβ in the circulation has been demonstrated to lead to diminution in brain plaque burden following active or passive immunization [72]. It is conceivable that, if statins lower circulating Aβ, this effect could secondarily cause diffusion of central nervous system interstitial Aβ down its concentration gradient and into the cerebrospinal fluid and circulation, from which it is cleared. To date, however, this mechanism is not supported by data from human clinical trials, where statin administration has shown no consistent effect on levels of circulating or cerebrospinal fluid Aβ [73].
The results reported here point to several areas for additional investigation. As described above, the key substrate or substrates linking cytoplasmic protein phosphorylation to intralumenal or cell-surface protelysis remain to be identified. Nonetheless, α-secretase activation has been validated as a viable therapeutic strategy for modulating cerebral amyloidosis [67]. Identification of the role of the Rho/ROCK pathway in regulating α-secretase provides a new avenue for its therapeutic activation, even though the potential relevance of atorvastatin-mediated ROCK1 inhibition in neurons may not explain the apparent clinical benefits of the drug. Still, if the reported disease-modifying effect of atorvastatin is confirmed in the National Institute on Aging's large, multi-center trial of simvastatin, one or more compounds of this class may be among the first disease-modifying compounds approved by the Food and Drug Administration for slowing the progression of Alzheimer disease.
Supporting Information
Accession Numbers
The GenBank (http://www.ncbi.nlm.nih.gov/Genbank/) accession numbers for the proteins discussed in this paper are ROCK1 (NP_005397) and APP695, 595–596 KM/NL (NP_958817).
Patient Summary
Background
Large-scale studies have found a link between taking cholesterol-lowering drugs called statins and a decreased risk of developing Alzheimer disease. But it is not clear why statins might protect people from getting the disease. The brains of people who have died from Alzheimer disease show remnants of damaged cells called “tangles” as well as “amyloid plaques” in the spaces between the cells. These plaques are mostly made up of collections of a protein called amyloid-beta. It is the buildup of this protein that is thought to cause the brain damage and dementia associated with Alzheimer disease. The protein itself is formed when another, larger protein called APP (Alzheimer amyloid-beta peptide precursor protein) is broken down (or cleaved). There are two ways in which APP can be broken down. “Bad cleavage” releases the toxic amyloid-beta, whereas “good cleavage” destroys it. When researchers gave statins to animals over a long period of time, they found that statins could slow down the formation of amyloid plaques. From the animal experiments, it seemed that statins somehow caused more good cleavage to occur.
Why Was This Study Done?
This study examined how statins can affect APP cleavage.
What Did the Researchers Do?
They studied cells to see which of the players known to be involved in APP cleavage were affected by statin.
What Did They Find?
Statin's ability to promote “good cleavage” of APP involves a molecular pathway called the Rho/ROCK1 pathway. It seems that when ROCK1 is active, less good cleavage takes place. But in the presence of statins, ROCK1 is less active, shifting the balance toward good cleavage. Consistent with this, when the scientists blocked the Rho/ROCK1 pathway, they saw the good cleavage pattern even without statin.
What Does This Mean for Patients?
Inhibition of the Rho/ROCK1 pathway could explain some of the beneficial effects of statins against Alzheimer disease. And the pathway itself seems worth more research to see whether it might be a good target for new ways to prevent and treat Alzheimer disease.
What Are the Limitations of the Study?
Statins are likely to influence the risk for Alzheimer disease by several different pathways, and future studies will need to show how important this particular pathway is in the overall picture. Moreover, studies like this one are by necessity done in cells under carefully controlled laboratory conditions and still a long way from the development of safe and effective drugs.
More Information Online
Factsheet on statins from the Alzheimer's Association: http://www.alz.org/Resources/TopicIndex/statins.asp
General information at the Alzheimer's Disease Education and Referral Center at the United States National Institute of Aging: http://www.alzheimers.org/index.html
Homepage of Alzheimer's Disease International, an umbrella organization of Alzheimer disease associations around the world: http://www.alz.co.uk/
The authors would like to thank the United States Public Health Service for support of salaries, supplies, and publication costs, via National Institute on Aging grant AG10491 to SG, National Institute on Neurological Diseases and Stroke grants NS42017 to SG and NS45913 to SP, and National Cancer Institute grant CA100123 to GCP. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Citation: Pedrini S, Carter TL, Prendergast G, Petanceska S, Ehrlich ME, et al. (2005) Modulation of statin-activated shedding of Alzheimer APP ectodomain by ROCK. PLoS Med 2(1): e18.
Abbreviations
ADAMa disintegrin and metalloproteinase
AICDAPP intracellular domain
APPAlzheimer amyloid-β peptide precursor protein
Aβamyloid-β peptide
CAconstitutively active
csAPPcell-surface Alzheimer amyloid-β peptide precursor protein
DNdominant negative
ERKextracellular-signal-regulated protein kinase
FTI-1farnesyl transferase inhibitor
GFPgreen fluorescent protein
PKCprotein kinase C
POphenylarsine oxide
ROCKRho-associated protein kinase
sAPPαα-secretase-cleaved soluble Alzheimer amyloid-β peptide precursor protein ectodomain
TGFαtransforming growth factor α
TNFαtumor necrosis factor α
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| 15647781 | PMC543463 | CC BY | 2021-01-05 10:38:14 | no | PLoS Med. 2005 Jan 11; 2(1):e18 | utf-8 | PLoS Med | 2,005 | 10.1371/journal.pmed.0020018 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 10.1371/journal.pmed.0020022SynopsisNeurosciencePharmacology/Drug DiscoveryGeriatricsNeurology/NeurosurgeryNeurologyDementiaPharmacology and ToxicologyHow Statins May Protect against Alzheimer Disease 1 2005 11 1 2005 2 1 e22Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Modulation of Statin-Activated Shedding of Alzheimer APP Ectodomain by ROCK
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Epidemiological studies suggest that statins, a class of cholesterol-lowering drugs, may lower the risk for Alzheimer disease. The mechanism for this effect is unclear. Alzheimer disease is characterized by accumulation of amyloid deposits in the brain. These deposits are composed of amyloid-beta (Aβ) peptide, a protein fragment that is cleaved off from the amyloid precursor protein APP. APP can be cleaved in two different ways. Amyloidogenic (“amyloid generating”) cleavage by an enzyme called beta-secretase yields “sticky” Aβ peptides that aggregate to form deposits, whereas non-amyloidogenic cleavage by alpha-secretases generates soluble peptides that do not form deposits. Studies in animal models and cell culture suggest that statins might modulate APP processing and shift the balance toward “healthy” (non-amyloidogenic) cleavage.
APP secretases (α, β, and γ)
In their quest to understand how statins affect APP processing, Sam Gandy and colleagues focused on a molecule called ROCK1, a kinase enzyme that had recently been implicated in APP processing. The theoretical link between statins and ROCK1 goes as follows: statins inhibit the isoprenoid pathway, isoprenoids are regulators of the enzyme Rho, and Rho in turn activates ROCK1. And while such potential connections could be drawn for any number of molecules, Gandy and colleagues went on to test whether statins exert their effect on APP cleavage by interfering with the isoprenoid/Rho/ROCK1 pathway.
Working in mouse neuroblastoma cells, they confirmed that two different statins increased healthy cleavage of APP. When they directly interfered with the isoprenoid/Rho/ROCK1 pathway by adding a drug that inhibits Rho activation, they saw effects similar to those of the statins (i.e., an increase in healthy cleavage). The same effects were seen when they transfected the cells with a dominant negative form of ROCK1 (which inactivates the normal ROCK1 molecules in the cell); this outcome shows that the pathway can influence APP cleavage. Most conclusively, when they added a version of ROCK1 that was constitutively (always) active, they reduced basal levels and abolished statin-stimulated levels of healthy cleavage.
Taken together, these results suggest that statins influence APP processing, at least in part, by modulating the isoprenoid pathway and inactivating the ROCK1 kinase. Future studies are necessary to determine whether this mechanism is actually responsible for the apparent clinical benefits of statins. Another question worth exploring further is whether ROCK1 might be a suitable target for therapeutic interventions that aim to decrease harmful, and promote healthy, cleavage of APP.
| 0 | PMC543464 | CC0 | 2021-01-05 10:39:35 | no | PLoS Med. 2005 Jan 11; 2(1):e22 | utf-8 | PLoS Med | 2,005 | 10.1371/journal.pmed.0020022 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 10.1371/journal.pmed.0020023SynopsisSexual HealthInfectious DiseasesHIV Infection/AIDSModeling the HIV Epidemic in Africa Synopsis1 2005 11 1 2005 2 1 e23Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Integrating HIV Prevention and Treatment: From Slogans to Impact
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The HIV epidemic is continuing to grow, year by year. According to the Joint United Nations Programme on HIV/AIDS (UNAIDS), in 2004 there were more people living with the virus than ever before, and in the same year more people than ever before died of it. So, although in the developed world HIV/AIDS is a controllable disease, one with which a treated person might expect to have a near normal lifespan, in much of the rest of the world AIDS is still a death sentence. Despite the fact that the cost of AIDS medicine has come down to around $150 per year in the developing world—a much lower cost than previously—the drugs are still unavailable to the vast majority of patients. What is more, every infected person has the chance of infecting many others. Although huge sums of money have been poured into combating HIV/AIDS—around US$4.7 billion in 2003—UNAIDS estimates this amount is less than half of what is required by 2005, and only a quarter of what will be required by 2007, to mount a comprehensive response to AIDS in low-income and middle-income countries. One of the real dilemmas, therefore, of HIV/AIDS policy is deciding whether it is better to concentrate resources on prevention of infections or on treatment of infected individuals. Each approach has ramifications for the other, as shown by the experience in some developed countries, where an increase in availability of treatment has been accompanied by an increase in risk behavior.
The best strategy is to combine prevention and treatment
An analysis by Joshua Salomon and colleagues in this month's PLoS Medicine suggests that trying to concentrate on one or the other of these alternatives is a false dichotomy, and that not integrating the two approaches could have a catastrophic effect on the global toll of HIV/AIDS by 2020. In this theoretical paper the authors analyze the epidemic in sub-Saharan Africa (where three-fourths of deaths from AIDS occur). With no change in current levels of prevention and care, it is predicted that there will be 3.7 million new HIV infections and 2.6 million adults dying of AIDS in this region each year within the next two decades. The authors predicted that concentrating on prevention alone could decrease yearly infections by half, and that concentrating on treatment could decrease yearly infections by 6%. However, combining both approaches could yield substantially greater benefits than the sum of the two alone—lowering projected new infections by 74% and projected annual mortality by half. These percentages translate into 29 million new infections and 10 million deaths averted between 2004 and 2020.
The challenge now is obviously how to put these policy suggestions into practice. The current World Health Organization treatment target of having three million people on antiretroviral therapy by the end of 2005 (the “3 by 5” objective) provides a yardstick for only one part of the equation. The authors comment that the mobilization of communities that will be needed to achieve the 3 by 5 objective should also be harnessed for prevention, and that those who teach prevention must also be allowed to get care for those infected. As the authors say, only by doing so “will we at last move from slogans to impact.”
| 0 | PMC543465 | CC0 | 2021-01-05 10:38:14 | no | PLoS Med. 2005 Jan 11; 2(1):e23 | utf-8 | PLoS Med | 2,005 | 10.1371/journal.pmed.0020023 | oa_comm |
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BMC GastroenterolBMC Gastroenterology1471-230XBioMed Central London 1471-230X-4-291552751110.1186/1471-230X-4-29Research ArticleElevated serum procollagen type III peptide in splanchnic and peripheral circulation of patients with inflammatory bowel disease submitted to surgery De Simone Matilde [email protected] Ugo [email protected] Ettore [email protected] Barbara [email protected] Roberta [email protected] Alberto [email protected] Gianni [email protected] Elide [email protected] Stefano [email protected] Fabio [email protected] Michele M [email protected] Department of Surgery, Ospedale Maggiore di Milano, IRCCS, University of Milan, V. F. Sforza, 35 – 20122, Milan, Italy2 Istituto di Medicina Cardiovascolare, Centro Interuniversitario di Fisiologia Clinica e Ipertensione, Ospedale Maggiore di Milano, IRCCS, University of Milan, V. F. Sforza, 35 – 20122, Milan, Italy3 II Cattedra di Anatomia Patologica, Dipartimento di Medicina Chirurgia e Odontoiatria, A.O. San Paolo and Ospedale Maggiore di Milano, IRCCS, University of Milan, V. A. di Rudinì – 20100, Milan, Italy2004 4 11 2004 4 29 29 30 4 2004 4 11 2004 Copyright © 2004 De Simone et al; licensee BioMed Central Ltd.2004De Simone et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
In the hypothesis that the increased collagen metabolism in the intestinal wall of patients affected by inflammatory bowel disease (IBD) is reflected in the systemic circulation, we aimed the study to evaluate serum level of procollagen III peptide (PIIIP) in peripheral and splanchnic circulation by a commercial radioimmunoassay of patients with different histories of disease.
Methods
Twenty-seven patients, 17 with Crohn and 10 with ulcerative colitis submitted to surgery were studied. Blood samples were obtained before surgery from a peripheral vein and during surgery from the mesenteric vein draining the affected intestinal segment. Fifteen healthy age and sex matched subjects were studied to determine normal range for peripheral PIIIP.
Results
In IBD patients peripheral PIIIP level was significantly higher if compared with controls (5.0 ± 1.9 vs 2.7 ± 0.7 μg/l; p = 0.0001); splanchnic PIIIP level was 5.5 ± 2.6 μg/l showing a positive gradient between splanchnic and peripheral concentrations of PIIIP. No significant differences between groups nor correlations with patients' age and duration of disease were found.
Conclusions
We provide evidence that the increased local collagen metabolism in active IBD is reflected also in the systemic circulation irrespective of the history of the disease, suggesting that PIIIP should be considered more appropiately as a marker of the activity phases of IBD.
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Background
Crohn's disease (CD) and Ulcerative Colitis (UC) are chronic inflammatory bowel diseases (IBD) of unknown origin of adolescent and young adulthood [1] where genetic polimorphisms [2,3], abnormal inflammation pathways activation [4], and environmental influences [5] seem to concur at different levels in the pathogenesis and the progression of IBD. These pathologic conditions are characterized by focal or diffuse inflammation of the alimentary tract, mucosal damage and epithelial destruction. IBD may be associated with an inability of the intestinal mucosa to protect itself from luminal challenges and inappropriate repair following intestinal injury [6-10]. CD differs from UC by the transmural granulomatous inflammation generally leading to fibrosis, strictures and fistulas [11].
Current opinions suggest that an increased synthesis of collagen type I, III, and V may play an important role in the pathophysiological mechanism leading to intestinal fibrosis [12-15]. An increased synthesis of collagen, namely an increased of procollagen type III, is well documented in fibrotic processes involving other organs such as liver, pancreas, and lung [16-18].
However, not all authors are in agreement regarding the increased serum levels of the aminoterminal propeptide (PIIIP) of collagen in peripheral and splanchnic circulation of patients with active IBD [13,14]. Below we present the results on the serum level of PIIIP in splanchnic and peripheral circulation in patients with active IBD submitted to surgery.
Methods
Twenty-seven patients affected by active IBD, 17 with CD (age 40.2 ± 13.1, yrs from diagnosis 9.2 ± 5.5), and 10 with UC (age 50.3 ± 15.6, yrs from diagnosis 9.8 ± 7.4) submitted to surgery, were enrolled in the study in a double blind fashion. The protocol was approved by local Ethical Committee and informed consent was obtained from all participants to the study. Three patients had CD in small bowel only, two in large bowel only, 12 had ileocolonic disease. Disease activity was assessed according to the Crohn's Disease Activity Index (CDAI) [19] and the Truelove-Witts index (TWI) [20] for CD and UC, respectively. According to CDAI, 2 patients were subclassified as having a moderate form of disease while 15 patients were subclassified as having a severe form of disease. According to TWI, 3 patients were classified as having moderate form of disease, 4 a mild form, and 3 a severe form of disease.
Patients affected by CD were operated on for recurrent obstruction, whereas patients with UC were submitted to surgery because of refractory to medical therapy.
The clinical diagnosis was confirmed by histology (Fig. 1); all cases under study fulfilled the histological criteria as follows:
Figure 1 Histological images obtained from two IBD patients enrolled in the study affected by CD (panel a) and UC (panel b) with 12.0 and 10.3 μg/l splanchnic levels of PIIIP, respectively. Panel a, CD: in the transmural section is clearly evident an ulceration (o) in the mucosa and submucosa with diffuse inflammatory infiltrations, pseudo-follicle nodules (arrow), and fibrosis of the intestinal wall. Panel b, UC: the inflammatory infiltration is more evident in the mucosa and submucosa with criptic abscesses (asterisks). A serpiginous linear ulcer is evident (arrow).
- for CD: deep ulcers, marked proliferation of small lymphoid nodules involving all layers of intestinal wall sometime with sarcoid-type granulomas and serosal inflammation;
- for UC: mucosal erosions and superficial ulcerations usually limited to the upper submucosa with cryptic abscesses and glandular destruction.
The clinical profile of the studied patients is reported (Table 1). Two patients did not receive any medication, whereas other patients received two or three drugs for the treatment of IBD. Table 2 shows the treatment protocol for all the studied patients. A control group of 15 healthy age and gender matched subjects was also studied to determine normal range for peripheral PIIIP.
Table 1 Clinical aspects of the studied patients
Disease N° of patients Age Sex Years from diagnosis Activity index
Crohn CDAI
1 34 F 12 Severe
2 35 M 8 Moderate
3 38 M 15 Severe
4 32 F 8 Severe
5 43 F 14 Severe
6 35 M 8 Severe
7 58 M 5 Severe
8 61 F 7 Severe
9 33 M 8 Severe
10 35 F 19 Severe
11 21 F 4 Severe
12 72 F 1 Severe
13 36 M 15 Moderate
14 42 M 7 Severe
15 25 F 6 Severe
16 51 F 18 Severe
17 34 M 1 Severe
Ulcerative colitis TWI
1 68 F 15 Severe
2 70 F 1 Moderate
3 21 F 3 Mild
4 54 F 20 Moderate
5 52 M 20 Mild
6 38 M 4 Moderate
7 64 M 6 Mild
8 38 F 16 Severe
9 41 F 3 Severe
10 57 M 10 Mild
CDAI: Crohn's Disease Activity Index; TWI: truelove-Witts index.
Table 2 Frequency distribution for therapy
Therapy N° of patients Crohn Ulcerative colitis
No 2 1 1
Aminosalicydic acid 9 8 1
Cortisone 2 2 0
Aminosalicydic acid + Cortisone 14 6 8
Total 27 17 10
Collagen metabolism (PIIIP)
Different kinds of collagen have been identified in humans. All of them derive from longer precursor molecules (procollagens). They are synthesized intracellularly and secreted in extracellular space where they are cleaved by aminoproteases [21-23]. Among the different kinds of precursors, type III is one of the most abundant interstitial procollagens. Since its aminoterminal propeptide, PIIIP, is formed in equimolar proportions to collagen, serum measurements of this fragment can provide an index of collagen synthesis [23]. The blood samples (two, 5-ml each) for PIIIP measurements were taken from the median cubital vein (p-PIIIP) before surgery after an overnight fast, during surgery from a mesenteric vein (s-PIIIP) draining the intestinal segment chosen for resection by the surgeon. Serum levels of PIIIP were assessed by commercial radioimmunoassay (Orion Diagnostics, Finland). The intra-assay and inter-assay variation were respectively 4% and 4.3%, mean 2.6 μg/l. Normal ranges of peripheral PIIIP concentrations assessed in the control group were 2.7 ± 0.7 μg/l.
Statistical analysis
Data were analyzed using a computer statistical software (SPSS-Rel 10; SPSS Inc., Chicago, Ill). All the quantitative variables were tested for Gaussian distribution with the Kolmogorov-Smirnov test. All that followed this distribution were presented as mean ± standard deviation.
Differences at baseline in collagen parameters between IBD patients and controls were tested for significance using the analysis of variance with the Bonferroni correction. The relation between collagen parameters and the estimated duration of the disease and indices of disease were tested with regression analysis. In all cases, a p value less than 0.05 was considered significant.
Results
Peripheral PIIIP assay
At baseline, before surgery, serum p-PIIIP in IBD patients were significantly higher if compared with healthy controls (5.0 ± 1.9 vs 2.7 ± 0.7 μg/l, respectively; p = 0.0001) (fig 2a). No significant differences were found when comparing CD and UC subgroups (5.0 ± 1.6 vs 4.9 ± 2.4 μg/l, respectively; p = ns) (fig 2b).
Figure 2 Panel a: Differences in baseline p-PIIIP values in Controls and IBD patients. Panel b: No significant differences in p-PIIIP values between CD and UC subgroups. Panel c: Differences between splancnic and periferic values of PIIIP, without significant differences in CD and UC sbgroups. p-PIIIP: periferic (median cubital vein) PIIIP; s-PIIIP: splancnic (mesenteric vein) PIIIP; Δ-PIIIP: differences between s- and p-PIIIP in IBD patients; IBD: inflammatory bowel diseases; CD: Crohn's Disease; UC: ulcerative colitis;
Splanchnic PIIIP assay
During surgery, serum s-PIIIP in IBD patients was 5.5 ± 2.6 μg/l. No significant differences were found when comparing CD and UC subgroups (5.4 ± 2.3 vs 5.7 ± 3.1 μg/l, respectively; p = ns). A positive gradient was found in IBD patients between splanchnic and peripheral serum concentrations of PIIIP (0.7 ± 1.9 μg/l). This gradient was confirmed when separately considering each disease, without significant differences between the two subgroups (CD 0.3 ± 1.3 vs UC 1.3 ± 2.6 μg/l; p = ns) (fig 2c).
Other variables and PIIIP levels
No significant correlation was found between peripheral and splanchnic levels of PIIIP and the age of the patients and the estimated duration of the disease. Regarding the activity indices, the number of patients belonging to each class was not enough to perform a statistical analysis. Notwithstanding, for the TWI in UC patients a significant difference in PIIIP levels was found between mild and severe form of the disease (Table 3). Finally, no significant differences were found in PIIIP levels between patients treated with glucocorticoids compared with patients not receiving this treatment.
Table 3 Baseline p-PIIIP levels in UC patients
TWI Activity Index Mean ± SD
Mild 7.05 ± 2.25
Moderate 3.9 ± 1.11
Severe 3.13 ± 1.57*
* p = 0.02 vs mild
Discussion
CD and UC are chronic pathologies characterized by an early onset followed by sporadic episodes of acute symptoms during lifetime, debilitating the affected patients to perform their daily functions [24]. Until now controversial theories exist about the synthesis and degradation of PIIIP, its level on systemic circulation, and its deposition far for main target organ [12,13,15,25].
In the present study we have found that intestinal collagen metabolism in IBD patients was increased and that it is reflected in local and systemic circulation. Differently from some experiences [12,13], we have found that serum PIIIP levels in IBD patients was significantly higher if compared with healthy subjects. No significant differences were found in peripheral and splanchnic circulation between patients affected by UC and CD. We also found a positive gradient between serum s-PIIIP and p-PIIIP levels in IBD patients. This gradient was confirmed when considering serum s-PIIIP and p-PIIIP in UC and CD separately, even if the differences between the two subgroups were not statistically significant. In our experience no significant differences were found when considering the age of the patients, the duration of the disease, and the activity indices. This fact implies that serum PIIIP should not be considered a long-term marker of the disease, probably reflecting the short-term fluctuation in the activity phases of the remodeling processes.
When comparing the mild with the severe form of the disease, a significant difference in PIIIP levels was found only in patients affected by UC. This data will probably be confirmed when the number of patients enrolled in each disease-related activity categories is extended as presently in our series the majority of the patients were classified as severe.
The effect of glucocorticoids on collagen synthesis, collagenase, and collagen degradation has not yet fully been clarified [25]. In our study the cortisone therapy did not have influence on the PIIIP levels, but the number of patients was too small and it was not possible to speculate on this regard.
Conclusions
In conclusion we provide evidence that collagen metabolism in IBD is reflected in the systemic and local circulation, without any differences between UC and CD, irrespective of the age of the patients and the duration of the disease. Therefore, this marker may give further information on the activity phases rather than on the entire history of the disease. Further data on the possible use of PIIIP as useful marker of choice for surgical option are attended from the follow-up at 6 and 12 months, which is still on-going [26].
List of abbreviations
Inflammatory bowel diseases = IBD
Procollagen III propeptide = PIIIP
Peripheral Procollagen III propeptide = p-PIIIP
Splanchnic Procollagen III propeptide = s-PIIIP
Crohn's disease = CD
Ulcerative Colitis = UC
Crohn's Disease Activity Index = CDAI
Truelove-Witts index = TWI
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
UC conception and design, interpretation of data, drafting the article
MDS conception and design, interpretation of data, drafting the article
ECA performed surgical operations, critical revision of the article, final approval of the version
BO patients' enrollement, blood samples collection
RP statistical analysis, interpretation of data, drafting the article
AP echocardiographic studies
GB radioimmunoassays
EO radioimmunoassays
SF histological examinations
FM interpretation of data, critical revision of the article, final approval of the version
MMC conception and design, interpretation of data, drafting the article
All Authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15527511 | PMC543466 | CC BY | 2021-01-04 16:29:55 | no | BMC Gastroenterol. 2004 Nov 4; 4:29 | utf-8 | BMC Gastroenterol | 2,004 | 10.1186/1471-230X-4-29 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-521557920310.1186/1471-2202-5-52Research ArticleMultiple synaptic and membrane sites of anesthetic action in the CA1 region of rat hippocampal slices Pittson Sky [email protected] Allison M [email protected] M Bruce [email protected] Department of Anesthesia, Stanford University School of Medicine, SUMC S288, Stanford, CA 94305-51172 Medical Student, University of California, San Francisco, CA 941432004 3 12 2004 5 52 52 17 9 2004 3 12 2004 Copyright © 2004 Pittson et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Anesthesia is produced by a depression of central nervous system function, however, the sites and mechanisms of action underlying this depression remain poorly defined. The present study compared and contrasted effects produced by five general anesthetics on synaptic circuitry in the CA1 region of hippocampal slices.
Results
At clinically relevant and equi-effective concentrations, presynaptic and postsynaptic anesthetic actions were evident at glutamate-mediated excitatory synapses and at GABA-mediated inhibitory synapses. In addition, depressant effects on membrane excitability were observed for CA1 neuron discharge in response to direct current depolarization. Combined actions at several of these sites contributed to CA1 circuit depression, but the relative degree of effect at each site was different for each anesthetic studied. For example, most of propofol's depressant effect (> 70 %) was reversed with a GABA antagonist, but only a minor portion of isoflurane's depression was reversed (< 20 %). Differences were also apparent on glutamate synapses-pentobarbital depressed transmission by > 50 %, but thiopental by only < 25 %.
Conclusions
These results, in as much as they may be relevant to anesthesia, indicate that general anesthetics act at several discrete sites, supporting a multi-site, agent specific theory for anesthetic actions. No single effect site (e.g. GABA synapses) or mechanism of action (e.g. depressed membrane excitability) could account for all of the effects produced for any anesthetic studied.
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Background
General anesthetics have been shown to depress neuronal responses in virtually all brain areas studied and this depression has been proposed to result from actions at GABAA-mediated inhibitory synapses and postsynaptic chloride channels [1-4], potassium channels [5-7], or calcium channels [8-11], and/or at glutamate-mediated excitatory synapses [12-17]. The last decade has seen a major shift in our understanding of general anesthetic mechanisms of action, away from a non-specific Unitary theory of action, towards a detailed view of anesthetic actions at membrane receptor and ion channel targets for these agents [18,19]. It is likely that several anesthetic actions occurring at independent sites contribute in additive ways to depress neuronal circuits in higher brain structures. Alternatively, anesthetic effects could result from actions at only a few sites and this should become evident by studying overall effects on the CA1 neural circuit and 'chasing down' the underlying actions. In the present study, the effects produced by five general anesthetics were studied at several possible sites of action within the well characterized Schaffer-collateral to CA1 neuron circuit using electrophysiological recordings from rat hippocampal slices. The CA1 circuit has previously been shown to be depressed by anesthetics from several chemical classes [20-26] at concentrations which alter hippocampal electrical activity in chronically instrumented rats during anesthesia [27-29]. The five agents chosen for this study are all clinically used anesthetics and provide a good representation from unique chemical classes: a halocarbon (halothane), halogenated ether (isoflurane), barbiturate (pentobarbital), sulfonated-barbiturate (thiopental), and a newer di-isopropylphenol compound, propofol.
Results and discussion
Anesthetics enhance GABA-mediated inhibition
All five anesthetics depressed synaptically evoked discharge, measured as a block of population spike (PS) responses recorded from CA1 neurons (Fig. 1). The two volatile anesthetics, halothane and isoflurane, produced a nearly complete depression (to 3.3 ± 3.5 and 5.6 ± 7.1 % of control respectively) at clinically effective concentrations: halothane (1.0 rat MAC; 1.25 vol % ~ 250 μM) and isoflurane (1.0 rat MAC; 1.55 vol % ~ 350 μM; for Sprague-Daley rats [30]; Minimum Alveolar Concentration – the expired anesthetic gas concentration for a 50 % loss of a tail clamp response – motor reflex in rats). The three intravenous agents, pentobarbital (400 μM), thiopental (80 μM) and propofol (30 μM), also depressed PS responses to a comparable degree: 1.7 ± 3.1, 3.4 ± 2.8 and 6.2 ± 5.8 % of control responses, respectively (p < 0.001, n ≥ 5 slices from individual rats, for all five agents compared with pre-anesthetic control responses, using ANOVA-Tukey). All anesthetic effects were reversible on washout of the agent with drug free ACSF. It should be noted that the more lipophilic intravenous anesthetics produce lower effect site concentrations in these brain slices than the applied concentrations shown, especially for these short time periods of application, because it can take several hours for these agents to diffuse 200 to 300 μm into brain slices and achieve steady-state levels [31]. For example, an applied concentration of 30 μM propofol would be expected to produce only ~ 1.0 to 3.0 μM at a recording depth of 250 microns within 30 minutes [32]. The volatile anesthetics, in contrast, rapidly equilibrate throughout the brain slice due to their relatively high aqueous solubility.
These equi-effective applied concentrations for PS depression were used in subsequent experiments to determine whether this depression resulted from enhanced GABAA-mediated inhibition.
A GABAA receptor antagonist, bicuculline, was applied in the continued presence of each anesthetic to attempt to reverse the anesthetic-induced PS depression. Bicuculline (10 μM) reversed anesthetic-induced PS depression to varying degrees for each agent: isoflurane – 16.2 ± 7.4 %, halothane – 22.3 ± 18.4 %, pentobarbital – 56.2 ± 12.4 %, thiopental – 64.9 ± 12.9 % and propofol – 69.5 ± 14.3 %. Similar degrees of reversal were observed using the GABA-chloride channel blocker, picrotoxin (100 μM; a supra-maximal blocking concentration). A GABAB receptor antagonist, CGP 55845A (10 μM) did not reverse PS depression for any of the anesthetics studied (see Table 1). None of the anesthetics produced a significant depression of antidromically stimulated PS responses (± 5 % depression, p > 0.15) indicating that CA1 neuron axonal conduction was not appreciably altered. Thus, enhanced GABAA-mediated inhibition appeared to play a major role for the PS depression produced by propofol and thiopental (~ 75 %), less so for pentobarbital (~ 50 %), and contributed only partially to the depressant effects of the volatile anesthetics (< 25 %; Fig. 1E).
Anesthetics depress glutamate-mediated excitatory synapses
To determine whether anesthetic-induced PS depression resulted from depressed glutamate-mediated excitatory synaptic inputs to CA1 neurons, field excitatory postsynaptic potentials (EPSPs) were recorded from dendritic regions in stratum radiatum. All five anesthetics depressed EPSP responses (e.g. Fig. 1C and 1D): isoflurane to 52.2 ± 7.6 (p < 0.001), halothane 61.3 ± 8.4 (p < 0.001), pentobarbital 54.5 ± 4.8 (p < 0.001), thiopental 75.5 ± 9.8 (p < 0.01) and propofol 72.7 ± 23.5 (p < 0.05) % of control responses. Bicuculline did not reverse volatile anesthetic-induced EPSP depression, but did partially reverse the effect for pentobarbital (11.4 ± 3.6 %) and completely reversed the EPSP depression produced by thiopental and propofol (Fig. 1F). Thus, depressed glutamate-mediated synaptic excitation appeared to play an important role for PS depression produced by isoflurane, halothane and pentobarbital. The thiopental and propofol-induced EPSP depression would also contribute to PS depression for these agents, but appeared to occur via enhanced GABA-mediated inhibition at a dendritic level, since this depression was reversed by bicuculline.
Pre- and postsynaptic sites of action at GABAA synapses
Whole cell voltage clamp recordings from CA1 neurons were used to examine more closely anesthetic effects on membrane currents at GABA synapses. Spontaneous GABA-mediated inhibitory postsynaptic currents (IPSCs) were observed in all CA1 neurons studied (n = 15) and were completely blocked by bicuculline (10 μM; Fig. 2A). In the presence of glutamate receptor antagonists (CNQX 17.2 μM and APV 100 μM) the anesthetics produced agent-specific effects on holding currents needed to clamp neurons at the control resting membrane potentials (-60 to -70 mV). Propofol was most effective at increasing holding currents (376 ± 83 pA, n = 3), followed by thiopental (320 ± 72 pA, n = 4) and pentobarbital (127 ± 65 pA, n = 3). Halothane (n = 6) and isoflurane (n = 3) produced weaker and more variable responses (< 50 pA). The changes in holding currents produced by propofol and the barbiturates were reversed by bicuculline (10 μM) or picrotoxin (100 μM), indicating that they involved activation of GABAA-mediated chloride channels.
The most dramatic effect produced by all five anesthetics was observed on IPSCs (e.g. Fig. 2B). Membrane charge transfer, for example, was increased by 3 to 4 fold in the presence of halothane and came about by at least two separate mechanisms. The first mechanism was a prolongation of IPSC time course (Fig. 2C) resulting in nearly a 3 fold increase in charge transfer for each IPSC (284 ± 33 % of control, p < 0.005, n = 6). This result was in good agreement with previous findings showing that anesthetics prolong IPSCs by increasing the open time of GABA-gated channels in the postsynaptic membrane [33-35]. The second mechanism appeared to involve presynaptic sites, observed as an increase in frequency of IPSCs (143 ± 28 % of control, p < 0.005, n = 6 neurons from separate slices) and occurred with a small, but significant, depression in IPSC amplitudes (92 ± 6 % of control, p < 0.05, n = 6). The anesthetic-induced IPSC frequency increase was also observed in the presence of tetrodotoxin, used to block action potentials (n = 5 for halothane, n = 4 for propofol), indicating a direct action on GABA nerve terminals. This confirms earlier findings that anesthetics can increase IPSC frequency and the release of GABA from nerve terminals [36-39]. This presynaptic effect combines with postsynaptic prolongation of IPSCs to account for the marked increase in membrane charge transfer observed, and would contribute to the anesthetic-induced postsynaptic hyperpolarization of CA1 neurons previously reported [4,40-42]. All of the anesthetics studied increased inhibitory charge transfer and the degree of enhancement corresponded well with the ability of bicuculline to reverse the anesthetic-induced depression of population spike responses (Fig. 1 and Table 1). For halothane and isoflurane, this enhanced inhibitory charge transfer played a relatively minor role in population spike depression compared with their ability to depress glutamate-mediated excitatory inputs to the CA1 neurons.
Anesthetics increase paired-pulse facilitation
To determine whether presynaptic actions also contribute to anesthetic effects at glutamate synapses, paired pulse (120 ms) facilitation of Schaffer-collateral evoked EPSPs were studied. In the presence of either halothane or isoflurane no apparent change in EPSP rise time or decay kinetics were observed (Fig. 3A), contrasting with the marked prolongation of IPSC decay time produced by the anesthetics. Facilitation was increased to nearly 115 % of control and this effect was independent of GABAA-mediated actions, since they persisted in the presence of the antagonist – bicuculline (Fig. 3B). This increase in facilitation is consistent with a presynaptic depression of glutamate release from nerve terminals, perhaps via depressant actions on voltage activated calcium or sodium channels which couple axon spike depolarization to the release of transmitter [8,9,17,43,44].
Anesthetics increase paired-pulse inhibition
Agent-specific effects were observed for paired pulse inhibitory responses (120 ms separation) recorded from CA1 neurons (Fig. 3C). Halothane and isoflurane produced no apparent change in paired pulse responses, both the first and second population spike following a pair of stimuli were depressed to a similar degree by these anesthetics. In contrast, propofol, thiopental and pentobarbital increased paired pulse inhibition, evident in a greater degree of depression for the second of a pair of responses. To quantify these increases in paired pulse inhibition, effects on second pulse responses were compared at concentrations that produced a half maximal depression of first spike responses. At a level of 50 % depression of first pulse responses, pentobarbital produced a 134 ± 8 % increase in second pulse inhibition, thiopental produced a 156 ± 15 % increase and propofol produced a 149 ± 13% increase (p < 0.001, n = 5 for each agent compared to first pulse responses). This effect is consistent with in vivo findings [45] and is thought to reflect a greater degree of GABA-mediated inhibition contributing to the second of a pair of stimuli, via recurrent (feedback) activation of inhibitory interneurons caused by the first pulse [26].
Anesthetics depress CA1 neuron excitability
To determine whether the anesthetics could alter postsynaptic membrane excitability, effects on action potentials evoked by direct current injection into CA1 neurons were studied. Differences in effect were apparent across anesthetic agents – hardly any effect was evident for halothane and isoflurane, but the barbiturates and propofol produced a significant depression of action potential discharge (Fig. 3D). When measured as a reduction in the number of action potentials produced in response to a one second long depolarizing current step, halothane produced an 8.2 ± 2.2 % depression and isoflurane an 11.6 ± 6.1 % depression (p < 0.01 for both agents compared to control responses). Propofol was much more effective at depressing CA1 discharge, producing a 93.5 ± 6.1 % depression (p < 0.001). Thiopental produced a 90.3 ± 9.9 % depression and pentobarbital a 79.5 ± 7.4 % depression (p < 0.001 for both anesthetics compared with control). The anesthetic-induced depression of spike discharge activity was accompanied by decreases in membrane resistance and to a lesser extent by small changes in membrane resting potential. In spite of the marked depressant effects observed for the intravenous anesthetics on spike discharge, none of the anesthetics appeared to alter action potential amplitude, rise time or decay profiles (Fig. 3D), suggesting that the major depressant effect was accounted for by actions on spike threshold – not on the sodium currents which underlie action potentials per se.
Conclusions
Two conclusions can be drawn from these results: 1) for a given anesthetic, like halothane, multiple sites of action contributed in an additive manner to produce an overall depression of transmission through the CA1 neuronal circuitry (Fig. 3E); 2) for each anesthetic the degree of effect was agent specific at some of these sites. Together the results support a Multisite Agent Specific (MAS) mechanism of action for general anesthetics. This represents a departure from traditional Unitary theories of action in several important respects. Unitary theories posit that all anesthetics act via a common molecular mechanism, such as to change the fluidity of nerve cell membranes, or to enhance a potassium current, or most recently to enhance GABA-mediated inhibition [2,3]. With the MAS theory, no common site of action is required (nor apparent) for anesthetics. This is consistent with observations at the molecular, [46-50] cellular [22,51] and behavioral levels [52-55].
Differing degrees of action (efficacy) were evident at both glutamate and GABA synapses for each anesthetic. For example, our results demonstrate that the two barbiturates studied appear to have differing degrees of effect at GABA synapses since thiopental's depressant effects were reversed ~ 65 % by a GABA antagonist, but pentobarbital's effects were only reversed by ~ 55 %. Similarly, these two barbiturates exhibited differing degrees of depression for glutamate-mediated excitatory inputs to the CA1 neurons, pentobarbital produced a 45 % depression in contrast to thiopental with only a 25 % depression. It was interesting that opposite actions were seen at presynaptic sites (GABA release was increased by anesthetics, while glutamate release was depressed) and at postsynaptic sites (GABA-mediated synaptic currents were prolonged, glutamate-mediated currents were not). The MAS theory can readily account for the unique agent-specific profiles of effects observed in various experimental models, and also seen clinically – a long standing weakness of Unitary theories [56]. Finally, the MAS theory predicts that agents which selectively target GABA and glutamate synapses could lead to the design of safer and more effective therapeutic agents for anesthesia, that exhibit fewer undesirable side effects.
Glutamate and GABA synapses in the hippocampus are among the best characterized synapses in the brain and appear to utilize receptor subtypes which are similar to those in neocortex, thalamus and other higher brain regions. Thus, the effects described in the present study would be expected to occur in these other brain regions as well, but it should be noted that different GABA and glutamate receptor subtype distributions are known to occur in cerebellum, spinal and some brain stem nuclei, and it remains to be determined whether anesthetics alter these synapses in a similar manner to their hippocampal counterparts. Ted Eger's group at UCSF has recently found that enhanced GABA-mediated synaptic transmission at the spinal level plays an important role for propofol-induced immobility in response to a noxious stimulus [54], but this was not the case for isoflurane-induced immobility [57]. This agrees well with our findings that the volatile anesthetic-induced depression of synaptic signaling involves mechanisms other than enhanced GABA inhibition (see also [58]), while the depression produced by the barbiturates and propofol are more dependent on enhanced GABA-mediated inhibition. Additional in vivo support comes from studies utilizing a GABA beta 3 receptor mutant mouse model – proprofol-induced anesthesia was blocked in these mice, while volatile anesthetic effects were not [59]. Taken together with these in vivo findings, our results indicate that effects on GABA synapses play a role in anesthetic actions, especially for propofol, thiopental and pentobarbital; but the results also indicate that effects on glutamate synapses and postsynaptic membrane excitability contribute to the CNS depression produced by all anesthetics. Given the multiple effects observed for anesthetic actions on the two types of synapses studied here, it is likely that effects on other neurotransmitter systems also contribute to anesthetic-induced depression of the CNS.
Methods
Male Sprague-Dawley rats were anesthetized with ether (22 vol % in air) and the brain was rapidly removed and placed in ice cold (5°C) and pregassed (95/5 % O2/CO2, carbogen) artificial cerebral spinal fluid (ACSF). The ACSF had the following composition (in mM): Na 151.25; K 2.5; Ca 2.0; Mg 2.0; Cl 131.5; HCO3 26.0; SO4 2.0; H2PO4 1.25; and glucose 10. Whole brain coronal slices (450 μm) were cut using a vibratome (Campden Instruments), following careful removal of the dura and pia membranes. Hemisected brain slices were equilibrated for at least one hour at room temperature in an incubation chamber filled with ACSF and continually bubbled with carbogen. Individual slices were transferred to a recording chamber and equilibrated for an additional 10 minutes prior to electrophysiological recording. Oxygenated ACSF solution was continuously perfused through the chamber at a flow rate of 3.0 ml/min and maintained at 22 ± 1°C. The present studies were carried out at room temperature because synaptic responses recorded from cooler brain slices exhibit considerably better baseline stability and the tissue remains viable for many more hours in vitro compared to slices maintained at physiological temperatures. Room temperature also facilitates the use of submerged preparations (oxygen solubility and delivery to slices is increased), which allows the use of 60× optics to visualize single neurons for the patch clamp recordings used in some experiments. Previous studies comparing both volatile and intravenous anesthetic effects at physiological and cooler temperatures in brain slices found that there were no apparent differences in effects [43,61,66]. The most important effect of lower temperature is to increase the aqueous solubility of the volatile anesthetics and previous work from our laboratory has described in detail the solubility changes observed at 22 vs. 35°C and our methods for measuring and compensating for changed aqueous solubility, as well as the remarkably similar physiological responses recorded from brain slices at these two temperatures [43,66].
To measure population spikes, bipolar tungsten microelectrodes were placed on Schaffer-collateral fibers to electrically stimulate inputs to hippocampal CA1 pyramidal neurons. Glass recording electrodes filled with ACSF (2 to 5 KOhm) were placed in stratum pyramidale to record stimulus-evoked population spike field potentials, or in stratum radiatum to record field EPSPs. Single stimulus pulses (0.01 to 0.05 ms duration; 10 to 80 μA @ 1.0 to 5.0 V) were delivered via constant current isolation units (Grass Instruments, SIU 6D) from a Grass S8800 two channel stimulator; at stimulus rates of 0.05 Hz. Field potential signals were amplified (× 1000), filtered (1 Hz to 10 KHz, bandpass), conditioned (DC offset), and digitally stored for later analysis (A/D with 20 μs resolution on a 486, and 50 MHz microcomputer using Data Wave Systems Corp. or Strathclyde Electrophysiological software).
Whole cell patch-clamp recordings were made using thin-walled borosilicate capillaries (1.5 mm O.D.) pulled in two stages on a Narishige PP83 pipette puller. Patch electrodes were filled with the following intracellular solution (in mM): potassium gluconate or CsCl2 – 100, EGTA – 10, MgCl2 – 5, HEPES free acid – 40, ATP disodium salt – 0.3, and GTP sodium salt -0.3. The electrode solution also contained the local anesthetic QX 314 (1.0 mM) in some experiments, to prevent action potential discharge that would contaminate recordings of IPSCs. Electrode solutions were filtered and pH adjusted to 7.2 using KOH or CsOH and had a final osmolarity of 260 to 270 mOSM. Patch electrodes with a DC resistance of 4 to 5 MOhm were used. Recordings were made using an Axoclamp 2A preamplifier (Axon Instruments) in single electrode voltage clamp mode with > 80 % series resistance compensation and > 5 GOhm seals. Patch-clamp current signals were filtered (0.1 Hz to 10 KHz, bandpass), amplified (× 100) and digitized (10 KHz) for storage and analysis. Frequency and amplitudes of IPSCs were analyzed using Data Wave Technologies and Strathclyde Electrophysiological software in a continuous data recording configuration.
The intravenous anesthetics (propofol and pentobarbital) were made fresh for each experiment, solubilized using 0.5% dimethyl sulfoxide (DMSO) and sonicated immediately prior to test administration in stock solutions and serially diluted into ACSF to achieve the final concentrations for testing. Volatile anesthetics (halothane and isoflurane) were applied in the perfusate at equilibrated concentrations, delivered from calibrated vaporizers and bubbled into the perfusate for at least 10 min prior to switching from control ACSF, to ensure steady-state concentrations were achieved. The concentration of volatile anesthetics in the gas phase were continually measured using a Puritan-Bennett anesthetic monitor. Only a single concentration of a given anesthetic was tested on each brain slice.
Data are expressed as the mean ± standard deviation and statistical analysis (ANOVA with post Tukey test) was performed using Instat from GraphPad Software. For drug effects on paired pulse inhibition, the percent change was first calculated as: Drug 1st/Control 1st = (0.5 × 100) - 100 % = 50 % depression; and Drug 2nd/Control 2nd = (X × 100) - 100 % = X % depression. Then the percent increase in paired pulse inhibition was = (X / 50) × 100 %. This approach has the advantage of normalizing paired responses with respect to varying degrees of population spike facilitation observed during control recordings, i.e. differing degrees of EPSP facilitation on a background of differing degrees of inhibition from preparation to preparation.
Authors' contributions
SP and AMH contributed equally to this study by conducting most of the experimental work and they also contributed to data analysis and helped with figure preparation. MBM conceived of the study and contributed to experimental design, data analysis and interpretation of results; as well as wrote and prepared the manuscript.
Acknowledgments
We thank Dr. Van Doze for technical assistance and Dr. Mark Bieda for help with manuscript preparation. This work was supported by NIH grants GM54767 and GM56308 to MBM.
Figures and Tables
Figure 1 Anesthetic-induced depression of CA1 neuron responses involve actions at both glutamate and GABA-mediated synapses. (A) Halothane depressed population spike (PS) responses at clinically relevant concentrations (1 rat MAC = 1.2 vol % ~ 250 μM) and this depression was only partially reversed using a GABAA receptor antagonist, bicuculline (BIC). (B) Propofol (30 μM) produced a comparable degree of population spike depression compared to halothane, but this depression was substantially reversed with BIC, indicating that enhanced GABA-mediated inhibition contributes > 75 % of the depressant effect of propofol. The representative recordings shown are for propofol effects at 10 minutes following exposure to anesthetic (i.e. at 30 min on the x axis for the grouped data) and after 30 minutes of exposure, when a nearly complete block of the population spike was apparent. (C) Excitatory postsynaptic potentials (EPSP) were also depressed by halothane and this effect was not reversed by BIC, indicating a direct effect of the anesthetic on glutamate-mediated synapses. (D) Propofol-induced depression of glutamate-mediated EPSPs, in contrast, appeared to involve enhanced GABA-mediated inhibition, since this depression was completely reversed by BIC. The two anesthetics exhibited quite different sensitivities to reversal by BIC, indicating that actions at GABA synapses vary for these agents. For each graph, data were normalized and each point represents the mean ± SD for at least five measures from different slices made from separate animals. Horizontal bars indicate the time of exposure to each drug. Sample recordings from representative experiments are shown in the top traces. (E) BIC reversal of anesthetic-induced population spike depression was agent specific for equieffective levels of depression, and data are summarized for four anesthetics as bar graphs. Shaded bars represent the degree of depression produced by each anesthetic and open bars show the extent of reversal produced by BIC, error bars indicate SD for at least five measures from different slices. Volatile agents (isoflurane – ISO, 350 μM; halothane – HAL, 250 μM) were only weakly reversed by BIC, while intravenous agents (pentobarbital – PEN, 400 μM; thiopental – THIO, 80 μM; propofol-PRO, 30 μM) were more sensitive to the GABA receptor antagonist. (F) A similar profile of agent specific effects for BIC reversal was evident for glutamate-mediated EPSP responses, volatile agent effects were poorly reversed while intravenous agents appeared to be more sensitive to the GABA antagonist.
Figure 2 Whole cell patch clamp recordings of spontaneous GABA-mediated inhibitory postsynaptic currents (IPSC) in CA1 neurons revealed two sites of action for anesthetic-induced enhanced inhibition. (A) Spontaneous synaptic currents were blocked by a GABAA-receptor antagonist bicuculline (BIC), indicating that they were GABAA-mediated Cl- currents. Traces on the top show 20 s of continuous recording from a CA1 neuron in control and in the presence of BIC. A rate meter graph (bottom) shows the relatively stable frequency of IPSCs during 20 min of control recording, followed by a rapid and complete block of responses produced by BIC (indicated by the bar). (B) Halothane produced a marked increase of the inhibitory charge transfer in CA1 neurons measured as the integral of current recordings (shaded area in top traces). A three fold increase of inhibitory charge (pico Colombs – pC) was reversibly produced by halothane and appeared to come about through both a prolongation of IPSC time course (C) and from an increased frequency of synaptic currents (D). Both effects persisted in the presence of 10 μM tetrodotoxin (TTX) and/or glutamate receptor antagonists indicating that action potential dependent activity and glutamate synapses were not required for anesthetic action. For the rate meter histograms in (A) and (D), each bin represents the number of events recorded in 4 s divided by 4 to give a frequency in Hz (events/second). For all IPSC recordings a CsCl based internal solution was used in the patch pipette.
Figure 3 Anesthetics act at several sites to depress CA1 neuron synaptically evoked discharge. (A) Halothane appears to act presynaptically to depress glutamate release, evidenced by an increase in paired pulse facilitation concomitant with EPSP depression. A similar increase in facilitation was produced by isoflurane and pentobarbital, but not by thiopental or propofol. No change in EPSP rise or decay time was apparent in the presence any anesthetic. (B) The increased facilitation produced by halothane, isoflurane and pentobarbital was not reversed by bicuculline (BIC) indicating a depressant effect on glutamate nerve terminals – independent of anesthetic effects at GABAA receptors. (C) Differential GABA effects were also evident for paired pulse inhibition of population spike responses. Volatile agents like halothane produced little or no paired pulse inhibition at concentrations that produced a half maximal depression of first pulse responses. In contrast, propofol increased paired pulse inhibition and similar effects were observed with thiopental and pentobarbital. This increase in paired pulse inhibition was reversed by bicuculline indicating that these anesthetics enhanced recurrent GABAA-mediated inhibition. (D) The anesthetics also appeared to act directly on CA1 pyramidal neuron membrane excitability to slow action potential discharge activity, although the intravenous agents were much more effective compared to volatile anesthetics. None of the anesthetics produced an appreciable effect on individual action potential amplitude or time course (right: control – solid line; anesthetic – dotted, for halothane on top and propofol on bottom). (E) Anesthetics act at multiple sites to depress the CA1 neuron circuit. Sites of action are indicated on a diagram of CA1 circuitry showing input from Schaffer-collateral fibers, local inhibitory interneurons (IN) and a CA1 pyramidal neuron (triangle). Action potential propagation in Schaffer-collateral fibers (1) was depressed by ~ 15% by halothane and this contributes about 25 % to EPSP depression [60, 61]; see also [17]. This effect did not contribute to anesthetic-induced increases in facilitation, because no change in facilitation occurred when a comparable amount of action potential depression was produced by tetrodotoxin [61]. Further presynaptic depression at glutamate nerve terminals (2) was evident from the increased facilitation observed (Fig. 3A&3B) and there is also good evidence for postsynaptic depressant effects on both NMDA and AMPA receptors (3) [16, 19, 62, 63]. Anesthetics also act pre- and postsynaptically at GABA-mediated synapses (4, see Fig. 2) and can also increase tonic GABA-mediated inhibition by acting as GABA agonists in the absence of synaptically released GABA (5) [46-48]. Perisynaptic and extrasynaptic tonic GABAA receptors (7) also contribute to the postsynaptic depression produced by isoflurane [64] as well as thiopental and propofol [4, 65]. Enhanced recurrent inhibition (6) plays and important role for anesthetics in vivo [45] and strong effects were evident in the present study for propofol, thiopental and pentobarbital (Fig. 3C), similar to effects previously reported for halothane in hippocampal slices [26]. In addition, anesthetics also directly depress CA1 neuron excitability by blocking calcium channels and enhancing potassium currents contributing to hyperpolarization (8) and increased discharge thresholds [40, 41, 42, also Nishikawa, Beida & Maclver, unpublished]. These latter effects could influence CA1 neuron discharge activity for near threshold responses, but for the stronger stimuli used in the present study, effects on GABA and glutamate synapses and on postsynaptic receptors for these transmitters appear to contribute most (~ 80 %) to the depressant actions observed.
Table 1 GABA antagonist effects on anesthetic-induced depression of population spike responses
Anesthetic Percent reversal of anesthetic-induced depression
Bicuculline Pictrotoxin CGP-GABAB
Propofol 69.5 ± 14.3 % 72.3 ± 8.2 % 3.1 ± 4.1%
Thiopental 64.9 ± 12.9 % 68.3 ± 9.7 % 1.3 ± 3.0%
Pentobarbital 56.2 ± 12.4 % 54.3 ± 11. 5% 0.8 ± 6.3 %
Halothane 22.3 ± 18.4 % 20.8 ± 15.3 % 0.5 ± 3.3 %
Isoflurane 16.2 ± 7.4 % 19.5 ± 10.2 % 3.4 ± 4.8 %
Notes: Bicuculline effects on propofol, thiopental and pentobarbital p < 0.01
Bicuculline effects on halothane and isoflurane p < 0.1
Picrotoxin effects on propofol, thiopental and pentobarbital p < 0.01
Picrotoxin effects on halothane and isoflurane p < 0.1
CGP effects were not significant for any anesthetic studied.
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| 15579203 | PMC543467 | CC BY | 2021-01-04 16:03:46 | no | BMC Neurosci. 2004 Dec 3; 5:52 | utf-8 | BMC Neurosci | 2,004 | 10.1186/1471-2202-5-52 | oa_comm |
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BMC Med EducBMC Medical Education1472-6920BioMed Central London 1472-6920-4-261556938510.1186/1472-6920-4-26Research ArticleStudent responses to the introduction of case-based learning and practical activities into a theoretical obstetrics and gynaecology teaching programme Massonetto Júlio Cesar [email protected] Cláudio [email protected] Paulo Sérgio Ribeiro [email protected] Toledo Sérgio Floriano [email protected] Department of Maternal-infantile Health, Medical Sciences, Centro Universitário Lusíada, Rua Dr. Oswaldo Cruz 179, CEP-11045-101, Santos, Brazil2004 29 11 2004 4 26 26 1 6 2004 29 11 2004 Copyright © 2004 Massonetto et al; licensee BioMed Central Ltd.2004Massonetto et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The fourth-year Obstetrics and Gynaecology course at our institution had previously been taught using theory classes alone. A new teaching model was introduced to provide a better link with professional practice. We wished to evaluate the impact of the introduction of case discussions and other practical activities upon students' perceptions of the learning process.
Methods
Small-group discussions of cases and practical activities were introduced for the teaching of a fourth-year class in 2003 (Group II; 113 students). Comparisons were made with the fourth-year class of 2002 (Group I; 108 students), from before the new programme was introduced. Students were asked to rate their satisfaction with various elements of the teaching programme. Statistical differences in their ratings were analysed using the chi-square and Bonferroni tests.
Results
Group II gave higher ratings to the clarity of theory classes and lecturers' teaching abilities (p < 0.05) and lecturers' punctuality (p < 0.001) than did Group I. Group II had greater belief that the knowledge assessment tests were useful (p < 0.001) and that their understanding of the subject was good (p < 0.001) than did Group I. Group II gave a higher overall rating to the course (p < 0.05) than did Group I. However, there was no difference in the groups' assessments of the use made of the timetabled hours available for the subject or lecturers' concern for students' learning.
Conclusions
Students were very receptive to the new teaching model.
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Background
In Brazil, medical school courses last for six years and demand full-time study. In our school, the curriculum follows the traditional model, and it is divided into the basic cycle (first and second years), clinical cycle (third and fourth years) and pre-intern cycle (fifth and sixth years; full-time outpatient and hospital practice).
The practice of institutional self-evaluation, especially for educational institutions, has become part of the country's recent culture [1]. It has come about as a result of the democratic transition that Brazil went through from 1986 onwards and with the introduction of quality control principles in the past decade. In our school, we introduced an annual subject evaluation programme (SEP) in 2000, for the purposes of self-evaluation. Every year, in the middle of the second semester, all students in each year-group fill out a standard questionnaire that aims to assess each subject according to the following variables: teaching ability, teaching quality, lecturer's punctuality, student's improvement and commitment to each subject, test evaluation, stimulus given to discussion and clinical reasoning, guidance on practical activities, emphasis on the doctor-patient relationship, clinical correlation between the subject taught, and general impression. Each of these items is rated by the students as very weak, weak, regular, good or very good. The annual report consists of the evaluations on each subject, for all the above-mentioned variables, and is handed in to each lecturer in charge and the department representative [2].
The teaching of medicine centred on diseases and hospital care and, consequently, centred on the less prevalent disorders, has arisen as a consequence of the current curricular model. This has proven to be inadequate, inefficient and onerous for Brazil's health care sector. Students' participation in the health system is practically non-existent in the basic cycle of the current medical curriculum. In this light, over the last 15 years or so, there has been a series of movements among institutions, aiming towards changing the Brazilian medical school system [3].
In addition to the implementation of curricular directives of greater efficacy, other measures may stimulate medical students and better prepare them for professional practice. Such invigorating measures may include the linking of basic sciences with all the phases of the professional cycle, scientific initiation programmes that are accessible to all students, support programmes and academic guidance [4].
To adapt our medical sciences course to these new concepts within the Brazilian setting of health care and medical teaching, a committee for discussing and drawing up a new teaching system was set up. The process began in the second semester of 2000 and has gradually involved more and more of the lecturers, students, members of the board of directors and the school's sponsoring foundation.
Thus, in the new model that was proposed, problem-solving techniques would be the main teaching tool [5]. The model was also grounded in the basic principles of adult education: adults have a profound need for self-motivation [6] and must therefore take on an active role in the learning process. Adults are motivated much more to learn because of their own inner needs, such as their drive to succeed and satisfaction in learning in order to reach specific personal objectives, than because of outside factors [7].
Consequently, the content of the fourth-year programme was restructured for the 2003 academic year, with the aim of providing the new teaching tool of clinical case discussions alongside the learning of theory. Students' responses to these changes were assessed in comparison with the 2002 course. Our working hypothesis was that we would be promoting four positive actions: a) integration of theoretical and practical learning from the beginning of the students' contact with the speciality; b) greater consolidation of knowledge of the speciality; c) optimisation of the time that is made available for the speciality in the fourth year of the course; and d) preparation of the staff for the curricular model that would be implemented in the coming years.
Methods
Each year-group of the medical course at Centro Universitário Lusíada (UNILUS) consists of 120 students. The Obstetrics and Gynaecology course is given in the fourth year (4 hours per week, giving a total of 120 hours) and the fifth and sixth years (a total of 925 hours for the pre-intern cycle). Up to and including 2002, the fourth-year course was taught by means of theory classes only, which aimed to cover all normal aspects of Obstetrics and Gynaecology and principal diseases encountered. Subsequently, during the pre-intern cycle, other relevant topics concerning disorders within the speciality were dealt with through visits to patients and seminars prepared by the students under staff supervision. In 2003, small-group discussions of cases and practical activities based on the normal aspects and diagnosis methods of the speciality were introduced for the fourth year. These activities replaced 50% of the theory content (the content relating to obstetrical and gynaecological pathology). The theory content taken from the fourth-year curriculum would be taught during the pre-intern cycle, together with the practical learning.
A large proportion of the staff was mobilised. Each week, during the four hours of teaching, students were divided into two groups. Sixty students attended two theory classes (one topic within Obstetrics and the other within Gynaecology), while the other sixty students were divided into six groups of ten students for discussions of clinical cases or practical activities. The two groups of sixty students alternated throughout the year. It should be stressed that the theory classes were taught by the same lecturers, using the same teaching material, during the two years of the present study.
The study began with a standardised SEP questionnaire that was administered to all fourth-year students in the second semester of the teaching year. This was done on the day of their assessment test, to ensure full attendance. The questionnaire bore the institution's official stamp and consisted of several questions, as described in the introduction, above. The questions used in the present study sought ratings for the use made of the timetabled hours available for the subject, lecturers' concern for students' learning, clarity of theory classes and lecturers' teaching abilities, lecturers' punctuality, quality of the assessment tests, students' learning of the subject and general evaluation of the subject. In the 2003 questionnaire, the variable new methodology – activities in small groups was introduced. Each of these items was rated by the students as very weak , weak, regular, good or very good. Group I consisted of 108 fourth-year students on the medical sciences course who answered the questionnaire in 2002 and group II consisted of 113 fourth-year students on the course in 2003.
The findings were tabulated using Microsoft ® Excel 2002 for later evaluation. For analysis purposes, positive evaluations were considered to be the sum of the good and very good ratings, and negative evaluations the sum of the weak and very weak ratings. For statistical analysis, the variables were represented by absolute (n) and relative (%) frequency, and the difference between them was analysed using the chi-square test (χ2). The significance level adopted was 0.05 (α = 5%), and descriptive levels (p) that were less than this value were considered significant and marked by an asterisk (*). Significant values were also submitted to the Bonferroni test to ratify their statistical value.
Results
Group I consisted of 66 female (61%) and 42 male students (39%), whose average age was 23.1 years, while group II consisted of 67 female (59%) and 46 male students (41%), whose average age was 23.6 years. There was no significant difference between their ages.
In group I, 95 students (88%) gave a positive rating for the use made of the timetabled hours available for the subject and 13 (12%) gave a regular rating for it, whereas 101 students in group II (89%) gave a positive rating and 12 (11%) a regular rating. There was no significant difference in this evaluation between the two groups (Table 1 – item 1). Eighty-five students (79%) in group I considered that the lecturers had great concern for students' learning, and 98 students (87%) in group II also believed this. Thus, there was no statistically significant difference between the groups (Table 1 – item 2).
Table 1 Distribution of course evaluation.
Question Rating Group I 2002 n (%) Group II 2003 n (%) χ2 p
1. Use made of timetabled hours available for the subject 1 95 (88%) 101 (89%)
2 13 (12%) 12 (11%) 0.1106079 > 0.05
3 0 0
2. Lecturers' concern for students' 1 85 (79%) 98 (87%)
learning 2 14 (13%) 9 (8%) 2.4986106 > 0.05
3 9 (8%) 6 (5%)
3. Clarity of theory classes and lecturers' teaching abilities 1 72 (67%) 98 (87%)
2 31 (29%) 12 (11%) 12.765231 < 0.05 *
3 5 (4%) 3 (2 %)
4. Lecturers' punctuality 1 108 (100%) 81 (72%)
2 0 32 (28%) 32.620532 < 0.001 *
3 0 0
5. Quality of knowledge assessment tests 1 38 (35%) 73 (65%)
2 49 (45%) 34 (30%) 21.978341 < 0.001 *
3 21 (20%) 6 (5 %)
6. Students' learning of the subject 1 60 (56%) 97 (86%)
2 46 (42%) 13 (12%) 24.619225 < 0.001 *
3 2 (2 %) 3 (2%)
7. General evaluation of the subject 1 80 (74%) 100 (89%)
2 25 (23%) 10 (9%) 7.6007955 < 0.05 *
3 3 (3 %) 3 (2%)
Ratings: 1 – Good and very good 2 – Regular 3 – Weak and very weak
The clarity of theory classes and lecturers' teaching abilities received a positive evaluation from 72 students in group I (67%) and from 98 students (87%) in group II. This was a statistically significant difference (Table 1 – item 3).
Lecturers' punctuality received a positive evaluation from all 108 students in 2002, but only from 72% (81 students) in 2003, and this was statistically significant (Table 1 – item 4). Only 35% of group I (38 students) gave a positive rating for the quality of the knowledge assessment test, whereas 65% of group II (73 students) gave this a positive rating, which was a significant difference (Table 1 – item 5).
In 2002, 60 students (56%) rated their learning of the subject as good or very good, while in 2003, 97 students (86%) rated it as positive, which was a significant difference (Table 1 – item 6). Finally, the general evaluation of the subject was rated as good or very good by 80 students (74%) in 2002 and by 100 students (89%) in 2003, which was a statistically significant difference (Table 1 – item 7).
The new methodology adopted in 2003 for the Obstetrics and Gynaecology course was considered to be good or very good by 89% of the students, regular by 8% and weak or very weak by 3%.
Discussion
The traditional curriculum model was developed with reference to the Flexner report of 1910 [8]. In this, medical education was considered to be a process of initiation in a science. The teachers' role was to establish what students must learn, to transmit information that was considered relevant, and to evaluate students' capacities to retain and reproduce the information presented. Theory would be dealt with before practice, with the aim of preparing students for the use of theory during students' internship and subsequent professional lives. In this model, medical practice is detached from scientific practice, thereby promoting fragmentation of knowledge and neglect of the psychosocial and cultural aspects of medical activities [9]. This teaching approach has been criticised for the excessive value given to content and for its low efficacy, which brings about the subsequent need for re-qualification. We believe that this "banking concept of education" that Freire [10] refers to is conclusively condemned to history.
On the other hand, the teaching concept of meaningful learning calls for linkage between the roles of universities, health care administrators and social services. It suggests that there should be co-operation in the selection of content, production of knowledge and development of professional competence. In meaningful learning, the teacher is no longer the main source of information, but the facilitator of the teaching-learning process. The teacher's aim is to stimulate the learner to take on an active, critical and reflective attitude in the knowledge building process. The content dealt with must have the potential to be meaningful (functionality and relevance for professional practice), giving value to matters that are pertinent and correlatable with students' cognitive structure. However, the absorption by students of knowledge of the so-called basic subjects in this context presents a great challenge [11].
The curricular directives for medical courses (Report 583/01, of August 7, 2001) from the Brazilian National Education Council (part of the Ministry of Education) give guidance on the changes to be made to the teaching model for courses. They indicate that courses must involve students in practical activities from the outset and promote active integration between health care service users and professionals from the beginning of their instruction, using methodology which reinforces students' active participation in knowledge-building, thereby bridging the gap between academic medical learning and the social needs of Brazilian health care. It is evident that the new curricular directives have used the concepts and logic of problem-based learning as their reference point. They have been based on various American and European curricula that, over the past decade, have been giving emphasis to free time for self-study instead of traditional lectures [12-14]. Thus, more than half of the medical schools in the United States are at present undergoing a process of curricular reform [15], as are a large proportion of the medical schools in the United Kingdom [16,17].
In the "problematization" methodology based on Maguerez's Arch, as presented by Bordenave [18], five phases develop from reality: observation, key points, formulation of theory, putting forward of solution and application to reality (Figure 1). This is an alternative methodology that is appropriate to higher education. It differs significantly from problem-based learning in some points that are summarised in Table 2 (adapted from Berbel, 1998 [19]).
Figure 1 Maguerez's Arch.
Table 2 Main differences between "problematization" and problem-based learning.
"Problematization" Problem-based learning
Observation of reality Problems constructed by the lecturers of subjects in which this methodology is used (subject option) Construction of problems by the lecturers, with complete vertical and horizontal integration (institutional option)
Key points Not defined Defined in the curriculum
Formulation of theory Investigation-guided study Investigation-guided study
Putting forward of solution Done after study Done by students before study, on the basis of previous knowledge
Application to reality (practice) Results must intervene in reality as much as possible Intervention in the social environment is considered to be fundamental
In problem-based learning, the cognitive objectives are all previously established, while in "problematization", total control over the resultant knowledge does not exist. The essence of problem-based learning is that the problems define objective concepts to be learned and non-objective concepts that can be excluded from the learning because they are not relevant to the study in question [16].
Although it may be difficult and scientifically dangerous to compare results from conventional curricula (lecture-based learning) and models like problem-based learning or "problematization" [20-23], this was not our intention. Our only objective was to evaluate a teaching tool that is already well known and make a contribution towards discussions on curricular reform.
The present study does not prove that the "modernised" curriculum is better than the previous one, but it emphasises that the strengths of the "new" curriculum are worthy of more exploration. In our opinion, the perception that a qualitative improvement in students' learning has taken place during the course is the first step towards a more substantial and effective change in the teaching-learning process. In the present study, the intention was to transform a totally theoretical course into a more stimulating and efficient course. In this, concepts acquired during classes would be applied clinically to real cases obtained by the students themselves in the wards. A recent study at Manchester University [16] has shown that changing a conventional course into a new integrated course, using problem-based learning throughout, has significantly improved recently graduated students' perceptions of their preparedness for entering the professional market.
There was no significant difference in students' evaluations of the use made of the time available for the subject between the two groups, because there was already a positive assessment among the 2002 group (Table 1 – item 1). Likewise, students gave positive evaluations regarding their perception of lecturers' concern for their learning. Although there was no significant difference between the groups in relation to this question, there was a mild tendency towards increased positive evaluation among the 2003 group (Table 1 – item 2).
An improvement in the assessment of the course can be seen from item 3 of Table 1 onwards. From 2002 to 2003, there was a significant increase in the positive rating given to clarity and teaching abilities in the classes taught. At first, this seemed odd to us, considering that the teaching material used and the staff who taught the theory classes were identical for the two groups. We concluded that the insertion of clinical cases and practical classes into the traditionally theoretical course was the decisive factor in students' perception that the 2003 lessons had improved. Although the fact that the questionnaire was administered at the time of the final assessment test may have had an influence on the data, the questionnaire was administered on the same occasion for each of the two year-groups.
The decrease in the rating of lecturers' punctuality can be easily explained by the fact that the theory classes were always predictably held in the same place in 2002 (group I), while group II used various locations that were specially booked for them. On some occasions in 2003, unexpected events occurred at the beginning of the activities (Table 1 – item 4).
Assessment tests for Obstetrics and Gynaecology are traditionally considered to be difficult. There was a perception in our school that they did not reflect the overall knowledge of the subject that is required. The tests consist of forty to fifty multiple-choice questions (each with five alternatives presented) and five essay-type questions. The former perception can be seen among the 2002 year-group in item 5 of Table 1, alongside the significant improvement among the 2003 group. This indicates to us that the 2003 year-group studied with greater satisfaction and interest, stimulated by the new process, and that this group consequently made the interpretation that there was greater coherence in the preparation of tests. Nonetheless, the tests did not undergo any substantial change from 2002 to 2003. Despite this improvement in the rating, we are still far from achieving the desired positive evaluation rate for the quality of our tests, and the present study shows us that the tests need to be improved.
One of the most important objectives in a change in the teaching system is to obtain greater course efficiency and increased student learning. Items 6 and 7 of Table 1 show us that, at least with regard to student perceptions, this aim has been achieved. Our assessment is that the change in the teaching system was very stimulating for the development of students' study routines. The holistic concept of modern education directs us towards integrating knowledge, understanding and practice for learners. In this, learning is taken to be an ongoing part of life and not just a preparation for it [24]. In keeping with this view, the medical curriculum needs to drum into students the ethos of self-evaluation [7].
Students responded well to the new method, as shown by the positive rating of 89% given by the 2003 year-group. This provides us with the basis for further advances in this subject in the years to come. It gives the staff the confidence to institute significant changes in the curricular reform that has been under discussion for three years.
Although the staff's level of satisfaction was not an objective of our study, initial observation of this indicates great commitment to the course and, probably, better performance. However, it will only be through future longitudinal studies that we will know whether there has really been greater consolidation of knowledge and course efficiency.
Conclusions
Students were very receptive to the new teaching model in this study. An active role in their learning process seems to be more pleasant and productive than usual method. Thus, active learning methodology should be stimulated on the medical courses throughout the world.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
All authors participated in the study design and application. JCM was the study coordinator. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
We would like to acknowledge professors Dr. Charles Arthur Santos de Oliveira and Dr. Maximiano Tadeu Vila Albers for the helpful statistical analysis work.
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| 15569385 | PMC543468 | CC BY | 2021-01-04 16:30:54 | no | BMC Med Educ. 2004 Nov 29; 4:26 | utf-8 | BMC Med Educ | 2,004 | 10.1186/1472-6920-4-26 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-2-641554118110.1186/1477-7525-2-64ResearchValidity of the AusTOM scales: A comparison of the AusTOMs and EuroQol-5D Unsworth Carolyn A [email protected] Stephen J [email protected] Dianne [email protected] Alison [email protected] Jemma [email protected] Nicholas [email protected] School of Occupational Therapy, La Trobe University, Melbourne Vic 3086, Australia2 School of Public Health, La Trobe University, Melbourne Vic 3086, Australia3 School of Human Communication Sciences, La Trobe University, Melbourne Vic 3086, Australia4 School of Physiotherapy, La Trobe University, Melbourne Vic 3086, Australia2004 13 11 2004 2 64 64 5 7 2004 13 11 2004 Copyright © 2004 Unsworth et al; licensee BioMed Central Ltd.2004Unsworth et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Clinicians require brief outcome measures in their busy daily practice to document global client outcomes. Based on the UK Therapy Outcome Measure, the Australian Therapy Outcome Measures were designed to capture global therapy outcomes of occupational therapy, physiotherapy and speech pathology in the Australian clinical context. The aim of this study was to investigate the construct (convergent) validity of the Australian Therapy Outcome Measures (AusTOMs) by comparing it with the EuroQuol-5D (EQ-5D).
Methods
The research was a prospective, longitudinal cohort study, with data collected over a seven month time period. The study was conducted at a total of 13 metropolitan and rural health-care sites including acute, sub-acute and community facilities. Two-hundred and five clients were asked to score themselves on the EQ-5D, and the same clients were scored by approximately 115 therapists (physiotherapists, speech pathologists and occupational therapists) using the AusTOMs at admission and discharge. Clients were consecutive admissions who agreed to participate in the study. Clients of all diagnoses, aged 18 years and over (a criteria of the EQ-5D), and able to give informed consent were scored on the measures. Spearman rank order correlation coefficients were used to analyze the relationships between scores from the two tools. The clients were scored on the AusTOMs and EQ-5D.
Results
There were many health care areas where correlations were expected and found between scores on the AusTOMs and the EQ-5D.
Conclusion
In the quest to measure the effectiveness of therapy services, managers, health care founders and clinicians are urgently seeking to undertake the first step by identifying tools that can measure therapy outcome. AusTOMs is one tool that can measure global client outcomes following therapy. In this study, it was found that on the whole, the AusTOMs and the EQ-5D measure similar constructs. Hence, although the validity of a tool is never 'proven', this study offers preliminary support for the construct validity of AusTOMs.
outcomesassessment
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Background
The costs of operating public health services in Australia are rapidly rising. Health administrators and practitioners are under pressure to document client outcomes and demonstrate the effectiveness of therapy interventions [1-3]. Increasingly, the allied health professions have come to see the need for quick, easy to use measures that describe the result of interventions in terms of client outcomes, and provide evaluative data for benchmarking between health service providers [4]. An outcome measure is a tool for documenting change in client status following therapist intervention. This involves the therapist administering a standardized measure at two time points (for example, at admission and at discharge) or at designated time points throughout therapy and then calculating how much change has occurred. The effectiveness of a therapy is shown when the therapist is able to demonstrate that the change in client status was attributable to treatment and not to other factors such as spontaneous recovery [3,5]. In response to the need for outcome measures, a study titled Australian Therapy Outcome Measures (AusTOMs) was funded by the Australian Department of Health and Ageing from 2001–2003. The goal of the study was to develop a reliable and valid measure of therapy outcome for the three largest allied health professions in Australia; occupational therapy (OT), physiotherapy (PT) and speech pathology (SP). The AusTOMs was based on the UK Therapy Outcome Measure (TOM) and adapted and developed to suit the current practices of therapists in Australia [6]. Clinicians can use AusTOMs data which show client change over time in a variety of ways. Clinicians can benchmark their service against other similar facilities which may prompt changes in the type or duration of therapy services offered [4]. Tools such as AusTOMs can also be used in research (such as randomised controlled trials) to evaluate the effectiveness of therapy services.
The original TOM was developed for use by speech and language therapists in the UK for therapists to measure client outcomes in a clinical setting [1]. Later, scales were developed to measure the effects of interventions by occupational therapists, physiotherapists, and rehabilitation nurses [7,8]. Both sets of tools were used to provide benchmarks for therapist practice between service providers [4,8-12]. The development of the TOM was considerably influenced by the International Classification of Impairments, Disabilities and Handicaps 1 and 2 (ICIDH 1&2) [13]. The TOM draws on the ICIDH domains and allows therapists to monitor client status over time in relation to Impairment, Disability, and Handicap. In addition, the developers of TOM added a domain to measure therapist perception of client Wellbeing or Distress (now referred to in this article as Wellbeing). The inter-rater reliability for the four domains of the TOM have been reported for Occupational Therapy as .84 for impairment, .85 for disability, .74 for handicap and .58 for Wellbeing. The reliability for physiotherapists was .66 for impairment, .74 for disability, .77 for handicap and .57 for Wellbeing and for Speech Pathologists the reliability was .89 for impairment, .90 for disability, .84 for handicap and .57 for Wellbeing [1,14].
The AusTOMs was designed to measure client therapy outcome separately for occupational therapists, speech pathologists and physiotherapists. Similar to TOM, AusTOMs provides a 'snapshot' rating that is determined by the clinical judgment of the therapist, which broadly reflects the client's status. The development of the scales and content validity of AusTOMs has been published [6], as has preliminary data concerning the reliability of the scales [15]. Attention has now turned to whether the instrument performs in a manner consistent with the theoretically derived hypotheses underpinning the constructs being measured [16]. The purpose of this paper is to continue the process of validating the AusTOMs, by establishing construct (convergent) validity. Construct validity refers in part to the ability of an instrument to measure an abstract concept or construct. Because constructs are not directly observable and are usually multidimensional, it is important to ascertain that the constructs adequately define and represent the variables that the instrument purports to measure [16]. In particular, convergent validity indicates the degree to which two instruments are measuring similar constructs. Therefore, examination of the construct validity of the AusTOM scales concerns whether the scales actually measure the intended underlying construct of global health-related outcomes.
The researchers attempted to find a 'gold standard' tool to investigate the concurrent validity of the AusTOMs. Health-related quality of life (HRQoL) tools have increasingly been used to assess multiple aspects of health-related quality of life in clinical trials [17]. Tools such as the General Sickness Impact Profile (SIP) [18] measures or infers aspects of activity and participation. The Medical Outcomes Study (MOS) Short Form Health Survey (SF-36) [19] and the Nottingham Health Profile (NHP) [20] measure or infers aspects of impairment, activity and participation. The widely used Functional Independence Measure [21] records the single health domain of activity limitation. However, no tools could be found that measure all four health domains as provided by the AusTOMs, and the tools that were reviewed required too much therapist administration time to be included in the present study. Since there is no gold standard global health status and therapist administered tools with which to compare AusTOMs, it was decided to compare the constructs of AusTOMs with those of EuroQoL-5D (EQ-5D) [22] in order to investigate the convergent validity of the tool [16]. The EQ-5D was chosen for this study since it is widely used in European [22] and Australian studies [23,24], has been used in number of clinical trials [17], is simple and quick to use [25] and similar to the AusTOM, purports to measure global health-related outcomes. However, the potential advantage of using the AusTOMs over the EQ-5D is that while the EQ-5D measures health related outcomes globally, the AusTOMS measures global outcomes in relation to the four specific domains of impairment, activity limitation, participation restriction and wellbeing/ distress. EQ-5D is a short and simple to administer generic HRQoL measure of health status [25]. EQ-5D provides a simple descriptive profile of client problems on five dimensions, an overall score for client self-rated health, and generates a single index value that can be used in the clinical and economic evaluation of health care and in population health surveys [17]. EQ-5D was initially developed in Dutch, English, Finnish, Norwegian and Swedish and is now available in 42 official translations and adaptations [22].
While in principle, health professionals support the notion of measuring health status, there is no consensus regarding the method of measurement [26,27]. While the AusTOMs is rated by therapists, the EQ-5D is rated by the client's themselves. This may be viewed as the main limitation in selecting the the EQ-5D for comparison with the AusTOMS. Nonetheless, it was expected that scores on the AusTOMs scale would vary in relation to scores generated on the EQ-5D since both seek to measure global health-related outcomes. Some researchers prefer the objectivity offered by therapist ratings from observation of client performance [28]. Others support client self-report [29,30] as an accurate reflection of the client's perception of their status, which is becoming increasingly important in consumer-driven heath services. Self -report tools are also considerably cheaper than therapist administered ones, hence, self-report assessments are typically used in a climate requiring cost containment [31]. However, it is also becoming increasingly clear that therapist and client ratings of client performance may not be related [27,32]. In view of the lack of therapist-administered tool suitable to validate the AusTOMs against, and given the time administration advantages of the use of the EQ-5D which were significant to the success of this research program, the EQ-5D was selected for inclusion in the present study.
The purpose of this study was to examine the measurement properties of the AusTOMs and to compare them with the EQ-5D in 'real practice'. The main question being; does AusTOMs perform in a similar manner to the EQ-5D? The study sought to investigate the following hypotheses:
1. There will be a clear pattern of correlations for the admission, discharge and change scores between the AusTOMs domains and the EQ-5D Health Status and Thermometer. Several scale-specific correlations are expected. For example :
a. There will be a moderate negative correlation of the admission, discharge and change scores between the PT AusTOMs Scale 'Pain', Impairment domain and the EQ-5D Health Status Subscale 'Pain'.
b. There will be a moderate negative correlation of the admission, discharge and change scores between OT AusTOMs Scale 'Functional Mobility and Walking', Activity Limitation Domain and the EQ-5D Health Status Subscale 'Mobility'.
c. There will be a moderate negative correlation of the admission, discharge and change scores between OT AusTOMs Scale 'Self-care', Activity Limitation domain, and the EQ-5D Health Status Subscale 'Self-care'.
2. There will be a moderate positive correlation of the admission, discharge and change scores between all the Physiotherapy, Occupational Therapy and Speech Pathology AusTOMs Scales for the Wellbeing /Distress scores and the EQ-5D Thermometer.
Methods
The research was designed as a prospective, longitudinal cohort study, with data collected over a seven month time period.
Participants
Thirty-eight occupational therapists, 30 physiotherapists and 47 speech pathologists were trained at 13 participating facilities to collect AusTOMs data, and to present the EQ-5D for clients to complete. However, it is possible that not all these therapists collected data (data collection forms did not require therapists to record their identity). The facilities included acute hospitals, rehabilitation hospitals, and community care facilities. Therapists recorded AusTOMs data and obtained client EQ-5D ratings from 205 clients (110 from Physiotherapy, 67 from Occupational Therapy and 28 from Speech Pathology). These clients were from a larger group of 1007 clients who participated in the study (284 from Physiotherapy, 466 from Occupational Therapy and 257 from Speech Pathology). While some of these participants refused to complete the EQ-5D, or the therapists chose not to burden the client with completing this form, many were children or non-cognizant adults and the EQ-5D is not validated for these groups. Otherwise, the sample was sequential admissions to therapist caseloads over a seven month period.
Instruments
AusTOMs is comprised of three separate sets of scales for Occupational Therapy (12 scales), Speech Pathology (6 scales) and Physiotherapy (9 scales). The title of each scale is provided in Table 1.
Table 1 AusTOMs scales for occupational therapists, speech pathologists and physiotherapists
Scale Occupational Therapy Speech Pathology Physiotherapy
1 Learning & Applying Knowledge Speech Balance & Postural Control
2 Functional Walking & Mobility Cognitive-Communication Cardiovascular System Related Functions
3 Upper Limb Use Language Musculoskeletal Movement Related Functions
4 Carrying Out Daily Life Tasks & Routines Voice Neurological Movement related Functions
5 Transfers, Swallowing Pain
6 Using Transport Fluency Respiratory Related Functions
7 Self-care Sensory functions
8 Domestic Life – Home Skin functions
9 Domestic Life – Managing Resources Urinary and bowel continence
10 Interpersonal Interactions & Relationships
11 Work, employment and Education and Community Life
12 Recreation, Leisure and Play.
Each scale requires a rating for four domains of client function, that is, Impairment, Activity Limitation, Participation Restriction and Wellbeing/Distress. An additional optional rating can be made of a caregiver's level of Wellbeing/Distress if the clinician has had contact with a caregiver, and feels that therapy is directed toward the caregiver in some way. Each of the domains are rated by therapists on an 11-point ordinal scale (6 defined points from 0 [most severe] to 5 [normal], and 5 undefined half points). Although clinicians are only required to use the 6 defined scale points, clinicians overwhelmingly chose to include the half points in the AusTOM scoring system to increase scale sensitivity. The use of the half points also facilitates international benchmarking of data against the UK TOM. A generic description of each of the domains of client function is presented in Table 2. Three of the AusTOM's four domains were drawn from the World Health Organisation (WHO)'s International Classification of Function (ICF) [33]. Based on TOM, the AusTOMs were developed by focus groups of expert clinicians in the state of Victoria in Australia who determined both the scale headings, and scalar descriptions for each of the 6 levels for each of the four domains. These scales were then sent out to clinicians across Australia for further refinement. More information on scale development was reported in an earlier publication [6]. In addition, a publication in press [15] reports the reliability of the AusTOM's domains for the majority of scales as ranging from 60–100% agreement, within .5 scalar points for most domains.
Table 2 Generic AusTOMs scales (Perry et al, 2004)
Impairment of either Structure or Function (as appropriate to age):
Impairments are problems in body structure (anatomical) or function (physiological) as a significant deviation or loss.
0 The most severe presentation of impairment (either structure or function)
1 Severe presentation of this impairment
2 Moderate/severe presentation
3 Moderate presentation
4 Mild presentation
5 No impairment of structure or function
Activity Limitations (as appropriate to age):
Activity limitation results from the difficulty in the performanceof an activity. Activity is the execution of a task by the individual.
0 Complete difficulty
1 Severe difficulty
2 Moderate/severe difficulty
3 Moderate difficulty
4 Mild difficulty
5 No difficulty
Participation Restrictions (as appropriate to age):
Participation restrictions are difficulties the individual may have in the manner or extent of involvement in their life situation. Clinicians should ask themselves: "given their problem, is this individual experiencing disadvantage?"
0 Unable to fulfill social, work, educational or family roles. No social integration. No involvement in decision-making. No control over environment. Unable to reach potential in any situation.
1 Severe difficulties in fulfilling social, work, educational or family roles. Very limited social integration. Very limited involvement in decision-making. Very little control over environment. Can only rarely reach potential with maximum assistance.
2 Moderately severe difficulties in fulfilling social, work, educational or family roles. Limited social integration. Limited involvement in decision-making. Control over environment in one setting only. Usually reaches potential with maximum assistance.
3 Moderate difficulties in fulfilling social, work, educational or family roles. Relies on moderate assistance for social integration. Limited involvement in decision-making. Control over environment in more than one setting. Always reaches potential with maximum assistance and sometimes reaches potential without assistance.
4 Mild difficulties in fulfilling social, work, educational or family roles. Needs little assistance for social integration and decision-making. Control over environment in more than one setting. Reaches potential with little assistance.
5 No difficulties in fulfilling social, work, educational or family roles. No assistance required for social integration or decision-making. Control over environment in all settings. Reaches potential with no assistance.
Wellbeing/Distress (as appropriate to age):
The level of concern experienced by the individual. Concern may be evidenced by anxiety, anger, frustration etc.
0. High and consistent levels of distress or concern.
1. Severe concern, becomes distressed or concerned easily. Requires constant reassurance. Loses emotional control easily.
2. Moderately severe concern. Frequent emotional encouragement and reassurance required.
3. Moderate concern. May be able to manage emotions at times, although may require some encouragement.
4. Mild concern. Able to manage emotions in most situations. Occasional emotional support or encouragement needed.
5. Able to cope with most situations. Accepts and understands own limitations.
The EQ-5D consists of two parts; the self -classifier or questionnaire, and the EQ-Vas or Thermometer. The EQ-5D self-classifier is a one-page questionnaire, which captures respondent descriptions of health problems on a 5-dimensional classification of mobility, self-care, usual activities, pain and discomfort and anxiety and depression. Each dimension is rated by respondents on a three-level scale from 1 (no problem) to 3 (unable or extreme problem) [22]. The EQ-Vas is a 20-centimeter visual analogue scale, portrayed as similar to a thermometer, on which the respondent rates his/her health state today between 0 (worst imaginable) to 100 (best imaginable). Overall, respondent's health status is either expressed as a score on the visual analogue scale (EQ-Vas), as a profile of their scores on each of the five dimensions (self-classifier), or by combining the scores on the five dimensions. This research utilised the combined scores from the 5 dimensions. The combined dimensions describe 243 theoretically possible health states, that can be converted into a weighted health index score (EQ-Index) for use in cost-effective analysis [26]. The EQ-5D has been shown to be both reliable and valid when used with adult clients with a wide variety of health-related conditions [17,22,25,26].
Procedure
Approval from the Human Ethics Committee at La Trobe University and the participating facilities was obtained. Study packs were collated for the collection of data. Each pack contained AusTOMs Scale Manual, AusTOMs and EQ-5D data collection forms, informed consent information and consent forms (if these were required by the facility ethics committee). The packs were sent to a contact person in occupational therapy, speech pathology and physiotherapy departments at each site participating in the project. The role of the contact at each site was to receive the packs, disseminate the packs to therapists, check the packs after completion and return them by postage paid envelope.
On admission, the therapists (who had each been previously trained in the use of the scales) briefed each client about the study and after verbal agreement, clients were given a statement of informed consent to read and sign. Clinicians then recorded relevant demographic information and established with the client a specific goal or set of goals for the first episode of care. The therapist then chose the AusTOMs scale/s that best described the main areas targeted for therapy intervention. An admission rating was made by the therapist for each of the four domains of AusTOMs (impairment, activity limitation, participation and wellbeing/distress) on a scale from 0 (most severe) to 5 (least severe). A rating for Wellbeing/ distress was also made for the client's carer if this was applicable to the client's situation. Therapists report that the AusTOMs takes approximately 5 minutes to complete. The therapist then asked the client to complete the self -classifier section of the EQ-5D and the EQ-Vas (Thermometer). Clients were instructed to indicate which statements best described their own health state today, by placing a tick in one box for each of the dimension of mobility, personal care, usual activities, pain/discomfort and anxiety depression. Finally, clients completed the EQ-Vas. Information on the form stated, 'to help people say how good or bad a health state is, we have drawn a scale (rather like a thermometer) on which the best state you can imagine is marked 100 and the worst state you can imagine is marked 0. We would like you to indicate on this scale how good or bad your own health is today, in your opinion. Please do this by drawing a line from the box below to whichever point on the scale indicates how good or bad your health state is today' [22]. Clients completed the EQ-5D in approximately 5 to 20 minutes. The therapist rating for AusTOMs was repeated at client discharge, and clients were asked to again complete both sections of the EQ-5D.
Data Analysis
The data were analyzed separately for each profession given the differences in the AusTOMs scales. Correlational analyses were performed to investigate the relationship between AusTOMs and EQ-5D. Given the ordinal nature of the scales, a non-parametric approach was adopted, hence all analyses use Spearman's rank-order correlation coefficients (Spearman's Rho). Given the number of correlations performed, alpha (to determine statistical significance) was set at .01, and magnitude of the relationship was considered using the guidelines from Colton [34] where .00 – .25 = little or no relationship, .25 – .50 = a weak to fair relationship, .50 – .75 moderate to good relationship and .76 and above considered good to excellent. In this paper, only relationships that are .5 – .75 (moderate to good), and .76 and above (good to excellent) are reported. In addition, only expected correlations are reported. The optional AusTOMs domain of 'Caregiver Wellbeing' was not included in the analyses since the sample sizes were generally too small to enable computations. Analyses were undertaken across the scales for each profession, and since sample sizes permitted, for the physiotherapy scales: Balance and Postural Control, Musculoskeletal and Neurological, and for the occupational therapy scales: Functional Walking and Mobility, Upper Limb Use, and Self-care.
Sample sizes were not sufficient to enable individual scale analysis for speech pathology scales. The analyses were conducted using only the first scaled selected by the therapist to rate the client. It is also important to note the directions of relationships reported. The EQ-5D Health Status subscales are 1 = no problem -> 3 = unable or extreme problem and the AusTOMs scores are 5 = Normal -> 0 = unable or extreme problem, hence, we expect to see negative correlations. However, the EQ-5D Thermometer scores 0 as the worst state and 100 as the best state and the overall EQ-5D Health Status self classifier score also indicates a better outcome as the score increases, and the AusTOMs scores are 5 = Normal -> 0 = unable or extreme problem. Hence, we expect to see positive correlations between these scores. In the 'Results' the statement is made that the results are in the 'expected direction'.
In line with the research aims and hypotheses, the following analyses were undertaken across each profession's data set. First, a correlation considering all the AusTOMs scales for each domain with EQ-5D Health Status (self classifier score and the 5 dimensions) and Thermometer at admission was performed. Next, AusTOMs scores for each domain for a subset of the most frequently used OT and PT scales with EQ-5D Health Status (self classifier score and the 5 dimensions) and Thermometer at admission were obtained. Then, considering all the AusTOMs scales for each domain were correlated with EQ-5D Health Status (self classifier score and the 5 dimensions) and Thermometer at discharge. Following this, AusTOMs scores for each domain for a subset of the most frequently used occupational therapy and physiotherapy scales were correlated with EQ-5D Health Status (self classifier score and the 5 dimensions) and the Thermometer at discharge. Finally, correlations were obtained for change from admission to discharge scores for AusTOMs (considering all the scales overall and for individual scales) with change from admission to discharge scores for EQ-5D Health Status (self classifier score and the 5 dimensions) and the Thermometer.
Results
A brief summary of demographic data from the sample is provided in Table 3. The results are presented in relation to the five analyses performed with the data set from each profession. The moderate to good, statistically significant correlations are reported in Table 4 (physiotherapy), Table 5 (occupational therapy), and Table 6 (speech pathology). Rather than present all correlations, only those that would be theoretically expected are presented. In Tables 4, 5, 6, an asterisk is also marked where correlations were expected that were not found, and the sample sizes the analyses were performed on are included since in many cases there is an inadequate sample to detect a relationship.
Table 3 Summary of client demographic data
Variable Occupational Therapy Clients (n = 67) Speech Pathology Clients (n = 28) Physiotherapy Clients (n = 110)
Mean Age 67.24 (SD 16.65) 64.44 (SD 13.43) 65.44 (SD 20.84)
SEX
No. Females 41 (61.2%) 11 (39.3.1%) 67 (60.9%)
No. Males 25 (37.3%) 16 (57.1%) 42 (38.2%)
Missing 1 (1.5%) 1 (3.6%) 1 (0.9%)
3 most frequently recorded aetiologies Acquired neurological 24 (35.8%)
Orthopaedic 12 (17.9%)
Spinal 6 (9%) Acquired neurological 14 (50%)
Oncology 7 (25%)
Neurosurgery 3 (10.7%) Orthopaedic 44 (40%)
Acquired neurological 19 (17.3%)
Spinal 9 (8.2%)
Musculoskeletal 9 (8.2%)
3 most frequently recorded disorders Inadequate muscle power 16 (23.9%)
Decreased general mobility 11 (16.4%)
Multifactorial 11 (16.4%)
Pain 9 (13.4%) Dysphagia (feeding) 9 (32.1%)
Acquired language disorder 5 (17.9%)
Disorders of voice 5 (17.9%)
Dysarthria 4 (14.3%)
Cognitive impairment 4 (14.3%) Abnormal joint mobility 29 (26.4%)
Decreased general mobility 24 (21.8%)
Inadequate muscle power 15 (13.6%)
SETTING
No. inpatient 44 (65.7%) 17 (60.7%) 78 (70.9%)
No. outpatient 21 (31.3%) 8 (28.6%) 32 (29.1%)
Missing 2 (3.0%) 3 (10.7%)
SERVICE TYPE
Acute 7 (10.4%) 1 (3.6%) 17 (15.5%)
Subacute 49 (73.1%) 24 (85.7%) 68 (61.8%)
Community 9 (13.4%) 2 (7.1%) 15 (13.6%)
Home 0 (0%) 0 (0%) 10 (9.1%)
Missing 2 (3.0%) 1 (3.6%)
Mean No. of occasions of service 9.05 (SD7.50) 23.28 (SD40.97) 12.36 (SD11.84)
Table 4 Summary of Physiotherapy Results: Moderate to strong, statistically significant Spearman's Rho correlations between AusTOMs and EQ-5D
EQ-5D Therm. EQ-5D Health status EQ-5D Mobility Subscale EQ-5D Self-care Subscale EQ-5D Usual activities subscale EQ-5D Pain/ Discom-fort EQ-5D Anxiety/ depression
AusTOM Over all Impairment
AusTOM Over all Activity Limitation * *
AusTOM Over all Participation
AusTOM Over all Wellbeing/ Distress 0.508
0.537 *
AusTOM n = 16 Balance & Pos control Impairment -0.691
-0.677
AusTOM Balance & Pos control Activity Limitation * *
AusTOM Balance & Pos control Participation
AusTOM Balance & Pos control Wellbeing/ Distress 0.655 -0.739
AusTOM n = 66 Musculoskeletal Impairment
AusTOM Musculoskeletal Activity Limitation -0.546 *
AusTOM Musculoskeletal Participation
AusTOM Musculoskeletal Wellbeing/ Distress 0.597
0.614 -0.539
AusTOM n = 18 Neurological Impairment
AusTOM Neurological Activity Limitation -0.801
-0.746 *
AusTOM Neurological Participation
AusTOM Neurological Wellbeing/ Distress 0.770 *
Key: Admission correlation coefficients in normal font
Discharge correlation coefficients in bold font
Change from admission to discharge correlation coefficients in italic
Correlations expected but not obtained marked with *
Table 5 Summary of Occupational Therapy Results: Moderate to strong, statistically significant Spearman's Rho correlations between AusTOMs and EQ-5D
EQ-5D Therm. EQ-5D Health status EQ-5D Mobility Subscale EQ-5D Self-care Subscale EQ-5D Usual activities subscale EQ-5D Pain/ Discom-fort EQ-5D Anxiety/ depression
AusTOM Over all Impairment
AusTOM Over all Activity Limitation * *
AusTOM Over all Participation
AusTOM Over all Wellbeing/ Distress * -0.612
AusTOM n = 13 Walk & Mobility Impairment
AusTOM Walk & Mobility Activity Limitation * *
AusTOM Walk & Mobility Participation
AusTOM Walk & Mobility Wellbeing/ Distress * *
AusTOM n = 18 Upper limb use Impairment
AusTOM Upper limb use Activity Limitation 0.707 *
AusTOM Upper limb use Participation
AusTOM Upper limb use Wellbeing/ Distress
AusTOM n = 16 Self-care Impairment
AusTOM Self-care Activity Limitation 0.748 -0.645
-0.623
-0.683
AusTOM Self-care Participation
AusTOM Self-care Wellbeing/ Distress * *
Key: Admission correlation coefficients in normal font
Discharge correlation coefficients in bold font
Change from admission to discharge correlation coefficients in italic
Correlations expected but not obtained marked with *
Table 6 Summary of Speech Pathology Results: Moderate to strong, statistically significant Spearman's Rho correlations between AusTOMs and EQ-5D
EQ-5D Therm. EQ-5D Health status EQ-5D Mobility Subscale EQ-5D Self-care Subscale EQ-5D Usual activities subscale EQ-5D Pain/ Discom-fort EQ-5D Anxiety/ depression
AusTOM Over all Impairment
AusTOM Over all Activity Limitation * *
AusTOM Over all Participation
AusTOM Over all Wellbeing/ Distress * *
Key: Admission correlation coefficients in normal font
Discharge correlation coefficients in bold font
Change from admission to discharge correlation coefficients in italic
Correlations expected but not obtained marked with *
Over all AusTOMs scales for each domain with EQ-5D Health Status (self-classifier score and the 5 dimensions) correlated with the Thermometer at admission (in other words, over all AusTOMs scales for each domain correlated with the EQ-5D Thermometer at admission).
These results are reported in the first 4 rows of Tables 4, 5, 6, normal font. The correlations found, that were expected are all in the expected direction.
AusTOMs scores for each domain for a subset of the most frequently used OT and PT scales with EQ-5D Health Status (self-classifier score and the 5 dimensions) correlated with the Thermometer at admission.
Several moderate to strong correlations were found that were expected and these are presented in Table 4, rows 5–16 for physiotherapy, and Table 5, rows 5–16 for occupational therapy, all in normal font. Again, all correlations were in the expected direction.
Over all AusTOMs scales for each domain with EQ-5D Health Status (self-classifier score and the 5 dimensions) correlated with the Thermometer at discharge.
In relation to this correlation, the results are reported in the first 4 rows of Tables 4, 5, 6, bold font. The correlations found (that were expected) for physiotherapy and occupational therapy were all in the expected direction.
AusTOMs scores for each domain for a subset of the most frequently used OT and PT scales (only) with EQ-5D Health Status (self-classifier score and the 5 dimensions) correlated with the Thermometer at discharge.
The correlations expected that were found are presented in Table 4, rows 5–16 for physiotherapy, and Table 5, rows 5–16 for occupational therapy, all in bold font. Again, all correlations were in the expected direction.
Change from admission to discharge scores for AusTOMs (overall and for individual scales) with change from admission to discharge scores for EQ-5D Health Status (self-classifier score and the 5 dimensions) correlated with the Thermometer.
The moderate to good, statistically significant correlations expected between change on the EQ-5D and AusTOMs overall, or in relation to the six AusTOMs scales where sample size permitted are presented as follows: for physiotherapy (see Table 4, rows 1–16, italic font), occupational therapy (see Table 5, rows 1–16, italic font), and speech pathology (see Table 6, rows 1–4, italic font).
Discussion
There was some support for the first hypothesis; '...There will be a clear pattern of correlations for the admission, discharge and change scores between the AusTOMs domains and the EQ-5D Health Status and Thermometer (except in relation to the EQ-5D Thermometer and the AusTOMs Wellbeing/ Distress domain as presented in the final hypothesis)'. There were several areas where relationships between constructs measured on AusTOMs and EQ-5D were expected (as described below), and it generally appeared that these two tools are measuring similar constructs. This lends some support to the construct (convergent) validity of AusTOMs. However, not all expected correlations were found and while AusTOMs seems to be measuring global change from the therapist's perspective in relation to four distinct domains (Impairment, Activity Limitation, Participation Restriction and Wellbeing), EQ-5D (as expected), is measuring client perceptions of how they feel about their health status. Hence, while both assessments attempt to capture global health-related outcomes, the differing perceptions of the raters (clinicians versus clients) does seem to impact on the establishment of construct validity. Suggestions for overcoming this problem are described below.
The next sub-hypotheses dealt with specific correlations that were expected in these data. Unfortunately, there were insufficient data to determine if a moderate negative correlation between admission, discharge and change scores between the PT AusTOMs Scale 'Pain', Impairment domain and the EQ-5D Health Status Subscale 'Pain' existed. Similarly, there were insufficient data (n = 13) to explore the hypothesis that '...there will be a moderate negative correlation between admission, discharge and change scores between the OT AusTOMs Scale 'Functional Mobility and Walking', Activity Limitation Domain and the EQ-5D Health Status Subscale Mobility'. The final scale-specific hypothesis predicted a moderate negative correlation between admission, discharge and change scores between the OT AusTOMs Scale 'Self-care', Activity Limitation domain and the EQ-5D Health Status Subscale Self-care. This hypothesis was supported. The results indicate that therapist and client perceptions of client self-care ability status on admission, discharge (and in relation to the change scores) were moderately correlated. Since many occupational therapists spend considerable time working on self-care with clients, and talking about progress in this area, it is reasonable that clients and therapists would rate client status in this area in a similar manner.
Finally, it was hypothesised that there would be a moderate positive correlation between admission, discharge and change scores across all the PT, OT and SP AusTOMs Scales for the Wellbeing domains and the EQ-5D Thermometer. There was only limited support for this hypothesis. However, in some cases the sample sizes were on the small side. There were no moderate, statistically significant correlations when analyzing across all combined OT and SP AusTOMs Scales for the Wellbeing domains and the EQ-5D Thermometer. However, there were moderate to good correlations at both admission and at discharge across all PT AusTOMs Scales for the Wellbeing domains and the EQ-5D Thermometer.
Study limitations and directions for further research
Given the number of correlations performed for this study, it is important not to over-interpret the relatively small number of moderate and good correlations found. When considering these findings it is also important to note the relatively small sample size since a larger EQ-5D data set may have produced more, significant correlations. The low EQ-5D return rate from speech pathology is not surprising considering that clients seen by speech pathologists often have communication/ cognitive difficulties, and this increases the difficulty in using a self-administered tool such as the EQ-5D. Clinicians also reported that it was difficult to ask quite acutely unwell clients to complete the EQ-5D although they were able to score the client using the AusTOMs.
The validity of a tool is never confirmed. Rather, many studies are required over time to demonstrate that a tool is operating in the manner which developers intended. Future validity studies could investigate the ability of AusTOMs to predict client discharge data from admission status, and to determine the capacity of the tool to discriminate between clients with differing severity levels of impairments and activity limitations. This has already been reported for the physiotherapy profession in relation to the UK TOM [4]. In addition, it would be interesting to compare therapist ratings of clients on the EQ-5D with client ratings on this tool. Such research would provide greater insights to the issue of how similar client and therapist views of clients' health status are. Future validity studies could also compare client data from the AusTOMs with data from other global measures such as the Medical Outcomes Study (MOS) Short Form Health Survey (SF-36) [19] or the Nottingham Health Profile (NHP) [20] measure.
Conclusion
The EQ-5D is used extensively in cost effectiveness analysis [22]. It is based on client's self report and is thus consistent with the theoretical basis of economic evaluation as the summation of individual utilities. In contrast, AusTOMs are based in therapists' assessment of clinical progress. In the introduction, it was stated that it might be expected that client scores for these two assessments could be different. However, this study revealed that client and therapist assessment appear to be somewhat similar on some domains, thus lending some support for the construct (convergent) validity of AusTOMs. Yet the fact that more, stronger correlations were not found helps to explain some of the differences in perceptions between policy makers, clients, and therapists. Therapists see a range of clients with a given condition and because of their training and experience, have an understanding of what might be achievable in therapy. Clients, on the other hand, make their assessment based on their own experiences and expectations. The differences between client and therapist expectations could perhaps be minimised with better and clearer communication between client and therapist, although neither party in that dyad may be able to accept the inherent limitations of the rehabilitation process. Nonetheless, client perceptions of the success of therapy are vitally important, and more research is required to investigate reasons for the different perceptions of 'therapy success' of these two groups.
The use of different tools across different disciplines to measure improvement can lead to different conclusions about benefits. If outcome measures of cost effectiveness are based on client perceptions it could well be the case that therapy interventions which are seen by therapists to lead to statistically significant improvements in outcome may not be so valued by clients. As a result, those interventions may not be found to be cost effective in an economic sense, if such an evaluation is based on measures of client perception, such as EQ-5D. These differences in perceptions may then contribute to conflict between policy makers, therapists and clients. Alternative economic measures of client outcome, such as return to work, may not be suitable in environments where a significant proportion of clients are beyond working age.
Although the tools appear to be measuring somewhat similar constructs, the results of this study suggest that therapy outcome measures such as AusTOMs may need to be supplemented by client-based measures. As part of the treatment process, differences between responses should be discussed to improve understanding between client and therapist about expectations and achievable outcomes from therapy. This may in turn assist goal setting for the therapy process.
Authors' contributions
CU and SD have made substantial contributions to conception and design of the study, analysis and interpretation of data and have been involved in drafting the article and revising it critically for important intellectual content. DD has made substantial contributions in the acquisition of data, and has been involved in drafting the article and revising it critically for important intellectual content. APand JS have made substantial contributions to conception and design of the study, acquisition of data, and have been involved in revising the article critically for important intellectual content. NT has made substantial contributions in the acquisition of data and has been involved in revising the article critically for important intellectual content. All authors have given final approval of the version to be published.
Acknowledgements
La Trobe University acknowledges that this publication draws heavily upon the "AusTOMs: Australian Therapy Outcome Measures project" commissioned by 'the Australian Government Department of Health and Ageing', Canberra, Australia. This paper is drawn from a larger study which involved the following team members from La Trobe University Faculty of Health Sciences: Professor Alison Perry (Principal Investigator), Professor Meg Morris (Co-investigator), Associate professor Carolyn Unsworth (Co-investigator), Professor Stephen Duckett, (Co-investigator), Ms. Jemma Skeat, Dr. Karen Dodd, Dr. Nicholas Taylor, Ms Karen Reilly and Ms Dianne Duncombe (Research Associates). Thanks also to Ms Sue Cotton, Biostatistician from the Orygen Centre, University of Melbourne, who assisted us with advice on data management and data analyses for this project. Ms D. Benetti provided administrative assistance throughout. Professor Pam Enderby assisted the research team at La Trobe University in the application to the Commonwealth to support this project and both Professor Enderby and Dr Alexandra John from Sheffield University, UK, were associate researchers to this project, providing the Research Team with advice, discussion and support in this development of the AusTOMs. The team is grateful for their guidance and encouragement.
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| 15541181 | PMC543469 | CC BY | 2021-01-04 16:38:12 | no | Health Qual Life Outcomes. 2004 Nov 13; 2:64 | utf-8 | Health Qual Life Outcomes | 2,004 | 10.1186/1477-7525-2-64 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-421552212110.1186/1471-2202-5-42Research ArticleAn information integration theory of consciousness Tononi Giulio [email protected] Department of Psychiatry, University of Wisconsin, Madison, USA2004 2 11 2004 5 42 42 10 8 2004 2 11 2004 Copyright © 2004 Tononi; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Consciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition?
Presentation of the hypothesis
This paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation – the availability of a very large number of conscious experiences; and integration – the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Φ value of a complex of elements. Φ is the amount of causally effective information that can be integrated across the informational weakest link of a subset of elements. A complex is a subset of elements with Φ>0 that is not part of a subset of higher Φ. The theory also claims that the quality of consciousness is determined by the informational relationships among the elements of a complex, which are specified by the values of effective information among them. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex.
Testing the hypothesis
The information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. As shown here, these include the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the time requirements on neural interactions that support consciousness.
Implications of the hypothesis
The theory entails that consciousness is a fundamental quantity, that it is graded, that it is present in infants and animals, and that it should be possible to build conscious artifacts.
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Background
Consciousness is everything we experience. Think of it as what abandons us every night when we fall into dreamless sleep and returns the next morning when we wake up [1]. Without consciousness, as far as we are concerned, there would be neither an external world nor our own selves: there would be nothing at all. To understand consciousness, two main problems need to be addressed. [2,3]. The first problem is to understand the conditions that determine to what extent a system has consciousness. For example, why is it that certain parts of the brain are important for conscious experience, whereas others, equally rich in neurons and connections, are not? And why are we conscious during wakefulness or dreaming sleep, but much less so during dreamless sleep, even if the brain remains highly active? The second problem is to understand the conditions that determine what kind of consciousness a system has. For example, what determines the specific and seemingly irreducible quality of the different modalities (e.g. vision, audition, pain), submodalities (e.g. visual color and motion), and dimensions (e.g. blue and red) that characterize our conscious experience? Why do colors look the way they do, and different from the way music sounds, or pain feels? Solving the first problem means that we would know to what extent a physical system can generate consciousness – the quantity or level of consciousness. Solving the second problem means that we would know what kind of consciousness it generates – the quality or content of consciousness.
Presentation of the hypothesis
The first problem: What determines to what extent a system has conscious experience?
We all know that our own consciousness waxes when we awaken and wanes when we fall asleep. We may also know first-hand that we can "lose consciousness" after receiving a blow on the head, or after taking certain drugs, such as general anesthetics. Thus, everyday experience indicates that consciousness has a physical substrate, and that that physical substrate must be working in the proper way for us to be fully conscious. It also prompts us to ask, more generally, what may be the conditions that determine to what extent consciousness is present. For example, are newborn babies conscious, and to what extent? Are animals conscious? If so, are some animals more conscious than others? And can they feel pain? Can a conscious artifact be constructed with non-neural ingredients? Is a person with akinetic mutism – awake with eyes open, but mute, immobile, and nearly unresponsive – conscious or not? And how much consciousness is there during sleepwalking or psychomotor seizures? It would seem that, to address these questions and obtain a genuine understanding of consciousness, empirical studies must be complemented by a theoretical analysis.
Consciousness as information integration
The theory presented here claims that consciousness has to do with the capacity to integrate information. This claim may not seem self-evident, perhaps because, being endowed with consciousness for most of our existence, we take it for granted. To gain some perspective, it is useful to resort to some thought experiments that illustrate key properties of subjective experience: its informativeness, its unity, and its spatio-temporal scale.
Information
Consider the following thought experiment. You are facing a blank screen that is alternately on and off, and you have been instructed to say "light" when the screen turns on and "dark" when it turns off. A photodiode – a very simple light-sensitive device – has also been placed in front of the screen, and is set up to beep when the screen emits light and to stay silent when the screen does not. The first problem of consciousness boils down to this. When you differentiate between the screen being on or off, you have the conscious experience of "seeing" light or dark. The photodiode can also differentiate between the screen being on or off, but presumably it does not consciously "see" light and dark. What is the key difference between you and the photodiode that makes you "see" light consciously? (see Appendix, i)
According to the theory, the key difference between you and the photodiode has to do with how much information is generated when that differentiation is made. Information is classically defined as reduction of uncertainty among a number of alternatives outcomes when one of them occurs [4]. It can be measured by the entropy function, which is the weighted sum of the logarithm of the probability (p) of alternatives outcomes (i): H = - Σpilog2pi. Thus, tossing a fair coin and obtaining heads corresponds to 1 bit of information, because there are just two alternatives; throwing a fair die yields log2(6) ≈ 2.59 bits of information, because there are six equally likely alternatives (H decreases if some of the outcomes are more likely than others, as would be the case with a loaded die).
When the blank screen turns on, the photodiode enters one of its two possible alternative states and beeps. As with the coin, this corresponds to 1 bit of information. However, when you see the blank screen turn on, the state you enter, unlike the photodiode, is one out of an extraordinarily large number of possible states. That is, the photodiode's repertoire is minimally differentiated, while yours is immensely so. It is not difficult to see this. For example, imagine that, instead of turning homogeneously on, the screen were to display at random every frame from every movie that was or could ever be produced. Without any effort, each of these frames would cause you to enter a different state and "see" a different image. This means that when you enter the particular state ("seeing light") you rule out not just "dark", but an extraordinarily large number of alternative possibilities. Whether you think or not of the bewildering number of alternatives (and you typically don't), this corresponds to an extraordinary amount of information (see Appendix, ii). This point is so simple that its importance has been overlooked.
Integration
While the ability to differentiate among a very large number of states is a major difference between you and the lowly photodiode, by itself it is not enough to account for the presence of conscious experience. To see why, consider an idealized one megapixel digital camera, whose sensor chip is essentially a collection of one million photodiodes. Even if each photodiode in the sensor chip were just binary, the camera as such could differentiate among 21,000,000 states, an immense number, corresponding to 1,000,000 bits of information. Indeed, the camera would easily enter a different state for every frame from every movie that was or could ever be produced. Yet nobody would believe that the camera is conscious. What is the key difference between you and the camera?
According to the theory, the key difference between you and the camera has to do with information integration. From the perspective of an external observer, the camera chip can certainly enter a very large number of different states, as could easily be demonstrated by presenting it with all possible input signals. However, the sensor chip can be considered just as well as a collection of one million photodiodes with a repertoire of two states each, rather than as a single integrated system with a repertoire of 21,000,000 states. This is because, due to the absence of interactions among the photodiodes within the sensory chip, the state of each element is causally independent of that of the other elements, and no information can be integrated among them. Indeed, if the sensor chip were literally cut down into its individual photodiodes, the performance of the camera would not change at all.
By contrast, the repertoire of states available to you cannot be subdivided into the repertoire of states available to independent components. This is because, due to the multitude of causal interactions among the elements of your brain, the state of each element is causally dependent on that of other elements, which is why information can be integrated among them. Indeed, unlike disconnecting the photodiodes in a camera sensor, disconnecting the elements of your brain that underlie consciousness has disastrous effects. The integration of information in conscious experience is evident phenomenologically: when you consciously "see" a certain image, that image is experienced as an integrated whole and cannot be subdivided into component images that are experienced independently. For example, no matter how hard you try, for example, you cannot experience colors independent of shapes, or the left half of the visual field of view independently of the right half. And indeed, the only way to do so is to physically split the brain in two to prevent information integration between the two hemispheres. But then, such split-brain operations yield two separate subjects of conscious experience, each of them having a smaller repertoire of available states and more limited performance [5].
Spatio-temporal characteristics
Finally, it is important to appreciate that conscious experience unfolds at a characteristic spatio-temporal scale. For instance, it flows in time at a characteristic speed and cannot be much faster or much slower. No matter how hard you try, you cannot speed up experience to follow a move accelerated a hundred times, not can you slow it down if the movie has decelerated. Studies of how a percept is progressively specified and stabilized – a process called microgenesis – indicate that it takes up to 100–200 milliseconds to develop a fully formed sensory experience, and that the surfacing of a conscious thought may take even longer [6]. In fact, the emergence of a visual percept is somewhat similar to the developing of a photographic print: first there is just the awareness that something has changed, then that it is something visual rather than, say, auditory, later some elementary features become apparent, such as motion, localization, and rough size, then colors and shapes emerge, followed by the formation of a full object and its recognition – a sequence that clearly goes from less to more differentiated [6]. Other evidence indicates that a single conscious moment does not extend beyond 2–3 seconds [7]. While it is arguable whether conscious experience unfolds more akin to a series of discrete snapshots or to a continuous flow, its time scale is certainly comprised between these lower and upper limits. Thus, a phenomenological analysis indicates that consciousness has to do with the ability to integrate a large amount of information, and that such integration occurs at a characteristic spatio-temporal scale.
Measuring the capacity to integrate information: The Φ of a complex
If consciousness corresponds to the capacity to integrate information, then a physical system should be able to generate consciousness to the extent that it has a large repertoire of available states (information), yet it cannot be decomposed into a collection of causally independent subsystems (integration). How can one identify such an integrated system, and how can one measure its repertoire of available states [2,8]?
As was mentioned above, to measure the repertoire of states that are available to a system, one can use the entropy function, but this way of measuring information is completely insensitive to whether the information is integrated. Thus, measuring entropy would not allow us to distinguish between one million photodiodes with a repertoire of two states each, and a single integrated system with a repertoire of 21,000,000 states. To measure information integration, it is essential to know whether a set of elements constitute a causally integrated system, or they can be broken down into a number of independent or quasi-independent subsets among which no information can be integrated.
To see how one can achieve this goal, consider an extremely simplified system constituted of a set of elements. To make matters slightly more concrete, assume that we are dealing with a neural system. Each element could represent, for instance, a group of locally interconnected neurons that share inputs and outputs, such as a cortical minicolumn. Assume further that each element can go through discrete activity states, corresponding to different firing levels, each of which lasts for a few hundred milliseconds. Finally, for the present purposes, let us imagine that the system is disconnected from external inputs, just as the brain is virtually disconnected from the environment when it is dreaming.
Effective information
Consider now a subset S of elements taken from such a system, and the diagram of causal interactions among them (Fig. 1a). We want to measure the information generated when S enters a particular state out of its repertoire, but only to the extent that such information can be integrated, i.e. each state results from causal interactions within the system. How can one do so? One way is to divide S into two complementary parts A and B, and evaluate the responses of B that can be caused by all possible inputs originating from A. In neural terms, we try out all possible combinations of firing patterns as outputs from A, and establish how differentiated is the repertoire of firing patterns they produce in B. In information-theoretical terms, we give maximum entropy to the outputs from A (AHmax), i.e. we substitute its elements with independent noise sources, and we determine the entropy of the responses of B that can be induced by inputs from A. Specifically, we define the effective information between A and B as EI(A→B) = MI(AHmax;B). Here MI(A;B) = H(A) + H(B) - H(AB) stands for mutual information, a measure of the entropy or information shared between a source (A) and a target (B). Note that since A is substituted by independent noise sources, there are no causal effects of B on A; therefore the entropy shared by B and A is necessarily due to causal effects of A on B. Moreover, EI(A→B) measures all possible effects of A on B, not just those that are observed if the system were left to itself. Also, EI(A→B) and EI(B→A) in general are not symmetric. Finally, note that the value of EI(A→B) is bounded by AHmax and BHmax, whichever is less. In summary, to measure EI(B→A), one needs to apply maximum entropy to the outputs from B, and determine the entropy of the responses of B that are induced by inputs from A. It should be apparent from the definition that EI(A→B) will be high if the connections between A and B are strong and specialized, such that different outputs from A will induce different firing patterns in B. On the other hand, EI(A→B) will be low or zero if the connections between A and B are such that different outputs from A produce scarce effects, or if the effect is always the same. For a given bipartition of a subset, then, the sum of the effective information for both directions is indicated as EI(A B) = EI(A→B) + EI(B→A). Thus, EI(A B) measures the repertoire of possible causal effects of A on B and of B on A.
Information integration
Based on the notion of effective information for a bipartition, we can assess how much information can be integrated within a system of elements. To this end, we note that a subset S of elements cannot integrate any information (as a subset) if there is a way to partition S in two parts A and B such that EI(A B) = 0 (Fig. 1b, vertical bipartition). In such a case, in fact, we would clearly be dealing with at least two causally independent subsets, rather than with a single, integrated subset. This is exactly what would happen with the photodiodes making up the sensor of a digital camera: perturbing the state of some of the photodiodes would make no difference to the state of the others. Similarly, a subset can integrate little information if there is a way to partition it in two parts A and B such that EI(A B) is low: the effective information across that bipartition is the limiting factor on the subset's information integration capacity. Therefore in order to measure the information integration capacity of a subset S, we should search for the bipartition(s) of S for which EI(A B) reaches a minimum (the informational "weakest link")." Since EI(A B) is necessarily bounded by the maximum entropy available to A or B, min{EI(A B)}, to be comparable over bipartitions, should be normalized by Hmax(A B) = min{Hmax(A); Hmax(B)}, the maximum information capacity for each bipartition. The minimum information bipartition MIBA B of subset S – its 'weakest link' – is its bipartition for which the normalized effective information reaches a minimum, corresponding to min{EI(A B)/Hmax(A B)}. The information integration for subset S, or Φ(S), is simply the (non-normalized) value of EI(A B) for the minimum information bipartition: Φ(S) = EI(MIBA B). The symbol Φ is meant to indicate that the information (the vertical bar "I") is integrated within a single entity (the circle "O", see Appendix, iii).
Complexes
We are now in a position to establish which subsets are actually capable of integrating information, and how much of it (Fig. 1c). To do so, we consider every possible subset S of m elements out of the n elements of a system, starting with subsets of two elements (m = 2) and ending with a subset corresponding to the entire system (m = n). For each of them, we measure the value of Φ, and rank them from highest to lowest. Finally, we discard all those subsets that are included in larger subsets having higher Φ (since they are merely parts of a larger whole). What we are left with are complexes – individual entities that can integrate information. Specifically, a complex is a subset S having Φ>0 that is not included within a larger subset having higher Φ. For a complex, and only for a complex, it is appropriate to say that, when it enters a particular state out if its repertoire, it generates and amount of integrated information corresponding to its Φ value. Of the complexes that make up a given system, the one with the maximum value of Φ(S) is called the main complex (the maximum is taken over all combinations of m>1 out of n elements of the system). Some properties of complexes worth pointing out are, for instance, that a complex can be causally connected to elements that are not part of it (the input and output elements of a complex are called ports-in and ports-out, respectively). Also, the same element can belong to more than one complex, and complexes can overlap.
In summary, a system can be analyzed to identify its complexes – those subsets of elements that can integrate information, and each complex will have an associated value of Φ – the amount of information it can integrate (see Appendix, iv). To the extent that consciousness corresponds to the capacity to integrate information, complexes are the "subjects" of experience, being the locus where information can be integrated. Since information can only be integrated within a complex and not outside its boundaries, consciousness as information integration is necessarily subjective, private, and related to a single point of view or perspective [1,9]. It follows that elements that are part of a complex contribute to its conscious experience, while elements that are not part of it do not, even though they may be connected to it and exchange information with it through ports-in and ports-out.
Information integration over space and time
The Φ value of a complex is dependent on both spatial and temporal scales that determine what counts as a state of the underlying system. In general, there will be a "grain size", in both space and time, at which Φ reaches a maximum. In the brain, for example, synchronous firing of heavily interconnected groups of neurons sharing inputs and outputs, such as cortical minicolumns, may produce significant effects in the rest of the brain, while asynchronous firing of various combinations of individual neurons may be less effective. Thus, Φ values may be higher when considering as elements cortical minicolumns rather than individual neurons, even if their number is lower. On the other hand, Φ values would be extremely low with elements the size of brain areas. Time wise, Φ values in the brain are likely to show a maximum between tens and hundreds of milliseconds. It is clear, for example, that if one were to stimulate one half of the brain by inducing many different firing patterns, and examine what effects this produces on the other half, no stimulation pattern would produce any effect whatsoever after just a tenth of a millisecond, and Φ would be equal to zero. After say 100 milliseconds, however, there is enough time for differential effects to be manifested, and Φ would grow. On the other hand, given the duration of conduction delays and of postsynaptic currents, much longer intervals are not going to increase Φ values. Indeed, a neural system will soon settle down into states that become progressively more independent of the stimulation. Thus, the search for complexes of maximum Φ should occur over subsets at critical spatial and temporal scales.
To recapitulate, the theory claims that consciousness corresponds to the capacity to integrate information. This capacity, corresponding to the quantity of consciousness, is given by the Φ value of a complex. Φ is the amount of effective information that can be exchanged across the minimum information bipartition of a complex. A complex is a subset of elements with Φ>0 and with no inclusive subset of higher Φ. The spatial and temporal scales defining the elements of a complex and the time course of their interactions are those that jointly maximize Φ.
The second problem: What determines the kind of consciousness a system has?
Even if we were reasonably sure that a system is conscious, it is not immediately obvious what kind of consciousness it would have. As was mentioned early on, our own consciousness comes in specific and seemingly irreducible qualities, exemplified by different modalities (e.g. vision, audition, pain), submodalities (e.g. visual color and motion), and dimensions (e.g. blue and red). What determines that colors look the way they do, and different from the way music sounds, or pain feels? And why can we not even imagine what a "sixth" sense would feel like? Or consider the conscious experience of others. Does a gifted musician experience the sound of an orchestra the same way you do, or is his experience richer? And what about bats [10]? Assuming that they are conscious, how do they experience the world they sense through echolocation? Is their experience of the world vision-like, audition-like, or completely alien to us? Unless we accept that the kind of consciousness a system has is arbitrary, there must be some necessary and sufficient conditions that determine exactly what kind of experiences it can have. This is the second problem of consciousness.
While it may not be obvious how best to address this problem, we do know that, just as the quantity of our consciousness depends on the proper functioning of a physical substrate – the brain, so does the quality of consciousness. Consider for example the acquisition of new discriminatory abilities, such as becoming expert at wine tasting. Careful studies have shown that we do not learn to distinguish among a large number of different wines merely by attaching the appropriate labels to different sensations that we had had all along. Rather, it seems that we actually enlarge and refine the set of sensations triggered by tasting wines. Similar observations have been made by people who, for professional reasons, learn to discriminate among perfumes, colors, sounds, tactile sensations, and so on. Or consider perceptual learning during development. While infants experience more than just a "buzzing confusion", there is no doubt that perceptual abilities undergo considerable refinement – just consider what your favorite red wine must have tasted like when all you had experienced was milk and water.
These examples indicate that the quality and repertoire of our conscious experience can change as a result of learning. What matters here is that such perceptual learning depends upon specific changes in the physical substrate of our consciousness – notably a refinement and rearranging of connections patterns among neurons in appropriate parts of the thalamocortical system (e.g [11]). Further evidence for a strict association between the quality of conscious experience and brain organization comes from countless neurological studies. Thus, we know that damage to certain parts of the cerebral cortex forever eliminates our ability to perceive visual motion, while leaving the rest of our consciousness seemingly intact. By contrast, damage to other parts selectively eliminates our ability to perceive colors. [12]. There is obviously something about the organization of those cortical areas that makes them contribute different qualities – visual motion and color – to conscious experience. In this regard, it is especially important that the same cortical lesion that eliminates the ability to perceive color or motion also eliminates the ability to remember, imagine, and dream in color or motion. By contrast, lesions of the retina, while making us blind, do not prevent us from remembering, imagining, and dreaming in color (unless they are congenital). Thus, it is something having to do with the organization of certain cortical areas – and not with their inputs from the sensory periphery – that determines the quality of conscious experiences we can have. What is this something?
Characterizing the quality of consciousness as a space of informational relationships: The effective information matrix
According to the theory, just as the quantity of consciousness associated with a complex is determined by the amount of information that can be integrated among its elements, the quality of its consciousness is determined by the informational relationships that causally link its elements [13]. That is, the way information can be integrated within a complex determines not only how much consciousness is has, but also what kind of consciousness. More precisely, the theory claims that the elements of a complex constitute the dimensions of an abstract relational space, the qualia space. The values of effective information among the elements of a complex, by defining the relationships among these dimensions, specify the structure of this space (in a simplified, Cartesian analogue, each element is a Cartesian axis, and the effective information values between elements define the angles between the axes, see Appendix, v). This relational space is sufficient to specify the quality of conscious experience. Thus, the reason why certain cortical areas contribute to conscious experience of color and other parts to that of visual motion has to do with differences in the informational relationships both within each area and between each area and the rest of the main complex. By contrast, the informational relationships that exist outside the main complex – including those involving sensory afferents – do not contribute either to the quantity or to the quality of consciousness.
To exemplify, consider two very simple linear systems of four elements each (Fig. 2). Fig. 2a shows the diagram of causal interactions for the two systems. The system on the left is organized as a divergent digraph: element number 1 sends connections of equal strength to the other three elements. The analysis of complexes shows that this system forms a single complex having a Φ value of 10 bits. The system on the right is organized as a chain: element number 1 is connected to 2, which is connected to 3, which is connected to 4. This system also constitutes a single complex having a Φ value of 10 bits. Fig. 2b shows the effective information matrix for both complexes. This contains the values of EI between each subset of elements and every other subset, corresponding to all informational relationships among the elements (the first row shows the values in one direction, the second row in the reciprocal direction). The elements themselves define the dimensions of the qualia space of each complex, in this case four. The effective information matrix defines the relational structure of the space. This can be thought of as a kind of topology, in that the entries in the matrix can be considered to represent how close such dimensions are to each other (see Appendix, vi). It is apparent that, despite the identical value of Φ and the same number of dimensions, the informational relationships that define the space are different for the two complexes. For example, the divergent complex has many more zero entries, while the chain complex has one entry (subset {1 3} to subset {2 4}) that is twice as strong as all other non-zero entries.
These two examples are purely meant to illustrate how the space of informational relationships within a complex can be captured by the effective information matrix, and how that space can differ for two complexes having similar amounts of Φ and the same number of dimensions. Of course, for a complex having high values of Φ, such as the one underlying our own consciousness, qualia space would be extraordinarily large and intricately structured. Nevertheless, it is a central claim of the theory that the structure of phenomenological relationships should reflect directly that of informational relationships. For example, the conscious experiences of blue and red appear irreducible (red is not simply less of blue). They may therefore correspond to different dimensions of qualia space (different elements of the complex). We also know that, as different as blue and red may be subjectively, they are much closer to each other than they are, say, to the blaring of a trumpet. EI values between the neuronal groups underlying the respective dimensions should behave accordingly, being higher between visual elements than between visual and auditory elements. As to the specific quality of different modalities and submodalities, the theory predicts that they are due to differences in the set of informational relationships within the respective cortical areas and between each area and the rest of the main complex. For example, areas that are organized topographically and areas that are organized according to a "winner takes all" arrangement should contribute different kinds of experiences. Another prediction is that changes in the quality and repertoire of sensations as a result of perceptual learning would also correspond to a refinement of the informational relationships within and between the appropriate cortical areas belonging to the main complex. By contrast, the theory predicts that informational relationships outside a complex – including those among sensory afferents – should not contribute directly to the quality of conscious experience of that complex. Of course, sensory afferents, sensory organs, and ultimately the nature and statistics of external stimuli, play an essential role in shaping the informational relationships among the elements of the main complex – but such role is an indirect and historical one – played out through evolution, development, and learning [14] (see Appendix, vii).
Specifying each conscious experience: The state of the interaction variables
According to the theory, once the quantity and quality of conscious experience that a complex can have are specified, the particular conscious state or experience that the complex will have at any given time is specified by the activity state of its elements at that time (in a Cartesian analogue, if each element of the complex corresponds to an axis of qualia space, and effective information values between elements define the angles between the axes specifying the structure of the space, then the activity state of each element provides a coordinate along its axis, and each conscious state is defined by the set of all its coordinates). The relevant activity variables are those that mediate the informational relationships among the elements, that is, those that mediate effective information. For example, if the elements are local groups of neurons, then the relevant variables are their firing patterns over tens to hundreds of milliseconds.
The state of a complex at different times can be represented schematically by a state diagram as in Fig. 2c (for the divergent complex on the left and the chain complex on the right). Each column in the state diagram shows the activity values of all elements of a complex (here between 0 and 1). Different conscious states correspond to different patterns of activity distributed over all the elements of a complex, with no contribution from elements outside the complex. Each conscious state can thus be thought of as a different point in the multidimensional qualia space defined by the effective information matrix of a complex (see Appendix, viii). Therefore, a succession or flow of conscious states over time can be thought of as a trajectory of points in qualia space. The state diagram also illustrates some states that have particular significance (second to fifth column). These are the states with just one active element, and all other elements silent (or active at some baseline level). It is not clear whether such highly selective states can be achieved within a large neural complex of high Φ, such as that one that is postulated to underlie human consciousness. To the extent that this is possible, such highly selective states would represent the closest approximation to experiencing that element's specific contribution to consciousness – its quality or "quale". However, because of the differences in the qualia space between the two complexes, the same state over the four elements would correspond to different experiences (and mean different things) for the two complexes. It should also be emphasized that, in every case, it is the activity state of all elements of the complex that defines a given conscious state, and both active and inactive elements count.
To recapitulate, the theory claims that the quality of consciousness associated with a complex is determined by its effective information matrix. The effective information matrix specifies all informational relationships among the elements of a complex. The values of the variables mediating informational interactions among the elements of a complex specify the particular conscious experience at any given time.
Testing the hypothesis
Consciousness, information integration, and the brain
Based on a phenomenological analysis, we have argued that consciousness corresponds to the capacity to integrate information. We have then considered how such capacity can be measured, and we have developed a theoretical framework for consciousness as information integration. We will now consider several neuroanatomical or neurophysiological factors that are known to influence consciousness. After briefly discussing the empirical evidence, we will use simplified computer models to illustrate how these neuroanatomical and neurophysiological factors influence information integration. As we shall see, the information integration theory not only fits empirical observations reasonably well, but offers a principled explanation for them.
Consciousness is generated by a distributed thalamocortical network that is at once specialized and integrated
Ancient Greek philosophers disputed whether the seat of consciousness was in the lungs, in the heart, or in the brain. The brain's pre-eminence is now undisputed, and scientists are trying to establish which specific parts of the brain are important. For example, it is well established that the spinal cord is not essential for our conscious experience, as paraplegic individuals with high spinal transactions are fully conscious. Conversely, a well-functioning thalamocortical system is essential for consciousness [15]. Opinions differ, however, about the contribution of certain cortical areas [1,16-21]. Studies of comatose or vegetative patients indicate that a global loss of consciousness is usually caused by lesions that impair multiple sectors of the thalamocortical system, or at least their ability to work together as a system. [22-24]. By contrast, selective lesions of individual thalamocortical areas impair different submodalities of conscious experience, such as the perception of color or of faces [25]. Electrophysiological and imaging studies also indicate that neural activity that correlates with conscious experience is widely distributed over the cortex (e.g [20,26-29]). It would seem, therefore, that the neural substrate of consciousness is a distributed thalamocortical network, and that there is no single cortical area where it all comes together (see Appendix, ix).
The fact that consciousness as we know it is generated by the thalamocortical system fits well with the information integration theory, since what we know about its organization appears ideally suited to the integration of information. On the information side, the thalamocortical system comprises a large number of elements that are functionally specialized, becoming activated in different circumstances. [12,30]. Thus, the cerebral cortex is subdivided into systems dealing with different functions, such as vision, audition, motor control, planning, and many others. Each system in turn is subdivided into specialized areas, for example different visual areas are activated by shape, color, and motion. Within an area, different groups of neurons are further specialized, e.g. by responding to different directions of motion. On the integration side, the specialized elements of the thalamocortical system are linked by an extended network of intra- and inter-areal connections that permit rapid and effective interactions within and between areas [31-35]. In this way, thalamocortical neuronal groups are kept ready to respond, at multiple spatial and temporal scales, to activity changes in nearby and distant thalamocortical areas. As suggested by the regular finding of neurons showing multimodal responses that change depending on the context [36,37], the capacity of the thalamocortical system to integrate information is probably greatly enhanced by nonlinear switching mechanisms, such as gain modulation or synchronization, that can modify mappings between brain areas dynamically [34,38-40]. In summary, the thalamocortical system is organized in a way that appears to emphasize at once both functional specialization and functional integration.
As shown by computer simulations, systems of neural elements whose connectivity jointly satisfies the requirements for functional specialization and for functional integration are well suited to integrating information. Fig. 3a shows a representative connection matrix obtained by optimizing for Φ starting from random connection weights. A graph-theoretical analysis indicates that connection matrices yielding the highest values of information integration (Φ = 74 bits) share two key characteristics [8]. First, connection patterns are different for different elements, ensuring functional specialization. Second, all elements can be reached from all other elements of the network, ensuring functional integration. Thus, simulated systems having maximum Φ appear to require both functional specialization and functional integration. In fact, if functional specialization is lost by replacing the heterogeneous connectivity with a homogeneous one, or if functional integration is lost by rearranging the connections to form small modules, the value of Φ decreases considerably (Fig 3b,3c). Further simulations show that it is possible to construct a large complex of high Φ by joining smaller complexes through reciprocal connections [8]. In the thalamocortical system, reciprocal connections linking topographically organized areas may be especially effective with respect to information integration. In summary, the coexistence of functional specialization and functional integration, epitomized by the thalamocortical system [30], is associated with high values of Φ.
Other brain regions with comparable numbers of neurons, such as the cerebellum, do not contribute to conscious experience
Consider now the cerebellum. This brain region contains more neurons than the cerebral cortex, has huge numbers of synapses, and receives mapped inputs from the environment and controls several outputs. However, in striking contrast to the thalamocortical system, lesions or ablations indicate that the direct contribution of the cerebellum to conscious experience is minimal. Why is this the case?
According to the theory, the reason lies with the organization of cerebellar connections, which is radically different from that of the thalamocortical system and is not well suited to information integration. Specifically, the organization of the connections is such that individual patches of cerebellar cortex tend to be activated independently of one another, with little interaction possible between distant patches [41,42]. This suggests that cerebellar connections may not be organized so as to generate a large complex of high Φ, but rather to give rise to many small complexes each with a low value of Φ. Such an organization seems to be highly suited for both the learning and the rapid, effortless execution of informationally insulated subroutines.
This concept is illustrated in Fig. 4a, which shows a strongly modular network, consisting of three modules of eight strongly interconnected elements each. This network yields Φ = 20 bits for each of its three modules, which form the system's three complexes. This example indicates that, irrespective of how many elements and connections are present in a neural structure, if that structure is organized in a strongly modular manner with little interactions among modules, complex size and Φ values are necessarily low. According to the information integration theory, this is the reason why these systems, although computationally very sophisticated, contribute little to consciousness. It is also the reason why there is no conscious experience associated with hypothalamic and brainstem circuits that regulate important physiological variables, such as blood pressure.
Subcortical centers can control consciousness by modulating the readiness of the thalamocortical system without contributing directly to it
It has been known for a long time that lesions in the reticular formation of the brainstem can produce unconsciousness and coma. Conversely, stimulating the reticular formation can arouse a comatose animal and activate the thalamocortical system, making it ready to respond to stimuli [43]. Groups of neurons within the reticular formation are characterized by diffuse projections to many areas of the brain. Many such groups release neuromodulators such as acetylcholine, histamine, noradrenaline, serotonin, dopamine, and glutamate (acting on metabotropic receptors) and can have extremely widespread effects on both neural excitability and plasticity [44]. However, it would seem that the reticular formation, while necessary for the normal functioning of the thalamocortical system and therefore for the occurrence of conscious experience, may not contribute much in terms of specific dimensions of consciousness – it may work mostly like an external on-switch or as a transient booster of thalamocortical firing.
Such a role can be explained readily in terms of information integration. As shown in Fig. 4b, neural elements that have widespread and effective connections to a main complex of high Φ may nevertheless remain informationally excluded from it. Instead, they are part of a larger complex having a much lower value of Φ.
Neural activity in sensory afferents to the thalamocortical system can determine what we experience without contributing directly to it
What we see usually depends on the activity patterns that occur in the retina and that are relayed to the brain. However, many observations suggest that retinal activity does not contribute directly to conscious experience. Retinal cells surely can tell light from dark and convey that information to visual cortex, but their rapidly shifting firing patterns do not correspond well with what we perceive. For example, during blinks and eye movements retinal activity changes dramatically, but visual perception does not. The retina has a blind spot at the exit of the optic nerve where there are no photoreceptors, and it has low spatial resolution and no color sensitivity at the periphery of the visual field, but we are not aware of any of this. More importantly, lesioning the retina does not prevent conscious visual experiences. For example, a person who becomes retinally blind as an adult continues to have vivid visual images and dreams. Conversely, stimulating the retina during sleep by keeping the eyes open and presenting various visual inputs does not yield any visual experience and does not affect visual dreams. Why is it that retinal activity usually determines what we see through its action on thalamocortical circuits, but does not contribute directly to conscious experience?
As shown in Fig. 4c, adding or removing multiple, segregated incoming pathways does not change the composition of the main complex, and causes little change in its Φ. While the incoming pathways do participate in a larger complex together with the elements of the main complex, the Φ value of this larger complex is very low, being limited by the effective information between each afferent pathway and its port in at the main complex. Thus, input pathways providing powerful inputs to a complex add nothing to the information it integrates if their effects are entirely accounted for by ports-in.
Neural activity in motor efferents from the thalamocortical system, while producing varied behavioral outputs, does not contribute directly to conscious experience
In neurological practice, as well as in everyday life, we tend to associate consciousness with the presence of a diverse behavioral repertoire. For example, if we ask a lot of different questions and for each of them we obtain an appropriate answer, we generally infer that a person is conscious. Such a criterion is not unreasonable in terms of information integration, given that a wide behavioral repertoire is usually indicative of a large repertoire of internal states that are available to an integrated system. However, it appears that neural activity in motor pathways, which is necessary to bring about such diverse behavioral responses, does not in itself contribute to consciousness. For example, patients with the locked-in syndrome, who are completely paralyzed except for the ability to gaze upwards, are fully conscious. Similarly, while we are completely paralyzed during dreams, consciousness is not impaired by the absence of behavior. Even lesions of central motor areas do not impair consciousness.
Why is it that neurons in motor pathways, which can produce a large repertoire of different outputs and thereby relay a large amount of information about different conscious states, do not contribute directly to consciousness? As shown in Fig. 4d, adding or removing multiple, segregated outgoing pathways to a main complex does not change the composition of the main complex, and does not change its Φ value. Like incoming pathways, outgoing pathways do participate in a larger complex together with the elements of the main complex, but the Φ value of this larger complex is very low, being limited by the effective information between each port-out of the main complex and its effector targets.
Neural processes in cortico-subcortico-cortical loops, while important in the production and sequencing of action, thought, and language, do not contribute directly to conscious experience
Another set of neural structures that may not contribute directly to conscious experience are subcortical structures such as the basal ganglia. The basal ganglia are large nuclei that contain many circuits arranged in parallel, some implicated in motor and oculomotor control, others, such as the dorsolateral prefrontal circuit, in cognitive functions, and others, such as the lateral orbitofrontal and anterior cingulate circuits, in social behavior, motivation, and emotion [45]. Each basal ganglia circuit originates in layer V of the cortex, and through a last step in the thalamus, returns to the cortex, not far from where the circuit started [46]. Similarly arranged cortico-ponto-cerebello-thalamo-cortical loops also exist. Why is it that these complicated neural structures, which are tightly connected to the thalamocortical system at both ends, do not seem to provide much direct contribution to conscious experience? (see Appendix, x)
As shown in Fig. 4e, the addition of many parallel cycles also generally does not change the composition of the main complex, although Φ values can be altered (see Appendix, xi). Instead, the elements of the main complex and of the connected cycles form a joint complex that can only integrate the limited amount of information exchanged within each cycle. Thus, subcortical cycles or loops implement specialized subroutines that are capable of influencing the states of the main thalamocortical complex without joining it. Such informationally insulated cortico-subcortical loops could constitute the neural substrates for many unconscious processes that can affect and be affected by conscious experience [3,47]. It is likely that new informationally insulated loops can be created through learning and repetition. For example, when first performing a new task, we are conscious of every detail of it, we make mistakes, are slow, and must make an effort. When we have learned the task well, we perform it better, faster, and with less effort, but we are also less aware of it. As suggested by imaging results, a large number of neocortical regions are involved when we first perform a task. With practice, activation is reduced or shifts to different circuits [48]. According to the theory, during the early trials, performing the task involves many regions of the main complex, while later certain aspects of the task are delegated to neural circuits, including subcortical ones, that are informationally insulated.
Many neural processes within the thalamocortical system may also influence conscious experience without contributing directly to it
Even within the thalamocortical system proper, a substantial proportion of neural activity does not appear to contribute directly to conscious experience. For example, what we see and hear requires elaborate computational processes dealing with figure-ground segregation, depth perception, object recognition, and language parsing, many of which take place in the thalamocortical system. Yet we are not aware of all this diligent buzzing: we just see objects, separated from the background and laid out in space, and know what they are, or hear words, nicely separated from each other, and know what they mean. As an example, take binocular rivalry, where the two eyes view two different images, but we perceive consciously just one image at a time, alternating in sequence. Recordings in monkeys have shown that the activity of visual neurons in certain cortical areas, such as the inferotemporal cortex, follows faithfully what the subject perceives consciously. However, in other areas, such as primary visual cortex, there are many neurons that respond to the stimulus presented to the eye, whether or not the subject is perceiving it [49]. Neuromagnetic studies in humans have shown that neural activity correlated with a stimulus that is not being consciously perceived can be recorded in many cortical areas, including the front of the brain. [26]. Why does the firing of many cortical neurons carrying out the computational processes that enable object recognition (or language parsing) not correspond to anything conscious?
The situation is similar on the executive side of consciousness. When we plan to do or say something, we are vaguely conscious of what we intend, and presumably these intentions are reflected in specific firing patterns of certain neuronal groups. Our vague intentions are then translated almost miraculously into the right words, and strung together to form a syntactically correct sentence that conveys what we meant to say. And yet again, we are not at all conscious of the complicated processing that is needed to carry out our intentions, much of which takes place in the cortex. What determines whether the firing of neurons within the thalamocortical system contributes directly to consciousness or not? According to the information integration theory, the same considerations that apply to input and output circuits and to cortico-subcortico-cortical loops also apply to circuits and loops contained entirely within the thalamocortical system. Thus, the theory predicts that activity within certain cortical circuits does not contribute to consciousness because such circuits implement informationally insulated loops that remain outside of the main thalamocortical complex. At this stage, however, it is hard to say precisely which cortical circuits may be informationally insulated. Are primary sensory cortices organized like massive afferent pathways to a main complex "higher up" in the cortical hierarchy? Is much of prefrontal cortex organized like a massive efferent pathway? Do certain cortical areas, such as those belonging to the dorsal visual stream, remain partly segregated from the main complex? Do interactions within a cortico-thalamic minicolumn qualify as intrinsic mini-loops that support the main complex without being part of it? Unfortunately, answering these questions and properly testing the predictions of the theory requires a much better understanding of cortical neuroanatomy than is presently available [50,51].
Consciousness can be split if the thalamocortical system is split
Studies of split-brain patients, whose corpus callosum was sectioned for therapeutic reasons, show that each hemisphere has its own, private conscious experience. The dominant, linguistically competent hemisphere does not seem to suffer a major impairment of consciousness after the operation. The non-dominant hemisphere, although it loses some important abilities and its residual capacities are harder to assess, also appears to be conscious. [5]. Some information, e.g. emotional arousal, seems to be shared across the hemispheres, probably thanks to subcortical common inputs.
Viewing consciousness as information integration suggests straightforward explanations for these puzzling observations. Consider the simplified model in Fig. 5a. A main complex having high Φ includes two sets of elements ("hemispheres") having similar internal architecture that are joined by "callosal" connections (top panel). When the callosal connections are cut (bottom panel), the single main complex splits and is replaced by two smaller complexes corresponding to the two hemispheres. There is also a complex, of much lower Φ, which includes both hemispheres and a "subcortical" element that provide them with common input. Thus, there is a sense in which the two hemispheres still form an integrated entity, but the information they share is minimal (see Appendix, xii).
Some parts of the thalamocortical system may contribute to conscious experience at one time and not at another
Until now, we have considered structural aspects of the organization of the nervous system that, according to the information integration theory, explain why certain parts of the brain contribute directly to consciousness and others do not, or much less so. In addition to neuroanatomical factors, neurophysiological factors are also important in determining to what extent a given neural structure can integrate information. For example, anatomical connections between brain regions may or may not be functional, depending on both pathological or physiological factors. Functional disconnections between certain parts of the brain and others are thought to play a role in psychiatric conversion and dissociative disorders, may occur during dreaming, and may be implicated in conditions such as hypnosis. Thus, functional disconnections, just like anatomical disconnections, may lead to a restriction of the neural substrate of consciousness.
It is also likely that certain attentional phenomena may correspond to changes in the neural substrate of consciousness. For example, when one is absorbed in thought, or focused exclusively on a given sensory modality, such as vision, the neural substrate of consciousness may not be the same as when we are diffusely monitoring the environment. Phenomena such as the attentional blink, where a fixed sensory input may at times make it to consciousness and at times not, may also be due to changes in functional connectivity: access to the main thalamocortical complex may be enabled or not based on dynamics intrinsic to the complex [52]. Phenomena such as binocular rivalry may also be related, at least in part, to dynamic changes in the composition of the main thalamocortical complex caused by transient changes in functional connectivity [53]. At present, however, it is still not easy to determine whether a particular group of neurons is excluded from the main complex because of hard-wired anatomical constraints, or is transiently disconnected due to functional changes.
Figure 5b (top panel) shows a simple model obtained by taking three subsets of elements of (relatively) high Φ and connecting them through reciprocal connections. Specifically, the first subset, which stands for supramodal areas of the brain, is reciprocally connected to the second and third subsets, which stand for visual and auditory areas, respectively. In this idealized example, the visual and auditory subsets are not connected directly among themselves. As one can see, the three subsets thus connected form a single main complex having a Φ value of 61 bits. In the bottom panel, the auditory subset has been disconnected, in a functional sense, by mimicking a profound deactivation of its elements. The result is that the main complex shrinks and the auditory subset ends up outside the main complex. Note, however, that in this particular case the value of Φ changes very little (57 bits), indicating that it might be possible for the borders of the main complex to change dynamically while the amount of consciousness is not substantially altered. What would change, of course, would be the configuration of the space of informational relationships. These simulations suggest that attentional mechanisms may work both by changing neuronal firing rates, and therefore saliency within qualia space, as well as by modifying neuronal readiness to fire, and therefore the boundaries of the main complex and of qualia space itself. This is why the set of elements underlying consciousness is not static, but can be considered to form a "dynamic complex" or "dynamic core" [1,9].
Depending on certain neurophysiological parameters, the same thalamocortical network can generate much or little conscious experience
Another example of the importance of neurophysiological parameters is provided by sleep – the most familiar of the alterations of consciousness, and yet one of the most striking. Upon awakening from dreamless sleep, we have the peculiar impression that for a while we were not there at all nor, as far as we are concerned, was the rest of the world. This everyday observation tells us vividly that consciousness can come and go, grow and shrink. Indeed, if we did not sleep, it might be hard to imagine that consciousness is not a given, but depends somehow on the way our brain is functioning. The loss of consciousness between falling asleep and waking up is relative, rather than absolute. [54]. Thus, careful studies of mental activity reported immediately after awakening have shown that some degree of consciousness is maintained during much of sleep. Many awakenings, especially from rapid eye movement (REM) sleep, yield dream reports, and dreams can be at times as vivid and intensely conscious as waking experiences. Dream-like consciousness also occurs during various phases of slow wave sleep, especially at sleep onset and during the last part of the night. Nevertheless, a certain proportion of awakenings do not yield any dream report, suggesting a marked reduction of consciousness. Such "empty" awakenings typically occur during the deepest stages of slow wave sleep (stages 3 and 4), especially during the first half of the night.
Which neurophysiological parameters are responsible for the remarkable changes in the quantity and quality of conscious experience that occur during sleep? We know for certain that the brain does not simply shut off during sleep. During REM sleep, for example, neural activity is as high, if not higher, than during wakefulness, and EEG recordings show low-voltage fast-activity. This EEG pattern is known as "activated" because cortical neurons, being steadily depolarized and close to their firing threshold, are ready to respond to incoming inputs. Given these similarities, it is perhaps not surprising that consciousness should be present during both states. Changes in the quality of consciousness, however, do occur, and they correspond closely to relative changes in the activation of different brain areas. [54].
During slow wave sleep, average firing rates of cortical neurons are also similar to those observed during quiet wakefulness. However, due to changes in the level of certain neuromodulators, virtually all cortical neurons engage in slow oscillations at around 1 Hz, which are reflected in slow waves in the EEG [55]. Slow oscillations consist of a depolarized phase, during which the membrane potential of cortical neurons is close to firing threshold and spontaneous firing rates are similar to quiet wakefulness, and of a hyperpolarized phase, during which neurons become silent and are further away from firing threshold. From the perspective of information integration, a reduction in the readiness to respond to stimuli during the hyperpolarization phase of the slow oscillation would imply a reduction of consciousness. It would be as if we were watching very short fragments of a movie interspersed with repeated unconscious "blanks" in which we cannot see, think, or remember anything, and therefore have little to report. A similar kind of unreadiness to respond, associated with profound hyperpolarization, is found in deep anesthesia, another condition where consciousness is impaired. Studies using transcranial magnetic stimulation in conjunction with high-density EEG are currently testing how response readiness changes during the sleep waking cycle.
From the perspective of information integration, a reduction of consciousness during certain phases of sleep would occur even if the brain remained capable of responding to perturbations, provided its response were to lack differentiation. This prediction is borne out by detailed computer models of a portion of the visual thalamocortical system (Hill and Tononi, in preparation). According to these simulations, in the waking mode different perturbations of the thalamocortical network yield specific responses. In the sleep mode, instead, the network becomes bistable: specific effects of different perturbations are quickly washed out and their propagation impeded: the whole network transitions into the depolarized or into the hyperpolarized phase of the slow oscillation – a stereotypic response that is observed irrespective of the particular perturbation (see Appendix, xiii). And of course, this bistability is also evident in the spontaneous behavior of the network: during each slow oscillation, cortical neurons are either all firing or all silent, with little freedom in between. In summary, these simulations indicate that, even if the anatomical connectivity of a complex stays the same, a change in key parameters governing the readiness of neurons to respond and the differentiation of their responses may alter radically the Φ value of the complex, with corresponding consequences on consciousness.
Conscious experience and time requirements
Consciousness not only requires a neural substrate with appropriate anatomical structure and appropriate physiological parameters: it also needs time. As was mentioned earlier, studies of how a percept is progressively specified and stabilized indicate that it takes up to 100–200 milliseconds to develop a fully formed sensory experience, and that the surfacing of a conscious thought may take even longer. Experiments in which the somatosensory areas of the cerebral cortex were stimulated directly indicate that low intensity stimuli must be sustained for up to 500 milliseconds to produce a conscious sensation [56]. Multi-unit recordings in the primary visual cortex of monkeys show that, after a stimulus is presented, the firing rate of many neurons increases irrespective of whether the animal reports seeing a figure or not. After 80–100 milliseconds, however, their discharge accurately predicts the conscious detection of the figure. Thus, the firing of the same cortical neurons may correlate with consciousness at certain times, but not at other times [57]. What determines when the firing of the same cortical neurons contributes to conscious experience and when it does not? And why may it take up to hundreds of milliseconds before a conscious experience is generated?
The theory predicts that the time requirements for the generation of conscious experience in the brain emerge directly from the time requirements for the build-up of effective interactions among the elements of the main complex. As was mentioned above, if one were to perturb half of the elements of the main complex for less than a millisecond, no perturbations would produce any effect on the other half within this time window, and Φ would be equal to zero. After say 100 milliseconds, however, there is enough time for differential effects to be manifested, and Φ should grow. This prediction is confirmed by results obtained using large-scale computer simulations of the thalamocortical system, where the time course of causal interactions and functional integration can be studied in detail [38,58,59], Hill and Tononi, unpublished results). For example, in a model including nine functionally segregated visual areas, the time it takes for functionally specialized neurons located in several different areas to interact constructively and produce a specific, correlated firing pattern is at least 80 milliseconds [38]. These correlated firing patterns last for several hundred milliseconds. After one or more seconds, however, the network settles into spontaneous activity states that are largely independent of previous perturbations. Thus, the characteristic time scale for maximally differentiated responses in thalamocortical networks appears to be comprised between a few tens of milliseconds and a few seconds at the most.
In summary, the time scale of neurophysiological interactions needed to integrate information among distant cortical regions appears to be consistent with that required by psychophysical observations (microgenesis), by stimulation experiments, and by recording experiments.
Summary: seeing blue
The previous examples show that the information integration theory is consistent with several empirical observations concerning the neural substrate of consciousness. Moreover, they show that the theory can provide a principled account of why consciousness is associated with certain parts of the brain rather than with others, and with certain global modes of functioning more than with others. To recapitulate the main tenets of the theory, it may be useful to reconsider the initial thought experiment.
Imagine again that you are comfortably facing a blank screen that is alternately on and off. When the screen turns on, you see a homogenous blue field, indeed for the sake of the argument we assume that you are having a "pure" perception of blue, unencumbered by extraneous percepts or thoughts (perhaps as can be achieved in certain meditative states). As you have been instructed, you signal your perception of blue by pushing a button. Now consider an extremely simplified scenario of the neural events that might accompany your seeing blue. When the screen turns on, a volley of activity propagates through the visual afferent pathways, involving successive stages such as retinal short wavelength cones, blue-yellow opponents cells, color constant cells, and so on. Eventually, this volley of activity in the visual afferent pathways leads to the firing of some neuronal groups in color-selective areas of the temporal lobe that, on empirical grounds, are our best bet for the neural correlate of blue: i) their activity correlates well with your perception of blue whether you see, imagine, or dream blue, in a way that is as stable and as susceptible to illusions as your perception of blue; ii) their microstimulation leads to the perception of blue; and iii) their selective lesion makes you unable to perceive blue. Let us assume, then, that these neuronal groups quickly increase their firing, and within a few tens of milliseconds they reach and then maintain increased levels of firing (see Appendix, xiv). We also assume that, at the same time, neuronal groups in neighboring cortical areas go on firing at a baseline level, largely unaffected by the blue light. These include neuronal groups in other visual areas that are selective for shape or movement; neuronal groups in auditory area that are selective for tones; and many others. On the other hand, the volley of activity originating in the retina does not exhaust itself by generating sustained activity in the color areas of the temporal lobe. Part of the volley proceeds at great speed and activates efferent motor pathways, which cause you to push the signaling button. Another part activates cortico-subcortico-cortical loops in your prefrontal cortex and basal ganglia, which almost make you speak the word "blue" aloud. In the meantime, many other parts of the brain are buzzing along, unaffected by what is going on in the visual system: cerebellar circuits are actively stabilizing your posture and gaze, and hypothalamic-brainstem circuits are actively stabilizing your blood pressure. What components in this simplified neural scenario are essential for your conscious experience of blue, and why?
The information integration theory makes several claims that lead to associated predictions. A first claim is that the neural substrate of consciousness as we know it is a complex of high Φ that is capable of integrating a large amount of information – the main complex. Therefore, whether a group of neurons contributes directly to consciousness is a function of its belonging to the main complex or not. In this example, the theory would predict that blue-selective neurons in some high-level color area should be inside the main complex; on the other hand, blue-sensitive neurons in afferent visual pathways, neurons in efferent pathways mediating the button-pressing response, neurons in cortico-subcortico-cortical and intracortical loops mediating subvocalization of the word "blue", neurons in the cerebellum controlling posture and neurons in hypothalamic-brainstem circuits controlling blood pressure should be outside. This even though these neurons may be equally active when you see blue, and even though some of them may be connected to elements of the main complex. In principle, joint microstimulation and recording experiments, and to some extent an analysis of patterns of synchronization, could determine participation in the main complex and test this prediction. The theory also predicts that blue-selective neurons in the main complex contribute to the conscious experience of blue only if their activation is sufficiently strong or sustained that they can make a difference, in informational terms, to the rest of the complex. Additional predictions are that, if a group of neurons that is normally part of the main complex becomes informationally disconnected from it, as could occur through attentional effects or in certain phases of sleep, the same group of neurons, firing in exactly the same way, would not contribute to consciousness. Moreover, according to the theory, the other groups of neurons within the main complex are essential to our conscious experience of blue even if, as in this example, they are not activated. This is not difficult to see. Imagine that, starting from an intact main complex, we were to remove one element after another, except for the active, blue-selective one. If an inactive element contributing to "seeing red" were removed, blue would not be experienced as blue anymore, but as some less differentiated color, perhaps not unlike those experienced by certain dichromats. If further elements of the main complex were removed, including those contributing to shapes, to sounds, to thoughts and so forth, one would soon drop to such a low level of consciousness that "seeing blue" would become meaningless: the "feeling" (and meaning) of the quale "blue" would have been eroded down to nothing. Indeed, while the remaining neural circuits may still be able to discriminate blue from other colors, they would do so very much as a photodiode does (see Appendix, xv).
A second claim of the theory is that the quality of consciousness is determined by the informational relationships within the main complex. Therefore, how a group of neurons contributes to consciousness is a function of its informational relationships inside the complex and not outside of it. In this example, blue-selective neurons within the main complex have become blue-selective no doubt thanks to the inputs received from the appropriate afferent pathways, and ultimately because of some aspects of the statistics of the environment and the resulting plastic changes throughout the brain. However, the theory predicts that their present firing contributes the quale "blue" exclusively because of their informational relationships within the main complex. If connections outside the main complex were to be manipulated, including the afferent color pathways, the experience elicited by activating the blue-selective neurons within the complex would stay the same. Conversely, if the relationships inside the main complex were to change, as could be done by changing the pattern of connections within the color-selective area and with the rest of the complex, so would the conscious experience of blue. That is, activating the same neurons would produce a different conscious experience.
Implications of the hypothesis
To conclude, it is worth mentioning some of the implications that derive from the information integration theory of consciousness. At the most general level, the theory has ontological implications. It takes its start from phenomenology and, by making a critical use of thought experiments, it argues that subjective experience is one and the same thing as a system's capacity to integrate information. In this view, experience, that is, information integration, is a fundamental quantity, just as mass, charge or energy are. It follows that any physical system has subjective experience to the extent that it is capable of integrating information, irrespective of what it is made of. Thus, an intriguing implication of the theory is that it should be possible to construct conscious artifacts by endowing them with a complex of high Φ. Moreover, it should be possible to design the quality of their conscious experience by appropriately structuring their effective information matrix.
It also follows that consciousness is not an all-or-none property, but it is graded: to varying degrees, it should exist in most natural (and artificial) systems. Because the conditions needed to build complexes of high Φ are apparently not easy to achieve, however, correspondingly high levels of experience are probably available to only a few kinds of systems, primarily complex brains containing the right type of architecture for maximizing functional specialization and integration. A related implication is that consciousness should also exist, to varying degrees, at multiple spatial and temporal scales. However, it is likely that, in most systems, there are privileged spatial and temporal scales at which information integration reaches a maximum.
Consciousness is characterized here as a disposition or potentiality – in this case as the potential differentiation of a system's responses to all possible perturbations, yet it is undeniably actual. Consider another thought experiment: you could be in a coma for days, awaken to consciousness for just one second, and revert to a coma. As long as your thalamocortical system can function well for that one second, you will be conscious. That is, a system does not have to explore its repertoire of states to be conscious, or to know how conscious it is supposed to be: what counts is only that the repertoire is potentially available. While this may sound strange, fundamental quantities associated with physical systems can also be characterized as dispositions or potentialities, yet have actual effects. For example, mass can be characterized as a potentiality – say the resistance that a body would offer to acceleration by a force – yet it exerts undeniable effects, such as attracting other masses. This too has intriguing implications. For example, because in this view consciousness corresponds to the potential of an integrated system to enter a large number of states by way of causal interactions within it, experience is present as long as such potential is present, whether or not the system's elements are activated. Thus, the theory predicts that a brain where no neurons were activated, but were kept ready to respond in a differentiated manner to different perturbations, would be conscious (perhaps that nothing was going on). Also, because consciousness is a property of a system, not of a state, the state the system is in only determines which particular experience becomes actual at any given time, and not whether experience is present. Thus, a brain where each neuron were microstimulated to fire as an exact replica of your brain, but where synaptic interactions had been blocked, would be unconscious.
The theory predicts that consciousness depends exclusively on the ability of a system to integrate information, whether or not it has a strong sense of self, language, emotion, a body, or is immersed in an environment, contrary to some common intuitions. This prediction is consistent with the preservation of consciousness during REM sleep, when both input and output signals from and to the body are markedly reduced. Transient inactivation of brain areas mediating the sense of self, language, and emotion could assess this prediction in a more cogent manner.
Nevertheless, the theory recognizes that these same factors are important historically because they favor the development of neural circuits forming a main complex of high Φ. For example, the ability of a system to integrate information grows as that system incorporates statistical regularities from its environment and learns [14]. In this sense, the emergence of consciousness in biological systems is predicated on a long evolutionary history, on individual development, and on experience-dependent change in neural connectivity. Indeed, the theory also suggests that consciousness provides an adaptive advantage and may have evolved precisely because it is identical with the ability to integrate a lot of information in a short period of time. If such information is about the environment, the implication is that, the more an animal is conscious, the larger the number of variables it can take into account jointly to guide its behavior.
Another implication of the theory is that the presence and extent of consciousness can be determined, in principle, also in cases in which we have no verbal report, such as infants or animals, or in neurological conditions such as coma and vegetative states, minimally conscious states, akinetic mutism, psychomotor seizures, and sleepwalking. In practice, of course, measuring Φ accurately in such systems will not be easy, but approximations and informed guesses are certainly conceivable.
At present, the validity of this theoretical framework and the plausibility of its implications rest on its ability to account, in a coherent manner, for some basic phenomenological observations and for some elementary but puzzling facts about the relationship between consciousness and the brain. Experimental developments, especially of ways to stimulate and record concurrently the activity of broad regions of the brain, should permit stringent tests of some of the theory's predictions. Equally important will be the development of realistic, large-scale models of the anatomical organization of the brain. These models should allow a more rigorous measurement of how the capacity to integrate information relates to different brain structures and certain neurophysiological parameters [38,50,59]. Finally, the theoretical framework presented here aims primarily at understanding the necessary and sufficient conditions that determine the quantity and quality of consciousness at the most general level. Further theoretical developments will be required to address several issues that are central to the study of consciousness in a biological and psychological context, such as the relationship of consciousness to memory and language, higher order aspects of consciousness [60,61], and its relationship to the self) [62]. Undoubtedly, a full understanding of how the brain generates human consciousness remains a formidable task. However, if experimental investigations can be complemented by a principled theoretical approach, it may not lay beyond the reach of science.
Appendix
i. The problem can also be posed in neural terms. When we see light, certain neurons in the retina turn on, as do other neurons higher up in the brain. Based on what we know, the activity of neurons in the retina is not directly associated with conscious experience of light and dark – they behave just like biological photodiodes that signal to higher centers. Somewhere in those higher centers, however, there seem to be some neurons whose activity is indeed tightly correlated with the conscious experience of light and dark. What is special about these higher neurons?
ii. Note that this information has nothing to do with how complicated the scene is, or how many different objects it appears to contain, but only with the number of alternative outcomes.
iii. This quantity is also called MIBcomplexity, for minimum information bipartition complexity. Note that, in most cases, the bipartitions for which the normalized value of EI will be at a minimum, everything else being equal, will be bipartitions that cut the system in two halves, i.e. midpartitions [2].
iv. Complexes can also be defined using mutual information instead of effective information, by exploiting the endogenous sources of variance that may exist in an isolated system [8]. A related measure could be constructed using the formalism of ε-machines [63]. Φ would then be related to the Hμ of the minimal ε-machine capable of reproducing the causal structure of a process, i.e. of the ε-machine that cannot be decomposed into a collection of lower Hμ ε-machines.
v. An elementary description of the qualia space is given by the author in [9], chapter 13.
vi. While the entries in the matrix contain all the relevant informational relationships defining this space, they do not reveal necessarily how the space is organized in an economical and explicit manner. This may be done by employing procedures akin to multidimensional scaling although, since the matrix is asymmetrical and involves high-order terms (among subsets of elements), this may not be easy. Satisfactorily mapping the phenomenological differences between modalities, submodalities and dimensions onto the structure of qualia space will require that we thoroughly characterize and understand the latter.
vii. Of course, sensory afferents usually play a role in determining which particular conscious experience we have at any given time (they better do so, if experience is to have an adaptive relationship to the environment). Nevertheless, particular experiences can be triggered even when we are disconnected from the environment, as in dreams.
viii. Note also that a "pure" sensation of blue defines a point in this N-dimensional qualia space as much as the experience of a busy city street, full of different objects, of sounds, smells, associations, and reflections defines another point.
ix. However, certain areas such as the posterior cingulate cortex and precuneus, some lateral parietal areas, and associated paramedian thalamic nuclei, may constitute strategic crossroads for coordinating the interactions among different sensory maps and frames of reference concerning the body and the environment. Bilateral lesions to such areas may lead to a virtual breakdown of information integration in the thalamocortical system [22,24]. A global, persistent disruption of consciousness can also be produced by focal lesions of paramedian mesodiencephalic structures, which include the intralaminar thalamic nuclei. Most likely, such focal lesions are catastrophic because the strategic location and connectivity of paramedian structures ensure that distributed cortico-thalamic loops can work together as a system.
x. Statements about the lack of direct contributions to consciousness of basal ganglia loops need to be qualified due to the difficulty of evaluating the precise effects of their selective inactivation, as well as to the unreliability of introspective assessments about the richness of one's experience, especially after brain lesions. Similar considerations apply to brain structures not discussed here, such as the claustrum, the amygdala, and the basal forebrain.
xi. A similar kind of analysis could be applied to other neurological disconnection syndromes.
xii. An explanation in terms of reduced degrees of freedom may also apply to loss of consciousness in absence and other seizures, during which neural activity is extremely high and near-synchronous over many cortical regions (Tononi, unpublished results).
xiii. While we do not yet have such a tight case for the neural correlate of blue, we are close to it with motion sensitive cells in area MT and in somatosensory cortex, at least in monkeys [64].
xv. In this sense, a particular conscious experience, its meaning, and the underlying informational relationships within a complex end up being one and the same thing. Such internalistic, relationally defined meanings generally relate to and ultimately derive from entities in the world. To the extent that the brain has a long evolutionary history and is shaped by experience, it is clear that internally specified meanings (and conscious states) bear an adaptive relationship to what is out there.
Acknowledgements
I thank Chiara Cirelli, Lice Ghilardi, Sean Hill, Marcello Massimini, and Olaf Sporns for helpful discussions.
Figures and Tables
Figure 1 Effective information, minimum information bipartition, and complexes. a. Effective information. Shown is a single subset S of 4 elements ({1,2,3,4}, blue circle), forming part of a larger system X (black ellipse). This subset is bisected into A and B by a bipartition ({1,3}/{2,4}, indicated by the dotted grey line). Arrows indicate causally effective connections linking A to B and B to A across the bipartition (other connections may link both A and B to the rest of the system X). To measure EI(A→B), maximum entropy Hmax is injected into the outgoing connections from A (corresponding to independent noise sources). The entropy of the states of B that is due to the input from A is then measured. Note that A can affect B directly through connections linking the two subsets, as well as indirectly via X. Applying maximum entropy to B allows one to measure EI(B→A). The effective information for this bipartition is EI(A B) = EI(A→B) + EI(B→A). b. Minimum information bipartition. For subset S = {1,2,3,4}, the horizontal bipartition {1,3}/{2,4} yields a positive value of EI. However, the bipartition {1,2}/{3,4} yields EI = 0 and is a minimum information bipartition (MIB) for this subset. The other bipartitions of subset S = {1,2,3,4} are {1,4}/{2,3}, {1}/{2,3,4}, {2}/{1,3,4}, {3}/{1,2,4}, {4}/{1,2,3}, all with EI>0. c. Analysis of complexes. By considering all subsets of system X one can identify its complexes and rank them by the respective values of Φ – the value of EI for their minimum information bipartition. Assuming that other elements in X are disconnected, it is easy to see that Φ>0 for subset {3,4} and {1,2}, but Φ = 0 for subsets {1,3}, {1,4}, {2,3}, {2,4}, {1,2,3}, {1,2,4}, {1,3,4}, {2,3,4}, and {1,2,3,4}. Subsets {3,4} and {1,2} are not part of a larger subset having higher Φ, and therefore they constitute complexes. This is indicated schematically by having them encircled by a grey oval (darker grey indicates higher Φ). Methodological note. In order to identify complexes and their Φ(S) for systems with many different connection patterns, each system X was implemented as a stationary multidimensional Gaussian process such that values for effective information could be obtained analytically (details in [8]). Briefly, in order to identify complexes and their Φ(S) for systems with many different connection patterns, we implemented numerous model systems X composed of n neural elements with connections CONij specified by a connection matrix CON(X) (no self-connections). In order to compare different architectures, CON(X) was normalized so that the absolute value of the sum of the afferent synaptic weights per element corresponded to a constant value w<1 (here w = 0.5). If the system's dynamics corresponds to a multivariate Gaussian random process, its covariance matrix COV(X) can be derived analytically. As in previous work, we consider the vector X of random variables that represents the activity of the elements of X, subject to independent Gaussian noise R of magnitude c. We have that, when the elements settle under stationary conditions, X = X * CON(X) + cR. By defining Q = (1-CON(X))-1 and averaging over the states produced by successive values of R, we obtain the covariance matrix COV(X) = <X*X> = <Qt * Rt * R * Q> = Qt * Q, where the superscript t refers to the transpose. Under Gaussian assumptions, all deviations from independence among the two complementary parts A and B of a subset S of X are expressed by the covariances among the respective elements. Given these covariances, values for the individual entropies H(A) and H(B), as well as for the joint entropy of the subset H(S) = H(AB) can be obtained as, for example, H(A) = (1/2)ln [(2π e)n|COV(A)|], where |•| denotes the determinant. The mutual information between A and B is then given by MI(A;B) = H(A) + H(B) - H(AB). Note that MI(A:B) is symmetric and positive. To obtain the effective information between A and B within model systems, independent noise sources in A are enforced by setting to zero strength the connections within A and afferent to A. Then the covariance matrix for A is equal to the identity matrix (given independent Gaussian noise), and any statistical dependence between A and B must be due to the causal effects of A on B, mediated by the efferent connections of A. Moreover, all possible outputs from A that could affect B are evaluated. Under these conditions, EI(A→B) = MI(AHmax;B). The independent Gaussian noise R applied to A is multiplied by cp, the perturbation coefficient, while the independent Gaussian noise applied to the rest of the system is given by ci, the intrinsic noise coefficient. Here cp = 1 and ci = 0.00001 in order to emphasize the role of the connectivity and minimize that of noise. To identify complexes and obtain their capacity for information integration, one considers every subset S of X composed of k elements, with k = 2,..., n. For each subset S, we consider all bipartitions and calculate EI(A B) for each of them. We find the minimum information bipartition MIB(S), the bipartition for which the normalized effective information reaches a minimum, and the corresponding value of Φ(S). We then find the complexes of X as those subsets S with Φ>0 that are not included within a subset having higher Φ and rank them based on their Φ(S) value. The complex with the maximum value of Φ(S) is the main complex. MATLAB functions used for calculating effective information and complexes are at .
Figure 2 Effective information matrix and activity states for two complexes having the same value of Φ. a. Causal interactions diagram and analysis of complexes. Shown are two systems, one with a "divergent" architecture (left) and one with a "chain" architecture (right). The analysis of complexes shows that both contain a complex of four elements having a Φ value of 10. b. Effective information matrix. Shown is the effective information matrix for the two complexes above. For each complex, all bipartitions are indicated by listing one part (subset A) on the upper row and the complementary part (subset B) on the lower row. In between are the values of effective information from A to B and from B to A for each bipartition, color-coded as black (zero), red (intermediate value) and yellow (high value). Note that the effective information matrix is different for the two complexes, even though Φ is the same. The effective information matrix defines the set of informational relationships, or "qualia space" for each complex. Note that the effective information matrix refers exclusively to the informational relationships within the main complex (relationships with elements outside the main complex, represented here by empty circles, do not contribute to qualia space). c. State diagram. Shown are five representative states for the two complexes. Each is represented by the activity state of the four elements of each complex arranged in a column (blue: active elements; black: inactive ones). The five states can be thought of, for instance, as evolving in time due the intrinsic dynamics of the system or to inputs from the environment. Although the states are identical for the two complexes, their meaning is different because of the difference in the effective information matrix. The last four columns represent four special states, those corresponding to the activation of one element at a time. Such states, if achievable, would correspond most closely to the specific "quale" contributed by that particular element in that particular complex.
Figure 3 Information integration for a thalamocortical-like architecture. a. Optimization of information integration for a system that is both functionally specialized and functionally integrated. Shown is the causal interaction diagram for a network whose connection matrix was obtained by optimization for Φ (Φ = 74 bits). Note the heterogeneous arrangement of the incoming and outgoing connections: each element is connected to a different subset of elements, with different weights. Further analysis indicates that this network jointly maximizes functional specialization and functional integration among its 8 elements, thereby resembling the anatomical organization of the thalamocortical system [8]. b. Reduction of information integration through loss of specialization. The same amount of connectivity, distributed homogeneously to eliminate functional specialization, yields a complex with much lower values of Φ (Φ = 20 bits). c. Reduction of information integration through loss of integration. The same amount of connectivity, distributed in such a way as to form four independent modules to eliminate functional integration, yields four separate complexes with much lower values of Φ (Φ = 20 bits).
Figure 4 Information integration and complexes for other neural-like architectures. a. Schematic of a cerebellum-like organization. Shown are three modules of eight elements each, with many feed forward and lateral connections within each module but minimal connections among them. The analysis of complexes reveals three separate complexes with low values of Φ (Φ = 20 bits). There is also a large complex encompassing all the elements, but its Φ value is extremely low (Φ = 5 bits). b. Schematic of the organization of a reticular activating system. Shown is a single subcortical "reticular" element providing common input to the eight elements of a thalamocortical-like main complex (both specialized and integrated, Φ = 61 bits). Despite the diffuse projections from the reticular element on the main complex, the complex comprising all 9 elements has a much lower value of Φ (Φ = 10 bits). c. Schematic of the organization of afferent pathways. Shown are three short chains that stand for afferent pathways. Each chain connects to a port-in of a main complex having a high value of Φ (61 bits) that is thalamocortical-like (both specialized and integrated). Note that the afferent pathways and the elements of the main complex together constitute a large complex, but its Φ value is low (Φ = 10 bits). Thus, elements in afferent pathways can affect the main complex without belonging to it. d. Schematic of the organization of efferent pathways. Shown are three short chains that stand for efferent pathways. Each chain receives a connection from a port-out of the thalamocortical-like main complex. Also in this case, the efferent pathways and the elements of the main complex together constitute a large complex, but its Φ value is low (Φ = 10 bits). e. Schematic of the organization of cortico-subcortico-cortical loops. Shown are three short chains that stand for cortico-subcortico-cortical loops, which are connected to the main complex at both ports-in and ports-out. Again, the subcortical loops and the elements of the main complex together constitute a large complex, but its Φ value is low (Φ = 10 bits). Thus, elements in loops connected to the main complex can affect it without belonging to it. Note, however, that the addition of these three loops slightly increased the Φ value of the main complex (from Φ = 61 to Φ = 63 bits) by providing additional pathways for interactions among its elements.
Figure 5 Information integration and complexes after anatomical and functional disconnections. a. Schematic of a split-brain-like anatomical disconnection. Top. Shown is a large main complex obtained by connecting two thalamocortical-like subsets through "callosum-like" reciprocal connections. There is also a single element that projects to all other elements, representing "subcortical" common input. Note that the Φ value for the main complex (16 elements) is high (Φ = 72 bits). There is also a larger complex including the "subcortical" element, but its Φ value is low (Φ = 10). Bottom. If the "callosum-like" connections are cut, one obtains two 8-element complexes, corresponding to the two "hemispheres", whose Φ value is reduced but still high (Φ = 61 bits). The two "hemispheres" still share some information due to common input from the "subcortical" element with which they form a large complex of low Φ. b. Schematic of a functional disconnection. Top. Shown is a large main complex obtained by linking with reciprocal connections a "supramodal" module of four elements (cornerstone) with a "visual" module (to its right) and an "auditory" module (below). Note that there are no direct connections between the "visual" and "auditory" modules. The 12 elements together form a main complex with Φ = 61 bits. Bottom. If the "auditory" module is functionally disconnected from the "supramodal" one by inactivating its four elements (indicated in blue), the main complex shrinks to include just the "supramodal" and "visual" modules. In this case, the Φ value is only minimally reduced (Φ = 57 bits).
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| 15522121 | PMC543470 | CC BY | 2021-01-04 16:03:46 | no | BMC Neurosci. 2004 Nov 2; 5:42 | utf-8 | BMC Neurosci | 2,004 | 10.1186/1471-2202-5-42 | oa_comm |
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BMC Musculoskelet DisordBMC Musculoskeletal Disorders1471-2474BioMed Central London 1471-2474-5-461556373210.1186/1471-2474-5-46Research ArticleNodular osteochondrogenic activity in soft tissue surrounding osteoma in neurogenic para osteo-arthropathy: morphological and immunohistochemical study Youssefian T [email protected] R [email protected] R [email protected] C [email protected] A [email protected] P [email protected] A [email protected] M [email protected] SYMPATHOS Laboratory, 67 boulevard du Général Martial Valin, 75015, Paris, France2 Medical Imaging department, Raymond Poincaré teaching hospital, APHP 104 Boulevard Raymond Poincaré, 92380, Garches, France3 Orthopedic deparment, Raymond Poincaré teaching hospital, APHP 104 Boulevard Raymond Poincaré, 92380, Garches, France4 Rehabilitation department, Raymond Poincaré teaching hospital, APHP 104 Boulevard Raymond Poincaré, 92380, Garches, France2004 25 11 2004 5 46 46 30 12 2003 25 11 2004 Copyright © 2004 Youssefian et al; licensee BioMed Central Ltd.2004Youssefian et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Neurogenic Para-Osteo-Arthropathy (NPOA) occurs as a consequence of central nervous system injuries or some systemic conditions. They are characterized by bone formation around the main joints.
Methods
In order to define some biological features of NPOAs, histological and immunohistological studies of the soft tissue surrounding osteoma and Ultrasound examination (US) of NPOA before the appearance of abnormal ossification on plain radiographs were performed.
Results
We have observed a great number of ossifying areas scattered in soft tissues. US examination have also shown scattered ossifying areas at the early stage of ossification. A high osteogenic activity was detected in these tissues and all the stages of the endochondral process were observed. Mesenchymal cells undergo chondrocytic differentiation to further terminal maturation with hypertrophy, which sustains mineralization followed by endochondral ossification process.
Conclusion
We suggest that periosteoma soft tissue reflect early stage of osteoma formation and could be a model to study the mechanism of osteoma formation and we propose a mechanism of the NPOA formation in which sympathetic dystony and altered mechanical loading induce changes which could be responsible for the cascade of cellular events leading to cartilage and bone formation.
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Background
Neurogenic Para Osteo-Arthropathies (NPOA) occurs in patients with brain or spinal cord injury, hemiplegias, various encephalopathies, tetanus [1] or neurological disregulation [2]. In this process, new bone named "osteoma" forms in extraskeletal areas which in normal condition do not ossify. NPOA were first described by Dejerine and Cellier [3] from observations of medullary wounded soldiers. They proposed the term NPOA, though other terms are used, such as: neurogenic osteoma, ossifying myositis in paraplegic, ectopic ossification, heterotopic ossification, etc. NPOAs have also been described as complications of many systemic diseases [4], acute pancreatitis, toxic syndromes and others [5].
The first clinical manifestations are local inflammatory signs, tumefaction and progressively limited range of motion of the involved joint region. Those appear between the second and tenth weeks after the onset of the pathological condition [6]. Despite anti-inflammatories treatment to prevent NPOA [7], excision of the newly formed bone called "osteoma", is the only known therapy.
As shown by radiographic and scintigraphic observations, heterotopic bone formation evolves from an early appearance of soft tissue densification and attenuation of the muscle signal to a mineral signal [8]. After 6 months, osteoma rarely increases in amount, but some further maturation occurs. As an assumption based on the fading of technetium fixation, the lesion is supposed to be mature after 1 to 1.5 years [9,10]. Hence, the process of NPOA formation seems to be frozen at the time of osteoma mineralization.
Very little is known about the pathophysiology of NPOA formation. Assuming such a freezing of the process of NPOA formation and an involvement of the periosteoma tissues in the reported relapses following surgery, we postulated that the periosteoma soft tissues could show some of the very early stages of the NPOA formation. We performed histological, histochemical and immunohistochemical studies of soft tissues dissected from the periphery of osteomas. We used samples of varying age lesions and searched for the main osteogenic and chondrogenic markers: alkaline phosphatase (ALP) activity, type I collagen and osteocalcin (OCN) for the bone [11-13], and type II collagen, sulfated and acid glycosaminoglycans, type X collagen and Vascular Endothelial Growth Factor (VEGF) for the cartilage [14]. In the light of our results, we propose a model of NPOA formation.
Methods
a)Specimen processing and histochemicals
The 28 specimens were obtained from 27 patients undergoing orthopedic surgery for osteoma excision. NPOA's were localized on: elbows (7), hips (18) and knees (3). The time from the neurologic insult ranged from 5 months to 216 months. The initial conditions were: 11 Brain Injuries (BI), 3 Spinal Cord Injury (SCI), 1 BI plus SCI, 4 strokes, and 9 patients sustained coma of various etiology (legionellose, anoxia, toxic condition, pneumonia, suicide attempt using neuroplegic).
Specimens obtained during the course of surgery, referred to in this paper as "osteoma", were immediately placed in sterile Gibco Hanks' balanced salts solution (Invitrogen, Cergy-Pontoise, France) at 4°C for transportation. The soft connective tissue was easily dissected off from the osteoma in order to exclude any part of the bony mass (Fig 1). The specimens were fixed in 4% paraformaldehyde in Phosphate Buffered Saline (PBS) with 0.5 M sucrose, frozen in isopentane in liquid nitrogen and stored at -86°C until embedding in OCT compound (Tissue-Tek, Sakuran Zoeterwoude, The Netherlands). Cryosectioning was performed on a Leica CM 3050 S cryostat (Leica Micro-Systems, Reil-Malmaison, France) at a thickness of 7 μm. Histochemical staining was performed according to standard protocols: Erlich's hematoxylin-eosin for general topographic staining, alcian blue pH1 for sulfated acid glycosaminoglycans, Von Kossa to show calcified areas, Oil red O to identify lipids, and Van Gieson Picro-Fuchsine for collagen distribution. The ALP-activity was demonstrated by using the Sigma procedure n°86 (Sigma Diagnostics, Saint Quentin Fallier, France). Slides were examined on a Leica DMR microscope (Leica Micro-Systems, Reuil-Malmaison, France), and pictures were recorded using a CCD colour camera with the Q Fluoro and Lida software systems (Leica Micro-Systems, Reil-Malmaison, France).
Figure 1 Dissection of part of the soft tissue surrounding a piece of osteoma.
b)Immunohistochemistry
The anti-human monoclonal mouse antibodies against type II collagen (NeoMarkers Inc., CA, USA), OCN (Interchim, Montlucon, France), anti-human polyclonal goat antibody against type I collagen (Santacruz Biotechnology, CA, USA), anti-rat polyclonal rabbit antibody against type X collagen (Calbiochem-Novabiochem, CA, USA) and anti-human polyclonal rabbit antibody against VEGF (Santacruz Biotechnology, CA, USA) were used at 1 mg/ml. Peroxidase-conjugated goat anti-mouse was purchased from Immunovision Technologies (CA, USA), peroxidase-conjugated goat anti-rabbit from Dako (Dako Corporation, CA, USA) and peroxidase-conjugated donkey anti-goat from Santacruz Biotechnology (CA, USA). They were used at a 1/20, 1/50 and 1/100 dilution, respectively. Frozen sections post-fixed with acetone were treated with hyaluronidase (5 mg/ml in Tris Buffered Saline) one hour at 37°C. For type X collagen immunostaining slides were treated first 15 minutes at 37°C with hyaluronidase (5 mg/ml in PBS), washed two times in PBS, then 15 minutes with chondroitinase (2 U/ml in PBS), and washed two times in PBS. Immunohistochemistry was performed using ABC method. Briefly, endogenous peroxydase activity was eliminated with 0.3% H2O2 until total clearing of oxygen bubbles. Non-specific protein binding was performed with 10% non-immune serum, same host as secondary antibody, in PBS with 1% Bovin Serum Albumin (Sigma, Saint Quentin Fallavier, France). Sections were then incubated with primary antibodies for 1 hour at room temperature, or 24 hours at 4°C with anti-type I collagen. Excess antibody was removed by washing the sections with PBS. Sections were incubated 1 hour with horseradish peroxydase-labeled secondary antibody diluted in PBS. 3-3'diaminobenzidine (DAB) solution (Dako Corporation, CA, USA) was then added in order to obtain staining. Sections were counter-stained with hematoxylin-eosin or nuclear red/eosin, dehydrated, and mounted with Mountex medium (Microm, France). Controls were systematically performed omitting the primary antibody. Slides were examined by light microscopy using a Leica DMR microscope (Leica Micro-Systems, Reil-Malmaison, France).
c)Ultrasound (US) Examination
Most of the patients were referred to Raymond Poincaré teaching hospital, at the time of surgery. US examination was performed when NPOA was clinically suspected in five patients whose rehabilitation has begun. Five hips were explored by US in two BI and three SCI. Linear 8 to 15 MHz and sectorial 4 MHz transducers (Sequoia Acuson-Siemens Erlangen) were used. A sectorial low frequency transducer was used because in the hip area NPOA can be very large and very deep especially in the gluteus area compared to the subcutaneous plane. US examination was combined with color and energy Doppler. In all cases a plain film was obtained the same day as the US examination.
Results
a)Histological and immunohistological studies
Non mineralized connective tissues from the periphery of the osteoma were examined by light microscopy on Erlich's hematoxylin-eosin-stained sections. Several kinds of tissues appeared on the slides so the diversity of these figures deserves a systematic analysis which will be completed in the next sections. Briefly, the ground basis of our preparations was a more or less fibro-cellular connective tissue displaying sometimes edema and/or necrosis. Suffering and degenerating tissues with vacuolized myofibers, thrombotic vessels and adipose tissue were often observed (Fig 2a). Muscular tissue underwent degeneration as shown by the vacuolisation or the disappearance of the internal eosinophily. Oil red-O stained adipocytes in the vicinity of some degenerating muscle with hyperplastic endomysium and perimysium (Fig 2b). In these regions, ALP activity was detected in the endomysium and perimysium cells of degenerated muscles (Fig 2b, inset).
Figure 2 a: Frozen section of soft tissue from a 72 months hip NPOA: Hematoxylin eosin (h&e): Thrombotic vessels (V), more or less advanced vacuolization/degeneration of muscular fibers (M), and adipous tissue (A). b: Oil Red-O staining : Degenerated muscle fibers (M) stained by eosin were embedded in a strongly hyperplastic perimysium. This structure was itself located inside an adipous tissue whose appearance and compartmentalization by endomysium-like sheets of cell layers suggests a muscular origin. Inset: ALP activity: the same area showed an intense ALP activity in some cells in hyperplastic perimysium. c: Hematoxylin eosin (h&e) staining showed hyperplastic intima and media.
Morphologically normal vessels were rarely observed and winding of the vasculature was an almost constant finding. Many of the vessels were thrombotic, sometimes with hyperplastic intimae or media (Fig 2c). Some perivascular cells showed ALP activity and seemed to migrate out from these proliferating zones (Fig 3). Clustered or isolated round cells with high ALP activity were also observed embedded in a high amount of collagen matrix (Fig 3, inset).
Figure 3 Frozen sections of soft tissue of an 8 months hip NPOA: Blue Alkaline Phosphatase (ALP) activity counter stained with nuclear red-eosin: perivascular cells show high ALP activity near by vessels (V) and some of these seems to migrate from these areas. Inset: Cluster and isolated rounded cells (C) with high ALP showed a more advanced stage of differentiation.
These findings point to a chondrogenic or osteogenic differentiation of formerly undifferentiated mesenchymal cells from the stroma and the vessel walls. More advanced stages of cartilaginous differentiation were frequently observed in avascular areas with varying degrees of chondrocyte maturation. Morphologically recognizable columns of chondrocyte-like cells presenting a high ALP activity were observed. Moreover, we could observe all the stages of progressive chondrocyte differentiation from quiescence to chondrocyte hypertrophy/matrix mineralization and endochondral ossification (Fig 4).
Figure 4 Frozen section of soft tissue of an 8 months hip NPOA: Blue Alkaline Phosphatase (ALP) activity counter stained with nuclear red-eosin: The successive stage of chondrocytes differentiation were observed: quiescent (Q), proliferative (P), prehypertrophic (D), hypertrophic (H) and mineralization zones. Inset: Lamellar bone deposition (L) is visualized with polarized light.
A high ALP activity was also found in cells surrounding the cartilage areas undergoing mineralization and embedded in a slight envelope of woven bone with ALP positive cells (Fig 5a). On the other hand, ALP activity of hypertrophic chondrocytes was progressively lost as mineralization occurred, thus Von Kossa staining seemed to be a negative image of ALP activity (Fig 5b).
Figure 5 Frozen sections of soft tissue of a 5 months elbow NPOA: a: Blue Alkaline Phosphatase (ALP) activity counter stained by nuclear red-eosin: A very strong ALP activity in multilayered cells surrounded a peripherically mineralized area. This area was made of woven bone (W) deposed on an hypertrophic cartilage (H) centered by prehypertrophic chondrocytes. Hypertrophic and prehypertrophic chondrocytes in the non-mineralized matrix displayed ALP activity. b: Vonkossa staining of a next section counter stained by h&e: Von Kossa stain was a negative image of the ALP activity. Osteoid matrix was deposed upon the calcified cartilage matrix and underwent mineralization. Palissade-arranged cells lined this osteoid and displayed morphological image of osteoblast-like cells (OB). c: Oscteocalcin immunolabelling of a next section of the same specimen counter stained by h&e: Osteocalcin immunoreactivity in the eosinophilic osteoid at the border of the mineralized woven bone and sligtly on osteoblast-like cells (OB) lining this zone. Slight labelling was also present in the woven bone (W), but not in the cartilage (C) areas. d: Type X collagen immunolabelling of a next section of the same specimen counter stained by h&e: Type X collagen immunoreactivity was found in the mineralized cartilage area up to the woven bone. It also streched over most of the fibrous tissue (F) surrounding the mineralized area. Inset: It was also present in hypertrophic chondrocytes and their matrix. e: Type II collagen immunolabelling of this specimen conter stain by h&e: The matrix of the columnar cartilage was immunolabelled with type II collagen antibody. Remnants of eosinophilic degenerated muscle fibers were interspersed among the cartilage. Inset: The type II colllagen immunoreactive areas displayed an heavy ALP activity.
Bands of eosinophilic material stained partially by Von Kossa underlined some borders of the mineralized cartilage. These osteoid-like bands were lined by cells which morphologically appeared to be osteoblast-like cells.
To confirm the osteoblastic nature of these cells, immunolabelling for OCN was performed (Fig 5c). In front of the osteoblastic-like cells a consistent matricial OCN immunoreactivity was evident onto the eosinophilic matrix underlining the mineralized cartilage. We have also observed deposition of OCN into woven bone formed adjacent to cartilage.
In order to document the collagenic composition of these tissues and confirm their osseous or cartilaginous nature, immunolabelling for type I, II and X collagens were performed.
Conspicuous immunolabelling for type X collagen was observed in most of the hypertrophic chondrocytes and in their matrix. The immunoreactivity became more intense nearby the mineralization front. In addition the matrix of the fibrous and non cartilage-like tissue around some mineralized areas was unexpectedly labelled (Fig 5d). Control samples in which primary antibody incubation was omitted were clearly negative (data not shown).
Distribution of type II collagen was limited to the matrix of non hypertrophic and prehypertrophic chondrocytes with high ALP activity (Fig 5e). Nevertheless some type II collagen immunoreactivity could sometimes be detected in the hypertrophic areas.
Bone deposition was frequently observed by polarized light and confirmed by type I collagen immunostaining (Fig 6). Type I collagen was located in the osteoblast-containing matrix which formed and lined up along spicules of calcified cartilage. Osteocytes were trapped in the lamellar and woven bone with type I collagen immunoreactivity.
Figure 6 Frozen section of soft tissue of a 24 months hip NPOA: Type I collagen immunolabelling counter stained by h&e: Type I collagen was the main constituant of the matrix in the primary osteons (OS) and non organized woven bone (W). Inset: lamellar bone deposition observed by polarized light
b)Serial sections
In order to determine the chronology of events at work in the described endochondral ossification, we performed serial cryosectioning of samples in which a cortical bone followed soft tissue. These samples seem to be appropriate to have all stages of osteogenesis
One of the specimens appeared to contain an aponeurotic tissue which showed signs of bursitis. In a highly cellular tissue we observed a high angiogenic activity. Bundles of vessels surrounded amorphous and avascular zones (Fig 7a). Some of these vessels expressed slight ALP activity which became more and more intense in the vicinity of the acellular areas. Then the avascular areas were replaced by nodules of cartilage with prehypertrophic and hypertrophic chondrocytes. These areas were stained by alcian blue pH1, showing chondroitin sulfate accumulation in their matrix (Fig 7b).
Figure 7 Serial sections of soft tissue of 24 months hip NPOA: a: ALP activity counter stained with nuclear red-eosin: Slide 118; Important angiogenesis encircles avascular areas. Many of these vessels express ALP activity (ALP+). b: Alcian bleu pH = 1 staining: Slide 70: chondroitin sulfate accumulation in cartilage. c: ALP activity counter stained with nuclear red-eosin: Slide 71. d: Von Kossa staining countre stained with h&e: Slide 65.
At this stage a strong ALP activity was observed in the cells surrounding the cartilage zone as well as in non mineralized hypertrophic areas (Fig 7c). Finally Von Kossa staining revealed the matrix mineralization (Fig 7d). Immunolabelling of these sections with type II collagen antibody demonstrated a circle of prehypertrophic chondrocytes (Fig 8a). The matrix of the fibrous tissue outside this mineralized nodule was immunoreactive to type I collagen antibody (Fig 8b). Type X collagen recovered the nodule of hypertrophic chondrocytes and the rest of this section showing a high osteogenic activity (Fig 8c). OCN immunolabelling revealed exactly the same zone stained by Von Kossa showing deposition of OCN on mineralized zone (Fig 8d). As previously described OCN was detected in the osteoblast-like cells lining the newly lay down osteoid as well as in the newly formed woven bone and on areas of membranous bone formation (Fig 8e). OCN was also observed in some cells around the vessels near by the areas of osteogenesis.
Figure 8 Immunological study of serial sections: a: Type II Collagen immunolabellingcountre stained with h&e: Slide 69: Type II collagen had the same pattern of ALP activity of nodule showing prehypertrophic chondrocytes in nonmineralized zone. b: Type I collagen Immunolabelling countre stained with h&e: Slide 72; Type I collagen expression encercled the mineralized nodule. c: Type X collagen immunolabelling countre stained with h&e:Slide 73: Type X collagen was expressed by most of cells and distributed in their matrix. d: OCN Immunolabelling countre stained with h&e: Slide 64: OCN was expressed by hypertrophic chondrocytes. e: OCN immunolabelling counter stained with h&e: Activated osteoblasts (OB) and woven bone (W) are strongly labelled. Some capillaries (C) near by these areas express osteocaline too. f: VEGF immunolabelling counter stained with h&e: VEGF was expressed by some hypertrophic chondrocytes (H). Activated and non activated osteoblasts-like cells (OB) lining the cartilage express also VEGF. Inset: Clustered and isolated cells in the matrix, surrounding hypertrophic chondrocytes, which could be destinated to capillary or osteoblast formation, are also labelled by VEGF.
To further confirm the process of endochondral ossification, we decided to search for VEGF expression. Immunolabelling of these tissues with VEGF monoclonal antibody, showed a labelling of the hypertrophic chondrocytes as well as an intense labelling of activated osteoblats lining the osteoid. Some clusters of rounded cells also expressed VEGF in the fibrous part of these preparations (Fig 8f).
c)Ultrasound examination and digital radiographs of suspected NPOA
US examination showed a huge focal disorganization of the muscles in the area of the suspected NPOA. Normal longitudinal muscular striation disappeared and was replaced by a relatively well defined mass with a very heterogeneous echostructure. The masses ranged from six to eleven centimeters of long axis. No scattered ossified areas were detected by US at this stage. Hypervascularization was detected on Doppler examination inside and outside the NPOA tumors (Fig 9a).
Figure 9 Ultrasound and color Doppler examination and digital radiographs of suspected NPOA a: Axial US view combined with color Doppler of the anterior side of the left hip in a paraplegic patient presenting acute limitation and inflammation of this joint. The striation of the psoas iliaque muscle, normally detectable at the anterior part of the hip joint with US examination, has disappeared. A relatively well defined mass (orange arrows) is detectable at the anterior part of the left femoral head (F). This mass is very heterogeneous with mixed hypo and hyper echoic areas. Color Doppler enables visualization of vessels in the mass (red and blue Doppler signals). A mass effect is visible on the femoral vessels (top right of the view). b: Same patient, one week later, axial US view of the posterior side of the left hip. The classical zone phenomenon (ZP) is detectable with a central hypoechoic area surrounded by hyper echoic nodules with posterior attenuation(black arrows). c: Axial US examination at the same day combined with color Doppler view of the posterior side of the left hip. A posterior mass (orange arrows) is also visible in the gluteal muscles, very heterogeneous with mixed hypo and hyper echoic areas. Color Doppler reveals large vessels in the mass (red and blue Doppler signals). d: Plain radiographs of the left hip obtained the same day as first US examination: Any sign of ossification is visible while a well defined mass is detected by US examination. e: Plain radiographs of the left hip obtained two weeks after: Early anterior and posterior NPOA ossification is only slightly visible two weeks (Orange arrows) after the initial clinical signs whereas the US examination was initially positive.
Classical zone phenomenon previously described in the literature [15,16] was visible with a central hypo echoic area surrounded by small (less than one centimeter) hyper echoic nodules with posterior attenuation (Fig 9b). At this stage color Doppler examination showed increasing angiogenesis with the appearance of large vessels in the tumor mass (Fig 9c).
The zone phenomenon became visible on the second US examination performed one week later (Fig 9d).
The opacity of the early ossification became slightly visible on plain films only two weeks after the US detection of zone phenomenon (Fig 9e).
Discussion
NPOA pathogenesis is still poorly understood, and the exact environmental and humoral conditions underlying the ossifying process are not clear. In this study we postulated that periosteoma soft tissues display interrupted early stages of osteoma formation, which could help us to understand the chronology as well as the mechanism of osteoma formation. Thus, histological and immunohistological experiments were performed on 28 specimens. Moreover, ultrasound examination of suspected NPOA tumor on five patients permitted to follow osteoma formation in the early stages before ossification.
Histological studies have shown varying amount of muscle and connective tissue degeneration in which some areas underwent reorganization. Islands of cartilage, woven bone, and mature lamellar bone were a constant finding in most of specimens, whatever the estimated age of the studied lesion. The spatial organization of chondrocytes was reminiscent of the epiphyseal growth plate or of the fracture callus organization [17]. In the developmental pathway leading to skeletogenesis, undifferentiated mesenchymal cells pass sequentially through at least four differentiation stages: committed mesenchymal cells which produce type I collagen and possibly basal level of type II collagen, quiescent chondrocytes, then proliferating chondrocytes characterized by the synthesis of a large amount of type II collagen and sulfated proteoglycans, and ultimately hypertrophic chondrocytes characterized by the synthesis of type X collagen. Then these hypertrophic chondrocytes allow the mineralization of the matrix elaborated and induce vascular invasion by releasing VEGF [18,19]. Studies of our specimens showed the same sequence of events. These results suggest that endochondral osteogenesis is the major pathway in the NPOA bone formation. Nevertheless in view of some features suggesting bone deposition without any cartilage scaffold we cannot exclude the occurrence of membranous bone formation.
Some sections showed a high expression of type X collagen in the hypertrophic chondrocytes areas, and unexpectedly in non differentiated mesenchymal tissue. As this labelling did not occur in other fibrocellular areas it was unlikely to be produced by a binding of the antibody to some other matricial component of the extra cellular matrix. It was shown, that type X collagen is not only associated with chondrocyte proliferation and hypertrophy, but also with resting chondrocytes, cells at the border of the perichondrium and resting cartilage of the fetal femoral head [20].
The finding of still degenerating muscular fibers and early chondro-osteogenesis accompanied by heavy ALP activity in large parts of the soft tissues in old lesions (till 8 years) is singular. Except when serial sectioning was performed the studied specimens were carefully dissected from the osteoma during the surgery or at the fixation time. Therefore the extra-osteoma localization of these tissues can without any doubt be assumed. In one study [21] "recent POA" was described as a kind of fibrocellular tissue including vascular stasis, overabundance of micro vessels, myolysis, edematous fibrocellular tissue, with chondrogenesis, osteogenesis and lamellar bone apposition on mineralized structures. This description of "recent POA" is perfectly in agreement with our description of the periosteoma tissues. However, we found each of these elements notwithstanding the advanced age of some lesions. Most of our specimens contained clusters of ALP positive cells in the undifferentiated fibrous connective tissue, suggesting the presence of preosteogenic cells. This fact and the presence of cartilage and bone at varying stages of maturity are indicative of a persistent chondro-osteogenic activity in these tissues. This point sounds of interest as regards to contingencies of heterotopic bone formation relapse following surgical excision. The occurrence of relapses was reported to correlate neither to the classical criteria of osteoma maturation nor to the amount of heterotopic bone left after excision nor to the age of the lesion [22-24]. The occurrence of relapses could be linked to the activation of the still process.
It was claimed that osteoma develops in the periphery of a muscle in which some myofibers undergo degeneration [25,26], and that osteoma involves the muscles. However the endochondral process of bone formation described here is in agreement with the results of various bone induction experiments in muscle [27-30]. Moreover, US examination of suspected NPOA tumor at the early stage showed huge focal disorganisation of muscle surrounding the hip joint and disappearance of normal longitudinal muscular striation of the psoas iliaque muscle replaced by masses with heterogeneous echostructure. This finding argues in favour of an intramuscular beginning of the process. We could not set up the place and part of the muscle degeneration process in the heterotopic bone formation. Though the process described here resembles by some aspects the Fibrodysplasia Ossificans Progressiva (FOP) where endochondral ossification was demonstrated in muscle and adjacent connective tissue [31,32]. These reports combined with our finding of endomysium-perimysium hyperplasia in the degenerating muscles with ALP activity and US result, could suggest a role for the muscular tissue and especially mesenchymal cells from endomysium and perimysium in the setting of the heterotopic bone formation process. Vascular disorders, such as vascular disruption or compression [33] and venous stasis together with a cascade of inflammatory reactions including release of enzymes from necrotic tissues and α-adrenergic mediated vasoconstriction, lead to the formation of hypoxic zones beneath the nervous injury.
On the other hand it has been shown that hypoxia promotes chondrocyte differentiation [34,35]. In our specimens, high amount of thrombotic vessels were indicative of the hypoxic status of these tissues. Color Doppler examination of tumor during the first clinical signs of NPOA, showed an avascular area in which some vessels appeared. These signals became more intense one week later. This tends to confirm hypoxic status of the initial lesion. This suggests that a timely defined hypoxic condition in tissue induces chondrocytes differentiation.
The volume of the tumor is acquired during the very early stages of NPOA. After tumefaction there is no real change in the tumor size [2]. Moreover chondrocytes in the hypertrophic stage increase volume 10 times [36]. So the chondrocyte hypertrophy could be the cause of the tumefaction and so determines the final size of the tumor. Then, the related vascular sign occurs and color Doppler showed the first sign of increasing angiogenesis.
On tissue section, VEGF immunolabelling revealed a more intense expression by osteoblasts and osteoblast-like cells, in addition to its expected expression by hypertrophic chondrocytes. VEGF induces neoangiogenesis and then endochondral ossification occurs. The restoration of normoxic conditions promote the onset of lamellar ossification and hamper any other de novo cartilaginous differentiation. US examination of the same NPOA tumor one week later showed scattered ossifying nodules. This nodular activity is in accordance with our histological finding in periosteoma soft tissue. Extense of the hypoxic area determines the size of each nodule. Areas with important hypoxia induce larger cartilage zones which could join together after ossification but small nodules remain scattered in periosteoma soft tissue. These results confirm that, periosteoma soft tissue has the same pattern of early stage of osteoma ossification, and could be a model of ossification for further studies.
Moreover, NPOA occurs in neurologically deficient patients with altered mechanical loading. Mechanical loading is of pivotal importance to the development, function and repair of all tissues in the musculoskeletal system. In nonfunctional joints, as it is the case of these patients, the absence or reduction of intermittent hydrostatic pressure in the articular cartilage could permit cartilage degeneration and the progressive advance of the ossification These mechanical influence could indeed shed light on the finding that osteomas only occur near the main joints.[37]
Moreover, Carter and associates have also shown that intermittently applied shear stresses (or strain energy) promote endochondral ossification and that intermittently applied hydrostatic compression inhibits or prevents cartilage degeneration and ossification. Thus, the imbalance of these forces among these patients can promote endochondral ossification of the cartilage nodules in areas of high shear (deviatoric) stresses[38].
Urist demonstrated that the induced endochondral bone is resorbed once the inductive agent has disappeared [30]. We have not seen many osteoclasts nor other multinucleated cells in ours preparations, and the literature does not report on NPOA regression except in people under 15 years of age [39]. Therefore it would be interesting to study the regulation of osteoclasts and the remodelling in such model.
We therefore propose a model of the NPOA lesion formation. The sympathetic hyperactivity causes major changes in the peripheral vascular dynamics. As related some of these changes end in vascular stasis and/or thrombosis [2]. Edema follows on in the connective tissue which sustains some amount of necrosis. The trauma, subsequent neurological conditions and perhaps systemic factors [40,41] induce major changes in these tissues. Regenerating celles under low oxygen pressure/high dilatational hydrostatic forces [34,42,43] transmogrify themselves into chondrogenic cells. Then cartilage differentiation gets moving on until hypertrophy of the chondrocytes and cartilage matrix mineralization. The cell hyperplasia and hypertrophy of the chondrocytes could account for the solid swelling clinically noticed soon after the onset of the clinical signs of NPOA. Like they did in the developing limb, some competent cells lining the cartilage rudiments undergo the osteoblastic differentiation and lay down osteoid on the cartilage. Concomitantly the cartilage hypertrophy induces angiogenesis, osteoid deposition, and some extent of cartilage resorption. Remodelling of the mineralized cartilage and woven bone occurs. The osteoma could then control the process and inhibit any further bone formation.
Some questions remain which deserve further studies. Why do NPOAs form only around the main joints? Why are they not resorbed as does any intramuscularly implanted bone graft [44]. What freezes the osteoma bone growth and the process of bone formation? Studies are ongoing in order to find some clues about the regulation of this heterotopic bone formation.
Conclusion
In conclusion, our results demonstrate that periosteoma soft tissues are a replica of the early stages of osteoma formation, and could be used as a model for NPOA formation. We propose also a mechanism for osteoma formation in which hypoxia is a major cause of nodular osteoinduction and chondrocyte differentiation. Combination of hypoxia and applied shear stresses induce endochodral ossification. Finally our results indicate implication of different types of mesenchymal cells in NPOA formation but US examination support specially muscular origin hypothesis.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15563732 | PMC543471 | CC BY | 2021-01-04 16:39:21 | no | BMC Musculoskelet Disord. 2004 Nov 25; 5:46 | utf-8 | BMC Musculoskelet Disord | 2,004 | 10.1186/1471-2474-5-46 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1721551129410.1186/1471-2105-5-172Methodology ArticleIncremental genetic K-means algorithm and its application in gene expression data analysis Lu Yi [email protected] Shiyong [email protected] Farshad [email protected] Youping [email protected] Susan J [email protected] Dept. of Computer Science, Wayne State University, Detroit, MI 48202, USA2 Department of Biological Sciences, the University of Southern Mississippi, Hattiesburg 39406, USA3 Division of Biology, Kansas State University, Manhattan, KS 66506, USA2004 28 10 2004 5 172 172 10 3 2004 28 10 2004 Copyright © 2004 Lu et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.
Results
In this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at
Conclusions
Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.
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Background
In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis (see [1] for an excellent survey). With the advancement in Microarray technology, it is now possible to observe the expression levels of thousands of genes simultaneously when the cells experience specific conditions or undergo specific processes. Clustering algorithms are used to partition genes into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.
Among the various clustering algorithms, K-means [2] is one of the most popular methods used in gene expression data analysis due to its high computational performance. However, it is well known that K-means might converge to a local optimum, and its result is subject to the initialization process, which randomly generates the initial clustering. In other words, different runs of K-means on the same input data might produce different solutions.
A number of researchers have proposed genetic algorithms [3-6] for clustering. The basic idea is to simulate the evolution process of nature and evolve solutions from one generation to the next. In contrast to K-means, which might converge to a local optimum, these genetic algorithms are insensitive to the initialization process and always converge to the global optimum eventually. However, these algorithms are usually computationally expensive which impedes the wide application of them in practice such as in gene expression data analysis.
Recently, Krishna and Murty proposed a new clustering method called Genetic K-means Algorithm (GKA) [7], which hybridizes a genetic algorithm with the K-means algorithm. This hybrid approach combines the robust nature of the genetic algorithm with the high performance of the K-means algorithm. As a result, GKA will always converge to the global optimum faster than other genetic algorithms.
In [8], we proposed a faster version of GKA, FGKA that features several improvements over GKA including an efficient evaluation of the objective value TWCV (Total Within-Cluster Variation), avoiding illegal string elimination overhead, and a simplification of the mutation operator. These improvements result that FGKA runs 20 times faster than GKA [9]. In this paper, we propose an extension to FGKA, Incremental Genetic K-means Algorithm (IGKA) that inherits all the advantages of FGKA including the convergence to the global optimum, and outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value TWCV and to cluster centroids incrementally. We then propose a Hybrid Genetic K-means Algorithm (HGKA) that combines the benefits of FGKA and IGKA. We show that clustering of microarray data by IGKA method has more tendencies to group the genes with the same functional category into a given cluster.
Results
Our experiments were conducted on a Dell PowerEdge 400SC PC machine with 2.24G Hz CPU and 512 M RAM. Three algorithms, FGKA, IGKA and HGKA algorithm were implemented in C language. GKA has convergence pattern similar to FGKA and IGKA, but its time performance is worse than FGKA, see [9] for more details. In the following, we compare the time performance of FGKA and IGKA along different mutation probabilities, and then we compare the convergence property of four algorithms, IGKA, FGKA, K-means and SOM (Self Organizing Map). At the end, we check how we can combine IGKA and FGKA algorithm together to obtain a better performance.
Data sets
The two data sets used to conduct our experiments are serum data, fig2data, introduced in [11]and yeast data, chodata, introduced in [2]. The fig2data data set contains expression data for 517 genes. Each gene has 19 expression data ranges from 15 minutes to 24 hours. In other words, the number of features D is 19. According to [11], 517 genes can be divided into 10 groups. The chodata is a yeast dataset, composed of expression data for 2907 genes and the expression data for each gene ranges 0 minutes to 160 minutes, which means that the number of features D is 15. According to the description in [2], the genes can be divided into 30 groups. Since the IGKA is a stochastic algorithm, for each experiment in this study, we obtain the results by averaging 10 independent run of the program. The mutation probability, the generation number, the population number all affect the performance and convergence of FGKA and IGKA. The detailed discussion of the parameters setting can be found in [8]. In this paper, we simply adopt the result in [8], the population number is set to 50, and the generation number is set to 100. These parameter setting are safe enough to guarantee the algorithm converge to the optima.
Comparison of IGKA with FGKA on time performance
As indicated in the implementation section, the mutation probability has great impact on IGKA algorithm. We check the performance impact on IGKA in this section, and the convergence in the next section. Figure 2 shows the time performance results for these two algorithms. We can see that when the mutation probability increases, the running time increases accordingly for both algorithms. However, when the mutation probability is smaller than some threshold (0.005 for fig2data, and 0.0005 for chodata), IGKA has a better performance. Figure 2 also indicates the thresholds vary from one dataset to another. In order to achieve better performance of IGKA in large data set, mutation probability may need to be set to smaller than that in small data set. For example, in larger data set chodata, we should set the mutation probability to 0.0005 to have IGKA outperform FGKA. On the other hand, in order to have IGKA outperform than FGKA, we only need to set the mutation probability to 0.005 in the small data set fig2data. In general, the threshold value depends on the number of patterns and the number of features in the data set. It is easy to understand that the performance gained in IGKA is mainly dependent on how many patterns change their cluster memberships. So, in a large data set, even small number of mutation probability may cause many patterns change their cluster memberships.
Comparison of IGKA with FGKA, K-means and SOM on convergence
Figures 3(A) and 3(B) show the convergence of IGKA versus FGKA across different mutation probabilities based on fig2data and chodata, respectively. These two algorithms have similar convergence results. When the mutation probability changes in these two data sets, it has little impact on these two algorithms during the range that is given in Figure 3, except for the case when the mutation probability is too large. It gives an opportunity to choose IGKA with better performance without losing the convergence benefit.
We also make an interesting comparison of IGKA with FGKA, K-means and SOM on TWCV convergence. We treat each algorithm as a black box. Two data sets, the fig2data and chodata, are fed into the algorithms, and the clustering results are exported as a text file. We then use an in-house program to calculate the TWCVs for each result. The experiments on K-means and SOM algorithm are conducted on an open source software [12]. As we can see in Table 2, the IGKA and FGKA have almost similar convergence result, and much better than the convergence of K-means algorithm. The TWCV convergence of SOM is much worse than the others although these four algorithms all use Euclidian distance as their measurement. The reason why we do not include another popular clustering algorithm, hierarchical clustering algorithm is because it is hard to define the boundary among the nested clusters, which means we cannot simply define the number of cluster before running the program.
Combination of IGKA with FGKA
Figure 4 compares three algorithms, IGKA, FGKA and HGKA, based on the running times for 100 iterations. The mutation probability is set to 0.0001 for all three algorithms. It is clearly that the running time for each iteration of FGKA is much stable than others. On the other hand, the running time for IGKA is much higher than FGKA at the beginning because there are a large number of patterns change their cluster belonging during the K-means operator which cause the IGKA spend a lot of computation time. However, the running time for each iteration of IGKA decrease very sharply at late iterations. The HGKA combines the advantage of two algorithms. The turning point when HGKA uses IGKA instead of FGKA as work horse is highly data dependent. In this particular case, we check the computation time every 15 iterations. The result shows that the performance can be really improved by using HGKA when the mutation probability is small.
Discussion
The clustering results of chodata using our IGKA algorithm were evaluated according to the scheme of gene classification of MIPS Yeast Genome Database [13]. We found that genes of similar function were grouped into the same cluster. Table 3 shows 8 main clusters including 16 functional categories of genes. The results are comparable to the data of [2]. The absolute number of ORFs with functional categories in some cluster may not be always higher than Tavazoie's result, but we found that the percentage of the ORF number within functional category of each cluster in the total ORF number of each cluster is usually higher than Tavazoie's result in most cases. For example, they found that there are 40 genes in the functional category of nuclear organization distributed in their cluster 2, in which there are 186 ORFs, so their percentage is 21.5%. But we found there are 50 genes of the same functional category distributed in our cluster 16, in which there are only 133 ORFs, and our percentage is 37.6% that is significantly higher than 21.5%.
Most interestingly, we found a remarkable enrichment of ORFs for the functional category of organization of mitochondria. They are mainly located in two clusters: cluster 3 and cluster 18. Cluster 3 has 156 ORFs in total, and 111 ORFs belong to the category, resulting in a very high percentage, 71.2%. Cluster 18, has 184 ORFs in total, in which there are 105 ORFs belonging to the category and the percentage is 57.1%. The percentage of ORFs within the same function category is only 18.8% in the previous paper. It looks that our IGKA method is more likely to increase the degree of enrichment of the genes within functional categories, and to make more biological sense. We also found a new function category: lipid and fatty isoprenoid metabolism distributed in cluster 25, which was not listed in Tavazoie's paper.
Conclusions
In this paper, we propose a new clustering algorithm called Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension of FGKA, which in turn was inspired by the Genetic K-means Algorithm (GKA) proposed by Krishna and Murty. The IGKA inherits the advantages of FGKA, and it outperforms FGKA when the mutation probability is small. Since both FGKA and IGKA might outperform each other, a hybrid approach that combines the benefits of them is very desirable. Our experimental results showed that not only the performance of our algorithm is improved but also the clustering result with gene expression data has some interesting biological discovery.
Methods
The problem of clustering gene expression data consists of N genes and their corresponding N patterns. Each pattern is a vector of D dimensions recording the expression levels of the genes under each of the D monitored conditions or at each of the D time points. The goal of IGKA algorithm is to partition the N patterns into user-defined K groups, such that this partition minimizes the Total Within-Cluster Variation (TWCV, also called square-error in the literature), which is defined as follows.
Let be the N patterns, and Xnd denotes the dth feature of pattern Xn(n = 1,...N). Each partition is represented by a string, a sequence of numbers a1....aN,, where an is the number of the cluster that pattern belongs to in this partition. Let Gk denote the kth cluster and Zk denote the number of patterns in Gk. The centroid ck = (ck1, ck2,...,ckD) of cluster Gk is defined as , (d = 1,2,...D) where SFkd is the sum of the dth features of all the patterns in Gk. and we use to denote the vector of sum of all patterns in cluster Gk.
IGKA maintains a population (set) of Z coded solutions, where Z is a parameter specified by the user. Each solution, also called a chromosome, is coded by a string a1...aN of length N, where each an, which is called an allele, corresponds to a gene expression data pattern and takes a value from {1, 2, ..., K} representing the cluster number to which the corresponding pattern belongs. For example, a1a2a3a4a5= "33212" encodes a partition of 5 patterns in which, patterns and belong to cluster 3, patterns and belong to cluster 2, and pattern belongs to cluster 1.
Definition (Legal strings, Illegal strings)
Given a partition Sz = a1 ....aN, let e(Sz) be the number of non-empty clusters in Sz divided by K, e(Sz) is called legality ratio. We say string Sz is legal if e(Sz) = 1, and illegal otherwise.
Hence, an illegal string represents a partition in which some clusters are empty. For example, given K = 3, the string a1a2a3a4a5 = "23232" is illegal because cluster 1 is empty.
Figure 1 gives the flowchart of IGKA. It starts with the initialization phase, which generates the initial population P0. The population in the next generation Pi + 1 is obtained by applying genetic operators on the current population Pi. The evolution takes place until a terminating condition is reached. The following genetic operators are used in IGKA: the selection, the mutation and the K-means operator.
Selection operator
We use the so-called proportional selection for the selection operator in which, the population of the next generation is determined by Z independent random experiments. Each experiment randomly selects a solution from the current population (S1, S2, ..., Sz) according to the probability distribution (p1, p2, ..., pK) defined by (z = 1,...Z), where F(Sz) denotes the fitness value of solution Sz with respect to the current population and will be defined in the next paragraph.
Various fitness functions have been defined in the literature [10] in which the fitness value of each solution in the current population reflects its merit to survive in the next generation. In our context, the objective is to minimize the Total Within-Cluster Variation (TWCV). Therefore, solutions with smaller TWCVs should have higher probabilities for survival and should be assigned with greater fitness values. In addition, illegal strings are less desirable and should have lower probabilities for survival, and thus should be assigned with lower fitness values. We define fitness value of solution Sz, F(Sz) as
where TWCVmax is the maxim TWCV that has been encountered till the present generation, Fmin is the smallest fitness value of the legal strings in the current population if they exist, otherwise Fmin is defined as 1. The definition of fitness function in GKA [7] paper inspired our definition, but we incorporate the idea of permitting illegal strings by defining the fitness values for them.
The intuition behind this fitness function is that, each solution will have a probability to survive by being assigned with a positive fitness value, but a solution with a smaller TWCV has a greater fitness value and hence has a higher probability to survive. Illegal solutions are allowed to survive too but with lower fitness values than all legal solutions in the current population. Illegal strings that have more empty clusters are assigned with smaller fitness values and hence have lower probabilities for survival. The reason we still allow illegal solution survive with low probability is that we believe the illegal solution may mutate to a good solution and the cost of maintain the illegal solution is very low.
We assume that the TWCV for each solution Sz (denoted by Sz.TWCV) and the maximum TWCV (denoted by TWCVmax), have already been calculated before the selection operator is applied.
Mutation operator
Given a solution (chromosome) that is encoded by a1 ....aN, the mutation operator mutates each allele an(n = 1, ..., N) to a new value an (an might be equal to an) with probability MP respectively and independently, where 0 <MP < 1 is a parameter called the mutation probability that is specified by the user. The mutation operator is very important to help reach better solutions. From the perspective of the evolutional theory, offsprings produced by mutations might be superior to their parents. More importantly, the mutation operator performs the function of shaking the algorithm out of a local optimum, and moving it towards the global optimum.
Recall that in solution a1 ....aN, each allele an corresponds to a pattern and its value indicates the number of the cluster to which belongs. During mutation, we replace allele an by an' for n = 1,...,N simultaneously, where an is a number randomly selected from (1,....,K) with the probability distribution (p1, p2, ..., pK) defined by:
where is the Euclidean distance between pattern and the centroid ck of the kth cluster, and . If the kth cluster is empty, then is defined as 0. The bias 0.5 is introduced to avoid divide-by-zero error in the case that all patterns are equal and are assigned to the same cluster in the given solution. Our definition of the mutation operator is similar to the one defined in the GKA paper [7]. However, we account for illegal strings, which are not allowed in the GKA algorithm.
The above mutation operator is defined such that (1) might be reassigned randomly to each cluster with a positive probability; (2) the probability of changing allele value an to a cluster number k is greater if is closer to the centroid of the kth cluster Gk; and (3) empty clusters are viewed as the closest clusters to . The first property ensures that an arbitrary solution, including the global optimum, might be generated by the mutation from the current solution with a positive probability; the second property encourages that each is moving towards a closer cluster with a higher probability; the third property promotes the probability of converting an illegal solution to a legal one. These properties are essential to guarantee that IGKA will eventually converge to the global optimum fast.
K-means operator
In order to speed up the convergence process, one step of the classical K-means algorithm, which we call K-means operator (KMO) is introduced. Given a solution that is encoded by a1 ....aN, we replace an by an' for n = 1,...,N simultaneously, where an' is the number of the cluster whose centroid is closest to in Euclidean distance. More formally,
To accommodate illegal strings, we define = +∞ if the kth cluster is empty. This definition is different from mutation operator, in which we defined = 0 if the kth cluster is empty. The motivation for this new definition here is that we want to avoid reassigning all patterns to empty clusters. Therefore, illegal string will remain illegal after the application of KMO.
In the following, we first present FGKA algorithm that is proposed in [9]. We then describe the motivation for IGKA based on the idea of incremental calculation of TWCV and centroids. Finally, we present a hybrid approach that combines the benefits of FGKA and IGKA.
Fast Genetic K-Means Algorithm (FGKA)
FGKA shares the same flowchart of IGKA given in Figure 1. It starts with the initialization of population P0 with Z solutions. For each generation Pi, we apply the three operators, selection, mutation and K-means operator sequentially which generate population , , and Pi + 1 respectively. This process is repeated for G iterations, each of which corresponds to one generation of solutions. The best solution so far is observed and recorded in So before the selection operator. So is returned as the output solution when FGKA terminates.
Incremental Genetic K-Means Algorithm (IGKA)
Although FGKA outperforms GKA significantly, it suffers from a potential disadvantage. If the mutation probability is small, then the number of allele changes will be small, and the cost of calculating centroids and TWCV from scratch can be much more expensive than calculating them in an incremental fashion. As a simple example, if a pattern is reassigned from cluster k to cluster k', then only the centroids and WCVs of these two clusters need to be recalculated. Furthermore, the centroids of these two clusters can be calculated incrementally since the memberships of other patterns have not changed; The TWCV can be calculated incrementally as well since the WCVs of other clusters have not changed. In the following, we describe how we can calculate TWCV and cluster centroids incrementally.
In order to obtain the new centroid , we maintain the difference values of ZkΔ, for old solution and new solution when allele changes. With these two values, incremental update of Zk and can be achieved as Zk = Zk + ZkΔ, and . Then the new centroids for new solution can be achieved by .
Similarly, in order to obtain the new TWCV, we can maintain a difference value TWCVΔ that denotes the difference between old TWCV and new TWCV for one solution. It is obvious that TWCVΔ is attributed from the difference of new WCVk and old WCVk for cluster k. However, WCVk has to be calculated from scratch since is changed. In this way, TWCV can be updated incrementally as well. Since the calculation of TWCV dominates all iterations, our incremental update of TWCV will have a better performance when mutation probability is small (which implies a small number of alleles changes). However, if the mutation probability is large, too many alleles change their cluster membership, the maintenance of Zk Δ and becomes expensive and IGKA becomes inferior to FGKA in performance, as confirmed in the experimental study.
Hybrid Genetic K-Means Algorithm (HGKA)
The above discussion presents a dilemma – both FGKA and IGKA are likely to outperform each other: when the mutation probability is smaller than some threshold, IGKA outperforms FGKA; otherwise, FGKA outperforms IGKA.
The key idea of HGKA is to combine the benefits of FGKA and IGKA. However, it is very difficult to derive the threshold value, which is dataset dependant. In addition, the running times of all iterations will vary as solutions converge to the optimum. We propose the following solution: we periodically run one iteration of FGKA followed by one iteration of IGKA while monitoring their running times, and then run the winning algorithm for the following iterations until we reach another competition point.
It has been proved in [8] that FGKA will eventually converge to the global optimum. By using the same flowchart and operators, IGKA and HGKA will also converge to the global optimum. We summarize the comparison of various clustering algorithms in Table 1.
Availability and requirements
IGKA algorithm is available at . The source code and database scheme are freely distributed to academic users upon request to the authors.
List of abbreviations
WCV: Within-Cluster Variation; TWCV: Total Within-Cluster Variation; IGKA: Incremental Genetic K-means Algorithm; FGKA: Fast Genetic K-means Algorithm; HGKA: Hybrid Genetic K-means Algorithm; ORF: Open Reading Frame.
Authors' contributions
YL carried out the study and drafted the manuscript. SL and FF designed the algorithms. YD designed the whole project, participated in analyzing gene functional data and wrote part of manuscript. SJB corrected English and helped to interpret the data analysis results.
Acknowledgements
We thank Mr. Jun Chen for helping us in dividing the gene function categories. The project described was supported by NIH grant P20 RR16475 from the BRIN Program of the National Center for Research Resources.
Figures and Tables
Figure 1 The flowchart of IGKA algorithm. It starts with the initialization phase, which generates the initial population P0. The population in the next generation Pi + 1 is obtained by applying genetic operators on the current population Pi. The evolution takes place until a terminating condition is reached. The selection, the mutation and the K-means operator are sequentially used in IGKA.
Figure 2 The impacts of mutation probability on time performance for IGKA and FGKA. The population size is set to 50; the generation size is set to 100. The mutation probability ranges from 0.001 to 0.1 for fig2data, and 0.0001 to 0.1 for chodata. (A) shows the running time for FGKA and IGKA on fig2data. (B) shows the running time for FGKA and IGKA on chodata. (C) shows the average and standard error of running time on fig2data when the mutation probability is set to 0.001 and 0.005. (D) shows the average and standard error of running time on chodata when the mutation probability is set to 0.0001 and 0.0005. When the mutation probability increases, the running time increases accordingly for both algorithms. However, when the mutation probability is smaller than some threshold (0.005 for fig2data, and 0.0005 for chodata), the IGKA has better performance. It indicates the thresholds vary from one dataset to another. It mainly depends on the number of patterns and the number of features in the data set.
Figure 3 The impacts of mutation probability on convergence for IGKA and FGKA. The population size is set to 50; the generation size is set to 100. The mutation probability ranges from 0.001 to 0.1 for fig2data, and 0.0001 to 0.1 for chodata. (A) shows the convergence with different mutation probability for FGKA and IGKA on fig2data. (B) shows the convergence with different mutation probability for FGKA and IGKA on chodata. These two algorithms have similar convergence results. When the mutation probability changes in these two data sets, it has little impact on two algorithms during the range that is given in the Figure, except for the case when the mutation probability is too large. It gives an opportunity to choose IGKA with better performance without losing the convergence benefit.
Figure 4 The performance comparison of IGKA, FGKA and HGKA based on iterations. The comparison is based on the chodata data set, the population number is set to 50 and the mutation probability is set to 0.0001. 100 iterations of three algorithms, IGKA, FGKA and HGKA, are shown in the Figure. The running time for each iteration of FGKA is almost fixed while the running time for IGKA is much higher than FGKA at the beginning and decrease very sharply at late iterations. The HGKA combines the advantage of two algorithms. The turning point in this test case is at iteration 30.
Table 1 Comparison of different algorithms on performance, convergence and stability. Five apporaches are compared based on time performance, convergence and stability. The K-means algorithm has better time performance than any other genetic algorithms, but it suffers from converging to local optimum and initialization dependent. Among the four genetic clustering approaches, Hybrid approach always has better time performance while FGKA performs well when the mutation probability is big, and IGKA performs well when the mutation probability is small. IGKA and FGKA outperform GKA. The convergence of four genetic algorithms has similar results, and all four are independent from the initialization.
K-means GKA FGKA IGKA Hybrid
Time Fastest Slow Good when the mutation Good when the mutation Good
Performance probability is large probability is small
Convergence Worse Good Good Good Good
Stability Unstable Stable Stable Stable Stable
Table 2 Comparison of different algorithms on TWCV convergence with two data sets. Four algorithms, IGKA, FGKA, K-means and SOM are experimented on the two data set, the fig2data, and chodata. The TWCVs of IGKA and FGKA algorithm are obtained by averaging 10 individual runs while the generation number is set to 100, the population number is set to 50, the mutation probability is set to 0.005 for fig2data, and 0.0005 for chodata. The TWCV of K-means algorithm is obtained by averaging 20 individual runs. The TWCV of SOM is obtainedby 8 individual runs with different setting on X and Y dimension. The IGKA and FGKA algorithms have better TWCV convergence than the K-means and SOM.
Algorithms Fig2data Chodata
IGKA (Average of 10 individual runs with generation 100, population 50, mutation probability 0.005 in fig2data, and 0.0005 in chodata) 4991.53889 16995.7
FGKA (Average of 10 individual runs with generation 100, population 50, mutation probability 0.005 in fig2data, and 0.0005 in chodata) 4992.13889 16995.4
K-means (Average of 20 individual runs) 5154.21434 17374.6758
SOM (Average of 8 individual runs with different setting) 24805.3661 21660.9049
Table 3 Distribution of ORF function categories in the clusters. Chodata set was clustered using IGKA algorithm. We identified the gene distribution of different functional categories into different clusters. The function categories were divided according to MIPS (Mewes et al., 2000). The total number of ORFs in each function category was indicated in parentheses. The cluster number to which the genes were grouped is denoted as "Cluster" column. The ORF number in each cluster is denoted as "Total". The ORF number within each functional category is denoted as "Function ORFs". The percentage of the ORF number within functional category of each cluster in the total ORF number of each cluster is denoted as "Percentage (%)".
Cluster MIPS functional category Total Function ORFs Percentage(%)
1 Mitotic cell cycle and cycle control(352) 86 24 27.9
Budding, cell polarity, filament form(170) 8 9.3
3 Organization of mitochondrion(366) 156 111 71.2
Respiration(88) 10 6.4
Nitrogen and sulpur metabolism(67) 9 5.6
16 Organization of nucleus(774) 133 50 37.6
17 Ribosome biogenesis(215) 88 50 56.8
Organization of cytoplasm(554) 31 35.2
18 Organization of mitochondrion(366) 184 105 57.1
25 DNA synthesis and replication(94) 164 23 14
DNA recombination and DNA repair(153) 11 6.7
Lipid and fatty isoprenoid metabolism(213) 9 5.5
29 Organization of nucleus chromosome(44) 93 14 15
Amino acid metabolism(204) 12 12.9
30 TCA pathway or Krebs cycle(25) 92 7 7.6
C-compound, carbohydrate metabolism(415) 14 15.2
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| 15511294 | PMC543472 | CC BY | 2021-01-04 16:02:47 | no | BMC Bioinformatics. 2004 Oct 28; 5:172 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-172 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1721551129410.1186/1471-2105-5-172Methodology ArticleIncremental genetic K-means algorithm and its application in gene expression data analysis Lu Yi [email protected] Shiyong [email protected] Farshad [email protected] Youping [email protected] Susan J [email protected] Dept. of Computer Science, Wayne State University, Detroit, MI 48202, USA2 Department of Biological Sciences, the University of Southern Mississippi, Hattiesburg 39406, USA3 Division of Biology, Kansas State University, Manhattan, KS 66506, USA2004 28 10 2004 5 172 172 10 3 2004 28 10 2004 Copyright © 2004 Lu et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.
Results
In this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at
Conclusions
Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.
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Background
In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis (see [1] for an excellent survey). With the advancement in Microarray technology, it is now possible to observe the expression levels of thousands of genes simultaneously when the cells experience specific conditions or undergo specific processes. Clustering algorithms are used to partition genes into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.
Among the various clustering algorithms, K-means [2] is one of the most popular methods used in gene expression data analysis due to its high computational performance. However, it is well known that K-means might converge to a local optimum, and its result is subject to the initialization process, which randomly generates the initial clustering. In other words, different runs of K-means on the same input data might produce different solutions.
A number of researchers have proposed genetic algorithms [3-6] for clustering. The basic idea is to simulate the evolution process of nature and evolve solutions from one generation to the next. In contrast to K-means, which might converge to a local optimum, these genetic algorithms are insensitive to the initialization process and always converge to the global optimum eventually. However, these algorithms are usually computationally expensive which impedes the wide application of them in practice such as in gene expression data analysis.
Recently, Krishna and Murty proposed a new clustering method called Genetic K-means Algorithm (GKA) [7], which hybridizes a genetic algorithm with the K-means algorithm. This hybrid approach combines the robust nature of the genetic algorithm with the high performance of the K-means algorithm. As a result, GKA will always converge to the global optimum faster than other genetic algorithms.
In [8], we proposed a faster version of GKA, FGKA that features several improvements over GKA including an efficient evaluation of the objective value TWCV (Total Within-Cluster Variation), avoiding illegal string elimination overhead, and a simplification of the mutation operator. These improvements result that FGKA runs 20 times faster than GKA [9]. In this paper, we propose an extension to FGKA, Incremental Genetic K-means Algorithm (IGKA) that inherits all the advantages of FGKA including the convergence to the global optimum, and outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value TWCV and to cluster centroids incrementally. We then propose a Hybrid Genetic K-means Algorithm (HGKA) that combines the benefits of FGKA and IGKA. We show that clustering of microarray data by IGKA method has more tendencies to group the genes with the same functional category into a given cluster.
Results
Our experiments were conducted on a Dell PowerEdge 400SC PC machine with 2.24G Hz CPU and 512 M RAM. Three algorithms, FGKA, IGKA and HGKA algorithm were implemented in C language. GKA has convergence pattern similar to FGKA and IGKA, but its time performance is worse than FGKA, see [9] for more details. In the following, we compare the time performance of FGKA and IGKA along different mutation probabilities, and then we compare the convergence property of four algorithms, IGKA, FGKA, K-means and SOM (Self Organizing Map). At the end, we check how we can combine IGKA and FGKA algorithm together to obtain a better performance.
Data sets
The two data sets used to conduct our experiments are serum data, fig2data, introduced in [11]and yeast data, chodata, introduced in [2]. The fig2data data set contains expression data for 517 genes. Each gene has 19 expression data ranges from 15 minutes to 24 hours. In other words, the number of features D is 19. According to [11], 517 genes can be divided into 10 groups. The chodata is a yeast dataset, composed of expression data for 2907 genes and the expression data for each gene ranges 0 minutes to 160 minutes, which means that the number of features D is 15. According to the description in [2], the genes can be divided into 30 groups. Since the IGKA is a stochastic algorithm, for each experiment in this study, we obtain the results by averaging 10 independent run of the program. The mutation probability, the generation number, the population number all affect the performance and convergence of FGKA and IGKA. The detailed discussion of the parameters setting can be found in [8]. In this paper, we simply adopt the result in [8], the population number is set to 50, and the generation number is set to 100. These parameter setting are safe enough to guarantee the algorithm converge to the optima.
Comparison of IGKA with FGKA on time performance
As indicated in the implementation section, the mutation probability has great impact on IGKA algorithm. We check the performance impact on IGKA in this section, and the convergence in the next section. Figure 2 shows the time performance results for these two algorithms. We can see that when the mutation probability increases, the running time increases accordingly for both algorithms. However, when the mutation probability is smaller than some threshold (0.005 for fig2data, and 0.0005 for chodata), IGKA has a better performance. Figure 2 also indicates the thresholds vary from one dataset to another. In order to achieve better performance of IGKA in large data set, mutation probability may need to be set to smaller than that in small data set. For example, in larger data set chodata, we should set the mutation probability to 0.0005 to have IGKA outperform FGKA. On the other hand, in order to have IGKA outperform than FGKA, we only need to set the mutation probability to 0.005 in the small data set fig2data. In general, the threshold value depends on the number of patterns and the number of features in the data set. It is easy to understand that the performance gained in IGKA is mainly dependent on how many patterns change their cluster memberships. So, in a large data set, even small number of mutation probability may cause many patterns change their cluster memberships.
Comparison of IGKA with FGKA, K-means and SOM on convergence
Figures 3(A) and 3(B) show the convergence of IGKA versus FGKA across different mutation probabilities based on fig2data and chodata, respectively. These two algorithms have similar convergence results. When the mutation probability changes in these two data sets, it has little impact on these two algorithms during the range that is given in Figure 3, except for the case when the mutation probability is too large. It gives an opportunity to choose IGKA with better performance without losing the convergence benefit.
We also make an interesting comparison of IGKA with FGKA, K-means and SOM on TWCV convergence. We treat each algorithm as a black box. Two data sets, the fig2data and chodata, are fed into the algorithms, and the clustering results are exported as a text file. We then use an in-house program to calculate the TWCVs for each result. The experiments on K-means and SOM algorithm are conducted on an open source software [12]. As we can see in Table 2, the IGKA and FGKA have almost similar convergence result, and much better than the convergence of K-means algorithm. The TWCV convergence of SOM is much worse than the others although these four algorithms all use Euclidian distance as their measurement. The reason why we do not include another popular clustering algorithm, hierarchical clustering algorithm is because it is hard to define the boundary among the nested clusters, which means we cannot simply define the number of cluster before running the program.
Combination of IGKA with FGKA
Figure 4 compares three algorithms, IGKA, FGKA and HGKA, based on the running times for 100 iterations. The mutation probability is set to 0.0001 for all three algorithms. It is clearly that the running time for each iteration of FGKA is much stable than others. On the other hand, the running time for IGKA is much higher than FGKA at the beginning because there are a large number of patterns change their cluster belonging during the K-means operator which cause the IGKA spend a lot of computation time. However, the running time for each iteration of IGKA decrease very sharply at late iterations. The HGKA combines the advantage of two algorithms. The turning point when HGKA uses IGKA instead of FGKA as work horse is highly data dependent. In this particular case, we check the computation time every 15 iterations. The result shows that the performance can be really improved by using HGKA when the mutation probability is small.
Discussion
The clustering results of chodata using our IGKA algorithm were evaluated according to the scheme of gene classification of MIPS Yeast Genome Database [13]. We found that genes of similar function were grouped into the same cluster. Table 3 shows 8 main clusters including 16 functional categories of genes. The results are comparable to the data of [2]. The absolute number of ORFs with functional categories in some cluster may not be always higher than Tavazoie's result, but we found that the percentage of the ORF number within functional category of each cluster in the total ORF number of each cluster is usually higher than Tavazoie's result in most cases. For example, they found that there are 40 genes in the functional category of nuclear organization distributed in their cluster 2, in which there are 186 ORFs, so their percentage is 21.5%. But we found there are 50 genes of the same functional category distributed in our cluster 16, in which there are only 133 ORFs, and our percentage is 37.6% that is significantly higher than 21.5%.
Most interestingly, we found a remarkable enrichment of ORFs for the functional category of organization of mitochondria. They are mainly located in two clusters: cluster 3 and cluster 18. Cluster 3 has 156 ORFs in total, and 111 ORFs belong to the category, resulting in a very high percentage, 71.2%. Cluster 18, has 184 ORFs in total, in which there are 105 ORFs belonging to the category and the percentage is 57.1%. The percentage of ORFs within the same function category is only 18.8% in the previous paper. It looks that our IGKA method is more likely to increase the degree of enrichment of the genes within functional categories, and to make more biological sense. We also found a new function category: lipid and fatty isoprenoid metabolism distributed in cluster 25, which was not listed in Tavazoie's paper.
Conclusions
In this paper, we propose a new clustering algorithm called Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension of FGKA, which in turn was inspired by the Genetic K-means Algorithm (GKA) proposed by Krishna and Murty. The IGKA inherits the advantages of FGKA, and it outperforms FGKA when the mutation probability is small. Since both FGKA and IGKA might outperform each other, a hybrid approach that combines the benefits of them is very desirable. Our experimental results showed that not only the performance of our algorithm is improved but also the clustering result with gene expression data has some interesting biological discovery.
Methods
The problem of clustering gene expression data consists of N genes and their corresponding N patterns. Each pattern is a vector of D dimensions recording the expression levels of the genes under each of the D monitored conditions or at each of the D time points. The goal of IGKA algorithm is to partition the N patterns into user-defined K groups, such that this partition minimizes the Total Within-Cluster Variation (TWCV, also called square-error in the literature), which is defined as follows.
Let be the N patterns, and Xnd denotes the dth feature of pattern Xn(n = 1,...N). Each partition is represented by a string, a sequence of numbers a1....aN,, where an is the number of the cluster that pattern belongs to in this partition. Let Gk denote the kth cluster and Zk denote the number of patterns in Gk. The centroid ck = (ck1, ck2,...,ckD) of cluster Gk is defined as , (d = 1,2,...D) where SFkd is the sum of the dth features of all the patterns in Gk. and we use to denote the vector of sum of all patterns in cluster Gk.
IGKA maintains a population (set) of Z coded solutions, where Z is a parameter specified by the user. Each solution, also called a chromosome, is coded by a string a1...aN of length N, where each an, which is called an allele, corresponds to a gene expression data pattern and takes a value from {1, 2, ..., K} representing the cluster number to which the corresponding pattern belongs. For example, a1a2a3a4a5= "33212" encodes a partition of 5 patterns in which, patterns and belong to cluster 3, patterns and belong to cluster 2, and pattern belongs to cluster 1.
Definition (Legal strings, Illegal strings)
Given a partition Sz = a1 ....aN, let e(Sz) be the number of non-empty clusters in Sz divided by K, e(Sz) is called legality ratio. We say string Sz is legal if e(Sz) = 1, and illegal otherwise.
Hence, an illegal string represents a partition in which some clusters are empty. For example, given K = 3, the string a1a2a3a4a5 = "23232" is illegal because cluster 1 is empty.
Figure 1 gives the flowchart of IGKA. It starts with the initialization phase, which generates the initial population P0. The population in the next generation Pi + 1 is obtained by applying genetic operators on the current population Pi. The evolution takes place until a terminating condition is reached. The following genetic operators are used in IGKA: the selection, the mutation and the K-means operator.
Selection operator
We use the so-called proportional selection for the selection operator in which, the population of the next generation is determined by Z independent random experiments. Each experiment randomly selects a solution from the current population (S1, S2, ..., Sz) according to the probability distribution (p1, p2, ..., pK) defined by (z = 1,...Z), where F(Sz) denotes the fitness value of solution Sz with respect to the current population and will be defined in the next paragraph.
Various fitness functions have been defined in the literature [10] in which the fitness value of each solution in the current population reflects its merit to survive in the next generation. In our context, the objective is to minimize the Total Within-Cluster Variation (TWCV). Therefore, solutions with smaller TWCVs should have higher probabilities for survival and should be assigned with greater fitness values. In addition, illegal strings are less desirable and should have lower probabilities for survival, and thus should be assigned with lower fitness values. We define fitness value of solution Sz, F(Sz) as
where TWCVmax is the maxim TWCV that has been encountered till the present generation, Fmin is the smallest fitness value of the legal strings in the current population if they exist, otherwise Fmin is defined as 1. The definition of fitness function in GKA [7] paper inspired our definition, but we incorporate the idea of permitting illegal strings by defining the fitness values for them.
The intuition behind this fitness function is that, each solution will have a probability to survive by being assigned with a positive fitness value, but a solution with a smaller TWCV has a greater fitness value and hence has a higher probability to survive. Illegal solutions are allowed to survive too but with lower fitness values than all legal solutions in the current population. Illegal strings that have more empty clusters are assigned with smaller fitness values and hence have lower probabilities for survival. The reason we still allow illegal solution survive with low probability is that we believe the illegal solution may mutate to a good solution and the cost of maintain the illegal solution is very low.
We assume that the TWCV for each solution Sz (denoted by Sz.TWCV) and the maximum TWCV (denoted by TWCVmax), have already been calculated before the selection operator is applied.
Mutation operator
Given a solution (chromosome) that is encoded by a1 ....aN, the mutation operator mutates each allele an(n = 1, ..., N) to a new value an (an might be equal to an) with probability MP respectively and independently, where 0 <MP < 1 is a parameter called the mutation probability that is specified by the user. The mutation operator is very important to help reach better solutions. From the perspective of the evolutional theory, offsprings produced by mutations might be superior to their parents. More importantly, the mutation operator performs the function of shaking the algorithm out of a local optimum, and moving it towards the global optimum.
Recall that in solution a1 ....aN, each allele an corresponds to a pattern and its value indicates the number of the cluster to which belongs. During mutation, we replace allele an by an' for n = 1,...,N simultaneously, where an is a number randomly selected from (1,....,K) with the probability distribution (p1, p2, ..., pK) defined by:
where is the Euclidean distance between pattern and the centroid ck of the kth cluster, and . If the kth cluster is empty, then is defined as 0. The bias 0.5 is introduced to avoid divide-by-zero error in the case that all patterns are equal and are assigned to the same cluster in the given solution. Our definition of the mutation operator is similar to the one defined in the GKA paper [7]. However, we account for illegal strings, which are not allowed in the GKA algorithm.
The above mutation operator is defined such that (1) might be reassigned randomly to each cluster with a positive probability; (2) the probability of changing allele value an to a cluster number k is greater if is closer to the centroid of the kth cluster Gk; and (3) empty clusters are viewed as the closest clusters to . The first property ensures that an arbitrary solution, including the global optimum, might be generated by the mutation from the current solution with a positive probability; the second property encourages that each is moving towards a closer cluster with a higher probability; the third property promotes the probability of converting an illegal solution to a legal one. These properties are essential to guarantee that IGKA will eventually converge to the global optimum fast.
K-means operator
In order to speed up the convergence process, one step of the classical K-means algorithm, which we call K-means operator (KMO) is introduced. Given a solution that is encoded by a1 ....aN, we replace an by an' for n = 1,...,N simultaneously, where an' is the number of the cluster whose centroid is closest to in Euclidean distance. More formally,
To accommodate illegal strings, we define = +∞ if the kth cluster is empty. This definition is different from mutation operator, in which we defined = 0 if the kth cluster is empty. The motivation for this new definition here is that we want to avoid reassigning all patterns to empty clusters. Therefore, illegal string will remain illegal after the application of KMO.
In the following, we first present FGKA algorithm that is proposed in [9]. We then describe the motivation for IGKA based on the idea of incremental calculation of TWCV and centroids. Finally, we present a hybrid approach that combines the benefits of FGKA and IGKA.
Fast Genetic K-Means Algorithm (FGKA)
FGKA shares the same flowchart of IGKA given in Figure 1. It starts with the initialization of population P0 with Z solutions. For each generation Pi, we apply the three operators, selection, mutation and K-means operator sequentially which generate population , , and Pi + 1 respectively. This process is repeated for G iterations, each of which corresponds to one generation of solutions. The best solution so far is observed and recorded in So before the selection operator. So is returned as the output solution when FGKA terminates.
Incremental Genetic K-Means Algorithm (IGKA)
Although FGKA outperforms GKA significantly, it suffers from a potential disadvantage. If the mutation probability is small, then the number of allele changes will be small, and the cost of calculating centroids and TWCV from scratch can be much more expensive than calculating them in an incremental fashion. As a simple example, if a pattern is reassigned from cluster k to cluster k', then only the centroids and WCVs of these two clusters need to be recalculated. Furthermore, the centroids of these two clusters can be calculated incrementally since the memberships of other patterns have not changed; The TWCV can be calculated incrementally as well since the WCVs of other clusters have not changed. In the following, we describe how we can calculate TWCV and cluster centroids incrementally.
In order to obtain the new centroid , we maintain the difference values of ZkΔ, for old solution and new solution when allele changes. With these two values, incremental update of Zk and can be achieved as Zk = Zk + ZkΔ, and . Then the new centroids for new solution can be achieved by .
Similarly, in order to obtain the new TWCV, we can maintain a difference value TWCVΔ that denotes the difference between old TWCV and new TWCV for one solution. It is obvious that TWCVΔ is attributed from the difference of new WCVk and old WCVk for cluster k. However, WCVk has to be calculated from scratch since is changed. In this way, TWCV can be updated incrementally as well. Since the calculation of TWCV dominates all iterations, our incremental update of TWCV will have a better performance when mutation probability is small (which implies a small number of alleles changes). However, if the mutation probability is large, too many alleles change their cluster membership, the maintenance of Zk Δ and becomes expensive and IGKA becomes inferior to FGKA in performance, as confirmed in the experimental study.
Hybrid Genetic K-Means Algorithm (HGKA)
The above discussion presents a dilemma – both FGKA and IGKA are likely to outperform each other: when the mutation probability is smaller than some threshold, IGKA outperforms FGKA; otherwise, FGKA outperforms IGKA.
The key idea of HGKA is to combine the benefits of FGKA and IGKA. However, it is very difficult to derive the threshold value, which is dataset dependant. In addition, the running times of all iterations will vary as solutions converge to the optimum. We propose the following solution: we periodically run one iteration of FGKA followed by one iteration of IGKA while monitoring their running times, and then run the winning algorithm for the following iterations until we reach another competition point.
It has been proved in [8] that FGKA will eventually converge to the global optimum. By using the same flowchart and operators, IGKA and HGKA will also converge to the global optimum. We summarize the comparison of various clustering algorithms in Table 1.
Availability and requirements
IGKA algorithm is available at . The source code and database scheme are freely distributed to academic users upon request to the authors.
List of abbreviations
WCV: Within-Cluster Variation; TWCV: Total Within-Cluster Variation; IGKA: Incremental Genetic K-means Algorithm; FGKA: Fast Genetic K-means Algorithm; HGKA: Hybrid Genetic K-means Algorithm; ORF: Open Reading Frame.
Authors' contributions
YL carried out the study and drafted the manuscript. SL and FF designed the algorithms. YD designed the whole project, participated in analyzing gene functional data and wrote part of manuscript. SJB corrected English and helped to interpret the data analysis results.
Acknowledgements
We thank Mr. Jun Chen for helping us in dividing the gene function categories. The project described was supported by NIH grant P20 RR16475 from the BRIN Program of the National Center for Research Resources.
Figures and Tables
Figure 1 The flowchart of IGKA algorithm. It starts with the initialization phase, which generates the initial population P0. The population in the next generation Pi + 1 is obtained by applying genetic operators on the current population Pi. The evolution takes place until a terminating condition is reached. The selection, the mutation and the K-means operator are sequentially used in IGKA.
Figure 2 The impacts of mutation probability on time performance for IGKA and FGKA. The population size is set to 50; the generation size is set to 100. The mutation probability ranges from 0.001 to 0.1 for fig2data, and 0.0001 to 0.1 for chodata. (A) shows the running time for FGKA and IGKA on fig2data. (B) shows the running time for FGKA and IGKA on chodata. (C) shows the average and standard error of running time on fig2data when the mutation probability is set to 0.001 and 0.005. (D) shows the average and standard error of running time on chodata when the mutation probability is set to 0.0001 and 0.0005. When the mutation probability increases, the running time increases accordingly for both algorithms. However, when the mutation probability is smaller than some threshold (0.005 for fig2data, and 0.0005 for chodata), the IGKA has better performance. It indicates the thresholds vary from one dataset to another. It mainly depends on the number of patterns and the number of features in the data set.
Figure 3 The impacts of mutation probability on convergence for IGKA and FGKA. The population size is set to 50; the generation size is set to 100. The mutation probability ranges from 0.001 to 0.1 for fig2data, and 0.0001 to 0.1 for chodata. (A) shows the convergence with different mutation probability for FGKA and IGKA on fig2data. (B) shows the convergence with different mutation probability for FGKA and IGKA on chodata. These two algorithms have similar convergence results. When the mutation probability changes in these two data sets, it has little impact on two algorithms during the range that is given in the Figure, except for the case when the mutation probability is too large. It gives an opportunity to choose IGKA with better performance without losing the convergence benefit.
Figure 4 The performance comparison of IGKA, FGKA and HGKA based on iterations. The comparison is based on the chodata data set, the population number is set to 50 and the mutation probability is set to 0.0001. 100 iterations of three algorithms, IGKA, FGKA and HGKA, are shown in the Figure. The running time for each iteration of FGKA is almost fixed while the running time for IGKA is much higher than FGKA at the beginning and decrease very sharply at late iterations. The HGKA combines the advantage of two algorithms. The turning point in this test case is at iteration 30.
Table 1 Comparison of different algorithms on performance, convergence and stability. Five apporaches are compared based on time performance, convergence and stability. The K-means algorithm has better time performance than any other genetic algorithms, but it suffers from converging to local optimum and initialization dependent. Among the four genetic clustering approaches, Hybrid approach always has better time performance while FGKA performs well when the mutation probability is big, and IGKA performs well when the mutation probability is small. IGKA and FGKA outperform GKA. The convergence of four genetic algorithms has similar results, and all four are independent from the initialization.
K-means GKA FGKA IGKA Hybrid
Time Fastest Slow Good when the mutation Good when the mutation Good
Performance probability is large probability is small
Convergence Worse Good Good Good Good
Stability Unstable Stable Stable Stable Stable
Table 2 Comparison of different algorithms on TWCV convergence with two data sets. Four algorithms, IGKA, FGKA, K-means and SOM are experimented on the two data set, the fig2data, and chodata. The TWCVs of IGKA and FGKA algorithm are obtained by averaging 10 individual runs while the generation number is set to 100, the population number is set to 50, the mutation probability is set to 0.005 for fig2data, and 0.0005 for chodata. The TWCV of K-means algorithm is obtained by averaging 20 individual runs. The TWCV of SOM is obtainedby 8 individual runs with different setting on X and Y dimension. The IGKA and FGKA algorithms have better TWCV convergence than the K-means and SOM.
Algorithms Fig2data Chodata
IGKA (Average of 10 individual runs with generation 100, population 50, mutation probability 0.005 in fig2data, and 0.0005 in chodata) 4991.53889 16995.7
FGKA (Average of 10 individual runs with generation 100, population 50, mutation probability 0.005 in fig2data, and 0.0005 in chodata) 4992.13889 16995.4
K-means (Average of 20 individual runs) 5154.21434 17374.6758
SOM (Average of 8 individual runs with different setting) 24805.3661 21660.9049
Table 3 Distribution of ORF function categories in the clusters. Chodata set was clustered using IGKA algorithm. We identified the gene distribution of different functional categories into different clusters. The function categories were divided according to MIPS (Mewes et al., 2000). The total number of ORFs in each function category was indicated in parentheses. The cluster number to which the genes were grouped is denoted as "Cluster" column. The ORF number in each cluster is denoted as "Total". The ORF number within each functional category is denoted as "Function ORFs". The percentage of the ORF number within functional category of each cluster in the total ORF number of each cluster is denoted as "Percentage (%)".
Cluster MIPS functional category Total Function ORFs Percentage(%)
1 Mitotic cell cycle and cycle control(352) 86 24 27.9
Budding, cell polarity, filament form(170) 8 9.3
3 Organization of mitochondrion(366) 156 111 71.2
Respiration(88) 10 6.4
Nitrogen and sulpur metabolism(67) 9 5.6
16 Organization of nucleus(774) 133 50 37.6
17 Ribosome biogenesis(215) 88 50 56.8
Organization of cytoplasm(554) 31 35.2
18 Organization of mitochondrion(366) 184 105 57.1
25 DNA synthesis and replication(94) 164 23 14
DNA recombination and DNA repair(153) 11 6.7
Lipid and fatty isoprenoid metabolism(213) 9 5.5
29 Organization of nucleus chromosome(44) 93 14 15
Amino acid metabolism(204) 12 12.9
30 TCA pathway or Krebs cycle(25) 92 7 7.6
C-compound, carbohydrate metabolism(415) 14 15.2
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| 15555062 | PMC543473 | CC BY | 2021-01-04 16:29:01 | no | BMC Evol Biol. 2004 Nov 19; 4:48 | latin-1 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-48 | oa_comm |
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BMC MicrobiolBMC Microbiology1471-2180BioMed Central London 1471-2180-4-441557162110.1186/1471-2180-4-44Research ArticleA simulation model of Escherichia coli osmoregulatory switch using E-CELL system Srividhya KV [email protected] Sankaran [email protected] Bioinformatics Centre, School of Biotechnology, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu India2004 30 11 2004 4 44 44 3 11 2003 30 11 2004 Copyright © 2004 Srividhya and Krishnaswamy; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Bacterial signal transduction mechanism referred to as a "two component regulatory systems" contributes to the overall adaptability of the bacteria by regulating the gene expression. Osmoregulation is one of the well-studied two component regulatory systems comprising of the sensor, EnvZ and the cognate response regulator, OmpR, which together control the expression of OmpC and OmpF porins in response to the osmolyte concentration.
Results
A quantitative model of the osmoregulatory switch operative in Escherichia coli was constructed by integrating the enzyme rate equations using E-CELL system. Using the substance reactor logic of the E-CELL system, a total of 28 reactions were defined from the injection of osmolyte till the regulated expression of porins by employing the experimental kinetic constants as reported in literature. In the case of low osmolarity, steady state production of OmpF and repression of OmpC was significant. In this model we show that the steady state – production of OmpF is dramatically reduced in the high osmolarity medium. The rate of OmpC production increased after sucrose addition, which is comparable with literature results. The relative porin production seems to be unaltered with changes in cell volume changes, ATP, EnvZ and OmpR at low and high osmolarity conditions. But the reach of saturation was rapid at high and low osmolarity with altered levels of the above components.
Conclusions
The E-CELL system allows us to perform virtual experiments on the bacterial osmoregulation model. This model does not take into account interaction with other networks in the cell. It suggests that the regulation of OmpF and OmpC is a direct consequence of the level of OmpRP in the cell and is dependent on the way in which OmpRP interacts with ompF and ompC regulatory regions. The preliminary simulation experiment indicates that both reaching steady state expression and saturation is delayed in the case of OmpC compared to OmpF. Experimental analysis will help improve the model. The model captures the basic features of the generally accepted view of EnvZ-OmpR signaling and is a reasonable starting point for building sophisticated models and explaining quantitative features of the system.
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Background
Among prokaryotes, a remarkable number of cellular functions are controlled by two component regulatory systems [1]. Dedicated circuits transduce and interpret specific signals such as pH, temperature, osmolarity, light, nutrients, ions, pheromones and toxins to regulate a wide range of processes including motility, virulence, metabolism, the cell cycle, development switches, antibiotic resistance and stress responses in a variety of systems from prokaryotes, archaea and eukaryotes [2].
Two component systems interact with each other and also with other regulatory systems mediating specific gene expression or cellular locomotion. Such complexes should be analysed quantitatively. Although quantitative data on cellular processes other than metabolism are still relatively sparse, scientists have modeled some systems with considerable success [3]. For example computer simulations of the chemotaxis two component system have been extensively studied [4].
Two-component regulatory systems (also called the HAP or His-Asp phosphotransfer mechanism) are widely used signaling machinery of bacterial adaptive responses. The system constitutes of sensor kinases and response regulators that can be phosphorylated and dephosphorylated by sensor kinases [5] (Figure 1).
The first component sensor-transmitter that spans the cytoplasmic membrane has two domains a sensory domain and a transmitter domain. The two domains are anchored in the cytoplasmic membrane by two membrane-spanning regions. The transmitter domain is capable of autophosphorylation utilizing ATP. During this reaction a phosphate group (PO4) is transferred to a specific Histidine (His) residue in the protein. The transmitter domain possesses a kinase activity that enables it to transfer the phosphate group to the receiver-regulator, when the environmental signal is detected [6]. The receiver-regulator that is cytoplasmically localized also consists of two domains. The first domain has a specific Aspartate (Asp) residue that can accept the phosphate group (PO4) from the transmitter domain. The second domain is the regulator that in response to phosphorylation can bind to specific DNA sequences near specific promoters to activate gene expression. These two domains are linked together by a flexible linker [7].
Osmoregulation is one of the well-studied two component regulatory systems operative in Escherichia coli, playing an important role in regulating the cellular response to different solute concentrations in their environment [8]. Among such types of osmotic responses, the expression of the major outer membrane proteins, OmpC and OmpF, has been the subject of extensive studies [9]. Both the OmpC and OmpF proteins form passive diffusion pores in the outer membrane, which facilitates the diffusion of small hydrophilic molecules across the membrane [10]. EnvZ the sensor kinase serves as a substrate for the phospho-transfer to aspartate-55 of OmpR, the response regulator [11,12]. EnvZ is a bifunctional histidine kinase that exhibits dual opposing functions, both phosphorylation and dephosphorylation of OmpR [13,14].
The sensor kinase is triggered by the osmolyte concentration of the environment and controls the production of phosphorylated regulator OmpR. This leads to the expression of outer membrane proteins OmpC or OmpF [15]. Phosphorylated OmpR binds to the regulatory sequences upstream of the ompC and ompF promoters [16,17]. OmpR undergoes a conformational change based on phosphorylation [18] and regulates the expression of the porin genes ompF and ompC in Escherichia coli [19]. The osmolyte works as the initial control element of the phosphorylation cascade. The primary signal for such a conformational change may be that caused by a change in the physical membrane-tension due to osmotic pressure [20]. First, the EnvZ-dimer, in the cytoplasmic membrane senses the environmental osmotic stimulus. The N-terminal membrane-spanning and periplasmic domains of the EnvZ-dimer presumably can take on two alternative conformational states (i.e., a high osmolarity form and a low osmolarity form) regulated by the osmotic signal and modulates the ratio of the kinase to phosphatase activity of EnvZ [21,22]. Under low osmolyte concentrations, Phosphatase activity of EnvZ predominates the kinase activity resulting in the binding of phosphorylated OmpR to the high affinity promoter of ompF gene triggering OmpF expression. In case of high osmolyte concentration the kinase activity of EnvZ is triggered resulting in the binding of the phosphorylated regulator to the low affinity promoters of ompC gene favouring OmpC expression [23].
Events associated with high osmolarity
In the high osmolarity state, EnvZ actively undergoes autophosphorylation at histidine residue-243 in the C-terminal kinase domain, and then efficiently transfers its phosphoryl group to the N-terminal receiver domain of OmpR at aspartate residue-55 through EnvZOmpR complex formation [24,25]. Upon phosphorylation, OmpR becomes an active dimer that exhibits enhanced DNA-binding ability specific for both the ompC and ompF genes. As the number of phosphorylated OmpR protein molecules increases, two events occur: OmpR binds not only to the high affinity binding sites upstream of the ompF promoter but also to the one low affinity-binding site. Binding to this low affinity site results in repression of ompF gene expression. So OmpF porin protein production is stopped. When OmpR binds to the three low affinity sites upstream of the OmpC promoters ompC gene expression is stimulated, more OmpC porin protein is made and appears in the outer membrane of the cell [26]. The summary of events is depicted in Figure 2.
Events associated with low osmolarity
In the low osmolarity state (Figure 3), however, EnvZ exhibits relatively low kinase activity (i.e., high phosphatase activity) towards OmpR. Such osmotic modulation of the kinase/phosphatase activity of EnvZ results in the relative amounts of the phosphorylated form of OmpR in cells varying in response to the medium osmolarity. When the medium osmolarity is low, the relative amount of the phosphorylated form of OmpR in cells is relatively small. In this particular situation, the ompF gene is first triggered, because the ompF promoter has relatively high-affinity OmpR-binding sites [27]. As the medium osmolarity increases, the relative amount of the phosphorylated form of OmpR increases proportionally, this in turn results in preferential activation of the ompC gene with low affinity OmpRP binding sites [9].
In summary, the relative amount of phosphorylated OmpR protein in the cell determines whether OmpF or OmpC is the predominantly expressed outer membrane porin. The relative amount of phosphorylated OmpR is determined by the perception of osmotic pressure by EnvZ [28].
Results and discussion
Simulation of low osmolarity
Reports have proposed that at low osmolarity, OmpRP binds cooperatively to F1, F1F2 and F1F2F3 sites resulting only in OmpF expression (Figure 3). Accordingly low osmolarity conditions were simulated assuming that Escherichia coli cells are grown in normal nutrient medium. The first event of start of simulation is the activation of phosphatase activity of EnvZ followed by autophosphorylation of EnvZp by ATP dissociation. Later EnvZpp-OmpR complex formation occurs (Figure 3) [29]. This short lived complex triggers phosphorylated regulator OmpRP. The phosphatase activity carries out the dephosphorylation of the phosphorylated regulator. Thus the concentration of cellular OmpRP is available only for binding of ompF promoters leading to OmpF porin expression.
In the case of Low osmolarity steady state production of OmpF and expression of OmpC could be seen initially at the start of simulation. However at the saturation of OmpF, repression of OmpC was significant. The steady state production of OmpF expression and OmpC repression is indicated in Figure 4a.
The sucrose levels were maintained as normal (around 150 molecules). OmpF synthesis seen to be triggered at the start of simulation. The entire trend of OmpF synthesis, gradual increase, steady state (at 50 seconds after the start of simulation with about a constant increase of 10 molecules for every 10 seconds) and final saturation (near 90 seconds with an constant value of 262 molecules) are represented graphically. (Figure 4a). A similar trend is seen with OmpC molecules reaching saturation at 70 seconds. The intermediates of low osmolarity pathway namely promoter binding is indicated in Figure 5a. It could be seen that EnvZpOmpR complex is formed at very basal levels thereby directly having control over OmpC molecules. Additionally only 3.5% of cellular OmpR was phosphorylated at minimal sucrose in low osmolarity conditions validating the model that is operative in low osmolarity.
Simulation of high osmolarity
For high osmolarity conditions, the model generated incorporates the concentration of sucrose with the assumption that Escherichia coli cells are grown in nutrient broth with 20% additional concentration of sucrose (1.11 M equivalent) [30]. With this injection stimulus, the kinase activity of EnvZ is enhanced leading to OmpC expression.
The first event of start of simulation is the activation of kinase activity of EnvZ followed by autophosphorylation of EnvZk by ATP dissociation. Shortly thereafter, EnvZkOmpR complex level rises and the dissociation of the complex raises the level of the phosphorylated regulator OmpR. This is followed by cooperative binding of OmpR to the ompF promoters and ompC promoters. OmpC molecules start accumulating. In the course of time there is complete dissociation of EnvZkOmpR complex, thereby both sensor and response protein are brought back to the pool (Figure 2). In our model sucrose levels turns the EnvZ to take up kinase activity by increasing EnvZk concentration [31]. In this model we show that the steady state production of OmpF is dramatically reduced in the high osmolarity medium. As the cooperative binding of OmpRP to ompC promoter sites is known to require a higher concentration of OmpRP than ompF promoter sites, this is achieved by reducing the EnvZ phosphatase activity. In this manner, a few fold increase in the OmpRP concentration on the cell is enough to induce OmpC expression and concomitant repression of OmpF expression by OmpRP binding to the F4 repressor site. Recent in vivo studies reveal that F4 site is key factor responsible for OmpF repression [32,28]. The rate of OmpC production increased notably after sucrose addition, which is comparable with literature results. OmpC expression reaching saturation and subsequent OmpF repression is indicated in Figure 4b.
In high osmolarity, the increase of sucrose level (to about 1.11 M equivalent) [30]in silico through the virtual pipette directly favours OmpC production. OmpC production follows the same trend as OmpF in steady state (from 60 seconds to 90 seconds with a constant increase of 13 molecules per second) and saturation reaches by 100 seconds. OmpF repression shows steady state increase of 6 molecules for every 10 seconds and finally at saturation takes up a constant value by 90 seconds (Figure 4b) but still the events are delayed compared to low osmolarity. Here again the key marker EnvZk is found in significant levels as seen in the graphical representation and so are the promoter-regulator complex (Figure 5b). Here 10% of cellular OmpR is finally phosphorylated at the end of simulation agreeing with the literature data.
Effect of Volume changes over porin production
Shrinkage of Escherichia coli is associated with osmotic change [33]. The effect of volume increase and decrease over the porin production was verified with the simulation model. A volume decrease of 10% and 20% from the specified 10-15 Litres was incorporated into the simulation model at low and high osmolarity conditions. In both cases the decrease in volume did not affect the relative porin production. Although the reach of saturation was rapid, the levels of porin and their relative ratios were found to be maintained with the same trend. This agrees with the data by Wood [34] with regard to the phases of the osmotic stress response by Escherichia coli K-12. The simulation model presented is at the first phase of physiological and structural responses triggered by osmotic shift where there is decreased cytoplasmic streaming. Our simulation time also corresponds with the time period of this first phase of the responses in the above literature. Table 2 represents the OmpC and OmpF levels at decreased volume levels.
Effect of ATP changes over porin production
To address the issue whether ATP levels has any effect over relative porin levels, simulation run was done at high and low levels of ATP. ATP level of 3 and 5 mM has been reported in exponentially growing Escherichia coli cells [35,36]. Simulation was carried out at these two concentrations of ATP. Table 2 indicates relative porin levels at high and low ATP levels. As has been reported earlier, the ATP increase leads to plasmolysis, thereby leading to crowding of molecules [34]. This does not seem to affect the ratio of porins in the simulated system even though the reach of simulation was rapid. This is possibly due to the robust nature of the system.
Porin production in the complete absence of the Sensor, EnvZ
Regulation of OmpC and OmpF expression in Escherichia coli in the absence of sensor, EnvZ has been studied [17]. We have thus examined the steady state production of OmpF and OmpC in the absence of EnvZ and also looked at the rate of production of the porins during osmolytic shift. As reported in vitro, EnvZ is required for the maximal OmpC production and for efficient induction of OmpC at high osmolarity. This is established in the in silico model. Also the lack of EnvZ in the simulation did not affect the OmpF at low osmolyte condition and incomplete OmpF repression could be noticed after osmolyte shift as reported by in vitro studies. The relative levels of porins without EnvZ is cross verified with the data reported by Frost et al. [17], over steady state production of OmpF and OmpC with minimal sucrose and osmolytic shift conditions (Table 2).
Effect of Elevated EnvZ levels over porin production
Within the context of the EnvZ/OmpR two component system, the mathematical model predicts the OmpF/OmpC transcription to be insensitive to variations in the level of EnvZ and OmpR. By increasing levels of EnvZ upto 10 fold (1000 molecules), the relative porin ratio was found to be constant during simulation, evidently agreeing with the robust nature of the switch as per the mathematical model [35].
Effect of OmpR levels over porin production
As with the case of EnvZ, through in silico model the OmpC/OmpF transcription was found to be independent of the OmpR levels upto 10-fold increase (20000 molecules). In either case both at high and low osmolarity, porins levels were found to be insensitive to elevated or decrease levels of response regulator, OmpR corresponding to the results of mathematical model (Table 2).
Conclusion
Signaling pathways, for example, commonly operate close to points of instability, frequently employing feedback and oscillatory reaction networks that are sensitive to the operation of small number of molecules [37,38]. The model simulated here is clearly a simplified description of the EnvZ/OmpR system. There are a number of aspects of the circuit that have not been included such as EnvZ dimerization, conformational changes of OmpR [6] or additional enzymatic steps. The simulation is based on the mathematical model of the EnvZ-mediated cycle of phosphorylation and dephosphorylation [39]. Thus, this model predicts that the regulation of OmpF and OmpC as a direct consequence of the level of OmpR-P in the cell and is dependent on the way in which OmpR-P interacts with sites in the ompF and ompC regulatory regions [40]. Previously, it was suggested on the basis of a simplified model for the EnvZ-mediated cycle of phosphorylation and dephosphorylation of OmpR that the output of the circuit (the concentration of OmpR-P) should be independent of the concentration of EnvZ and OmpR in the cell [41]. We have shown porin regulation at high and low osmolyte concentrations where the dual activity of EnvZ is primarily controlled by the concentration of osmolyte stimulus at the start of simulation. The preliminary simulation experiment indicates that both reaching steady state expression and saturation is delayed in the case of OmpC compared to OmpF. The relative porin production seems to be unaltered with changes in cell volume, ATP, EnvZ and OmpR at low and high osmolarity conditions. But the reach of saturation was rapid at high and low osmolarity with altered levels of the above components. Experimental analysis will help improve the model. The model captures the basic features of the generally accepted view of EnvZ-OmpR signaling and is a reasonable starting point for building sophisticated models and explaining quantitative features of the system. At the same time, beyond its applicability to EnvZ-OmpR the model provides an interesting mechanism for achieving robust behavior with a bi-functional enzyme that may be broadly applicable to the other regulatory circuits within cells.
Methods
The E-CELL Windows version 2.25 was employed for simulation [42,43]. The software was installed with the third party software namely Active Perl, JRE (java runtime environment) and Borland C++ compiler [44] essential for running simulations. The information defining all the components of the osmoregulatory switch, reactions and appropriate reactors and rate constants and environmental parameters describing volume was incorporated in the rule file. This file was further compiled through Active perl. The order of reaction kinetics, time of simulation and time interval was specified in script file. The reactor is basically a file describing /defining the kinetics of the equation along with rate constants. Reactor file were complied using C++ compiler. Figure 6 summarizes the method of construction of quantitative model.
Creation of rule file based on the mathematical model
The computational model of osmoregulatory switch is based on the mathematical model by Goulian and Batchelor [39]. The entire model is described in the rule file. The cell system and cell environment was defined first. Changes in volume could not be incorporated, hence in this in silico approach, volume parameters were assumed constant. Also simulations with other system have assumed volume as a constant parameter irrespective of the system simulated. Simulations could not be defined and shown visually for volume parameter, as E-CELL has no provision for spatial information. Table 3 details the list of substances in osmoregulatory with their respective substance IDs.
Reactor Specifications
E-Cell is based on an object-oriented modeling theory, structured Substance-Reactor Model (SRM). The simulation models are constructed with three fundamental object classes, Substance, Reactor and System. Substances represent state variables, Reactors represent operations on the state variables, and Systems represent logical and/or physical compartments containing other objects. The distributed package of version employed for carrying out simulation has 18 different classes of standard Reactors, such as for Michaelis-Menten formula and generalized chemical equilibrium [43]. In the simulation systems, the rate equations of all the reactions are defined. Every reaction follows different kinetics based on the substrate involved and is dependent on reaction type. The reactors employed for osmoregulatory switch includes mass action, Catalysed mass action (specified in Table 4) defining all reactions from sucrose injection till the regulated expression of the porins. Reactor is the term employed here, as in E-CELL, to describe the reaction rate. The volume of the system is assumed to be unchanged during simulation using a constant parameter reactor. Molecular binding such as osmolyte interaction and response regulator DNA binding was modeled using Mass Action reactor, which computes velocity as a product of concentration of substrates and a kinetic constant. The expression of porin was modeled on mass action principles with catalyst embedded using a Catalyzed Mass Action reactor. Autophosphorylation of sensor, sensor-regulator complex formation and ATP dissociation was modeled using MichaelisUniUni reactor. Auto dephosphorylation reactions was modeled using MichaelisUniUnireactor. Zeroreactor, which calculates velocity independent of concentration of molecular species, was employed for modeling complex dissociation. A Decay reactor was employed for defining the disintegration or decay of components. Table 4 details the reaction type and reactors with their respective chemical constants employed for constructing the model.
The present model is built on the assumption of the in vivo condition considering Escherichia coli cells grown in mid-log phase. Accordingly the levels of OmpR and EnvZ are reported to be 3500 and 100 molecules in cell respectively. OmpR and EnvZ levels were almost the same from cells grown in L-broth medium or in a high osmolarity medium (NB (Nutrient Broth) +20% sucrose) [26]. The ratio of OmpR to EnvZ is reported to be constant, assuming the cell volume to be 10-15 liters [45]. At low osmolarity the phosphorylation of only 3.5%(120 nM or 70 OmpRP molecules/cell) of total OmpR molecules in a cell (2024 molecules OmpR molecules per cell) would be enough to activate the expression of OmpF, whereas at high osmolarity the phosphorylation of about 10%(590 nM or 350 OmpRP molecules /cell) of total OmpR molecules in a cell (3500 molecules per cell) would be sufficient to promote the expression of OmpC and to repress the expression of OmpF [46](Table 1). The majority of OmpR still remains unphosphorylated, as it's pool is very large. It is important to note that the osmoregulation of the OmpF and OmpC gene is finely tuned by having a very large pool of OmpR molecules [30]. The list of key substances participating in osmoregulatory switch with their initial concentration at the start of simulation is summarized in Table 5. The data was adapted from Cai and Inouye [30]. As the data with regard to the number of promoters was not available, different values were taken and checked with the porin production and their relative ratios. The relative ratios were found be unaltered with any promoter levels.
Authors contribution
KVS was responsible for data collection and analysis. SK conceived of the study, and participated in its design and analysis. Two referees and an advisor of the journal helped to bring this information into the biological context.
Acknowledgements
We thank the anonymous referee and adviser of the journal who helped us improve the manuscript substantially. We acknowledge the use of Bioinformatics centre facility funded by DBT, Govt of India for project support under the NMITLI programme and under the Centre of Excellence in Bioinformatics.
Figures and Tables
Figure 1 Two Component Regulatory Systems. The first component, sensor kinase autophosphorylates and transfers phosphate to the response regulator. These are also called the HAP systems indicating the involvement of Histidine of sensor kinase and Aspartate of Response regulators playing a key role in signal transduction.
Figure 2 Molecular Model of Osmoregulatory switch operative at High osmolarity in Escherichia coli. With the injection stimulus of sucrose as osmolyte the sensor EnvZ is triggered to take up either kinase or phosphatase activity. In high osmolarity conditions the higher osmolyte medium (NB (Nutrient Broth)+20% sucrose) makes the kinase activity of EnvZ (EnvZk) predominate the phosphatase activity resulting in the formation of EnvZkpOmpR complex. Finally after the phosphotransfer of phosphate group to OmpR, the promoter sites of OmpF are occupied in cooperative manner with F1, F1F2 and F1F2F3, high affinity binding sites. The cellular concentration of OmpRP makes it available for the OmpC promoter C1, C1C2, C1C2C3. Additionally OmpRP binds to F4 after binding F1F2F3 promoter directly repressing OmpF expression, thereby facilitating OmpC expression.
Figure 3 Molecular Model of Osmoregulatory switch operative at Low Osmolarity in Escherichia coli. The low osmolarity conditions favour the phosphatase activity of EnvZ. The Phosphatase domain (EnvZp) upon autophosphorylation leads to formation of EnvZppOmpR complex and later favours the dephosphorylation of the cellular OmpRP thus making OmpRP available only for cooperative binding to the high affinity promoter of OmpF namely F1, F1F2, and F1F2F3 favouring OmpF expression.
Figure 4 Porin production at Low and High osmolarity conditions Indicated in the X-axis is the time in seconds and number of molecules of the component in the osmoregulatory switch in Y-axis. (a) The start of simulation with OmpF (indicated in blue) synthesis gradually triggered. By mid term of simulation steady state production and final saturation of OmpF molecules could be seen. Shown in b is the OmpC (indicated in pink) production and reaching saturation levels at high osmolarity conditions.
Figure 5 Regulator-promoter complex formation simulation The intermediate products of simulation namely the regulator-promoter complexes for regulation of (a) OmpF: f1omprp – blue; f1f2omprp – pink; f1f2f3omprp – yellow and (b) OmpC: c1omprp-yellow; c1c2omprp-pink; c1c2c3omprp-blue respectively.
Figure 6 Schematic representation of steps in creation of simulation model using E-CELL system
Table 1 Sensor and response regulator molecules at high and low osmolarity. Based on the assumed in vivo data as reported by Cai and Inouye, 2002 [30], the levels of the components EnvZ, OmpR and OmpRP at high and low osmolarity conditions are listed. At high osmolarity 10% of cellular OmpR gets phosphorylated, sufficient enough to promote OmpC expression. Contrarily at low osmolarity only 3.5% of cellular OmpRP would be sufficient enough to activate the expression of OmpF.
SUBSTANCE High osmolarity (no. of molecules/cell) Low osmolarity (no. of molecules/cell)
EnvZ 100 60
OmpR 3500 2100
OmpRP 350(10%) 70(3.5%)
Table 2 Effect of ATP, EnvZ, OmpR and volume at high and low osmolarity conditions. The levels of low and high ATP taken were 3 mM and 5 mM respectively. The volume taken was 10% and 20% decrease with respect to 10-15. At low EnvZ, the level was taken as nil and was elevated till 10 fold (1000 molecules with respect to 102 reported value). Similarly for OmpR a 10 fold increase and decrease from the reported value of 2100 was taken (20000 and 200 molecules respectively)
Condition Low Osmolarity High Osmolarity
Saturation time OmpF Number of OmpF Number of OmpC Ratio OmpF/OmpC Saturation time OmpF Number of OmpC Number of OmpF Ratio OmpC/Omp F
Low ATP 30 244 100 2.4 18 557 129 4.3
High ATP 26 242 106 2.2 13 557 130 4.2
Low EnvZ 120 246 6 41 85 550 246 2.2
High EnvZ 135 244 104 2.3 80 550 125 4.4
Low OmpR 125 35 13 2.6 70 52 14 3.7
High OmpR 130 2176 889 2.4 115 5309 1210 4.3
Low Volume 150 245 108 2.2 73 537 124 4.3
High Volume 130 243 100 2.4 80 548 125 4.3
Table 3 List of components involved in osmoregulatory switch
Substance Substance Description Substance_ID
Sucrose Sucrose s
EnvZ Sensor kinase EnvZ envz
EnvZk/EnvZp EnvZ with Kinase / phosphatase activity envzk/envzk
EnvZkP/EnvZpP Phosphorylated EnvZ with Kinase / phosphatase activity envzkp/envzpp
OmpR/OmpRP Response regulatory OmpR/Phosphorylated OmpR ompr/omprp
EnvZpPOmpR
EnvZkPOmpR
EnvZpOmpRP
EnvZkOmpRP Sensor kinase-regulatory complexes envzppompr
envzkpompr
envzpomprp
envzkomprp
F1
F1F2
F1F2F3
F1F2F3F4 ompF gene promoter sites f1
f1f2
f1f2f3
f1f2f3f4
C1
C1C2
C1C2C3 ompC gene promoter sites c1
c1c2
c1c2c3
F1OmpRP
F1F2 OmpRP
F1F2F3 OmpRP
F1F2F3F4 OmpRP
C1 OmpRP
C1C2 OmpRP
C1C2C3 OmpRP Response regulator-promoter complex f1omprp
f1f2omprp
f1f2f3omprp
f1f2f3f4 omprp
c1omprp
c1c2omprp
c1c2c3omprp
ATP ATP ATP
OmpF Porin OmpF ompf
OmpC Porin OmpC ompc
Table 4 Rate equations, reactors and respective rate constants employed in simulation. Represented in the table are the details of the reaction and reactor type along with kinetic constants. Theoretical and Kinetic data used for defining the rate equations taken from literature are quoted. Those indicated in * are defined as concentration/second.
Reaction Equation Constant Comments
Formation of EnvZk and EnvZp v = k [envz] [s]
v = k [envz] [s] k = 15.6 μM
k = 0.15 μM (High osmolarity)
(low osmolarity)
Catalysed Mass action reactor, velocity is calculated as a product of concentrations of substrates and kinetic constants
[Data as presented by Yoshida et al [29]]
Formation of EnvZkp and EnvZpp
KmS = 1 μM*
KcF = 10 μM*
KmS = 10 μM*
KcF = 100 μM* As per chemotaxis data.
Kinetics of the reaction described by Henri Michaelis Menten equation derived from rapid equilibrium assumptions [(Bray et al [4]]
Formation of EnvZkpompr and EnvZppompr complex
KmS = 0.51 μM*
KcF = 10 μM*
KmS = 0.42 μM*
KcF = 20 μM* As per chemotaxis data Kinetics of the reaction described by Henri Michaelis Menten equation derived from rapid equilibrium assumptions. [(Bray et al [4]]
Dissociation of EnvZkpompr, EnvZppompr, EnvZkomprp and EnvZpomprp complexes v = rate Rate = 1.20 μM Represented by zero reactor, velocity is independent of concentration of molecular species [data as presented by Yoshida et al [29]]
Formation of F1omprp, F1F2omprp, F1F2F3omprp v = k [f1] v = k [f1f2]
v = k [f1f2f3]
v = k [f1f2f3f4] k = 6.8 nM
k = 10.7 nM
k = 15.4 nM
k = 21.2 nM Mass action reactor, velocity is calculated as a product of concentrations of substrates and kinetic constants [data as presented Head et al [28]]
Formation of C1omprp, C1C2omprp, C1C2C3omprp v = k [c1]
v = k [c1c2]
v = k [c1c2c3] k = 7.7 μM
k = 18.9 μM
k = 31.4 μM Mass action reactor, velocity is calculated as a product of concentrations of substrates and kinetic constants [data as presented Head et al [28]]
Degradation of F1F2F3F4 OmpRp (OmpF repression) [f1f2f3f4omprp] In the reactor class decay process, substrate reduced according to the half-life inputted. [Bergstrom et al [28]]
Formation of OmpC and OmpF v(OmpC) = k [c1c2c3omprp] [envzk]
v(OmpF) = k [f1f2f3fomprp] [envzp] k = 1 μM
k = 1 μM Catalysed Mass action reactor, velocity is calculated as a product of concentrations of substrates and kinetic constants (Batchelor and Goulian [39])
Table 5 Initial levels of molecular species at the start of simulation. Indicated in the table are initial concentrations of substances at the start of simulation for low and high osmolarity conditions. At high molarity the sucrose molecules added (virtually through substance window) to the medium is indicated in the table. EnvZ-Sensor kinase, OmpR-Response regulator [data as presented by Cai and Inouye] [30], F1, F1F2, F1F2F3-ompF gene promoter sites and C1, C1C2, C1C2C3-ompC gene promoter sites. Envzp, envzk, envzppompr, envzkpompr, envzpomprp, envzkomprp, f1omprp, f1f2omprp, f1f2f3omprp, f1f2f3f4omprp, c1omprp, c1c2omprp, c1c2c3omprp are initially nil at the start of simulation. During the course of simulation, these complexes are formed and dissociated at the end of simulation
Species Number of molecules
High Low
EnvZ 113 102
OmpR 3500 2100
Sucrose 150 3000
f1 - 100
f1f2 - 100
f1f2f3 - 100
c1 100 -
c1c2 100 -
c1c2c3 100 -
ATP 100 100
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E-CELL
Takahashi K Ishikawa N Sadamoto Y Sasamoto H Ohta S Shiozawa S Miyoshi F Naito Y Nakayama Y Tomita M E-Cell 2: Multi-platform E-Cell simulation system Bioinformatics 2003 19 1727 1729 15593410 10.1093/bioinformatics/btg221
Borland C++ compiler
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| 15571621 | PMC543474 | CC BY | 2021-01-04 16:03:38 | no | BMC Microbiol. 2004 Nov 30; 4:44 | utf-8 | BMC Microbiol | 2,004 | 10.1186/1471-2180-4-44 | oa_comm |
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BMC MicrobiolBMC Microbiology1471-2180BioMed Central London 1471-2180-4-441557162110.1186/1471-2180-4-44Research ArticleA simulation model of Escherichia coli osmoregulatory switch using E-CELL system Srividhya KV [email protected] Sankaran [email protected] Bioinformatics Centre, School of Biotechnology, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu India2004 30 11 2004 4 44 44 3 11 2003 30 11 2004 Copyright © 2004 Srividhya and Krishnaswamy; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Bacterial signal transduction mechanism referred to as a "two component regulatory systems" contributes to the overall adaptability of the bacteria by regulating the gene expression. Osmoregulation is one of the well-studied two component regulatory systems comprising of the sensor, EnvZ and the cognate response regulator, OmpR, which together control the expression of OmpC and OmpF porins in response to the osmolyte concentration.
Results
A quantitative model of the osmoregulatory switch operative in Escherichia coli was constructed by integrating the enzyme rate equations using E-CELL system. Using the substance reactor logic of the E-CELL system, a total of 28 reactions were defined from the injection of osmolyte till the regulated expression of porins by employing the experimental kinetic constants as reported in literature. In the case of low osmolarity, steady state production of OmpF and repression of OmpC was significant. In this model we show that the steady state – production of OmpF is dramatically reduced in the high osmolarity medium. The rate of OmpC production increased after sucrose addition, which is comparable with literature results. The relative porin production seems to be unaltered with changes in cell volume changes, ATP, EnvZ and OmpR at low and high osmolarity conditions. But the reach of saturation was rapid at high and low osmolarity with altered levels of the above components.
Conclusions
The E-CELL system allows us to perform virtual experiments on the bacterial osmoregulation model. This model does not take into account interaction with other networks in the cell. It suggests that the regulation of OmpF and OmpC is a direct consequence of the level of OmpRP in the cell and is dependent on the way in which OmpRP interacts with ompF and ompC regulatory regions. The preliminary simulation experiment indicates that both reaching steady state expression and saturation is delayed in the case of OmpC compared to OmpF. Experimental analysis will help improve the model. The model captures the basic features of the generally accepted view of EnvZ-OmpR signaling and is a reasonable starting point for building sophisticated models and explaining quantitative features of the system.
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Background
Among prokaryotes, a remarkable number of cellular functions are controlled by two component regulatory systems [1]. Dedicated circuits transduce and interpret specific signals such as pH, temperature, osmolarity, light, nutrients, ions, pheromones and toxins to regulate a wide range of processes including motility, virulence, metabolism, the cell cycle, development switches, antibiotic resistance and stress responses in a variety of systems from prokaryotes, archaea and eukaryotes [2].
Two component systems interact with each other and also with other regulatory systems mediating specific gene expression or cellular locomotion. Such complexes should be analysed quantitatively. Although quantitative data on cellular processes other than metabolism are still relatively sparse, scientists have modeled some systems with considerable success [3]. For example computer simulations of the chemotaxis two component system have been extensively studied [4].
Two-component regulatory systems (also called the HAP or His-Asp phosphotransfer mechanism) are widely used signaling machinery of bacterial adaptive responses. The system constitutes of sensor kinases and response regulators that can be phosphorylated and dephosphorylated by sensor kinases [5] (Figure 1).
The first component sensor-transmitter that spans the cytoplasmic membrane has two domains a sensory domain and a transmitter domain. The two domains are anchored in the cytoplasmic membrane by two membrane-spanning regions. The transmitter domain is capable of autophosphorylation utilizing ATP. During this reaction a phosphate group (PO4) is transferred to a specific Histidine (His) residue in the protein. The transmitter domain possesses a kinase activity that enables it to transfer the phosphate group to the receiver-regulator, when the environmental signal is detected [6]. The receiver-regulator that is cytoplasmically localized also consists of two domains. The first domain has a specific Aspartate (Asp) residue that can accept the phosphate group (PO4) from the transmitter domain. The second domain is the regulator that in response to phosphorylation can bind to specific DNA sequences near specific promoters to activate gene expression. These two domains are linked together by a flexible linker [7].
Osmoregulation is one of the well-studied two component regulatory systems operative in Escherichia coli, playing an important role in regulating the cellular response to different solute concentrations in their environment [8]. Among such types of osmotic responses, the expression of the major outer membrane proteins, OmpC and OmpF, has been the subject of extensive studies [9]. Both the OmpC and OmpF proteins form passive diffusion pores in the outer membrane, which facilitates the diffusion of small hydrophilic molecules across the membrane [10]. EnvZ the sensor kinase serves as a substrate for the phospho-transfer to aspartate-55 of OmpR, the response regulator [11,12]. EnvZ is a bifunctional histidine kinase that exhibits dual opposing functions, both phosphorylation and dephosphorylation of OmpR [13,14].
The sensor kinase is triggered by the osmolyte concentration of the environment and controls the production of phosphorylated regulator OmpR. This leads to the expression of outer membrane proteins OmpC or OmpF [15]. Phosphorylated OmpR binds to the regulatory sequences upstream of the ompC and ompF promoters [16,17]. OmpR undergoes a conformational change based on phosphorylation [18] and regulates the expression of the porin genes ompF and ompC in Escherichia coli [19]. The osmolyte works as the initial control element of the phosphorylation cascade. The primary signal for such a conformational change may be that caused by a change in the physical membrane-tension due to osmotic pressure [20]. First, the EnvZ-dimer, in the cytoplasmic membrane senses the environmental osmotic stimulus. The N-terminal membrane-spanning and periplasmic domains of the EnvZ-dimer presumably can take on two alternative conformational states (i.e., a high osmolarity form and a low osmolarity form) regulated by the osmotic signal and modulates the ratio of the kinase to phosphatase activity of EnvZ [21,22]. Under low osmolyte concentrations, Phosphatase activity of EnvZ predominates the kinase activity resulting in the binding of phosphorylated OmpR to the high affinity promoter of ompF gene triggering OmpF expression. In case of high osmolyte concentration the kinase activity of EnvZ is triggered resulting in the binding of the phosphorylated regulator to the low affinity promoters of ompC gene favouring OmpC expression [23].
Events associated with high osmolarity
In the high osmolarity state, EnvZ actively undergoes autophosphorylation at histidine residue-243 in the C-terminal kinase domain, and then efficiently transfers its phosphoryl group to the N-terminal receiver domain of OmpR at aspartate residue-55 through EnvZOmpR complex formation [24,25]. Upon phosphorylation, OmpR becomes an active dimer that exhibits enhanced DNA-binding ability specific for both the ompC and ompF genes. As the number of phosphorylated OmpR protein molecules increases, two events occur: OmpR binds not only to the high affinity binding sites upstream of the ompF promoter but also to the one low affinity-binding site. Binding to this low affinity site results in repression of ompF gene expression. So OmpF porin protein production is stopped. When OmpR binds to the three low affinity sites upstream of the OmpC promoters ompC gene expression is stimulated, more OmpC porin protein is made and appears in the outer membrane of the cell [26]. The summary of events is depicted in Figure 2.
Events associated with low osmolarity
In the low osmolarity state (Figure 3), however, EnvZ exhibits relatively low kinase activity (i.e., high phosphatase activity) towards OmpR. Such osmotic modulation of the kinase/phosphatase activity of EnvZ results in the relative amounts of the phosphorylated form of OmpR in cells varying in response to the medium osmolarity. When the medium osmolarity is low, the relative amount of the phosphorylated form of OmpR in cells is relatively small. In this particular situation, the ompF gene is first triggered, because the ompF promoter has relatively high-affinity OmpR-binding sites [27]. As the medium osmolarity increases, the relative amount of the phosphorylated form of OmpR increases proportionally, this in turn results in preferential activation of the ompC gene with low affinity OmpRP binding sites [9].
In summary, the relative amount of phosphorylated OmpR protein in the cell determines whether OmpF or OmpC is the predominantly expressed outer membrane porin. The relative amount of phosphorylated OmpR is determined by the perception of osmotic pressure by EnvZ [28].
Results and discussion
Simulation of low osmolarity
Reports have proposed that at low osmolarity, OmpRP binds cooperatively to F1, F1F2 and F1F2F3 sites resulting only in OmpF expression (Figure 3). Accordingly low osmolarity conditions were simulated assuming that Escherichia coli cells are grown in normal nutrient medium. The first event of start of simulation is the activation of phosphatase activity of EnvZ followed by autophosphorylation of EnvZp by ATP dissociation. Later EnvZpp-OmpR complex formation occurs (Figure 3) [29]. This short lived complex triggers phosphorylated regulator OmpRP. The phosphatase activity carries out the dephosphorylation of the phosphorylated regulator. Thus the concentration of cellular OmpRP is available only for binding of ompF promoters leading to OmpF porin expression.
In the case of Low osmolarity steady state production of OmpF and expression of OmpC could be seen initially at the start of simulation. However at the saturation of OmpF, repression of OmpC was significant. The steady state production of OmpF expression and OmpC repression is indicated in Figure 4a.
The sucrose levels were maintained as normal (around 150 molecules). OmpF synthesis seen to be triggered at the start of simulation. The entire trend of OmpF synthesis, gradual increase, steady state (at 50 seconds after the start of simulation with about a constant increase of 10 molecules for every 10 seconds) and final saturation (near 90 seconds with an constant value of 262 molecules) are represented graphically. (Figure 4a). A similar trend is seen with OmpC molecules reaching saturation at 70 seconds. The intermediates of low osmolarity pathway namely promoter binding is indicated in Figure 5a. It could be seen that EnvZpOmpR complex is formed at very basal levels thereby directly having control over OmpC molecules. Additionally only 3.5% of cellular OmpR was phosphorylated at minimal sucrose in low osmolarity conditions validating the model that is operative in low osmolarity.
Simulation of high osmolarity
For high osmolarity conditions, the model generated incorporates the concentration of sucrose with the assumption that Escherichia coli cells are grown in nutrient broth with 20% additional concentration of sucrose (1.11 M equivalent) [30]. With this injection stimulus, the kinase activity of EnvZ is enhanced leading to OmpC expression.
The first event of start of simulation is the activation of kinase activity of EnvZ followed by autophosphorylation of EnvZk by ATP dissociation. Shortly thereafter, EnvZkOmpR complex level rises and the dissociation of the complex raises the level of the phosphorylated regulator OmpR. This is followed by cooperative binding of OmpR to the ompF promoters and ompC promoters. OmpC molecules start accumulating. In the course of time there is complete dissociation of EnvZkOmpR complex, thereby both sensor and response protein are brought back to the pool (Figure 2). In our model sucrose levels turns the EnvZ to take up kinase activity by increasing EnvZk concentration [31]. In this model we show that the steady state production of OmpF is dramatically reduced in the high osmolarity medium. As the cooperative binding of OmpRP to ompC promoter sites is known to require a higher concentration of OmpRP than ompF promoter sites, this is achieved by reducing the EnvZ phosphatase activity. In this manner, a few fold increase in the OmpRP concentration on the cell is enough to induce OmpC expression and concomitant repression of OmpF expression by OmpRP binding to the F4 repressor site. Recent in vivo studies reveal that F4 site is key factor responsible for OmpF repression [32,28]. The rate of OmpC production increased notably after sucrose addition, which is comparable with literature results. OmpC expression reaching saturation and subsequent OmpF repression is indicated in Figure 4b.
In high osmolarity, the increase of sucrose level (to about 1.11 M equivalent) [30]in silico through the virtual pipette directly favours OmpC production. OmpC production follows the same trend as OmpF in steady state (from 60 seconds to 90 seconds with a constant increase of 13 molecules per second) and saturation reaches by 100 seconds. OmpF repression shows steady state increase of 6 molecules for every 10 seconds and finally at saturation takes up a constant value by 90 seconds (Figure 4b) but still the events are delayed compared to low osmolarity. Here again the key marker EnvZk is found in significant levels as seen in the graphical representation and so are the promoter-regulator complex (Figure 5b). Here 10% of cellular OmpR is finally phosphorylated at the end of simulation agreeing with the literature data.
Effect of Volume changes over porin production
Shrinkage of Escherichia coli is associated with osmotic change [33]. The effect of volume increase and decrease over the porin production was verified with the simulation model. A volume decrease of 10% and 20% from the specified 10-15 Litres was incorporated into the simulation model at low and high osmolarity conditions. In both cases the decrease in volume did not affect the relative porin production. Although the reach of saturation was rapid, the levels of porin and their relative ratios were found to be maintained with the same trend. This agrees with the data by Wood [34] with regard to the phases of the osmotic stress response by Escherichia coli K-12. The simulation model presented is at the first phase of physiological and structural responses triggered by osmotic shift where there is decreased cytoplasmic streaming. Our simulation time also corresponds with the time period of this first phase of the responses in the above literature. Table 2 represents the OmpC and OmpF levels at decreased volume levels.
Effect of ATP changes over porin production
To address the issue whether ATP levels has any effect over relative porin levels, simulation run was done at high and low levels of ATP. ATP level of 3 and 5 mM has been reported in exponentially growing Escherichia coli cells [35,36]. Simulation was carried out at these two concentrations of ATP. Table 2 indicates relative porin levels at high and low ATP levels. As has been reported earlier, the ATP increase leads to plasmolysis, thereby leading to crowding of molecules [34]. This does not seem to affect the ratio of porins in the simulated system even though the reach of simulation was rapid. This is possibly due to the robust nature of the system.
Porin production in the complete absence of the Sensor, EnvZ
Regulation of OmpC and OmpF expression in Escherichia coli in the absence of sensor, EnvZ has been studied [17]. We have thus examined the steady state production of OmpF and OmpC in the absence of EnvZ and also looked at the rate of production of the porins during osmolytic shift. As reported in vitro, EnvZ is required for the maximal OmpC production and for efficient induction of OmpC at high osmolarity. This is established in the in silico model. Also the lack of EnvZ in the simulation did not affect the OmpF at low osmolyte condition and incomplete OmpF repression could be noticed after osmolyte shift as reported by in vitro studies. The relative levels of porins without EnvZ is cross verified with the data reported by Frost et al. [17], over steady state production of OmpF and OmpC with minimal sucrose and osmolytic shift conditions (Table 2).
Effect of Elevated EnvZ levels over porin production
Within the context of the EnvZ/OmpR two component system, the mathematical model predicts the OmpF/OmpC transcription to be insensitive to variations in the level of EnvZ and OmpR. By increasing levels of EnvZ upto 10 fold (1000 molecules), the relative porin ratio was found to be constant during simulation, evidently agreeing with the robust nature of the switch as per the mathematical model [35].
Effect of OmpR levels over porin production
As with the case of EnvZ, through in silico model the OmpC/OmpF transcription was found to be independent of the OmpR levels upto 10-fold increase (20000 molecules). In either case both at high and low osmolarity, porins levels were found to be insensitive to elevated or decrease levels of response regulator, OmpR corresponding to the results of mathematical model (Table 2).
Conclusion
Signaling pathways, for example, commonly operate close to points of instability, frequently employing feedback and oscillatory reaction networks that are sensitive to the operation of small number of molecules [37,38]. The model simulated here is clearly a simplified description of the EnvZ/OmpR system. There are a number of aspects of the circuit that have not been included such as EnvZ dimerization, conformational changes of OmpR [6] or additional enzymatic steps. The simulation is based on the mathematical model of the EnvZ-mediated cycle of phosphorylation and dephosphorylation [39]. Thus, this model predicts that the regulation of OmpF and OmpC as a direct consequence of the level of OmpR-P in the cell and is dependent on the way in which OmpR-P interacts with sites in the ompF and ompC regulatory regions [40]. Previously, it was suggested on the basis of a simplified model for the EnvZ-mediated cycle of phosphorylation and dephosphorylation of OmpR that the output of the circuit (the concentration of OmpR-P) should be independent of the concentration of EnvZ and OmpR in the cell [41]. We have shown porin regulation at high and low osmolyte concentrations where the dual activity of EnvZ is primarily controlled by the concentration of osmolyte stimulus at the start of simulation. The preliminary simulation experiment indicates that both reaching steady state expression and saturation is delayed in the case of OmpC compared to OmpF. The relative porin production seems to be unaltered with changes in cell volume, ATP, EnvZ and OmpR at low and high osmolarity conditions. But the reach of saturation was rapid at high and low osmolarity with altered levels of the above components. Experimental analysis will help improve the model. The model captures the basic features of the generally accepted view of EnvZ-OmpR signaling and is a reasonable starting point for building sophisticated models and explaining quantitative features of the system. At the same time, beyond its applicability to EnvZ-OmpR the model provides an interesting mechanism for achieving robust behavior with a bi-functional enzyme that may be broadly applicable to the other regulatory circuits within cells.
Methods
The E-CELL Windows version 2.25 was employed for simulation [42,43]. The software was installed with the third party software namely Active Perl, JRE (java runtime environment) and Borland C++ compiler [44] essential for running simulations. The information defining all the components of the osmoregulatory switch, reactions and appropriate reactors and rate constants and environmental parameters describing volume was incorporated in the rule file. This file was further compiled through Active perl. The order of reaction kinetics, time of simulation and time interval was specified in script file. The reactor is basically a file describing /defining the kinetics of the equation along with rate constants. Reactor file were complied using C++ compiler. Figure 6 summarizes the method of construction of quantitative model.
Creation of rule file based on the mathematical model
The computational model of osmoregulatory switch is based on the mathematical model by Goulian and Batchelor [39]. The entire model is described in the rule file. The cell system and cell environment was defined first. Changes in volume could not be incorporated, hence in this in silico approach, volume parameters were assumed constant. Also simulations with other system have assumed volume as a constant parameter irrespective of the system simulated. Simulations could not be defined and shown visually for volume parameter, as E-CELL has no provision for spatial information. Table 3 details the list of substances in osmoregulatory with their respective substance IDs.
Reactor Specifications
E-Cell is based on an object-oriented modeling theory, structured Substance-Reactor Model (SRM). The simulation models are constructed with three fundamental object classes, Substance, Reactor and System. Substances represent state variables, Reactors represent operations on the state variables, and Systems represent logical and/or physical compartments containing other objects. The distributed package of version employed for carrying out simulation has 18 different classes of standard Reactors, such as for Michaelis-Menten formula and generalized chemical equilibrium [43]. In the simulation systems, the rate equations of all the reactions are defined. Every reaction follows different kinetics based on the substrate involved and is dependent on reaction type. The reactors employed for osmoregulatory switch includes mass action, Catalysed mass action (specified in Table 4) defining all reactions from sucrose injection till the regulated expression of the porins. Reactor is the term employed here, as in E-CELL, to describe the reaction rate. The volume of the system is assumed to be unchanged during simulation using a constant parameter reactor. Molecular binding such as osmolyte interaction and response regulator DNA binding was modeled using Mass Action reactor, which computes velocity as a product of concentration of substrates and a kinetic constant. The expression of porin was modeled on mass action principles with catalyst embedded using a Catalyzed Mass Action reactor. Autophosphorylation of sensor, sensor-regulator complex formation and ATP dissociation was modeled using MichaelisUniUni reactor. Auto dephosphorylation reactions was modeled using MichaelisUniUnireactor. Zeroreactor, which calculates velocity independent of concentration of molecular species, was employed for modeling complex dissociation. A Decay reactor was employed for defining the disintegration or decay of components. Table 4 details the reaction type and reactors with their respective chemical constants employed for constructing the model.
The present model is built on the assumption of the in vivo condition considering Escherichia coli cells grown in mid-log phase. Accordingly the levels of OmpR and EnvZ are reported to be 3500 and 100 molecules in cell respectively. OmpR and EnvZ levels were almost the same from cells grown in L-broth medium or in a high osmolarity medium (NB (Nutrient Broth) +20% sucrose) [26]. The ratio of OmpR to EnvZ is reported to be constant, assuming the cell volume to be 10-15 liters [45]. At low osmolarity the phosphorylation of only 3.5%(120 nM or 70 OmpRP molecules/cell) of total OmpR molecules in a cell (2024 molecules OmpR molecules per cell) would be enough to activate the expression of OmpF, whereas at high osmolarity the phosphorylation of about 10%(590 nM or 350 OmpRP molecules /cell) of total OmpR molecules in a cell (3500 molecules per cell) would be sufficient to promote the expression of OmpC and to repress the expression of OmpF [46](Table 1). The majority of OmpR still remains unphosphorylated, as it's pool is very large. It is important to note that the osmoregulation of the OmpF and OmpC gene is finely tuned by having a very large pool of OmpR molecules [30]. The list of key substances participating in osmoregulatory switch with their initial concentration at the start of simulation is summarized in Table 5. The data was adapted from Cai and Inouye [30]. As the data with regard to the number of promoters was not available, different values were taken and checked with the porin production and their relative ratios. The relative ratios were found be unaltered with any promoter levels.
Authors contribution
KVS was responsible for data collection and analysis. SK conceived of the study, and participated in its design and analysis. Two referees and an advisor of the journal helped to bring this information into the biological context.
Acknowledgements
We thank the anonymous referee and adviser of the journal who helped us improve the manuscript substantially. We acknowledge the use of Bioinformatics centre facility funded by DBT, Govt of India for project support under the NMITLI programme and under the Centre of Excellence in Bioinformatics.
Figures and Tables
Figure 1 Two Component Regulatory Systems. The first component, sensor kinase autophosphorylates and transfers phosphate to the response regulator. These are also called the HAP systems indicating the involvement of Histidine of sensor kinase and Aspartate of Response regulators playing a key role in signal transduction.
Figure 2 Molecular Model of Osmoregulatory switch operative at High osmolarity in Escherichia coli. With the injection stimulus of sucrose as osmolyte the sensor EnvZ is triggered to take up either kinase or phosphatase activity. In high osmolarity conditions the higher osmolyte medium (NB (Nutrient Broth)+20% sucrose) makes the kinase activity of EnvZ (EnvZk) predominate the phosphatase activity resulting in the formation of EnvZkpOmpR complex. Finally after the phosphotransfer of phosphate group to OmpR, the promoter sites of OmpF are occupied in cooperative manner with F1, F1F2 and F1F2F3, high affinity binding sites. The cellular concentration of OmpRP makes it available for the OmpC promoter C1, C1C2, C1C2C3. Additionally OmpRP binds to F4 after binding F1F2F3 promoter directly repressing OmpF expression, thereby facilitating OmpC expression.
Figure 3 Molecular Model of Osmoregulatory switch operative at Low Osmolarity in Escherichia coli. The low osmolarity conditions favour the phosphatase activity of EnvZ. The Phosphatase domain (EnvZp) upon autophosphorylation leads to formation of EnvZppOmpR complex and later favours the dephosphorylation of the cellular OmpRP thus making OmpRP available only for cooperative binding to the high affinity promoter of OmpF namely F1, F1F2, and F1F2F3 favouring OmpF expression.
Figure 4 Porin production at Low and High osmolarity conditions Indicated in the X-axis is the time in seconds and number of molecules of the component in the osmoregulatory switch in Y-axis. (a) The start of simulation with OmpF (indicated in blue) synthesis gradually triggered. By mid term of simulation steady state production and final saturation of OmpF molecules could be seen. Shown in b is the OmpC (indicated in pink) production and reaching saturation levels at high osmolarity conditions.
Figure 5 Regulator-promoter complex formation simulation The intermediate products of simulation namely the regulator-promoter complexes for regulation of (a) OmpF: f1omprp – blue; f1f2omprp – pink; f1f2f3omprp – yellow and (b) OmpC: c1omprp-yellow; c1c2omprp-pink; c1c2c3omprp-blue respectively.
Figure 6 Schematic representation of steps in creation of simulation model using E-CELL system
Table 1 Sensor and response regulator molecules at high and low osmolarity. Based on the assumed in vivo data as reported by Cai and Inouye, 2002 [30], the levels of the components EnvZ, OmpR and OmpRP at high and low osmolarity conditions are listed. At high osmolarity 10% of cellular OmpR gets phosphorylated, sufficient enough to promote OmpC expression. Contrarily at low osmolarity only 3.5% of cellular OmpRP would be sufficient enough to activate the expression of OmpF.
SUBSTANCE High osmolarity (no. of molecules/cell) Low osmolarity (no. of molecules/cell)
EnvZ 100 60
OmpR 3500 2100
OmpRP 350(10%) 70(3.5%)
Table 2 Effect of ATP, EnvZ, OmpR and volume at high and low osmolarity conditions. The levels of low and high ATP taken were 3 mM and 5 mM respectively. The volume taken was 10% and 20% decrease with respect to 10-15. At low EnvZ, the level was taken as nil and was elevated till 10 fold (1000 molecules with respect to 102 reported value). Similarly for OmpR a 10 fold increase and decrease from the reported value of 2100 was taken (20000 and 200 molecules respectively)
Condition Low Osmolarity High Osmolarity
Saturation time OmpF Number of OmpF Number of OmpC Ratio OmpF/OmpC Saturation time OmpF Number of OmpC Number of OmpF Ratio OmpC/Omp F
Low ATP 30 244 100 2.4 18 557 129 4.3
High ATP 26 242 106 2.2 13 557 130 4.2
Low EnvZ 120 246 6 41 85 550 246 2.2
High EnvZ 135 244 104 2.3 80 550 125 4.4
Low OmpR 125 35 13 2.6 70 52 14 3.7
High OmpR 130 2176 889 2.4 115 5309 1210 4.3
Low Volume 150 245 108 2.2 73 537 124 4.3
High Volume 130 243 100 2.4 80 548 125 4.3
Table 3 List of components involved in osmoregulatory switch
Substance Substance Description Substance_ID
Sucrose Sucrose s
EnvZ Sensor kinase EnvZ envz
EnvZk/EnvZp EnvZ with Kinase / phosphatase activity envzk/envzk
EnvZkP/EnvZpP Phosphorylated EnvZ with Kinase / phosphatase activity envzkp/envzpp
OmpR/OmpRP Response regulatory OmpR/Phosphorylated OmpR ompr/omprp
EnvZpPOmpR
EnvZkPOmpR
EnvZpOmpRP
EnvZkOmpRP Sensor kinase-regulatory complexes envzppompr
envzkpompr
envzpomprp
envzkomprp
F1
F1F2
F1F2F3
F1F2F3F4 ompF gene promoter sites f1
f1f2
f1f2f3
f1f2f3f4
C1
C1C2
C1C2C3 ompC gene promoter sites c1
c1c2
c1c2c3
F1OmpRP
F1F2 OmpRP
F1F2F3 OmpRP
F1F2F3F4 OmpRP
C1 OmpRP
C1C2 OmpRP
C1C2C3 OmpRP Response regulator-promoter complex f1omprp
f1f2omprp
f1f2f3omprp
f1f2f3f4 omprp
c1omprp
c1c2omprp
c1c2c3omprp
ATP ATP ATP
OmpF Porin OmpF ompf
OmpC Porin OmpC ompc
Table 4 Rate equations, reactors and respective rate constants employed in simulation. Represented in the table are the details of the reaction and reactor type along with kinetic constants. Theoretical and Kinetic data used for defining the rate equations taken from literature are quoted. Those indicated in * are defined as concentration/second.
Reaction Equation Constant Comments
Formation of EnvZk and EnvZp v = k [envz] [s]
v = k [envz] [s] k = 15.6 μM
k = 0.15 μM (High osmolarity)
(low osmolarity)
Catalysed Mass action reactor, velocity is calculated as a product of concentrations of substrates and kinetic constants
[Data as presented by Yoshida et al [29]]
Formation of EnvZkp and EnvZpp
KmS = 1 μM*
KcF = 10 μM*
KmS = 10 μM*
KcF = 100 μM* As per chemotaxis data.
Kinetics of the reaction described by Henri Michaelis Menten equation derived from rapid equilibrium assumptions [(Bray et al [4]]
Formation of EnvZkpompr and EnvZppompr complex
KmS = 0.51 μM*
KcF = 10 μM*
KmS = 0.42 μM*
KcF = 20 μM* As per chemotaxis data Kinetics of the reaction described by Henri Michaelis Menten equation derived from rapid equilibrium assumptions. [(Bray et al [4]]
Dissociation of EnvZkpompr, EnvZppompr, EnvZkomprp and EnvZpomprp complexes v = rate Rate = 1.20 μM Represented by zero reactor, velocity is independent of concentration of molecular species [data as presented by Yoshida et al [29]]
Formation of F1omprp, F1F2omprp, F1F2F3omprp v = k [f1] v = k [f1f2]
v = k [f1f2f3]
v = k [f1f2f3f4] k = 6.8 nM
k = 10.7 nM
k = 15.4 nM
k = 21.2 nM Mass action reactor, velocity is calculated as a product of concentrations of substrates and kinetic constants [data as presented Head et al [28]]
Formation of C1omprp, C1C2omprp, C1C2C3omprp v = k [c1]
v = k [c1c2]
v = k [c1c2c3] k = 7.7 μM
k = 18.9 μM
k = 31.4 μM Mass action reactor, velocity is calculated as a product of concentrations of substrates and kinetic constants [data as presented Head et al [28]]
Degradation of F1F2F3F4 OmpRp (OmpF repression) [f1f2f3f4omprp] In the reactor class decay process, substrate reduced according to the half-life inputted. [Bergstrom et al [28]]
Formation of OmpC and OmpF v(OmpC) = k [c1c2c3omprp] [envzk]
v(OmpF) = k [f1f2f3fomprp] [envzp] k = 1 μM
k = 1 μM Catalysed Mass action reactor, velocity is calculated as a product of concentrations of substrates and kinetic constants (Batchelor and Goulian [39])
Table 5 Initial levels of molecular species at the start of simulation. Indicated in the table are initial concentrations of substances at the start of simulation for low and high osmolarity conditions. At high molarity the sucrose molecules added (virtually through substance window) to the medium is indicated in the table. EnvZ-Sensor kinase, OmpR-Response regulator [data as presented by Cai and Inouye] [30], F1, F1F2, F1F2F3-ompF gene promoter sites and C1, C1C2, C1C2C3-ompC gene promoter sites. Envzp, envzk, envzppompr, envzkpompr, envzpomprp, envzkomprp, f1omprp, f1f2omprp, f1f2f3omprp, f1f2f3f4omprp, c1omprp, c1c2omprp, c1c2c3omprp are initially nil at the start of simulation. During the course of simulation, these complexes are formed and dissociated at the end of simulation
Species Number of molecules
High Low
EnvZ 113 102
OmpR 3500 2100
Sucrose 150 3000
f1 - 100
f1f2 - 100
f1f2f3 - 100
c1 100 -
c1c2 100 -
c1c2c3 100 -
ATP 100 100
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| 0 | PMC543571 | CC BY | 2021-01-04 16:37:32 | no | Biomed Eng Online. 2004 Nov 25; 3:43 | latin-1 | Biomed Eng Online | 2,004 | 10.1186/1475-925X-3-43 | oa_comm |
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Respir ResRespiratory Research1465-99211465-993XBioMed Central 1465-9921-5-261558506710.1186/1465-9921-5-26ReviewApplication of microarray technology in pulmonary diseases Tzouvelekis Argyris [email protected] George [email protected] Demosthenes [email protected] Department of Pneumonology, Medical School, Democritus University of Thrace, Greece2004 7 12 2004 5 1 26 26 26 9 2004 7 12 2004 Copyright © 2004 Tzouvelekis et al; licensee BioMed Central Ltd.2004Tzouvelekis et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Microarrays are a powerful tool that have multiple applications both in clinical and cell biology arenas of common lung diseases. To exemplify how this tool can be useful, in this review, we will provide an overview of the application of microarray technology in research relevant to common lung diseases and present some of the future perspectives.
Microarrayspulmonary fibrosisasthmachronic obstructive pulmonary diseaseacute lung injurypulmonary edemasarcoidosispulmonary diseases
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Introduction
Microarray technology is rapidly becoming a standard technology used in research laboratories all across the world. Since its first application in the mid 1990s [1] microarray technology has been successfully applied to almost every aspect of biomedical research [2-7] with over 250 papers in respiratory research alone. Research conducted the last ten years has elevated the status of microarray technology from poorly understood and doubtfully applied in the fields of medicine to one that requires attention when the examination of clusters of genes in a single experiment is considered. Far more progress has been made toward an understanding of the pivotal role of microarrays in respiratory research by providing the scientists well-established knowledge concerning numerous genes that can be used as potential drug targets, mediators and inflammatory molecules with important cellular functions, evidence that captured the interest of both clinicians and researchers and caused a consecutive year by year rise of the applications of microarrays in experiments designed to study pulmonary diseases. Thus microarray technology since its first application [8] in the field of respiratory medicine has already been used the past five years in almost every aspect of respiratory research with an increased rate of application resulting to an overall of approximately 250 published papers until now (Figures 1, 2). Though the majority of experiments using microarray platforms was designed to study lung cancer (Figure 2) we excluded from this review this data, because considering the number of published papers, the enormous data derived from these experiments could compile a separate review article on its own. The scope of this review was based on the fact that although there are numerous original published papers using this pioneering method, the number of review articles summarizing the importance of microarrays in the research field relevant with pulmonary diseases still remains inadequately small.
Figure 1 Diagram showing the number of published papers using microarray technology in respiratory research the last ten years since 1995 when microarrays were first applied in clinical medicine.
Figure 2 Diagram showing the number of published papers in research relevant to common lung diseases such as asthma, COPD, IPF, ALI/PE, SARC, SCL, lung cancer, the last ten years since 1995 when microarrays were first applied in clinical medicine.
Applications of microarrays in medicine
DNA microarrays, microscopic arrays of large sets of DNA sequences immobilized on solid substrates, are valuable tools in areas of research that require the identification or quantitation of many specific DNA sequences in complex nucleic acid samples [8]. They are ordered samples of DNA and each sample represents a particular gene. These arrays can then be assayed for changes in the expression patterns of the representative genes after different treatments, different conditions or tissue sources. There are numerous ways to measure gene expression including northern blotting, differential display, serial analysis of gene expression and dot-blot analysis. The problem with all these techniques is that they are unsuitable for the parallel testing of multiple genes' expression. Microarrays, based on Southern's method of nucleotide hybridization, contain multiple DNA sequences (probes) spotted or synthetized on a relatively small surface. This feature of microarrays allows the simultaneous monitoring of the expression of thousands of genes, thus providing a functional aspect to sequence information, in a given sample [9]. Currently, genomic microarrays are used in medicine for the following purposes: [10-13]
1. Determination of transcriptional programs of cells for a given cellular function (e.g., cell function, cell differentiation, etc.) or when they are exposed to certain conditions leading to activation, inhibition or apoptosis.
2. Compare and contrast transcriptional programs to aid diagnosis of diseases, predict therapeutic response and provide class discovery and sub-classification of diseases.
3. Identification of genome-wide binding sites for transcriptional factors that regulate the transcription of genes.
4. Prediction of gene function.
5. Identification of new therapeutic targets (target identification, target validation, and drug toxicity).
6. Development of public databases that will help us understand the functioning of complex biological systems.
7. Genetics of gene expression: Although this is a relatively new study field, it is advancing rapidly with major implications in complex clinical traits by the identification of promising candidate genes. Thus, we briefly review the current implementations of this novel approach highlighting its necessity in the research field. Treating mRNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Unlike classical quantitative traits, the genetic linkages associated with transcript abundance permits a more precise look at cellular biochemical processes. Schadt et al. [14] described comprehensive genetic screens of three specific transcriptomes by considering gene expression values as quantitative traits. Authors treated the gene expression levels derived by a microarray analysis in mice liver tissues as quantitative traits in a standard linkage analysis using evenly spaced autosomal markers. Interestingly they found that a substantial portion of these genes had at least one significant gene expression quantitative trait locus (eQTL) depending on the LOD (log odds ratios) scores. Since transcript abundances are increasingly used as surrogates for clinical traits, knowledge about their genetic control can help dissect the genetics of complex traits. In the same study investigators revealed the importance of LOD scores to differentiate whether the expression levels of the genes under study is regulated by variations within the gene itself (cis) or at a separate locus (trans). They found that eQTL with LOD scores are cis acting (gene affects transcription of the gene itself) in most cases, whereas moderately significant eQTL are trans acting (genes acting on the transcription of other genes). Furthermore this study undertook an investigation on how the heritability of gene expression can be studied within and between families and demonstrated that a significant portion of differentially expressed genes derived from reference families had a detectable genetic component. The latter finding suggests that this group of genes may serve as novel therapeutic targets for complex human diseases, given that their degree of genetic control was so readily identifiable in a small number of families.
Microarray technologies
DNA microarrays are used to estimate the levels of mRNA in the cell. The process can be described in three steps:
1) Array construction: Currently, there are two widely used microarray technologies:
• In situ synthetized oligonucleotide (20–25 mers) microarrays-mainly oligonucleotides synthetized by photolithography or ink-jet technology on a glass surface.
• Spotted, in glass or nylon membrane matrices, microarrays-mostly created by robotic printing of pre-prepared cDNAs or oligonucleotides (polymerase chain reaction-PCR-generated products from cDNA libraries or clone collections) [9,15].
2) Sample preparation and array hybridization: The next step in the microarray experiment is to prepare the material that will be hybridized with the microarray. Gene expression is measured by the amount of mRNA therefore it must be extracted from the sample cells or tissues. For high-density microarrays one has to convert mRNA into cRNA, whereas for spotted arrays one can use mRNA, cDNA, or cRNA. The RNA needs to be labeled so that the detecting machinery can measure the quantity of RNA present. In oligonucleotide microarrays mRNA is extracted from experimental samples and is labeled with a fluorescent oligonucleotide (biotin). The biotin labeled cRNA and each sample is hybridized to a separate array, the array is scanned and absolute expression levels are obtained for each probe by using a dedicated laser scanner. In contrast, in spotted microarrays, mRNA is extracted from a sample and a control and one is labeled with cy-5 (red fluorescent dye) and the other with cy-3 (green fluorescent dye). Expression values are based on the competitive hybridization of the two samples being directly compared on a single array. Conventionally, in radioactive nylon membrane arrays RNA probes are labeled with P33 or P32 dCTP during a reverse transcription reaction. The great advantage of nylon microarrays is that they require relatively small amounts of PCR products because radioactive targets have a higher intrinsic detectability whereas in glass arrays the quality of RNA is not as critical as it is in nylon arrays. Therefore nylon arrays are mainly used when sample material is scarce or a small number of genes need to be assayed [16,17].
3) Image analysis and data acquisition: The laser causes excitation of fluorescently or radioactively labeled cDNA probes. Only the spots representing mRNAs in the sample give emission signals. The emission is measured using a scanning confocal laser microscope for fluorescently labeled arrays or a flat-bed scanner for radioactive nylon arrays and finally data is analyzed by appropriate software. In spotted microarrays using fluorescent probes if particular mRNA from the sample is in abundance, the spot with a complementary probe will be red; if the concentration of the particular mRNA is higher in the control, the spot will be green. If both samples contain the same amount of a given mRNA, the spot will be yellow (Figure 3). In nylon membrane microarrays using radioactive probes acquisition of phosphor-representations of radioactive hybridizations is performed with a high resolution digital autoradiography system displaying in real time the quantitative image of radioisotopes deposited on biological samples [18]. In high density oligonucleotide microarrays the absence of the fluorescence of the specific spots means that complementary mRNA is not present in the sample. If the fluorescence is present, the intensity of the signal is a measure of the level of particular mRNAs in the examined cell population [11-19].
Figure 3 Image from laser scanning confocal microscope of a DNA microarray slide. mRNA, is extracted from a sample and a control and after its transcription into more stable cDNA, one is labeled with cy-5 (red fluorescent dye) and the other with cy-3 (green fluorescent dye). The two cDNA populations are allowed to hybridize to the same microarray slide. If particular mRNA from the sample is in abundance, the spot with a complementary probe will be red (induction of gene expression in sample condition); if the concentration of the particular mRNA is higher in the control, the spot will be green (induction of gene expression in control condition). If both samples contain the same amount of a given mRNA the spot will be yellow (equal gene expression in both conditions). (Adapted with permission of Dr Karameris Andreas.)
Application of microarrays in pulmonary diseases
1. Microarrays in idiopathic pulmonary fibrosis (Table 1)
Table 1 Studies utilizing microarray technology to analyze IPF
Investigator Microarray type Species/Sample size Summary/Key findings Normalization procedure
Number of genes Type of tissue Replications per data point
Zuo et al.22 Oligonucleotide 8.400 genes 5 patients with IPF Lung tissue specimens Gene expression analysis reveals matrilysin as a key regulator of PF in mice and humans. Gene expression levels normalized by a scaling factor multiplied to the average of differences of probe pairs (matched-mismatched) / 2 replicates
Kaminski et al.23 Oligonucleotide 6.000 genes 30 mice Lung tissue specimens Global analysis of gene expression in PF reveals distinct programs regulating lung inflammation and fibrosis. Mean hybridization intensities of all probe sets on each array were scaled to an arbitrary, fixed level/4 replicates
Katsuma et al. 24 cDNA 4.224 genes 22 mice Lung tissue samples Molecular monitoring of bleomycin-induced pulmonary fibrosis by cDNA microarray-based gene expression profiling. Quantified signal intensities were converted by logarithms of base two 4 replicates
Chambers et al. 25 Oligonucleotide 6.000 genes Human lung fibroblasts Global expression profiling of fibroblast responses to transforming growth factor-beta1 reveals the induction of ID1. Gene expression levels normalized by a scaling factor multiplied to the average of differences of probe pairs (matched-mismatched)/ 2 replicates
Liu et al.26 cDNA 10.000 genes 12 rats Lung tissue specimens FIZZ1 stimulation of myofibroblast differentiation. Average median ratios Cy3/Cy5 values normalized to 1.0/ 4 replicates
Abbreviations: ID: Inhibitor of Differentiation, IPF: Idiopathic Pulmonary Fibrosis, PF: Pulmonary Fibrosis
Idiopathic pulmonary fibrosis (IPF) is a refractory and lethal interstitial lung disease characterized by fibroblast proliferation, extracellular matrix (ECM) deposition and progressive lung scarring. The incidence of IPF is estimated at 15–40 cases per 100.000 per year, and the mean survival from the time of diagnosis is 3–5 yr regardless of treatment [20,21]. The etiology of IPF has remained elusive and the molecular mechanisms are poorly understood. To elucidate the molecular mechanisms that lead to end-stage human pulmonary fibrosis Zuo et al. [22] analyzed lung biopsy samples from five patients with clinically, radiologically and histologically proven pulmonary fibrosis (usual interstitial pneumonia-UIP) and compared to samples from three resected lungs with normal histologic findings, by using oligonucleotide microarrays. Using a combined set of scoring systems they determined that matrilysin (matrix metalloproteinase-MMP-7), a metalloprotease not previously associated with pulmonary fibrosis, was the most informative increased gene in their data set. Immunochemistry demonstrated increased expression of matrilysin protein in fibrotic lungs derived from different patients. Furthermore, in a separate set of experiments matrilysin knockout mice were dramatically protected from pulmonary fibrosis in response to intratracheal bleomycin. Their results identify matrilysin as a mediator of pulmonary fibrosis and a potential therapeutic target. Nevertheless potential limitations of the study include the small sample size and the relative inability of microarray analysis using whole lung homogenates to assess the exact cells that were overexpressing the informative genes. The application of analytic microarray approaches using gene expression signatures of specific cell types coupled with advanced data mining computational tools will ameliorate this hardship.
In another study Kaminski et al. [23] used oligonucleotide microarrays to analyze the gene expression programs that underlie pulmonary fibrosis in response to bleomycin, in two strains of susceptible mice. Microarray analysis performed by different investigators and at different time points demonstrated a considerable overlap between genes induced by bleomycin in these two distinct strains of mice. Differential gene expression in response to bleomycin included upregulation of genes known to be associated with bleomycin-induced lung injury and fibrosis such as transforming growth factor-β1 (TGF-β1), as well as genes not previously associated with the disease. Confirmational studies performed and further verified a portion of the microarray data. Surprising insights were derived from comparing gene expression patterns in response to bleomycin of mice homozygous for a null mutation of the integrin β6 subunit gene (β6 -/-), thus protected from pulmonary fibrosis, and wild type mice. Interestingly a simple hierarchical cluster analysis identified most of the known TGF-β1 inducible genes preferentially induced in wild type mice. The latter finding provides support for the hypothesis that β6 knockout mice are protected from pulmonary fibrosis as a consequence of failure to activate TGF-β. The great importance of this study results from the identification and the global availability of several genes that are likely to be directly relevant to the fibrotic process. However the inability of microarray technology to detect genes that are not included in the array, identify critical proteins that participate in biological responses and ascribe changes in gene expression in specific cellular types limit the scientific rigidity of the data derived and highlights the necessity for combined application of novel approaches.
Furthermore driven by the same perspective idea to investigate the gene expression pattern in bleomycin-induced pulmonary fibrosis, Katsuma et al. [24] constructed a lung chip derived from a normalized lung cDNA. They performed large-scale analyses of gene expression and illuminated a time-dependent change in the expression profile of genes related to the inflammatory and fibrotic responses in this model of pulmonary fibrosis, similar to that observed by Kaminski et al [18]. Using cluster analysis they classified genes into groups based on a time-dependent gene expression. Interestingly this profile was well correlated with observed histopathological changes and data was confirmed with real time-(RT)-PCR methods. Nevertheless apparent inconsistencies with the gene expression pattern revealed by Kaminski et al. [23] highlight the inability of microarray approach in distinguishing changes in transcriptional regulation from changes in cellular composition of the organ being studied. Thus, it is likely these discrepancies between findings of the two studies to represent differences in cellular composition, rather than differences in transcriptional regulation.
One of the most informative studies scrutinizing the global gene expression profile of fibroblasts in response to one of their most potent activators, TGF-β1, has been published by Chambers et al. [25] Gene expression analysis of human lung fibroblasts treated with TGF-β1 has led investigators to uncover novel TGF-β1-inducible genes including genes encoding inhibitors of differentiation (ID) as well as genes that are usually expressed by highly differentiated smooth muscle cells. The induction of these genes was further confirmed at the mRNA level (Northern blot analysis) and the protein level (Western blot analysis) for primary cultures of adult lung fibroblasts. The potential relevance of these observations in vivo was established in a separate set of confirmational experiments in rats where it was revealed an overexpression of the ID by myofibroblasts within the fibrotic regions. These novel suggestions have major impact on our understanding of the crucial role of TGF-β1 as a fibroblast differentiation factor in response to fibrogenic stimuli. Nonetheless the present study does not determine the precise role of ID in regulating fibroblast responses to TGF-β1. To do so this study should be coupled with independent methods using ID blocking strategies. Moreover the use of incomplete arrays that detect only the included genes, the variability of cellular composition of tissues studied even from the same organ and the inability of this technology to distinguish changes in cellular composition from transcriptional changes pose major limitations to the global application of these results. Furthermore these observations offer plausible explanations for the lack of similar effects of TGF-β1 in previous studies.
Moreover, bleomycin-induced pulmonary fibrosis rat model was extensively studied by Liu et al. [26] with the use of rat cDNA microarray platforms, in an attempt to highlight genes that may be involved in fibrosis. Interestingly a novel and unexpected finding of this microarray analysis was the identification of FIZZ1 as the most highly and prominently induced gene in bleomycin-treated lungs, evidence consistent with the RT-PCR results. Further analysis of its protein product, demonstrated a unique pattern of localization primarily to alveolar epithelial cells (AECs) derived from bleomycin-injured lungs. To illuminate the exact role of FIZZ1 in inflammation and fibrosis, the effects of co-culturing FIZZ1-expressing AECs on fibroblasts were examined. These analyses demonstrated the significant higher stimulation of normal lung fibroblasts, by high FIZZ1-expressing-AECs, as compared to that observed by control AECs. The major contribution of this new molecule in the differentiation of fibroblasts to myofibroblasts was suggested and verified by its transfection into normal lung fibroblasts which provoked their stimulation independently of TGF-β activation.
These preliminary results suggest that this technology could identify unexpected molecular participants in IPF and might help in the development of novel targets for improved treatment. The method may also allow molecular fingerprinting that could improve the ability to identify subclassifications of pulmonary fibrosis that might be more informative than the current classification based primarily on histologic and radiographic patterns [27]. Nonetheless these studies characterized as "fishing expeditions" are limited by the inability of microarrays to detect the final expression product (protein), identify genes that are not included in the array and ascribe changes in gene expression in specific cellular types. However our view is that there is nothing wrong with a "fishing expedition" if what you are after is "fish", such as new genes involved in a pathway, potential drug targets or expression markers that can be used in predictive or diagnostic fashion. Hence, these observations are not to diminish their value for understanding basic biological processes and even for understanding, predicting and eventually treating human disease (Table 1).
2. Microarrays in asthma (Tables 2 and 3)
Table 2 Studies utilizing cDNA microarray technology to study asthma
Investigator Microarray type Species/Sample size Summary/Key findings Normalization procedure
Number of genes Type of tissue Replications per data point
Zou et al.32 cDNA 40.000 elements 10 monkeys Lung tissue samples Microarray profile of differentially expressed genes in a monkey model of allergic asthma. Ratios of Cy5/Cy3 multiplied to the balance coefficient of the microarray / 3 replicates
Brutsche et al.33 cDNA 600 genes 40 subjects Mononuclear cells CAGE score for atopy and asthma. Absolute difference of the expression of CAGE scored genes 1 replicate
Sayama et al.38 cDNA 14.000 genes human umbilical cord mast cells Transcriptional response of human mast cells stimulated via the Fc (epsilon) RI and identification of mast cells as a source of IL-11. Array-specific normalization coefficient was calculated by centering in log base 2 space a dataset consisting of all elements with an I/D> 3-fold / 2 replicates
Brutsche et al.41 cDNA 600 genes 40 subjects Mononuclear cells Apoptosis signals in atopy and asthma measured with cDNA arrays G.I was normalized to the housekeeping G.I / 1 replicate
Syed et al. 42 cDNA 12.228 genes Human CD4+ T cells CCR7 down-regulation in asthma Median G.I of each filter normalized any differences in cDNA probe activity between filters/ 1 replicate
Banerjee et al.43 cDNA 1.176 genes 18 mice Lung tissue samples Gene expression profiling in inflammatory airway disease associated with elevated adenosine G.I was normalized to the housekeeping G.I / 2 replicates
Abbreviations: CAGE: Composite atopy gene expression, CCR7: Chemokine receptor 7, G.I: Gene Intensity, I/D: Intensity/Background ratio, Th: T helper, RI: Immunoglobulin receptor
Table 3 Studies utilizing oligonucleotide microarray technology to study asthma
Investigator Microarray type Species/Sample size Summary/Key findings Normalization procedure
Number of genes Type of tissue Replications per data point
Lee et al.34 Oligonucleotide 6.500 genes Human airway cells IL-13 induces dramatically different transcriptional programs in three human airway cell types. Gene expression levels normalized by a scaling factor multiplied to the average of differences of probe pairs (matched-mismatched)/ 1 replicate
Temple et al.35 Oligonucleotide 6800 genes Human eosinophils Microarray analysis of eosinophils reveals a number of candidate survival and apoptosis genes. Geometric mean of the scaling (standard experiment) factor served as normalization factor/ 2 replicates
Hakonarson et al.36 Oligonucleotide 5.000 genes Rabbit and human ASM Association between IL-1beta/TNF-alpha-induced glucocorticoid-sensitive changes in multiple gene expression and altered responsiveness in ASM. Gene expression levels normalized by a scaling factor multiplied to the average of differences of probe pairs (matched-mismatched) / 2 replicates
Laprise et al.37 Oligonucleotide 12.000 probe sets 8 subjects Lung tissue samples Functional classes of bronchial mucosa genes that are differentially expressed in asthma. Mean hybridization intensities of all probe sets on each array were scaled to a fixed level/ 2 replicates
Nakajima et al.39 Oligonucleotide 12.000 genes Human MCs and eosinophils Gene expression screening of human mast cells and eosinophils using high-density oligonucleotide probe arrays: abundant expression of MBP in MCs Mean hybridization intensities of all probe sets on each array were scaled to an arbitrary, fixed level / 1 replicate
Abbreviations: ASM: Airway Smooth Muscle cells, MBP: Major Basic Protein, MCs: Mast cells, TNF: Tumor Necrosis Factor
Asthma is one of the most serious allergic diseases associated with both genetic and environmental factors such as allergens, respiratory tract infections, and atmospheric pollutants. Most asthma is associated with atopy, a predisposition to generate immunoglobulin (Ig)-E against environmental allergens [28]. However, only a proportion of atopic individuals develop lower airways symptoms consistent with an asthmatic phenotype. It is therefore tempting to speculate that the development of asthma requires combined inheritance of genes which alter the immune cell response to the environment, and at the same time, render the airways structural and neural regulation susceptible to injury caused by inflammation. Several asthma/atopy associated genes have been identified from linkage and association studies within families and revealed that there are multiple chromosomal regions, containing potential candidate genes, associated with various asthma phenotypes [29-31].
Microarray technology offers a new opportunity to gain insight into global gene expression profiles in asthma, leading to the identification of asthma associated genes. Several experimental models have been used for this purpose although no animal disease model is identical to human disease. Zou et al.[32] were the first attempted to produce an allergen-induced gene expression profile in the lung of a non-human primate using genomics tools such as microarrays and real time-(RT)-PCR in an independent way. Microarray data generated from this study and validated by RT-PCR using same lung samples, revealed a differential gene expression pattern between control and challenged animals. Furthermore investigators established that genes identified by microarray technology represented genes truly regulated by inhalation antigen challenge. This was done by determining that the regulated expression levels identified by microarray assay from a single animal were confirmed by RT-PCR studies using multiple similarly treated animals. Potential limitations of this study include the time-limited gene expression profile tested which may not reflect the chronic aspect of asthma and the absence of evidence that the antigens used would produce the same allergic reaction in humans.
Brutsche et al. [33] designed an array based composite atopy gene expression (CAGE) score to evaluate the diagnosis of atopy and asthma and assess disease activity in order to guide therapeutic decisions. The CAGE score was determined by using 10 genes dysregulated atopic individuals according to a specific algorithm. The application of this score in a group of asthmatic patients revealed that this approach had a better sensitivity and specificity than total IgE in differentiating atopic from non-atopic subjects. Correlation between CAGE score and total IgE was found, and there was a trend for correlation with asthma severity. It is noteworthy that the CAGE score was able to quantify phenotype-specific alteration in gene expression of atopic individuals. Perspectively the CAGE score can be further improved through a better reproducibility of microarray systems compared with the filter arrays and the possibility of a better selection of genes. Therefore it may be used as a prognostic and diagnostic tool or to monitor the effects and side-effects of asthmatic therapy in the not distant future.
Several morphologic changes in the airways of patients with asthma have been attributed to the Th-2 produced cytokines such as IL-13 and IL-5. However the molecular mechanisms underlying the contributions of these cytokines to asthma remain largely unknown.
Towards this direction Lee et al. [34] applied oligonucleotide microarray technology in primary cultures of three human airway cell types (epithelial, smooth muscle cells and lung fibroblasts) to elucidate the effects of IL-13 in these cell types. Interestingly, the results of this study demonstrated that despite initiation of an identical signaling pathway (STAT6), IL-13 induced highly distinct transcriptional programs in each of the three cell types suggesting a coordinate and distinct contribution to asthma pathogenesis by each of the cell types examined. Although the quality of the genechip analysis was estimated and validated by RT-PCR methods applied in a small number of selective genes, however there are important limitations in this study including the possible differences between transcriptional responses and gene expression profile of a cell type in vivo and in vitro.
One of the greatest disadvantages of microarrays and at the same time challenges for most of the investigators is the objective difficulty dealing with the results of the experiments resulting from the large quantities of information. Currently, the hurdle faced is the routine interpretation of this information to identify among thousands of dysregulated genes, those who are informative, causal and specific to the phenotypic change of interest. Thus, Temple et al. [35] compared the results derived from the application of oligonucleotide microarray technology in eosinophils isolated from human peripheral blood before and after treatment with IL-5 and in an alternative cellular model, TF1.8 cells, whose survival was known to be dependent on IL-5. Comparison of these two models facilitated the identification of the genes that rule the apoptosis and survivability of eosinophils and demonstrated a small group of genes whose regulation was similarly coordinated in both systems. Authors combined different cellular models focused on the same experimental paradigm and looked for common changes. This approach helped the scientists to focus attention on a subset of genes most likely to be causal and relevant to the phenotypic change of interest and filter out non-specific gene expression change. Combination of this method with proteomics approaches and tissue distribution analysis can add another filter for genes of interest and generate data of sufficient scientific rigidity.
Microarrays apart from their remarkable effectiveness in identifying novel gene expression patterns can also be used to clarify physiological mechanisms underlying the actions of numerous drugs, such as those applied in the management of patients with asthma. Several studies have utilized microarray technology to assess the gene expression profile of cells and tissues before and after treatment with commonly applied drugs such as corticosteroids. Two of them are reviewed here.
Recently, Hakonarson et al. [36] in addition with the role of two pleiotropic cytokines, IL-1β and tumor necrosis factor (TNF)-α, in the pathophysiology of asthma, have reported the effectiveness of dexamethasone in the treatment of asthma with the use of oligonucleotide microarrays. The accumulation of the two cytokines in a medium where human airway smooth muscle-(ASM) – cells were cultured, elicited an overexpression of several proinflammatory genes known to regulate smooth muscle contractility and relaxation. The latter finding noted in four separate microarray experiments was consistent with the increased responsiveness of rabbit cytokine-treated tissues in acetylcholine. The administration of dexamethasone provoked a repression to the majority of the microarray-study derived genes as well as to the contractility of the cytokine-treated ASM. Collectively these findings suggest a crucial role of ASM in expressing a host of glucocorticoid-sensitive proinflammatory gene patterns that affect the structure and the function of the airways. However these observations were based on studies using rabbit and human ASM cells. Hence the issue of potential species differences warrants consideration. Further studies using samples derived from homogeneous species and validation techniques are necessary to streamline these observations.
Microarray analysis performed by Laprise et al. [37] indicated a differential gene expression pattern in bronchial tissues from healthy and asthmatic individuals, a profile that included not only genes previously implicated in the pathogenesis of asthma but also new potential candidates. The remarkable ascertainment of this study, conducted with bronchial tissues which are known as a primary site for airway inflammation and remodeling, was that the expression of one third of the genes was partially or completely corrected by inhaled corticosteroid treatment. The latter evidence further illuminates the true impact of first line therapy offered to asthmatic patients. However application of this technology may be limited by the disease's spatial and temporal heterogeneity due to differences in cellular composition between asthmatic and control tissue. Ultimately the results obtained using microarrays need to be verified firstly with confirmational studies (RT-PCR and in situ hybridization) and secondly with separate experiments.
Mast cells represent key cells in the initiation and progression of asthma, releasing several mediators of inflammation, such as certain cytokines and chemokines. The past few years several studies have been focused on the identification of new mast cell products through the gene expression analysis. In one of them published by Sayama et al. [38] application of cDNA microarrays in only two populations of stimulated human mast cells exhibited among other genes a significant upregulation of the gene encoding IL-11. The latter finding was further confirmed by a separate set of experiments where an increased secretion of IL-11 by activated human mast cells was noted. However further microarray analyses coupled with functional approaches and independent studies examining the potential role of IL-11 in the pathogenetic mechanisms of asthma as well as in the alterations of mast cell proliferation and survival, are required.
Furthermore, Nakajima et al. [39] in their attempt to evaluate the significance of protein products present in mast cells applied oligonucleotide microarray technology in human mast cells derived from different sources and in eosinophils. The most impressive finding of this study was the abundant expression of major basic protein (MBP) among the transcripts for expected mast cells specific proteins such as tryptase. Authors also confirmed in independent studies using RT-PCR and flow cytometry that MBP was expressed at both the transcript and protein levels in various types of mast cells. While this result is really intriguing and opposes to the already known data which indicates the unique presence of MBP to eosinophils [40], it is incomplete and unable to determine the biologic significance of MBP present in mast cells.
Microarrays using nylon membrane radioactive cDNAs have already been applied in the research field of asthma and much good work has been done with this technology.
Brutsche et al. [41] applied nylon membrane cDNA microarray technology in blood samples derived from atopic asthmatic and nonasthmatic patients, and healthy control subjects to investigate the systemic activation of apoptosis pathways of inflammatory cells in lung tissue. They identified significantly altered expression of several apoptosis-related genes in atopy and asthma compared with the healthy subjects suggesting that these alterations could be due to genetic or environmental factors. Several verification experiments have been used to further validate a considerable amount of differentially expressed genes on mRNA level (RT-PCR), as well as protein (ELISA) and receptor level (double stained fluorescence methods). The profile of altered gene expression did not show a definite pattern that was suggestive of survival or apoptosis. Potential criticisms of this approach include the large amounts of data variability derived from the heterogeneity of the studied samples and the lack of proteomics analyses. Thus this study is unable to give any statement on the activity of the apoptotic pathways. To do so the data should be combined with the proteomics analysis of proteins involved in apoptosis. The latter will contribute to the characterization of protein patterns and will allow for the assessment of overall changes in the protein content associated with apoptosis.
One of the first gene-profiling studies highlighting the potential role of chemokines and their receptors in the pathogenesis of asthma was conducted by Syed et al. [42] They used nylon membrane radioactive arrays compiled from mixed biological samples to determine the gene expression pattern of T-cells from patients with atopic and non-atopic asthma and found altered gene expression profile for CCR7 (chemokine receptor 7) between patients and controls, findings that were confirmed by RNA dot plot analysis. Data derived from this analysis is indicative of a possible role of this molecule in the progression of asthma. However the small number of patients recruited in this study and the lack of functional genomic analysis allow us to make only speculations on the exact role of this factor in the disease process.
In another study Banerjee et al. [43] in their attempt to identify and characterize biological roles for adenosine-regulated genes applied radioactive cDNA microarray technology in lung specimens derived from normal and adenosine deaminase (ADA)-deficient mice. The results of this study profile the differential expression of a vast number of genes, that may be regulated by adenosine and hence play a pivotal role in modulating the underlying lung pathology. The reliability of the results derived from the microarray approach was also confirmed with gene specific RT-PCR analysis. Moreover authors used a separate set of experiments and demonstrated both with microarray analysis and protein localization that therapy with ADA in the deficient group of mice regulated expression of several genes modulating pulmonary inflammation and cell adhesion. The consistency of findings derived from the two experiments provides to microarray analysis a high degree of confidence. However critical limitations of this study originate from the disability of gene expression analysis to distinguish changes in transcriptional regulation from changes in cellular composition. Hence a more in-depth analysis is required to quantify the gene expression and establish a direct regulation of these genes by adenosine signaling.
Accumulated evidence from these analyses revealed that microarray analysis can be a powerful tool for identifying mediators of allergic asthmatic disease through a genomic-based strategy using non-human primates and provide us a novel large scale of differentially expressed genes. Additionally, authors compared different cellular models sharing similar experimental paradigm to focus on the most likely informative genes and filter out the bystanders. The application of this approach further streamlined the pivotal role of microarrays in determining transcriptional responses of genes to several inflammatory cytokines and in identifying gene expression patterns and important mediators associated with the initiation and the progression of asthma. Moreover, microarrays coupled with separate set of experiments have provided the investigators with useful knowledge concerning the efficacy of the already applied drugs in the treatment of asthma and helped them to understand their anti-inflammatory role in terms of physiology and molecular biology. Nevertheless, most of the studies exhibited essential weaknesses generated by the heterogeneity of samples studied and compared and by the disability of microarrays to quantify gene expression and yield information about transcriptional responses and post-translational protein modifications. Therefore more in depth analysis of the microarray results is needed in combination with novel approaches that will help us focus on the specific genes and elucidate their role in the cellular function and the pathogenesis of asthma (Tables 2,3).
3. Microarrays in Chronic Obstructive Pulmonary Disease (Table 4)
Table 4 Studies utilizing microarray technology to study COPD
Investigator Microarray type Species/Sample size Summary/Key findings Normalization procedure
Number of genes Type of tissue Replications per data point
Koike et al.45 cDNA 450 genes Rats AM cDNA microarray analysis of gene expression in rat alveolar macrophages in response to organic extract of diesel exhausts particles. G.I was normalized to the housekeeping G.I 2 replicates
Yamanaka et al.46 cDNA 18.432 genes Human AEC Gene expression profiles of human small airway epithelial cells treated with low doses of 14- and 16-membered macrolides. G.I was normalized to the housekeeping G.I 3 replicates
Fuke et al.47 cDNA 77 genes 30 patients Lung tissue specimens Chemokines in bronchiolar epithelium in the development of chronic obstructive pulmonary disease. Signal normalized to a given gene transcript 3 replicates
Vuillemenot et al.48 Oligonucleotide 12.000 genes 10 mice Lung tissue specimens Lymphoid tissue and emphysema in the lungs of transgenic mice inducibly expressing tumor necrosis factor-alpha. Signal normalized to internal control 2 replicates
Hackett et al.50 cDNA 4.600 genes 22 individuals AEC Variability of antioxidant-related gene expression in the airway epithelium of cigarette smokers. Mean hybridization intensities of all probe sets on each array were scaled to an arbitrary, fixed level / 2 replicates
Morris et al.52 Oligonucleotide 6.500 genes Mice Lung tissue samples Loss of integrin alpha (v) beta6-mediated TGF-beta activation causes MMP-12-dependent emphysema. Mean hybridization intensities of all probe sets on each array were scaled to an arbitrary, fixed level / 2 replicates
Golpon et al.53 Oligonucleotide 6.500 genes Human/mice Lung tissue samples HOX genes in human lung: altered expression in primary pulmonary hypertension and emphysema. Gene expression levels normalized by a scaling factor multiplied to the average of differences of probe pairs/ 3 replicates
Abbreviations: AEC: Airway Epithelial Cells, AM: Alveolar Macrophages, COPD: Chronic Obstructive Pulmonary Disease, G.I: Gene Intensity, TGF-b: Transforming Growth Factor-beta, MMP: Metalloproteinase
Chronic obstructive pulmonary disease (COPD) is a chronic disease characterized by progressive airflow obstruction, chronic cough and dyspnea in advanced stages, caused by smoking, environmental, and hereditary factors. It is associated with two clinical entities, chronic bronchitis and emphysema. In nowadays, the invention and application of microarray technology offers scientists the opportunity to gain a better understanding on the pathophysiology of COPD through the identification of novel gene expression patterns, leading to illumination of genes candidates for modern therapeutical approaches [44].
It is already known that chronic bronchitis can be induced by several types of environmental pollutants such as diesel exhaust particles (DEP). Though recently a microarray study has been published by Koike et al. [45] addressing the effect of such pollutants on the gene expression profiles of alveolar macrophages, however a complete analysis including the transcriptome and proteome, is needed to elucidate the toxic effect of air pollutants on pulmonary cells.
Microarray approach is already being applied in respiratory clinical pharmacology with the identification of genes {Yamanaka et al. [46]} that can serve as potential molecular targets of common drugs applied in the management of patients with chronic bronchitis. However, studies being published in the field of respiratory pharmacogenomics lack of scientific rigidity primarily due to incomplete available arrays that will help scientists to determine much larger numbers of pharmacologically relevant genotypes. Far more progress should be made towards this direction.
One of the major limitations in our attempt to elucidate the exact role of specific cell types in the pathogenesis of COPD is the compact anatomy of the lung which makes unraveling specific cell type gene expression changes difficult, requiring immunoelectron microscopy or laser capture microdissection. The first study to perform quantitative cell type-specific gene expression analysis using the pioneering technology of laser capture microdissection in human tissue samples coupled with RT-PCR and cDNA approach was recently published by Fuke et al. [47] Authors performed individual analyses and revealed a specific cell type upregulation of three inflammatory chemokines reportedly relevant to the pathogenesis of COPD emphasizing the pivotal role of these cells and their products in driving the inflammation. Although data was not fully confirmed by microarray analysis, however discrepancies between methods illustrate more the potential danger of depending solely on array approach rather than limit the scientific consistency of these results. Further research investigating the functional consequences of these changes is required.
Several inflammatory cytokines have been implicated in the pathogenesis of emphysema, including TNFa, a molecule with versatile pathogenetic mechanisms. As a means to investigate some of them that culminate to lung-pathology, Vuillemenot et al. [48] applied oligonucleotide microarray technology coupled with independent studies (histologic and immunohistochemical analyses) in an experimental model they developed. Results derived both by microarray approach and independent studies revealed a direct correlation between TNFa and emphysema. However functional approaches should be applied in combination with gene expression analysis to shed further light in the mechanisms by which TNF promotes airspace enlargement.
Despite the clear link of smoking to the risk for chronic bronchitis, only 15–20% of cigarette smokers develop COPD, [49] suggesting that there must be risk factors other than smoking that contribute to the susceptibility to this disease. To address this issue Hackett et al. [50] implemented microarray technology in human airway epithelial cells of smokers and non-smokers and demonstrated differential anti-oxidant related gene expression between the two groups of volunteers. One of the most intriguing aspects of this study is that individual assessment by hierarchical clustering of anti-oxidant related gene expression in response to smoking displayed a remarkable variability suggesting variability in the responses of different individuals to the chronic oxidant stress of smoking. However the extent of this variability may be explained by the nature of the microarray assay and the large amounts of data variation derived from these studies. Thus it is of high risk to speculate that these genes may serve as useful genetic markers in future epidemiologic studies determining susceptibility to smoking induced COPD. Further studies applying high-tech computational clustering tools coupled with independent validation tests are required.
One of the most exciting aspects of microarrays is their use as tools for actively introducing serendipity to one's research [51]. In experiments designed to identify global transcriptional programs responsible for regulating lung inflammation and pulmonary fibrosis, as described previously, [23] microarray experiments were performed by Morris et al. [52] on lung tissue from wild-type mice and mice lacking a member of the integrin family (avβ6) known to be involved in activation of latent TGF-β. In addition to identifying distinct cluster of genes involved in these processes, these studies combined with RT-PCR validation tests and independent experiments led to the identification of novel pathways by which TGF-β can regulate emphysema through the upregulation of Mmp-12, the most highly induced gene in the lungs of β6 knockout mice. The role of Mmp-12 in the pathogenesis of emphysema was verified in an independent cohort where β6 knockout mice deficient in the expression of Mmp-12 displayed no alveolar enlargement. Although these results do not eliminate the possibility that other proteases may interact with Mmp-12 in the development of emphysema, however suggest that abnormalities in any of the steps in this pathway of TGF-β activation may contribute to genetic or acquired susceptibility to emphysema in humans.
Presently, very few studies dealing with the role of HOX genes in the adult respiratory system are available in the literature. Golpon et al. [53] investigated the expression pattern of HOX genes, in fetal and diseased lung specimens (emphysema, primary pulmonary hypertension), by applying two microarray survey techniques and their analysis reflects one of the most detailed and informative studies in this field. They compared the HOX gene expression pattern in human and mouse lungs and found that HOX genes are selectively expressed in the human lung. This study also yielded an altered HOX-gene expression pattern among fetal, adult and lung specimens with emphysema and pulmonary hypertension, by identifying different types of HOX genes overexpressed in each of these conditions indicating differential HOX gene expression as a potential factor that contributes to the development of certain pulmonary diseases. Though the overall size of tissue samples studied was small data from this study comprises evidence with high degree of confidence, validated and confirmed both in an independent cohort (degenerate RT-PCR) and by alternative methods (quantitative RT-PCR and in situ hybridization). Possible limitations include small number of tissues studied, incomplete microarray survey techniques and minor discrepancies between the findings generated from validation studies.
Collectively these results suggest that microarray analysis with its ability to highlight gene expression profiles on a large scale and coupled with progressive technologies and independently validated data has led researchers to shed further light into transcriptional programs regulating emphysema and to the identification of common mediators and molecular pathways involved in the pathogenesis of both COPD and pulmonary fibrosis, indicating novel targets for therapeutic interventions and useful genetic markers assessing susceptibility to COPD. Although limitations such as inconsistency between findings derived by microarray approach and independent studies, lack of functional changes assessment and significant data variability can be detectable in these studies, however evidence derived from these analyses is valuable and heavily informative (Table 4).
4. Microarrays in acute lung injury and pulmonary edema (Table 5)
Table 5 Studies utilizing microarray technology to study ALI/PE
Investigator Microarray type Species/Sample size Summary/Key findings Normalization procedure
Number of genes Type of tissue Replications per data point
McDowell et al.56 cDNA 8.374 genes 6 mice Lung tissue samples Differential gene expression in the initiation and progression of nickel-induced ALI. Ratios of Cy5/Cy3 multiplied to the balance coefficient of each microarray / 5 replicates
Olman et al.58 cDNA 588 genes 36 patients Pulmonary edema Lung fibroblasts Microarray analysis indicates that pulmonary edema fluid from patients with ALI mediates inflammation, mitogen gene expression, and fibroblast proliferation through bioactive IL-1. G.I normalized to the housekeeping G.I 2 replicates
Kupfner et al.61 Oligonucleotide Mice Lung neutrophils Role of NF-κB in endotoxemia-induced alterations of lung neutrophil apoptosis. Gene expression levels normalized by a scaling factor multiplied to the average of differences of probe pairs / 3 replicates
Cher et al.62 Oligonucleotide 8.800 genes 21 rats Whole lungs Pulmonary inflammation and edema induced by Phospholipase A2. Each gene was divided by the median of its values in all samples / 3 replicates
Sabbadini et al.63 Oligonucleotide 12.600 genes 14 rabbits Lung tissue samples Gene expression analysis in interstitial lung edema induced by saline infusion. Gene expression levels normalized by a scaling factor multiplied to the average of differences of probe pairs / 2 replicates
Perkowski et al.64 cDNA 8.374 genes 20 mice Lung tissue samples Gene expression profiling of the early pulmonary response to hyperoxia in mice. Difference between observed log-ratio and corresponding fitted ratio/ 5 replicates
Ward et al.65 cDNA 7.398 genes 6 rats Lung and other organ samples Molecular signatures of sepsis: multiorgan gene expression profiles of systemic inflammation. Gene expression levels normalized by a scaling factor multiplied to the average of differences of probe pairs / 4 replicates
Abbreviations: ALI: Acute Lung Injury, G.I: Gene Intensity, PE: Pulmonary Edema, NF-κB: Nucleus Factor-κB
Acute lung injury (ALI), a severe respiratory syndrome, develops in response to numerous insults. This syndrome that responds poorly in therapeutic interventions and has a poor prognosis has been associated with a myriad of mediators including cytokines, reactive oxygen and nitrogen species, growth factors and proteolytic enzymes [54,55]. Despite extensive research since the initial description of ALI over 30 yr ago, questions remain about the basic pathophysiologic mechanisms that are critical to the diminished survival and their relationship to therapeutic strategies. McDowell et al. [56] in their attempt to determine the interactions between the great amount of factors that have been associated with the development of ALI, analyzed 8,374 murine cDNAs for temporal changes and functional relationships throughout the initiation and progression of ALI in mice exposed to particulate NiSO4. Novel interactions between factors (antioxidant genes) previously associated with ALI and factors (surfactant proteins) previously not associated with ALI emerged from the application of functional genomics during nickel-induced ALI. Data derived from this experiment and partially confirmed by Northern blot analysis and nuclease protection assays is valuable and consistent with the ongoing attempts to treat ALI with exogenous surfactant-associated proteins [57] in combination with antioxidant therapy and may determine new therapeutical interventions. This study reveals the great importance of functional genomics not simply to provide a catalogue of all the genes and information about their functions, but to help scientists to understand the possible interplay of components contributing to lung injury.
Although the fibroproliferative response to lung injury occurs in high frequency in patients with ALI, the mechanisms of this response are largely unknown. One of the most meaningful and informative studies addressing this important issue was recently published by Olman et al [58]. Authors applied radioactively nylon membrane arrays in human lung fibroblasts exposed either to ALI or hydrostatic pulmonary edema fluids and revealed a potential mitogenic activity of IL-1β and its importance as a modulator of fibroblast proliferation. Data derived from this study of a considerable number of patients and replicated both in an independent cohort (use of IL-1 antagonist receptor) and by alternative laboratory methods (RT-PCR, Northern blot analysis) is of fundamental value and may provide scientists with several independent lines of evidence that IL-1 amplifies the inflammatory and fibroproliferative process through regulation of fibroblast mitogenesis and gene expression. A major limitation mentioned in this study was the pooling of pulmonary edema samples due to their limited volume that did not allow authors to perform clinical-molecular correlations in an individual way.
ALI is frequently associated with endotoxemia and is characterized by the accumulation in the lungs of large populations of neutrophils activated to produce proinflammatory mediators. Many studies had demonstrated a critical role of the endotoxemia-induced activation of NF-κB in neutrophils in the development of ALI [59,60]. One of the most important studies focused on the role of NF-κB activation in lung neutrophils apoptosis after endotoxemia was conducted by Kupfner et al. [61]. Though gene expression analysis revealed a significant role of NF-κB as a modulator of neutrophil apoptosis and data was confirmed by proteomics analysis there were major discrepancies between these findings and the results derived from individual experiments utilizing an inhibitor of nuclear translocation of NF-κB. The latter demonstrated no significant alterations in the percentage of the endotoxemia -induced apoptotic lung neutrophils in mice treated with the inhibitor evidence that reflects anti-apoptotic mechanisms not solely dependent on NF-κB. Although these findings can be informative indicating novel anti-apoptotic pathways for modern therapeutic approaches they should be re-evaluated in the context of new microarrays analyses of considerable sample size coupled with confident validation steps and independent experiments.
To gain a more comprehensive understanding on the complex interplay between several inflammatory cytokines involved in the pathogenesis of pulmonary edema, several group of investigators {Cher et al. [62], Sabbadini et al.[63]} applied oligonucleotide microarray techniques in different experimental models of lung edema. Interestingly, these analyses revealed differential expression patterns for many inflammatory genes implicating some of them in the pathogenesis of pulmonary edema. Though these studies applied several confirmational tests (RT-PCR, Northern blot, Western blot) to validate results derived form microarray experiments, there are substantial weaknesses and concerns such as discrepancies between findings determined by these techniques that pose major limitations and limit their scientific rigidity.
The study of Perkowski et al. [64] has been on of the most extensive and informative studies of the effect of 100% oxygen on the mouse lung. The authors used the cDNA microarray approach to evaluate the molecular profiling occurring during the early response to hyperoxia in mice. Among the vast amount of data derived from two different array sets they distinguished a cluster of genes of great interest (antioxidant enzymes, cell cycle progression regulators, endothelial cell and ECM genes) whose expression was substantially altered in response to hyperoxic stimuli. Authors were encouraged to observe that, of those genes where array data was compared with RT-PCR, changes occurred in same direction and were of similar magnitude. Further validation of the data with Northern-blot analysis for some of these genes reassuringly confirmed these alterations whereas for only one gene investigators performed functional activity assessment demonstrating similar notable findings. Though the results of this microarray study were double validated by standard molecular biology techniques there are still a number of caveats that should be kept in mind, including the post-transcriptional modifications that cannot be readily detected by gene expression changes and the large amounts of data variability that limits the careful analysis of every gene and highlights the necessity for further confirmatory tests and studies utilizing human lung samples.
One of the first gene-profiling studies to address an important disease process, such as sepsis at a multisystem level, was that of Ward et al. [65] Using DNA microarray platforms, authors examined the sepsis-induced gene expression patterns at a multiorgan level, in mice. One of the most intriguing aspects of this study is the identification of genes (many of them not previously associated with sepsis response) that have a distinctive organ-specific expression profile as well as of genes with a relatively universal response to sepsis indicating interesting associations between organs. Validation of the data was performed by Northern blot analysis in only four selected genes and similar quantitative concordance between the two analyses was achieved. Although the microarray analysis provides an informative insight in the pathogenetic mechanisms of a complex disease process the lack of sufficient confirmatory tests applied in all studied genes and the absence of functional genomics that will help us understand the exact role of the newly characterized genes in the septic response pose major limitations in the study and illuminates the need for further characterization of the sepsis-induced gene expression profiles.
In summary, these studies exhibit the crucial role of a novel molecular technology in discovering, through global analysis of gene expression, genes previously identified only by their DNA sequence. Although the array analysis provides in some studies a comprehensive overview of gene expression in the lung during ALI [56,58,61,64], and sepsis [65] and after hyperoxia [64], however there are numerous concerns arising from the large amounts of data variability, the lack of proteomics approaches in most of them and the controversial findings of microarray analysis and confirmational techniques. Unfortunately only three studies [58,61,64] used independent methodological criteria to validate a relatively small portion of their results, evidence that highlight the necessity for further more widespread evaluation of these findings. With the use of these approaches, more precise diagnosis and risk assessment of ALI based on expression profiles can be achievable in the next ten years, leading to more accurate determination of prognosis and new therapeutical interventions (Table 5).
5. Microarrays in sarcoidosis
Sarcoidosis is a chronic systemic disorder characterized by the presence of non-caseating granulomas and accumulation of T-lymphocytes and macrophages in multiple organs [66]. The mechanisms leading to the persistent accumulation of inflammatory cells are not fully understood. Apoptosis, a dynamic process involved in the control of the "tissue load" of immune effecter cells at inflamed sites, limits inflammatory tissue injury and promotes resolution of inflammation [67]. Whether or not reduced apoptosis is involved in the pathogenesis of sarcoidosis is unclear. Rutherford et al. [68] in their attempt to shed further light on apoptosis signals in the peripheral blood of sarcoidosis patients with self limited and progressive disease in comparison with healthy controls used high-density probe arrays containing 12.626 genes. Though this study demonstrated significant differences in the expression of apoptosis-related genes in peripheral blood of patients with acute onset sarcoidosis compared to controls, ultimately did not manage to show a definite profile that was suggestive of survival or apoptosis. Although authors applied functional genomics a potential criticism of their approach is that they cannot give any statement on the activity of the apoptotic pathways. To do so the data should be combined with the proteomics analysis [69] of proteins involved in apoptosis. The latter will contribute to the characterization of protein patterns and will allow for the assessment of overall changes in the protein content associated with apoptosis.
Collectively these findings not only reveal the importance of the microarray platforms in identifying gene expression patterns that give the scientists the opportunity to elucidate the pathophysiological processes of complex diseases, such as sarcoidosis but also illuminate some of their origin disadvantages.
Future directions, challenges and limitations of microarray technology
The last five years has seen the emergence of a novel technology applied in almost every aspect of respiratory research, a technology that has also great future perspectives and may provide scientists with numerous avenues of investigation that have clinical implications. Since nowadays, microarray technology has been successfully used for the identification of potential target genes for therapeutic intervention in IPF,[22] mechanistic studies in animal models of asthma [32,45] and IPF,[23,24] and helped the investigators to shed further light into the transcriptional programs involved in cytokine signaling [34,36,42] and apoptosis [35,41,61]. Furthermore, this technology increased our hopes in the field of diagnosis and clinical assessment of complex diseases such as asthma [33] and revealed modern approaches in therapeutic interventions in asthma [37,43] as well as COPD [46]. Finally, it provided scientists with useful information in their attempt to gain better understanding in molecular mechanisms regulating several pathological processes such as IPF, [22-26] asthma, [33,35-39,42] COPD, [45-48,50,52] lung fibrosis in acute lung injury [56,61,64,65] and pulmonary edema [58,63,64]. However, the feeling of excitement arising from the relative ease of producing the results of microarray experiments comes to contrast with the confusion arising from the objective difficulty of dealing with the results.
Managing and mining the huge amount of data generated by microarray experiments still remains a major challenge and limitation for most investigators. Whether gene expression changes are considered primary vs. reactive for a given disease is a complicated issue, and one can only begin to judge that if the methods and approaches used to generate the data are of sufficient scientific rigidity. The diversity and scope of the data require the creation of multidisciplinary teams consisting of physicians, biologists and bioinformaticians (mathematicians, computational biologists and database managers) [10]. Thus, we can conclude, that these diverse experimental schemes pose diverse computational requirements, such as advanced data mining, clustering and analysis tools, including interpreting patterns of gene expression with self-organizing maps.
Because of the statistical issues raised by microarray technology, it is necessary for any meaningful interpretation that the data is replicated using independent methodological criteria, preferably with separate samples rather than the tissue or RNA used to derive the original targets. A rapid high through-put method for confirmation of microarray data is quantitative RT-PCR. Alternatively, Northern blots or ribonuclease protection assays provide the benefit of direct quantification. So far some studies have started to adapt these approaches (Figure 4) but there are still limitations. Because a microarray analysis may reveal putative changes in the expression of tens or hundreds of genes, it is practically impossible to validate all of the data. However, it is incumbent upon investigators to evaluate a reasonable number of biased in their selection genes. Thus genomics and gene expression experiments are sometimes derided as "fishing expeditions". Hence it is necessary that these conventional techniques should be coupled with advanced data mining tools to help the scientists to face the greatest challenge, namely the extraction of biological meaning from microarray data and the prioritization of candidate genes for follow up [11]. Another challenge is to gain a holistic view of the human genome and biology, by applying genomic microarray in combination with the proteomic microarray to overpass the origin disadvantage of microarray technology that gives the users the view of inducible genes only. Sequence and gene expression analysis alone is insufficient to fully inform the investigator on the cell state and function. To the best of our knowledge only few of the studies utilizing microarray platforms in respiratory research has taken advantage of this approach and in a limited number of genes [69]. This combination would be crucial to better understand the functional aspects of disease and to bridge the long way between genotype and phenotype due to environment-gene interactions [12,70]. Additionally, it will be critical to develop improved methods for unbiased amplification of small RNA samples so that meaningful data can be obtained by applying microarrays on small tissue samples and pure cell populations, such as are samples obtained by microdissection of tissue sections [47]. This approach will solve the problem created by dramatic differences in cellular composition of affected and unaffected tissue and by spatial and temporal heterogeneity of disease that limits the optimal application of microarrays to the study of diseases [27]. Finally it is noteworthy to be mentioned that microarrays like other new diagnostic and research tools are highly cost-intensive. Considering the high costs of microarray based experiments it is important to say that this disadvantage will inevitably limit the speed with which they are introduced into clinical practice and restrict their application in university hospitals and other medical institutes [71]. Therefore it is crucial that academic centers and other specialized units should understand that joint ventures with biotechnological and pharmaceutical companies are critical to overlap all the financial obstacles so that microarray technology will be likely to reach most large hospitals with huge potential gain in clinically relevant information for individual patients and their diseases.
Figure 4 Diagram showing the number of studies cited in this review article that validated the data derived from the microarray analysis either by confirmational studies (RT-PCR, Northern blot analysis, or both) or independent experiments (protein analysis, in situ hybridization, transgenic mice etc) in comparison with the total number of studies reviewed in this article. The majority of the studies cited in this review manuscript have used at least one confirmational test to replicate the microarray findings.
Conclusion
Currently, the use of microarray technology in respiratory research is limited by the tissue sample, incomplete available arrays and the analysis of data generated from this technology. Clinical use of microarrays technology is still in its infancy and remains exploratory. For these array-based methods to become truly revolutionary, they must become an integral part of the daily activities of the typical molecular biology laboratory and biomedical institute. There is plenty of room for technical improvements, further development, and more widespread acceptance and accessibility. Optimistically thinking we expect that over the next few years the pattern of development and use of microarrays will be on a similar trajectory to that seen for computers and other high-tech electronic devices, which started out as exotic and expensive tools in the hands of the few developers and then moved quickly to become easier to use, more available and less expensive. Alternatively authors believe that so far microarrays have not added much to our understanding and the possibility to live up to the great 'hype' that was generated belongs to the distant future.
Our view is that the application of these approaches will improve dramatically the effectiveness and reliability of microarray technology in studies of diseases of complex organs like the lung, and will have a major impact on our understanding of molecular pathogenesis for the foreseeable future. Whether our hopes will be fulfilled or disproved remains to be seen.
List of abbreviations
ADA: Adenosine Deaminase
ALI: Acute Lung Injury
ASM: Airway Smooth Muscle
a-SMA: a-Smooth Muscle Actin
BAL: Bronchoalveolar lavage
CAGE: Composite Atopy Gene Expression
COPD: Chronic Obstructive Pulmonary Disease
CCR7: Chemokine Receptor 7
DEP: Diesel Exhaust Particles
ECM: Extracellular Matrix
eQTL: expression quantitative trait locus
G.I: Gene Intensity
Ig: Immunoglobulin
ID: Inhibitor of Differentiation
I/D: Intensity/Background ratio
IPF: Idiopathic Pulmonary Fibrosis
MBP: Major Basic Protein
MCs: Mast Cells
MCP: Monocyte Chemoattractant Protein
MMP: Matrix Metalloproteinase
NF-κB: Nuclear Factor-Kb
nsPLA2: snake venom phospholipase A2
RT-PCR: real time-polymerase chain reaction
TGF-b: Transforming Growth Factor-b
TNF: Tumor Necrosis Factor
UIP: Usual Interstitial Pneumonia
VEGF: Vascular Endothelial Growth Factor
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
AT, GP and DB were involved with the study conception. AT performed the data acquisition and interpretation. DB and GP were involved in revising the article for important intellectual content. All authors read and approved the final manuscript.
Acknowledgements
The authors thank Panagiotis Pantelidis (PhD) for his constructive and helpful comments especially on the methods section of our review. The authors are also grateful to Dr.Karameris Andreas. for providing us the microarray image from his illustration courtesy.
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| 15585067 | PMC543572 | CC BY | 2021-01-04 16:47:22 | no | Respir Res. 2004 Dec 7; 5(1):26 | utf-8 | Respir Res | 2,004 | 10.1186/1465-9921-5-26 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-481558142910.1186/1475-2875-3-48ResearchPopulation genetic structure of Anopheles gambiae mosquitoes on Lake Victoria islands, west Kenya Chen Hong [email protected] Noboru [email protected] John [email protected] Guiyun [email protected] Department of Biological Sciences, State University of New York, Buffalo, NY 14260, USA2 Global Public Health Research Group, Department of Epidemiology and Public Health, University of Miami School of Medicine, Miami, FL 33136, USA2004 6 12 2004 3 48 48 28 9 2004 6 12 2004 Copyright © 2004 Chen et al; licensee BioMed Central Ltd.2004Chen et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Understanding the genetic structure of island Anopheles gambiae populations is important for the current tactics in mosquito control and for the proposed strategy using genetically-modified mosquitoes (GMM). Genetically-isolated mosquito populations on islands are a potential site for testing GMM. The objective of this study was to determine the genetic structure of A. gambiae populations on the islands in Lake Victoria, western Kenya.
Methods
The genetic diversity and the population genetic structures of 13 A. gambiae populations from five islands on Lake Victoria and six villages from the surrounding mainland area in the Suba District were examined using six microsatellite markers. The distance range of sampling sites varied between 2.5 and 35.1 km.
Results
A similar level of genetic diversity between island mosquito populations and adjacent mainland populations was found. The average number of alleles per locus was 7.3 for the island populations and 6.8 for the mainland populations. The average observed heterozygosity was 0.32 and 0.28 for the island and mainland populations, respectively. A low but statistically significant genetic structure was detected among the island populations (FST = 0.019) and between the island and mainland populations (FST = 0.003). A total of 12 private alleles were found, and nine of them were from the island populations.
Conclusion
A level of genetic differentiation between the island and mainland populations was found. Large extent of gene flow between the island and mainland mosquito populations may result from wind- or human-assisted dispersal. Should the islands on Lake Victoria be used as a trial site for the release program of GMM, mosquito dispersal between the islands and between the island and the mainland should be vigorously monitored.
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Background
Despite 50 years of malaria vector control efforts, malaria remains a major public health threat in tropical and subtropical countries [1-3]. In recent years, malaria has caused increased human mortality and morbidity as malaria epidemics have spread to areas where it was previously rare [4,5]. The current strategies for malaria control involve the treatment of infected individuals with antimalarial drugs to kill the parasites and vector management to reduce human-vector contacts via residual spraying and the use of insecticide-impregnated bednets. As demonstrated in multisite trials throughout Africa, the large-scale use of insecticide-treated bednets can reduce overall mortality by up to 30% [6] and morbidity in young children [7]. The emergence of insecticide resistance in mosquito vectors [8] and antimalarial drug resistance in Plasmodium [9] has significantly reduced the viability of many malaria control programs. An efficacious malaria vaccine will not be available in the near future [10].
One potential alternative malaria control strategy is based on the genetic disruption of mosquito vector competence [11-13]. This genetic control approach requires identification and cloning of parasite-inhibiting genes in the mosquito vectors, development of stable and efficient mosquito transformation tools and the development of strategies for spreading the parasite-inhibiting genes. Over the past several years, remarkable progress has been made in the development of mosquito germline transformation and in the identification of parasite-inhibiting molecules. For example, A. gambiae cell lines were successfully transformed with the Hermes element [14,15], and the Minos transposable element bearing an exogenous gene was efficiently integrated into the genome of Anopheles stephensi [16,17]. Genetic linkage maps have been constructed for A. gambiae [18], and genes conferring mosquito refractoriness to malaria parasites have been mapped [19]. Availability of complete A. gambiae genome sequences will greatly facilitate identification and cloning of parasite-inhibiting genes [20].
The success of the transgenic mosquito approach depends on the spread and even fixation of parasite-inhibiting genes into natural populations. Presently, releasing transgenic mosquitoes to the field is premature. Isolated islands have been suggested as an ideal natural site for testing transgenic mosquito release strategies and spatial spreading of transgenes [13,21,22]. Information on mosquito population genetic structure and gene flow on islands and the surrounding mainland area is critical. Using microsatellite markers, the A. gambiae population genetic structure in the African continent has been examined [23-28]. These studies revealed that the Great Rift Valley in East Africa is a substantial gene flow barrier for A. gambiae; however, no significant genetic structure was detected for mosquito populations between western Kenya and West Africa. The minimum area associated for a deme of A. gambiae in western or coastal Kenya is larger than 50 km [24]. Simard et al. [29] found a high degree of genetic differentiation of the Anopheles arabiensis populations from the high plateau of Madagascar and those from Réunion and Mauritius islands (FST ranges from 0.080 to 0.215). Population substructure was also detected on the island of São Tomé, West Africa [22].
The present study examined the genetic diversity and the population genetic structures of A. gambiae mosquitoes from five islands on Lake Victoria and the surrounding mainland in western Kenya. This information is valuable for selecting field sites to test transgene release strategies and evaluating the spread of transgenes in nature.
Materials and Methods
Study sites and mosquito collection
Anopheline female mosquitoes were collected from seven villages on five islands in Lake Victoria and from six villages in the mainland Suba District, western Kenya (Fig. 1). The sampled islands were Kibuogi, Mfangano (Sena village), Ngodhe, Takawiri and Rushinga. Mosquitoes were collected from three villages (Kamsengere, Utajo and Wanyama) on Rushinga Island and one village on each of the other islands. Mfangano Island is the largest and the most offshore (about 10 km away from the nearest mainland village). Rushinga Island is the most populated among the five islands and is connected to the mainland by a walkway. The islands are about 2.5–21.0 km apart. Also, five mainland villages (Ragwe, Roo, Gingo, Mbita and Kasunga) along the shore of Lake Victoria and one inland village (Ruri) about 11 km away from the lakeshore were selected. The distance between the islands and the mainland sites ranges from 4.9 to 35.1 km. Malaria on these islands and the mainland area is holoendemic, and A. gambiae mosquitoes are the major malaria vectors in this region [30].
Figure 1 Map of study area showing the distribution of Anopheles gambiae populations on the Lake Victoria islands and the surrounding mainland area in Suba District, western Kenya.
At least 170 anopheline mosquitoes were collected from four to 28 houses within each village using the pyrethrum spray collection method [31]. Mosquitoes from Mbita, Kasgunga and Ruri were sampled in May 1997; collection in other villages was conducted in April and May 1999. A. gambiae sensu lato (s.l.) specimens were separated from other anophelines according to the identification key provided by Gillies and Coetzee [32] and then preserved in 95% ethanol and kept at -20°C until further analyzed.
PCR assay for species identification
PCR analysis was conducted for species identification using the rDNA-PCR method because individual species within the A. gambiae species complex cannot be identified by morphology alone [33]. About 100 A. gambiae s.l. females per village were tested. If the initial PCR testing failed to amplify for a sample, then the PCR analysis was repeated once or twice until successful amplification was achieved. If a sample could not be identified after three PCR amplifications, it was scored as unknown.
Microsatellite loci and genotyping
Six microsatellite markers were used for specimen genotyping, including AGXH1D1 and AGXH131 of Chromosome X, AG2H46 and AG2H79 of Chromosome 2, and AG3H29C and AG3H33C of Chromosome 3 [17,18,22,23]. Microsatellite analyses were conducted on 51–70 individuals per village (See the additional file: A table of sample size, allelic number, heterozygosities and breeding coefficient of 13 A. gambiae populations from the Lake Victoria islands and the surrounding mainland in western Kenya).
A Li-Cor Model 4200 Automated DNA Analyzer (Li-Cor Inc., Lincoln, NE) was used for gel electrophoresis. For the apparatus to detect PCR products, one primer in every pair of microsatellite primers must be fluorescently labelled. To reduce the cost associated with synthesis of fluorescently labelled primers, we used the "tailed primer" method [34,35]; that is, the forward primer for each microsatellite locus was synthesized with an additional 19 bp sequence (5' CACGACGTTGTAAAACGAC 3') added to the 5' end of the primer. A third primer with the same 19 bp sequence was directly labelled with the fluorescence and was used as the sole type of labelled primer for the detection of all microsatellite alleles. The tailed primer method reduced the cost of oligonucleotide synthesis by >80%. The 10 μl PCR reaction contained 1X Taq buffer, 0.2 mM dNTPs, 1.5 pmol forward and reverse primers, 1.5 pmol fluorescently labelled 19 bp sequences, 1.5 mM MgCl2, 1.0 μg BSA, 1.0 unit Taq polymerase and about 20 ng genomic DNA. Cycling conditions in a MJ Research PTC-220 thermocycler were 35–40 cycles of 94°C for 30 seconds, 55°C for 30 seconds and 72°C for 45 seconds. Allele sizes were determined using Gene ImagIR computer software [36]. The allele sizes used in the analysis were true allele sizes that have been adjusted for the 19 bp tail in the forward primer.
Data analysis
Microsatellite polymorphism was measured by the number of alleles and heterozygosity at each locus. Using the probability test available in the GENEPOP computer program [37], conformance with Hardy-Weinberg Equilibrium (HWE) was tested for each locus and population, and the Bonferroni correction was applied for multiple comparisons. The FIS statistics and probability test were used to determine whether distortion from HWE resulted from heterozygosity deficiency or excess using. Because the probability test is robust to low allele frequencies, rare alleles were not pooled. Variations in heterozygosity among the populations were analyzed following Weir's method [38], using the analysis of variance (ANOVA) with subpopulations, individuals, loci and interactions of loci, and individuals as factors. All factors were treated as random effects except loci. The Fisher exact test was performed to detect linkage disequilibrium for pair-wise loci in each population and the pooled population. Population genetic structure was examined with Wright's F-statistics (FST) using FSTAT 2.8 [39]. FST statistic appears to be more sensitive to detect intraspecific differentiation than RST [40,41]. The standard deviations of the F-statistics were obtained for each locus by a jackknife procedure over all the alleles and were used to test the statistical significance. Nei's unbiased genetic distances [42] were calculated for all pairs of populations based on microsatellite allele frequencies at six loci using TFPGA [43]. A dendrogram was created based on the pair-wise genetic distances using the unweighted pair group method with arithmetic mean (UPGMA). The bootstrap confidence values were generated by 1,000 permutations.
The isolation-by-distance model of population genetic structure was tested by linear regression of pair-wise FST/(1 - FST) against the natural logarithm of straight-line geographical distance between population pairs [44]. Statistical significance of the regression was tested using the Mantel test with 10,000 permutations [45].
Results
Population genetic variability
A moderate to high level of polymorphism was found in six loci across the 13 populations (See the additional file). The three populations from Rushinga Island had a similar number of alleles per locus (ANOVA, F = 0.02, df = 2, P > 0.05) and observed heterozygosities (F = 0.029, df = 2, P > 0.05). Among the island populations, the average observed number of alleles per locus was not significantly different (F = 0.08, df = 6, P > 0.05), but observed heterozygosity varied significantly (F = 4.52, df = 6, P < 0.01). The three populations on Rushinga Island, Kamsengere, Utajo and Wanyama, showed significantly lower heterozygosity than other islands. Similarly, the six mainland populations did not differ in the number of alleles per locus (F = 0.29, df = 5, P > 0.05), but they varied significantly in the observed heterozygosities (F = 5.45, df = 5, P < 0.01). In particular, the Ruri population had the highest observed heterozygosity (0.343), about two-fold higher than the Mbita population (See the additional file). Overall, there was no significant difference between the island and mainland populations in the number of alleles per locus (7.3 vs. 6.8; t = 0.67, df = 74, P > 0.05) and observed heterozygosities (0.32 vs. 0.28; t = 1.82, df = 74, P > 0.05). A total of 12 private alleles were identified, nine of them from the island populations.
A total of 14.1% loci (11 out of 78 tests) showed significant departure from Hardy-Weinberg equilibrium, all due to heterozygote deficiency. This was caused entirely by heterozygote deficiency in the locus AG2H46, a locus known for the presence of null alleles in western Kenyan A. gambiae populations [46]. The Fisher exact test revealed linkage disequilibrium in 13 out of 195 pairs of loci (6.7%; data not shown), suggesting a low level of linkage disequilibrium among the six loci scored.
Population genetic structure
A low, but significant, genetic structure was detected among the seven island and the six mainland populations (Table 1). The genetic differentiation in the seven island populations (FST = 0.019, P < 0.001) was almost twice as high as the six mainland populations (FST = 0.010, P < 0.001). Genetic differentiation between island and mainland populations was also small (FST = 0.010, P < 0.001). Pair-wise comparisons between all populations revealed that only seven pairs (Kibougo/Kamsengere, Kasgunga/Kamsengere, Takawiri/Ruri, Sena/Ruri, Utajo/Ruri, Ngodhe/Ruri and Ngodhe/Gingo) exhibited significant FST values, and six of them were between an island and a mainland population.
Table 1 FST estimates of Anopheles gambiae populations on the islands of Lake Victoria and from surrounding mainland sites in western Kenya
Locus Among seven island populations Among six mainland populations Between island and mainland areas Among all populations
AGXH1D1 0.082*** 0.022*** 0.002*** 0.042***
AGXH131 0.000 0.009*** 0.005*** 0.006***
AG2H46 0.008*** 0.006 0.006*** 0.009***
AG2H79 0.011*** 0.011*** 0.000 0.010***
AG3H29C 0.026*** 0.008*** 0.000 0.007***
AG3H33C 0.003*** 0.009*** 0.000 0.005***
Overall 0.019*** 0.010*** 0.003*** 0.012***
* P < 0.05, ** P < 0.01, *** P < 0.001.
The Mantel test revealed a significant correlation between geographic distance and pair-wise FST/(1 - FST) (P < 0.001), suggesting that the population genetic structure of A. gambiae populations from the island and mainland is consistent with the isolation-by-distance model. When the Ruri population is removed from the analysis, the correlation was still statistically significant (P = 0.015). Therefore, the population genetic structure of our study populations is consistent with the isolation-by-distance model. The cluster analysis revealed that the Ruri population, located farther inland from the other populations, was out-grouped from other populations with a significant bootstrap value, while other mainland and island populations were intermixed with non-significant branch bootstrap values (Fig. 2).
Figure 2 A UPGMA tree based on Nei's unbiased distance showing genetic divergence among Anopheles gambiae populations. The numbers above branches indicate those with >50% bootstrap support. The populations marked with an asterisk are the mainland populations.
Discussion
The present study demonstrated a similar level of genetic diversity between the island A. gambiae populations in the Lake Victoria and adjacent mainland populations in the Suba District, western Kenya. For the seven island populations, the average number of alleles at six microsatellite loci was 7.3 and the observed heterozygosity was 0.32. For the six mainland populations, the average number of alleles was 6.8 and the observed heterozygosity was 0.28. The population genetic diversity at most loci in this study was similar to other western Kenyan populations [23,47-49]. Compared with West Africa populations [23,27,48,49], lower heterozygosities, particularly at loci AG2H46, AG2H79 and AG3H33, were reported in this study, caused by fewer alleles detected in the studied populations. The comparable level of genetic diversity between island and mainland populations suggests that the island mosquito populations have a similar effective population size as the mainland populations, and they have not suffered severe genetic bottleneck during the previous vector control efforts. For each population, all loci except the AG2H46 locus did not show a significant deviation from Hardy-Weinberg equilibrium, suggesting that the microsatellite markers used in the study are not under strong selection and mosquito populations are in random mating. A heterozygote deficit at the locus AG2H46 was observed for all populations in this study. Heterozygote deficiency at the locus AG2H46 was also demonstrated in other western Kenya populations by Lehmann et al. [23,24]; the presence of null alleles as a result of mutations in the primer-annealing region was the cause.
A small but statistically significant genetic structure was detected for A. gambiae populations among the five islands in Lake Victoria (FST = 0.019) and among the six villages in the mainland in an area of approximately 40 × 20 km2 (FST = 0.010). The degree of genetic differentiation between the island populations in this study was less than for the island A. gambiae populations of São Tomé, western Africa (FST = 0.032) [22]. The lower FST estimates in the populations in this study were probably caused by shorter distance between islands (3–15 vs. 23–38 km) [22] and a lack of mountainous topography as gene flow barriers. The FST estimates for the mainland populations in this study were comparable to other studies on the western Kenya populations (FST = 0.0033) [24,27]. The genetic differentiation between island and mainland populations was small but statistically significant (FST = 0.003). Thus, there is a very small degree of genetic isolation between island and mainland populations. This estimation is consistent with the private allele distribution in the studied populations, in which nine of the 12 private alleles were from the island populations. Further evidence for a small degree of genetic differentiation between island and mainland populations is from pair-wise population comparisons in which six out of the seven pairs that exhibited significant genetic differentiation were between an island population and a mainland population.
The low level of genetic differentiation between island and mainland mosquito populations implies large gene flow between the two areas (83.1 migrants per generation). The normal flight range of A. gambiae is usually less than 1 km [50]. The distance to the lake shore of the mainland from the islands ranges from 2.5 to 15 km, farther than the normal flight range of the mosquitoes. Thus, mosquito migration is likely assisted by wind. Lindsay et al. [51] found that the spatial distribution of A. gambiae mosquitoes was related to the predominant wind direction at night, suggesting that wind assisted the dispersal of mosquitoes from their breeding site. A. gambiae have been shown to fly up to 7 km with the assistance of wind [52,53]. This distance is in the range for mosquitoes to disperse between the closest islands and between islands and their closest mainland in this study area. Mosquitoes may also use one island as a stepping-stone to extend their dispersal distance.
Mosquito migration may also be assisted by human activities. A study on Aedes polynesiensis populations from islands found no significant effect of geographic distance on the population genetic structure, but detected a significant correlation between gene flow and commercial traffic by planes and/or boats between islands [54]. The introduction of A. arabiensis to the Mascarene islands and Madagascar was thought to be caused by human transportation by steamship lines [55,56]. In Lake Victoria, small wooden boats may transport mosquito larvae between the islands and the mainland. A. gambiae larvae were collected at the bottom of a wooden fishing boat [57]. Rushinga Island in the study area was connected to the mainland by a walkway, and the island mosquito larvae could be moved to the mainland by vehicle transportation.
The results of this study of the population genetic stricture of island and mainland A. gambiae populations have implications for the ecological safety evaluation of the transgenic mosquito release program. During the initial field test of environmental safety and public health consequences by transgenic mosquito release, ideal sites would be islands that are totally genetically isolated from other islands and the mainland, with a sufficient number of human inhabitants and active malaria transmission on the island. Such an island may be extremely difficult to find, so islands with some genetic isolation from the mainland may have to be chosen. If so, the Lake Victoria islands could be used as field test sites; however, due to potential gene flow between the islands and between the islands and the mainland, mosquito dispersal between the islands and between the islands and the mainland should be vigorously monitored. After the release of the genetically modified mosquitoes, long-term monitoring programs should be launched to evaluate the spread of the transgenes to any unintended areas. In addition, methods to minimize the negative effects of transgene leak need to be developed prior to the field trial of transgene release [58].
Conclusions
This study showed that a low level of genetic differentiation existed between the island and mainland populations and no any genetically-isolated population was found among the 13 mosquito populations. If the islands on Lake Victoria were used as a trial site for the program to release genetically-modified mosquitoes, short-term and long-term mosquito dispersal between the islands and between the island and the mainland should be vigorously monitored.
Authors' Contributions
HC conducted species identification using PCR, microsatellite analyses and drafting the manuscript. NM was responsible for sample collection, and participated in species identification and drafting the manuscript. JB and GY supervised the study, and assisted data analysis and manuscript preparations.
Supplementary Material
Additional File 1
A table of sample size, allelic number, heterozygosities and breeding coefficient of 13 A. gambiae populations from the Lake Victoria islands and the surrounding mainland in western Kenya.
Click here for file
Acknowledgments
We thank L. Carson and two anonymous reviewers for critical comments. This work was supported by WHO TDR grant A10429 and NIH grants D43 TW01505 and R01 AI50243.
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| 15581429 | PMC543573 | CC BY | 2021-01-04 16:37:27 | no | Malar J. 2004 Dec 6; 3:48 | utf-8 | Malar J | 2,004 | 10.1186/1475-2875-3-48 | oa_comm |
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Nutr JNutrition Journal1475-2891BioMed Central London 1475-2891-3-231558830610.1186/1475-2891-3-23Case ReportLifetime total and beverage specific - alcohol intake and prostate cancer risk: a case-control study Barba Maddalena [email protected] Susan E [email protected]ünemann Holger J [email protected] Saverio [email protected] Barbara [email protected] Placido Sabino [email protected] Giuseppe [email protected] Jo L [email protected] Maurizio [email protected] Marcia [email protected] Tom [email protected] Paola [email protected] Department of Social and Preventive Medicine, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY 14214, USA2 Departments of Endocrinology and Oncology, Federico II Medical School, University of Naples, Naples, Italy3 Department of Epidemiology, Division of Cancer Prevention and Population Sciences, Roswell Park Cancer Institute, Buffalo, NY 14263, USA4 Department of Medicine, University at Buffalo, State University of New York, Buffalo, NY, USA5 Department of Preventive Medical Sciences, Federico II Medical School, University of Naples, Naples, Italy6 Gynecologic Oncology Group, Roswell Park Cancer Institute, NY, USA7 Institutes of Oncology, School of Medicine, University of Palermo, Palermo, Italy8 Prevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, CA, USA9 School of Social Work, State University of New York at Buffalo, Buffalo, NY, USA2004 9 12 2004 3 23 23 23 9 2004 9 12 2004 Copyright © 2004 Barba et al; licensee BioMed Central Ltd.2004Barba et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
We investigated lifetime alcohol consumption and prostate cancer risk in a case-control study conducted in Buffalo, NY (1998–2001).
Methods
The study included 88 men, aged 45 to 85 years with incident, histologically-confirmed prostate cancer and 272 controls. We conducted extensive in-person interviews regarding lifetime alcohol consumption and other epidemiologic data.
Results
Prostate cancer risk was not associated with lifetime intake of total and beverage specific ethanol. In addition we found no association with number of drinks per day (average drinks per day over the lifetime) or drinks per drinking day (average drinks per day on drinking days only over the lifetime). However, we observed an inverse association with the total number of drinking years. Men in the lowest tertile of total drinking years had a two-fold prostate cancer risk than men in the highest tertile (OR 2.16, 95% CI 0.98–4.78, p for trend <0.05).
Conclusion
Our results suggest that alcohol intake distribution across lifetime may play a more important role in prostate cancer etiology than total lifetime consumption.
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Background
Prostate cancer is the most frequently diagnosed malignancy and the second leading cause of cancer death among men in the Western countries [1]. Notwithstanding the importance of this malignancy, little is understood about its cause. To date the only well established risk factors are age, family history of disease, race and country of residence [2], while the body of the evidence about the role of alcohol intake is still controversial. Since alcohol consumption is a common lifestyle factor and potentially modifiable, the finding of an association with prostate cancer could have an important impact on public health.
Among the population-based case-control studies, those carried out by Heyes et al. [3] and Sharpe et al. [4] found an increased risk of prostate cancer associated with alcohol consumption. Risk increased with increasing frequency of alcohol consumption [3] and among those who drank regularly over a longer period [4]. Sesso et al., in their prospective cohort study, confirmed the finding of a higher risk associated with alcohol consumption [5]. However, numerous studies published since 1998 have not found an association between alcohol intake and prostate cancer [6-17]. In a review by Breslow and Weed, only 6 of 32 studies reported a positive association between alcohol use and prostate cancer [18]; however, they noted that many of the studies had biases that could have attenuated the risk estimates.
Although prostate cancer is known to have a long latency period, lifetime alcohol consumption was not addressed in the studies carried out until the late 1990s, and rarely in the more recent studies [18]. Furthermore, investigators focusing on this topic have considered lifetime alcohol consumption as the average total amount of alcohol consumed over the lifetime, rarely taking into account such characteristics as number of drinks consumed on a typical drinking day or other descriptions of drinking pattern. The distribution of an equivalent volume of alcohol across multiple drinking occasions rather than a single occasion (e.g., one drink per day vs. seven drinks on single day) is likely to have different physiologic effects and impact on cancer risk. Likewise, an examination of average total lifetime alcohol intake does not address the possibility that, although the total lifetime volume may not differ, the duration of intake may, thus effectively resulting in a higher dose over a shorter time period.
Alcohol may act as a carcinogen itself and may also modulate risk from other carcinogen exposures. It has been implicated in risk of cancer at a number of sites [19,20]. In the present case-control study we examined the association between lifetime alcohol intake, duration of alcohol use, and drinks per day with risk of prostate cancer in western New York.
Methods
We conducted a case-control study of prostate cancer and hormones and alcohol intake (the PROMEN STUDY) in Erie and Niagara Counties, NY, USA, between December 1998 and April 2001. The methods for this study have been previously described in detail [21]. Participants provided informed consent; the Institutional Review Board of the University at Buffalo, School of Medicine and Biomedical Science, and each of the participating hospitals approved the procedures for the protection of human subjects recruited for the study.
Cases were men aged 45 to 85 years with incident, primary, histologically confirmed prostate cancer. Men with a previous history of cancer (except non-melanoma skin cancer), or already on hormonal or chemotherapy treatment (current or in the 6 months prior to diagnosis), as well as those affected by chronic or acute liver diseases, were excluded. Cases aged 35–65 years were also required to have a driver's license, because we used driver's license records to identify age matched controls.
During the study period, 504 men were identified with incident prostate cancer. Of these, 336 men did not meet the eligibility criteria; we invited the remaining 163 patients to participate in the PROMEN study. After being contacted, 50 men refused to participate resulting in a participation rate of 70%. Ninety-six patients had complete data for the variables of interest.
Controls aged between 35 and 65 years were selected from a list of individuals holding a New York State driver's license and residing in Erie and Niagara Counties. Those aged 65 and over were selected from the rolls of the Health Care Financial Administration. As with cases, men on hormonal treatment (current or in the 6 months prior the diagnosis), or diagnosed with metabolic or endocrine disease were excluded, as well as participants with a previous story of cancer other than non-melanoma skin cancer. Since it is well known that latent prostatic carcinoma has a high prevalence in men over 50 [22,23], we evaluated prostate specific antigen (PSA) in the blood samples obtained from controls. Controls found to have a PSA value higher than 4 ng/ml were excluded from the control group, in accordance with the criterion established by the American Cancer Society Prostate Cancer Detection Project [24] until the completion of further diagnostic procedures to clarify their true case-control status.
During the study period, 1373 potential controls were contacted. One hundred and seventy nine of these individuals were deceased or were too ill to participate, 293 did not meet the eligibility criteria and we were not able to contact 272 persons. We identified eight prostate cancer cases as a result of PSA determination in subjects who initially were recruited as controls. Three hundred and seventeen of the remaining 513 subjects (60%) were enrolled and interviewed: 304 had complete data for analysis.
Extensive data on demographics, smoking history, alcohol consumption, and other study variables were collected by trained interviewers during in-person computer-assisted interviews [25] and with self-administered questionnaires. Height, weight, waist and hip circumferences were measured by trained technicians using a standardized protocol. Body mass index (BMI) was calculated as weight in kilograms divided by square of the height in meters (kg/m2). Waist to hip ratio (WHR) was calculated as waist circumference divided by hip circumference.
Alcohol intake
Detailed information on alcohol consumption throughout the lifetime was collected using the Cognitive Lifetime Drinking History [26,27]. Prior to the interview, participants completed a lifetime events calendar on which they recorded the date and their age when significant events in their life occurred. The calendar was used during the interview to help them remember what they were doing during specified periods of their lives and whether drinking alcohol was involved. Participants reported the age when they started drinking alcohol regularly (at least once a month for six months) and when their drinking changed over the years. When changes were reported, participants were asked whether they continued regular drinking; if not, they were asked if they ever resumed regular drinking. Using this information, we defined intervals during each participant's life when drinking patterns were relatively homogeneous and computed the total number of drinking years and the total number of abstinent years. Lists of alcoholic beverages, beer, wine, wine coolers, and liquor, and models of glasses and bottles were used to help respondents recall what beverages they drank over their lifetimes; their usual drink size of each beverage; and whether drink size changed over their lifetimes. This provides information used to: (1) calculate absolute alcohol intakes and (2) tailor the computer-assisted interview to the each respondent's drinking history. Patterns of drinking were ascertained for intervals during which respondents drank weekly or more often by asking how often respondents drank on Fridays, Saturdays, Sundays, and weekdays, and how many drinks they usually had on each. For intervals during which respondents drank less often than weekly, they were asked standard quantity and frequency questions. Quantity and frequency for times when they drank more than usual were assessed for all intervals, as was the frequency of intoxication; the proportion of drinks they had with meals/snack/without eating; and the proportion of drinks from beer, wine, wine coolers, and liquor.
Drinks per interval was estimated by multiplying quantity by frequency for days of the week and more than usual and adding. Drinks per interval was translated into ounces of ethanol per interval based on the proportion of drinks represented by specific beverages, respondents' beverage-specific drink size in ounces, and factors representing the average percent per ounce of absolute alcohol for a given beverage to estimates of drinks per interval. Factors used were 0.048, 0.12, 0.04 and 0.40, for beer, wine, wine cooler and hard liquor, respectively. These estimates were summed across drinking intervals to yield lifetime totals.
We considered several variables in these analyses: total number of years alcohol was consumed, number of drinks per day during the drinking years (total number of drinks/total number of days in drinking years), number of drinks per drinking day (total number of drinks/total number of days on which alcohol was consumed in drinking years), total lifetime ounces of ethanol and beverage-specific total lifetime ounces of ethanol. Because few participants consumed wine coolers, wine and wine coolers were combined. A drink was defined as 12 ounces of beer, 5 ounces of wine, and 1.5 ounces of liquor.
Statistical analysis
Statistical analyses were conducted using SPSS for Windows version 11.0. Differences between cases and controls in demographic characteristics and alcohol consumption were assessed using t-tests for continuous variables and χ2 for categorical variables. Lifetime abstainers, defined as those subjects who had less than 12 drinks in any one year over their lifetimes, were excluded from our analyses. The biological and social differences between lifetime abstainers and both former and current drinkers [28,29] and the very low number of these subjects in our sample (5 cases and 11 controls) represent the reasons for their exclusion from our analyses. Our final sample size for analysis included 88 cases and 272 controls.
In analyses of risk associated with lifetime alcohol intake, tertiles of total and beverage specific ounces and total drinking years were computed based on the distribution in the controls. For the beverage specific analyses, non-drinkers were those respondents not consuming that particular alcoholic beverage. For risk associated with drinks per day and drinks per drinking day, we categorized consumption as two drinks or less per day and greater than two drinks per day. Odds ratios (OR) and 95% confidence intervals (CI) for risk of prostate cancer associated with alcohol consumption were computed using unconditional logistic regression adjusting for age, cigarette smoking status, education, body mass index (BMI), and waist to hip ratio (WHRATIO). The beverage specific analyses were further mutually adjusted for the other beverages.
Results
Characteristics of the participants in the PROMEN study are shown in Table 1. Compared to cases, controls were slightly more educated (13.0 vs. 12.3 years) and more likely to be Caucasian (93.0% vs. 67%). No statistically significant differences between cases and controls were observed for age, body mass index, waist to hip ratio, smoking or drinking status.
Table 1 Characteristics of prostate cancer cases and controls, PROMEN Study
Cases (n = 88) Controls (n = 272)
Mean (SDa)
Age, years 69.3 (8.4) 70.0 (6.3)
Education, years 12.3 (2.7)b 13.0 (2.8)
Body mass index, kg/m2 29.2 (5.2) 28.6 (4.6)
Waist to hip ratio
Percent
Race
White 67.0c 93.4
Non white 33.0 6.6
Smoking statusd
Never 23.8 28.3
Former 61.4 61.8
Current 14.8 9.9
Drinking statuse
Non-current drinkers 36.4 23.5
Current drinkers 63.6 76.5
astandard deviation; b p < 0.05, t-tests for differences in means between cases and controls; c p < 0.001, χ2 for differences in categorical variables between cases and controls; dsmoking status at the time of diagnosis in cases or interview in controls; edrinking status in the 12–24 months prior to diagnosis or interview, non-current drinkers stopped drinking at least 12–24 months prior to interview
Means and standard deviations for aspects of lifetime alcohol consumption for the sample overall and by current drinking status are shown in Table 2. Among drinkers overall and current drinkers, cases drank for fewer years than did controls (38.2 vs. 43.7 years, p < 0.05 and 41.3 vs. 46.8 years, p < 0.05, overall and current drinkers, respectively) and, consequently, had greater numbers of years abstaining. Few differences in lifetime total and beverage-specific ounces consumed, drinks per day, or drinks per drinking day were observed between cases and controls for drinkers overall or current drinkers. However, although not statistically significant, we observed several differences in alcohol consumption between cases and controls who were former drinkers. Among former drinkers, cases consumed more total ethanol, beer and liquor, more drinks per day and more drinks per drinking day, but consumed less ethanol from wine and wine coolers compared to controls.
Table 2 Selected lifetime alcohol consumption characteristics among prostate cancer cases and controls, PROMEN study
All drinkers
(n = 360) Former drinkers
(n = 96) Current drinkers
(n = 264)
Cases
(n = 88) Controls
(n = 272) Cases
(n = 32) Controls
(n = 64) Cases
(n = 56) Controls
(n = 208)
Mean (SD) Mean (SD) Mean (SD)
Total drinking years 38.2 a (16.5) 43.7 (14.9) 32.9 (18.5) 33.8 (17.2) 41.3 a (14.5) 46.8 (12.7)
Total abstaining years 11.4 a (15.0) 6.6 (12.5) 19.8 (16.4) 18.2 (15.3) 6.6a (11.9) 3.0 (8.8)
Drinks per day 2.6 (7.3) 1.6 (3.4) 4.7 (11.6) 2.5 (5.8) 1.3 (1.7) 1.3 (2.2)
Drinks per drinking day 4.5 (7.3) 3.6 (4.3) 6.8 (11.3) 5.0 (6.3) 3.2 (2.5) 3.2 (3.4)
Total lifetime ethanol, ounces 12904.7 (18681.0) 11735.3 (12904.7) 19051.0 (26382.6) 13498.8 (21019.7) 9392.6 (11187.8) 11192.7 (16880.9)
Total lifetime ethanol from beer, ounces 6282.5 (11321.0) 6024.3 (9250.0) 7771.0 (15173.8) 5992.6 (12284.7) 5431.9 (8422.3) 6034.1 (8129.3)
Total lifetime ethanol from liquor, ounces 5654.2 (14571.6) 4067.2 (12815.8) 10307.0 (22051.6) 5233.7 (11480.5) 2995.5 (6480.2) 3708.3 (13204.5)
Total lifetime ethanol from wine/wine coolers, ounces 953.1 (2715.6) 1634.6 (4168.8) 958.9 (3588.4) 2271.0 (6154.0) 949.8 (2099.5) 1438.8 (3326.0)
a p < 0.05, t-tests for differences in means between cases and controls
Odds ratios and 95% confidence intervals for the risk of prostate cancer associated with lifetime alcohol consumption are shown in Table 3. We observed no associations with risk with lifetime ounces of total ethanol, beer, wine, or liquor. Risk associated with total drinking years, years of abstaining (ever/never), drinking status, drinks per day, and drinks per drinking day are shown in Table 4. Compared to the highest tertile of total drinking years, men in the lowest tertile had a marginally significant increased risk (OR 2.16, 95% CI 0.98–4.78, p for trend <0.05) and, similarly, men reporting ever abstaining compared to those who never abstained had increased prostate cancer risk (OR 1.79, 95% CI 1.05–3.03). No associations with risk were observed for former vs. current drinkers, drinks per day, or drinks per drinking day.
Table 3 Odds ratios (OR)a and 95% confidence intervals (CI) for risk of prostate cancer associated with lifetime alcohol consumption
Cases (n = 88) Controls (n = 272) Odds Ratios (95% CI)
Total lifetime ethanol, ounces
≤2647.62 29 90 1.00
2647.62 – 11048.28 34 90 1.20 (0.65–2.23)
>11048.28 25 92 0.83 (0.43–1.60)
Total lifetime ethanol from beer, ouncesb
≤1941.78 42 120 1.00
1941.78 – 6237.30 25 75 1.16 (0.62–2.16)
>6237.30 21 77 0.89 (0.46–1.72)
Total lifetime ethanol from liquor, ouncesb
≤932.23 51 152 1.00
932.23 – 3976.79 15 59 0.71 (0.35–1.44)
>3976.79 22 61 0.91 (0.47–1.76)
Total lifetime ethanol from wine and wine cooler, ounces b
≤511.66 67 177 1.00
511.66 – 2283.00 10 47 0.76 (0.35–1.65)
>2283.00 11 48 0.60 (0.27–1.30)
a Adjusted for race, age (years), smoke, education (years), BMI, WHRATIO; bfurther mutually adjusted for other beverages
Table 4 Odds ratios (OR)a and 95% confidence intervals (CI) for risk of prostate cancer associated with lifetime alcohol consumption: duration, drinking status, drinks per day, and drinks per drinking day.
Cases (n = 88) Controls (n = 272) Odds Ratios (95% CI)
Total drinking years
>53 14 80 1.00
42 – 53 27 94 1.44 (0.66–3.14)
≤42 47 92 2.16b (0.98–4.78)
Ever abstained from drinking
never abstained 39 173 1.00
ever abstained 49 99 1.79b (1.05–3.03)
Drinking status c
current drinkers 56 208 1.00
former drinkers 32 64 1.40 (0.77–2.53)
Drinks per day
≤2 62 218 1.00
>2 26 54 1.38 (0.76–2.51)
Drinks per drinking day
≤2 24 106 1.00
>2 64 166 1.57 (0.88–2.79)
a Adjusted for race age, smoke, education (years), BMI, WHRATIO; bp for trend <0.05; cdrinking status in the 12–24 months prior to diagnosis or interview. Former drinkers stopped drinking at least 12–24 months prior to interview.
Discussion
The assessment of lifetime alcohol consumption in cancer etiology has been predominantly expressed through the calculation of either total lifetime volume or an average volume per specified time period across the lifetime. Few investigations have evaluated lifetime drinking patterns in relation to prostate cancer risk. While methodological difficulties challenge the evaluation of drinking patterns, our results suggest that failure to take into account aspects of drinking pattern such as the relative duration and dose of consumption may reduce our ability to clearly elucidate the role alcohol may be playing in cancer development. Although we observed no associations with risk for total lifetime alcohol intake or when alcohol was expressed as average drinks per day or even average drinks per drinking day, our results suggest that the impact may differ when the same volume of alcohol consumption takes place in fewer drinking years over a lifetime.
Furthermore, it is notable that alcohol consumption was much higher among the cases compared with controls who were former drinkers. As alcohol consumption has been positively related to many causes of morbidity, a proportion of these men may have stopped drinking in response to poor health. Whether pre-existing morbid conditions or heavier drinking is related to subsequent development of prostate cancer remains to be clarified.
Our study has several strengths and limitations. A limitation of our study is the small sample size, especially for cases. However, because one of the original aims of the study was an examination of hormones and prostate cancer, both cases and controls were carefully identified. To eliminate the effect on hormone levels by treatment, cases were enrolled in the study prior to starting chemotherapy or hormone therapy; thus increasing the difficulty of case ascertainment. On the other hand, the exclusion of controls with high circulating PSA levels helped to reduce misclassification and to ensure that the control group was free from prostate cancer. The data used in the present analysis were collected as a part of an in-person interview, and the questionnaire about lifetime alcohol consumption was very detailed allowing us to compute both the dose and frequency aspects of alcohol consumption.
Given the difficulties involved in measuring alcohol consumption, studies utilizing data collected before diagnosis would appear more likely to lead to valid inferences. Recently, Dennis in his meta-analysis [30] pointed out that in many of the published cohort studies alcohol consumption was assessed only at a baseline, often many years before the diagnosis, with no subsequent assessment to quantify changes in drinking pattern. While retrospective assessment of lifelong alcohol consumption at diagnosis may appear to be more likely to lead to recall bias, such an assessment may also be more likely to capture relevant attributes of exposure, such as overall duration of alcohol use and timing of potentially important changes in use, such as quitting. These differences are not always taken into account [30].
The plausibility of alcohol as a risk factor for prostate cancer relates to evidence that alcohol may act as a carcinogen or may modulate risk from other known carcinogens through generation of free radicals, affecting the metabolism of detoxification enzymes, impairment of immune system and depression of DNA repair enzymes [31]. It remains unclear to what extent alcohol could affect the early phases of cancer development. Some studies suggest that the critical period of exposure may be as early as adolescence as the development of prostate gland begins prenatally, continuing until the end of puberty [31]. If alcohol contributes to cancer promotion, duration and relative intensity of exposure during a specified period of time, instead of the total amount of the agent itself over the entire life time course may be important.
Conclusions
Further studies focusing on lifetime exposure and more specifically on patterns of consumption may help in prevention of a disease with considerable public health impact.
List of Abbreviations
BMI body mass index
PSA prostate-specific antigen
WHRATIO waist-to-hip ratio
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
MB performed statistical analyses, interpreted the results and drafted the manuscript
SEM performed statistical analyses, interpreted the results and revised the manuscript
HJS interpreted the results and revised the manuscript
SS interpreted the results and revised the manuscript
BJF performed data analysis, interpreted the results and revised the manuscript
SDP interpreted the results and revised the manuscript
GC interpreted the results and revised the manuscript
JLF interpreted the results and revised the manuscript
MT revised the manuscript
MR defined the exposure variables and revised the manuscript
TN defined the exposure variables and revised the manuscript
PM designed and implemented the study, interpreted the study results, revised the manuscript
Acknowledgements
Supported in part by grant 1179818559 from Department of Defense, Prostate Cancer Program and grant 5K07CA89123 from the National Cancer Institute, and in part by an American Italian Cancer Foundation Fellowship.
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| 15588306 | PMC543574 | CC BY | 2021-01-04 16:39:29 | no | Nutr J. 2004 Dec 9; 3:23 | utf-8 | Nutr J | 2,004 | 10.1186/1475-2891-3-23 | oa_comm |
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Environ HealthEnvironmental Health1476-069XBioMed Central London 1476-069X-3-141558506310.1186/1476-069X-3-14ResearchRespiratory symptoms in relation to residential coal burning and environmental tobacco smoke among early adolescents in Wuhan, China: a cross-sectional study Salo Päivi M [email protected] Jiang [email protected] C Anderson [email protected] Yan [email protected] Grace E [email protected] Edward L [email protected] Chunhong [email protected] Stephanie J [email protected] Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, MD A3-05, PO Box 12233, Research Triangle Park, NC 27709, USA2 Wuhan Public Health and Anti-Epidemic Station, No. 24 N. Jianghan Road, Wuhan, Hubei 430022, China3 Institute for Health Promotion & Disease Prevention Research, USC Keck School of Medicine, 1000 South Fremont Ave., Unit 8, Alhambra, CA 91803, USA4 Wuhan Health Bureau, 2 YiYuan Road, Wuhan, Hubei 430014, China5 Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, MD A3-03, PO Box 12233, Research Triangle Park, NC 27709, USA6 Department of Occupational & Environmental Health, USC Keck School of Medicine, CHP 236, 1540 Alcazar St., Los Angeles, CA 90089, USA2004 7 12 2004 3 14 14 26 8 2004 7 12 2004 Copyright © 2004 Salo et al; licensee BioMed Central Ltd.2004Salo et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Cigarette smoking and coal burning are the primary sources of indoor air pollution in Chinese households. However, effects of these exposures on Chinese children's respiratory health are not well characterized.
Methods
Seventh grade students (N = 5051) from 22 randomly selected schools in the greater metropolitan area of Wuhan, China, completed an in-class self-administered questionnaire on their respiratory health and home environment.
Results
Coal burning for cooking and/or heating increased odds of wheezing with colds [odds ratio (OR) = 1.57, 95% confidence interval (CI): 1.07–2.29] and without colds (OR = 1.44, 95% CI: 1.05–1.97). For smoking in the home, the strongest associations were seen for cough (OR = 1.74, 95% CI: 1.17–2.60) and phlegm production (OR = 2.25, 95% CI: 1.36–3.72) without colds among children who lived with two or more smokers.
Conclusions
Chinese children living with smokers or in coal-burning homes are at increased risk for respiratory impairment. While economic development in China may decrease coal burning by providing cleaner fuels for household energy use, the increasing prevalence of cigarette smoking is a growing public health concern due to its effects on children. Adverse effects of tobacco smoke exposure were seen despite the low rates of maternal smoking (3.6%) in this population.
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Background
Residential coal burning and cigarette smoking are the most common sources of indoor air pollution in Chinese households [1-3]. Although use of coal stoves and smoking have been linked to respiratory morbidity among adult populations in mainland China [1,4-6], little is known about how these exposures affect Chinese children's respiratory health. Children's developing lungs are especially vulnerable to indoor air pollution because children spend much of their time indoors at home [7,8].
Coal has been widely used for cooking and heating in China [1,9]. Domestic coal stoves and boilers produce high indoor concentrations of sulfur dioxide (SO2), carbon monoxide (CO), particulate matter, and other pollutants [2,3,10,11]. Some studies suggest that indoor concentrations of emitted pollutants may exceed international guidelines and national ambient air pollution standards in China [1,2].
An increasing proportion of Chinese children are exposed to tobacco smoke because smoking prevalence in China has increased rapidly, especially among men, during the last decades [1,12]. Over two thirds of the Chinese population is currently exposed to environmental tobacco smoke (ETS) in the home [12]. Literature from Western populations has generally shown that maternal smoking is more strongly associated with children's respiratory symptoms than paternal smoking [13,14]. China provides a unique opportunity to examine effects of parental smoking because prevalence of smoking remains very low among women.
We examined the associations between respiratory symptoms and exposure to residential coal burning and environmental tobacco smoke in a cross-sectional study of seventh grade students in the greater metropolitan area of Wuhan, China.
Methods
Participants and study procedure
In the spring of 1999, 5231 seventh grade students at 22 public schools in and around Wuhan, China were invited to complete a self-administered questionnaire on respiratory symptoms and home environment. Two schools were randomly selected from each of the 11 administrative units governed by the city of Wuhan, the capital of Hubei Province. The resulting schools included 14 schools from urban (53.0% of the students), three schools from suburban (25.1%), and five schools from rural areas (21.9%). Of the 5231 students, 5051 (97 %) provided parental consent and completed the questionnaire in class with study staff in attendance. Before completing the questions students viewed a video demonstrating wheezing [15]. The study protocol was approved by the Institutional Review Boards at the Wuhan Public Health and Anti-Epidemic Station and the National Institute of Environmental Health Sciences.
Questionnaire data
The questionnaire included questions on respiratory health and potential risk factors such as exposure to cooking and heating fuels, smokers in the home, and personal smoking. We incorporated respiratory health items from a standardized questionnaire (ATS-DLD-78-C) translated into Chinese for a previous study in Wuhan and other Chinese cities [16]. Our primary outcome measures were responses to questions regarding respiratory symptoms in the past 12 months. We asked whether children had cough and/or phlegm production almost everyday during the past 12 months, with and without colds. Additionally, we asked whether children had wheezing over the past 12 months, with and without colds.
We assessed exposure to residential coal burning by questions defining the types of fuels used for cooking and heating in the child's home. We combined information on cooking and heating with coal into a single variable with the following categories: no coal stove, coal stove used only for heating, coal stove used only for cooking, and coal stove used for both cooking and heating. To assess environmental tobacco smoke exposure, we asked the child to list all household members and indicate whether each person currently smoked. We created three exposure categories: no smokers in the home, one smoker (1) in the home, and two or more smokers (2 +) in the home. Personal smoking was not considered in the analysis because of very low prevalence (0.6%).
Statistical analysis
We calculated prevalence odds ratios (95% confidence intervals) for each of the six outcome measures (cough, phlegm production and wheezing in the past 12 months, each with colds and without colds) by unconditional logistic regression (Proc Genmod in SAS System for Windows, Version 8.01). Although the odds ratio is the most common measure of association in cross-sectional studies [17], divergence between odds ratios and risk ratios increases as the outcome becomes more common [18,19]. However, we present odds ratios as our effect measures to estimate the associations between respiratory symptoms and residential exposures (coal burning and environmental tobacco smoke); using the log-binomial model (Proc Genmod in SAS System for Windows, Version 8.01) [20] to estimate prevalence proportion ratios for the outcomes did not alter any of our conclusions.
We excluded 521 subjects with missing data on any of the outcome or exposure variables leaving 4,530 subjects for the analysis. The following covariates were considered as potential confounders or modifying factors within the logistic models: child's gender, presence of animals in the household, presence of pests (cockroaches, ants, rodents), crowding in the household, presence of older siblings, parental asthma, physical activity, living area (school district), and time spent indoors and outdoors. To account for variation due to the type of neighborhood the children lived in, we included school district (22 districts) in the models using CLASS and REPEATED statements within Proc Genmod in SAS. The models reported here are adjusted for coal use, smokers in the home, school district, and child's sex because inclusion of the other variables did not appreciably change the associations.
Results
Characteristics of the study population are presented in Table 1. The mean age of the seventh grade students was 13.6 years (SD = 0.7 years). The majority of the students (94.2%) were life-long residents of the Wuhan area. Although 7.1% of the students reported wheezing without colds, doctor-diagnosed asthma was relatively uncommon in this population (3.2%). Coal was used for cooking and/or heating in almost half of the homes. Few children smoked (0.6%), but 73.2% of the students lived with household members who smoked. The prevalence of ETS exposure was similar across the study area (74.5% in urban areas, 70.2% in suburban areas, and 73.5% in rural areas). Fathers (69.1%) were much more likely to smoke than mothers (3.6%).
Table 1 Characteristics of the study population of 4530 students at 22 schools in greater Wuhan, China
Characteristic %
Subjects
Age (mean, SD) in years 13.6, 0.7
Gender
Male
Female 52.5
47.5
Respiratory symptoms
Wheezing with colds 19.4
Wheezing without colds 7.1
Bringing up phlegm with colds 16.7
Bringing up phlegm without colds 5.7
Coughing with colds 24.7
Coughing without colds 4.5
Exposures
Smokers in child's household
No smokers
1 smoker
2+ smokers
Father smokes
Mother smokes
Personal smoking by students 26.8
62.3
10.9
69.1
3.6
0.6
Exposure to coal burning
No coal use
Coal used only for heating
Coal used only for cooking
Coal used for cooking and heating 54.2
8.8
25.9
11.1
After adjusting for gender, ETS, and living area, residential coal burning was primarily associated with wheezing in the past 12 months (Table 2). For those who used coal only for cooking or only for heating, wheezing was more strongly associated with cooking. However, the association between coal use and recent wheezing tended to strengthen when coal was used for both cooking and heating (OR = 1.78, 95% CI: 1.08–2.91 for wheezing with colds; OR = 1.57, 95% CI: 0.94–2.64 for wheezing without colds).
Table 2 Respiratory symptoms in relation to residential coal burning
Cough with colds Cough without colds
No Yes No Yes
Exposure N N OR* (95% CI) N N OR* (95% CI)
Total 3413 1117 4327 203
Coal use
No 1833 622 1.00 2347 108 1.00
Yes 1580 495 0.92 (0.76,1.11) 1980 95 1.03 (0.80,1.33)
Heating 300 99 0.96 (0.76,1.22) 381 18 1.02 (0.67,1.55)
Cooking 926 249 0.79 (0.67,0.94) 1120 55 1.04 (0.74,1.46)
Both 354 147 1.22 (0.93,1.59) 479 22 0.99 (0.66,1.49)
Phlegm with colds Phlegm without colds
No Yes No Yes
N N OR* (95% CI) N N OR* (95% CI)
Total 3772 758 4274 256
Coal use
No 2051 404 1.00 2315 140 1.00
Yes 1721 354 1.04 (0.90,1.20) 1959 116 0.96 (0.75,1.23)
Heating 331 68 1.04 (0.86,1.27) 376 23 1.02 (0.62,1.66)
Cooking 994 181 0.92 (0.76,1.10) 1114 61 0.86 (0.64,1.16)
Both 396 105 1.34 (1.05,1.73) 469 32 1.12 (0.81,1.54)
Wheeze with colds Wheeze without colds
No Yes No Yes
N N OR* (95% CI) N N OR* (95% CI)
Total 3652 878 4210 320
Coal use
No 2058 397 1.00 2309 146 1.00
Yes 1594 481 1.57 (1.07,2.29) 1901 174 1.44 (1.05,1.97)
Heating 329 70 1.10 (0.76,1.57) 368 31 1.35 (0.86,2.15)
Cooking 892 283 1.66 (1.01,2.73) 1077 98 1.42 (1.05,1.92)
Both 373 128 1.78 (1.08,2.91) 456 45 1.57 (0.94,2.64)
* Odds ratios (OR) adjusted for gender, ETS, and school district. Dichotomous and multilevel odds ratios are computed in separate models.
After adjusting for gender, coal use, and living area, living with smokers (Table 3) was significantly associated with chronic cough and phlegm production in the past 12 months. The strongest associations were seen for cough (OR = 1.74, 95% CI: 1.17–2.60) and phlegm production (OR = 2.25, 95% CI: 1.36–3.72) without colds among children who lived with two or more smokers. Living with smokers was not appreciably associated with wheezing.
Table 3 Respiratory symptoms in relation to living with smokers
Cough with colds Cough without colds
No Yes No Yes
Exposure N N OR* (95% CI) N N OR* (95% CI)
Total 3413 1117 4327 203
Smokers in the home
No 954 259 1.00 1165 48 1.00
Yes 2459 858 1.29 (1.05,1.58) 3162 155 1.19 (0.86,1.65)
1 smoker 2105 717 1.26 (1.02,1.55) 2700 122 1.10 (0.77,1.57)
2+ smokers 354 141 1.47 (1.11,1.95) 462 33 1.74 (1.17,2.60)
Phlegm with colds Phlegm without colds
No Yes No Yes
N N OR* (95% CI) N N OR* (95% CI)
Total 3772 758 4274 256
Smokers in the home
No 1036 177 1.00 1164 49 1.00
Yes 2736 581 1.24 (1.08,1.43) 3110 207 1.60 (1.11,2.29)
1 smoker 2327 495 1.25 (1.09,1.43) 2657 165 1.49 (1.04,2.14)
2+ smokers 409 86 1.23 (0.92,1.64) 453 42 2.25 (1.36,3.72)
Wheeze with colds Wheeze without colds
No Yes No Yes
N N OR* (95% CI) N N OR* (95% CI)
Total 3652 878 4210 320
Smokers in the home
No 993 220 1.00 1125 88 1.00
Yes 2659 658 1.11 (0.93,1.31) 3085 232 0.96 (0.74,1.25)
1 smoker 2265 557 1.10 (0.93,1.30) 2619 203 0.99 (0.75,1.30)
2+ smokers 394 101 1.13 (0.85,1.49) 466 29 0.78 (0.45,1.37)
* Odds ratios (OR) adjusted for gender, coal use, and school district. Dichotomous and multilevel odds ratios are computed in separate models.
Discussion
Domestic coal use and exposure to ETS in the home were both associated with adverse respiratory effects in this population of Chinese adolescents. Coal burning was associated with increased wheezing, whereas living with smokers was associated with increased cough and phlegm production.
Coal burning produces high concentrations of particulate matter, SO2, and other pollutants [2,3,11]. Exposure to these pollutants may impair clearance mechanisms, and lead to airway inflammation [21,22]. Decreased pulmonary function has been associated with exposure to particulate matter and SO2 in several air pollution studies during the past decades [21]. Although residential coal burning has been linked to decreased pulmonary function and asthma among children [23-25], conflicting data exist. In two European studies, domestic coal burning has been associated with lower risk for childhood asthma and allergic diseases [26,27]. The findings in these two studies, however, may reflect some early life or other lifestyle factors related to coal use in Europe.
In our study, residential coal burning was predominantly associated with wheezing. Coal cooking was a stronger risk factor for wheezing than was coal heating. This may be explained by relatively low heating use in the Wuhan area, whereas cooking is a year around activity. The greater association with coal use for both cooking and heating may suggest an exposure-dependent relationship.
Although wheezing is often closely related to asthma, coal use was not positively associated with asthma diagnosis (data not shown) in this population. The majority of the diagnosed asthmatics (76.4%) lived in urban areas, where prevalence of coal use was lower than in non-urban areas. The diagnostic ascertainment of asthma most likely was greater in the urban than in the rural areas.
The harmful effects of ETS in children, primarily from living with smokers, have been widely studied [14,28-31]. In general, evidence that ETS causes cough, phlegm, and wheezing has not been as strong for school-aged children as it has been for infants and preschool children [28]. There are few data among Chinese populations where smoking behavior differs from Western populations. In utero exposure, via maternal smoking, that is believed to contribute to adverse effects of ETS in children [32,33] is uncommon in China. Thus, it is of interest that in this group of middle school children, where maternal and personal smoking were low, exposure to ETS in the home was clearly associated with chronic cough and phlegm production, with and without colds.
Our results indicated an exposure dependent response to ETS; having two or more smokers in the household increased the odds of cough and phlegm production compared to having only one smoker in the household. We did not find strong evidence suggesting modifying effects by gender, although the effect of ETS on persistent cough without colds was more pronounced among boys than girls (data not shown). Exposure levels may be influenced by time-activity patterns that can differ by gender. Boys may be more likely to spend time in close proximity with their smoking fathers or male relatives than girls.
Mechanisms responsible for the respiratory effects of ETS have been proposed in the literature [28]. In addition to decreased mucociliary clearance and goblet cell hypertrophy/hypersecretion, local and central nervous system components are thought be involved in cough and phlegm production [28,34]. Although exposure to ETS may affect childhood lung growth and result in lower pulmonary function [14,35], wheezing was not appreciably related to the presence of smokers in our study. Genetic susceptibility may influence the effects of ETS on bronchial obstruction. For example, parental atopy was found to modify the effects of ETS on bronchial obstruction and asthma considerably in a Norwegian birth cohort study [36]. However, we were unable to examine potential interactions between family history and ETS in relation to atopic illness in our population because, consistent with previously published data on Chinese children [37,38], the prevalence of asthma (3.2%) and hay fever (1.8%) was very low.
In general, our findings agree with available data on Chinese children's respiratory health [16,23]. However, residential exposures in the current study were more selectively associated with the respiratory symptoms than in previous studies. This may reflect differences in the study settings. In the previous studies [16,23], for example, most of the children were younger in age than in the current study. Prevalence of symptoms and factors associated with childhood respiratory symptoms may differ between different age groups [39]. It is also possible that using students rather than parents as a source of information on child's symptoms may contribute to the observed differences.
Exposure to indoor air pollutants is not only influenced by the source strength and other emission characteristics, but also by air exchange rates. A recent study showed that ventilation could modify effects between respiratory health outcomes and indoor air pollutants [40]. In that study, the modifying effects were found most relevant when air exchange rates were low. Residences in Wuhan, however, were not energy-efficiently built [16]. Air conditioning was uncommon, and most of the homes, both in urban and non-urban areas, relied on natural ventilation. In this study, we were unable to evaluate the effects of ventilation rates, because we did not collect detailed information on ventilation practices. We thought that children would not be able to give this information accurately.
The composition of pollutants produced by residential coal burning and smoking can be highly variable, but both exposures contribute substantially to inhalable and respirable particulate matter in indoor environments [2,3,41]. Existing data suggests that coal burning and smoking may have synergistic effects on respiratory symptoms [5]. In our data, we did not find consistent evidence of interaction between coal burning and ETS exposure.
Our outcome and exposure measures were determined by questionnaire alone, which is one of the major limitations of the study. Nonetheless, large epidemiological studies of respiratory health often rely on reports on recent symptom history because self-reported measures are cost efficient, practical and their repeatability is good [42,43]. Generally, respiratory symptoms have been reported consistently across populations [43]. To improve the quality of our self-reported outcomes we included audiovisual presentation of wheezing symptoms [15]. Because the temporal relationship between outcome(s) and exposure(s) can be difficult to determine in cross-sectional studies we focused on respiratory symptoms in the past 12 months to minimize recall bias. We did not use parents as source of information on child's symptoms. Some studies suggest that Chinese parents may deny or underreport child's symptoms or illnesses [44,45]. In addition, parents living in non-urban areas around Wuhan have lower educational level than parents living in urban areas [16], and their literacy level may be lower than their children attending middle school. Therefore, adolescents' reports on their own symptoms and health status may be more accurate than their parents'. Because children were answering in school about exposures in their home, we were not able to acquire very detailed information on exposure characteristics. Given that questionnaires have limited ability to quantify exposures, the possibility of exposure misclassification cannot be excluded. However, serious differential misclassification either of the exposures or outcomes is unlikely because health hazards of indoor air pollutants were not widely known among Chinese school children at the time when the survey was conducted [46].
Although urban air pollution has long been a major environmental concern in China, we do not believe that outdoor air pollution alone could explain the observed associations. Exposures to indoor air pollutants are likely to dominate the total exposure burden [47], especially among children, who spend much of their time inside the home [8]. In Chinese homes with coal stoves and smokers, not only levels of particulate matter, but also levels of many other air pollutants, including concentrations of SO2, often exceed the levels outdoors [2,3]. In Wuhan, where coal stoves are not usually vented via flue, concentrations of respirable particulate matter (291 μg/m3) and SO2 (173 μg/m3) can reach high levels indoors [2]. Concentrations of these pollutants have been found to be lower in ambient air. For example, a study investigating long-term air pollution in Wuhan estimated that the annual means for PM2.5, PM10, and SO2 in urban areas were 73 μg/m3, 129 μg/m3, and 73 μg/m3, respectively [48]. Because indoor air quality is influenced by infiltration of outdoor air, we cannot fully exclude possible confounding effects of ambient air pollution [1]. However, the effects of living area, as measured by school districts, were taken into account in our models, providing some control for differing air pollution levels in the study area.
The major strength of this study is that the public school system ensured a large and representative sample of rural, suburban, and urban populations in the Wuhan area. Our study is one of the few studies that have examined effects of major indoor pollutants in relation to children's respiratory health in mainland China [16,23-25].
Conclusions
Coal burning and living with smokers contributed to persistent respiratory symptoms in this cohort of Chinese adolescents. Adverse effects of tobacco smoke in the home were seen despite the very low prevalence of maternal smoking. Even if exposure to residential coal burning declines in response to economic changes in China, the increasing prevalence in smoking augur an increase in children's exposure to environmental tobacco smoke. Because many men initiate smoking during adulthood, and the rate of quitting and desire to quit smoking are low [49], future prospects for children's health are worrisome. The rise in cigarette smoking in China is a growing public health concern, not only in the adult population but because its effects on children. Although rates of childhood asthma have remained low in China, common indoor air pollutants, coal and tobacco smoke, impair children's respiratory health.
List of abbreviations
CI = confidence interval
CO = carbon monoxide
ETS = environmental tobacco smoke
OR = odds ratio
PM10 = particulate matter with an aerodynamic diameter less or equal to 10 μm
PM2.5 = particulate matter with an aerodynamic diameter less or equal to 2.5 μm
SO2 = sulfur dioxide
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
Contributors: PMS analyzed the data and wrote the manuscript with input from all investigators. YL, JX and CL are key investigators for the data collection. ELA assisted with data collection, and GK assisted with data analysis. CAJ was involved in design of the study. SJL is the principal investigator and guarantor of the manuscript.
Acknowledgements
This study was supported by the National Cancer Institute / National Institute of Drug Abuse Transdisciplinary Tobacco Use research Center grant (1 P50 CA84735-01) awarded to the University of Southern California and the project Z01 ES 49019 of the Division of Intramural Research, National Institute of Environmental Health Sciences.
We wish to acknowledge the contribution of the Wuhan Education Committee and the Wuhan Public Health and Anti-Epidemic Station for their assistance with data collection and entry, and Ms. Gong Jie for her help with data management. We thank Dr. Chris Lai from The Chinese University of Hong Kong, China, for sharing questionnaire materials, and Dr. Grace Chiu from Westat Inc, Research Triangle Park, NC, for assistance with programming.
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| 15585063 | PMC543575 | CC BY | 2021-01-04 16:36:31 | no | Environ Health. 2004 Dec 7; 3:14 | utf-8 | Environ Health | 2,004 | 10.1186/1476-069X-3-14 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-1-421558828510.1186/1742-4690-1-42Short ReportAlterations in the expression of DEAD-box and other RNA binding proteins during HIV-1 replication Krishnan Vyjayanthi [email protected] Steven L [email protected] HIV and AIDS Malignancy Branch, National Cancer Institute, National Institutes of Health, Building 10, Room 10S255 MSC1868, Bethesda, MD 20892 USA2004 8 12 2004 1 42 42 2 12 2004 8 12 2004 Copyright © 2004 Krishnan and Zeichner; licensee BioMed Central Ltd.2004Krishnan and Zeichner; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Recent results showed that certain DEAD box protein RNA helicases, DDX3 and DDX1, play an important role in the HIV infection cycle by facilitating the export of long, singly spliced or unspliced HIV RNAs from the nucleus via the CRM1-Rev pathway. Close examination of an extensive microarray expression profiling dataset obtained from cells latently infected with HIV induced to undergo lytic viral replication indicated that additional DEAD box proteins, beyond DDX3 and DDX1, exhibit differential expression during lytic HIV replication, and in latently infected cells prior to induction into active replication. This finding provides additional evidence that the involvement of DEAD box proteins and other RNA-binding proteins may play roles in active HIV replication and in the control of viral latency. Agents targeting these functions may offer new approaches to antiretroviral therapy and the therapeutic manipulation of HIV latency.
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Findings
The DEAD box proteins, a family of RNA helicases containing the conserved amino acid motif Asp-Glu-Ala-Asp (D-E-A-D in the single letter amino acid code), play an essential role in many aspects of cellular RNA metabolism (reviewed in [1,2]), including RNA transport, transcription, spliceosome function, ribosome assembly, the initiation of translation, and RNA degradation. The HIV Rev protein regulates a key aspect of the HIV replication cycle by mediating the switch between the early pattern of HIV gene expression, in which short, multiply spliced messages encoding the viral regulatory genes Tat, Rev, and Nef are exported from the nucleus, and the late pattern of viral gene expression in which larger singly spliced and unspliced messages that encode the viral structural proteins and that constitute the RNA genomes of progeny virions are exported from the nucleus [3-5]. Recent work, reviewed in reference [6], from the Jeang [7] and Pomerantz [8] laboratories implicate the DDX3 and DDX1 DEAD box proteins as additional critical co-factors for the Rev-mediated export of the long HIV singly spliced and unspliced mRNAs.
In a directed analysis of a large data set, which describes global changes in cellular gene patterns before and after a latently infected cell line was induced into active viral replication [9], we found that genes encoding many DEAD box proteins, and other RNA and DNA binding and modification proteins, in addition to DDX3 and DDX1, showed differential regulation, suggesting that HIV replication may be associated with generalized changes in the expression of many DEAD box proteins and other RNA binding proteins.
Yedavalli et al. [7] identified DDX3 in a differential display-based screen for cellular messages upregulated in the presence of HIV Tat. They found that DDX3 binds the nuclear export protein CRM1, which is essential for nuclear export mediated by the HIV Rev protein [10,11], shuttles between the cytoplasm and nucleus, facilitates the nuclear export of RRE-containing RNA in the presence of Rev, and advances HIV replication. Fang et al. [8] identified DDX1 in a two hybrid screen as a Rev- and RRE-binding protein that enhanced HIV replication, improved the expression of RRE-containing RNA, and modified the subcellular distribution of Rev from a predominantly nuclear to a predominantly cytoplasmic distribution.
Our work on large scale expression profiling in HIV actively and latently infected cells has been guided by the hypothesis that there is one set of cellular conditions which is ideal for normal cellular growth and homeostasis, that there is another set of conditions which may be better suited to supporting viral replication, and that HIV has evolved ways of altering the host cell so as to better support viral replication. An interesting corollary of the hypothesis is that targeting the products of the differentially expressed genes may inhibit viral replication by making the host cell environment less hospitable to viral replication (reviewed in [12,13]). Several laboratories have conducted large scale expression profiling studies investigating changes in cellular gene expression during HIV replication [14-16], and have, in some cases, identified new potential targets for antiviral therapy. The observations that altering the intracellular environment by, for example, targeting kinases involved in signal transduction and cell cycle regulation inhibits HIV replication lends additional support for this hypothesis [17,18]. Investigators studying other viruses, for example Kaposi's sarcoma-associated herpesvirus, have also noted changes in host cell gene expression patterns that accompany infection and transformation [19,20], and have identified targets for potential therapeutic intervention based on those studies. In examining our data, we therefore try to note interesting and potentially targetable host cell genes that show alterations in expression during HIV replication and latency. In our earlier study, we identified cellular genes encoding proteins that constituted new targets for agents aimed at activating latently infected cells into active viral replication [9]. In our initial examination of our dataset, among the several classes of genes showing discrete, temporally-dependent changes in expression during lytic replication, we noticed that several DDX genes and genes encoding other factors involved in RNA metabolism were differentially expressed. However, prior to the work by Yedavalli et al. and Fang et al., we had no clear sense of how the differential expression of those genes might contribute to facilitating HIV replication. Those recent studies prompted us to undertake a more detailed analysis of our data.
In our study, we compared RNA samples obtained from HIV latently infected cell lines prior to induction of active replication by the integrated HIV-1 provirus and following such induction with phorbol myristyl acetate (PMA). We profiled ACH-2 cells treated with PMA side by side with similarly treated HIV-1 naïve parental A3.01 cells to assess differential cellular gene expression patterns associated with HIV lytic replication. The dataset was generated from samples obtained from three independent biological replicate experiments and at least two microarray hybridizations were done for each time point from each biological replicate (for a minimum of 6 microarrays per time point). A detailed description of the cell culture, induction, RNA isolation, and microarray labeling and hybridization methods are contained in reference [9]. Following microarray data acquisition, data were analyzed using commercial (GenePix Pro software, Axon Instruments) and in-house software (microarray database system (mAdb), hosted by Center for Information Technology, NIH). Using BRB -ArrayTools , the data were subjected to statistical analyses using univariate parametric and multivariate permutation analyses, based on the one sample random variance t-statistic, where significance was based on P < 0.001 and the proportion of false discoveries was limited to 0.10 with a 90% confidence level. 1740 genes showed differential gene expression at a minimum of one timepoint during lytic replication. Hierarchical clustering analyses were performed using mAdb clustering tools, as well as Treeview . Since the data was obtained from latently infected cell lines, there may be some concern that infected primary cells may behave in a somewhat different fashion. However, the advantages of using latently infected cell lines are significant: Activation into active replication is reasonably synchronous and includes essentially all the cells, so that the signal from the cells supporting active replication is not diluted by the signal from uninfected cells or cells with virus at different stages of viral replication. Also, the signal comes only from infected cell and not from cells responding to effects from the exposure to very large numbers of defective viral particles that are in high multiplicity of infection inocula.
Figure 1 shows the expression patterns for genes encoding DEAD box proteins, and other genes encoding RNA helicases and RNA binding proteins, as assigned by the gene ontology database [21]. We found that a number of DEAD-box proteins were significantly up regulated (P < 0.001) immediately following induction (0.5 hr post induction p.i). These included DDX10, a DEAD box protein with expression in many tissues having tumorigenic activity when fused to the nucleoporin NUP98 [22,23], DDX21, a DEAD-box protein originally identified as a nucleolar protein thought to be involved in ribosomal RNA metabolism[24], DDX23, a DEAD box protein first identified in U5 SnRNPs with significant homology to the yeast Prp28p splicing factor[25], and DDX52, a human DEAD box protein identified through its homologies to a yeast gene [26].
Figure 1 A compilation of the expression profiles of genes with known or putative involvement in RNA binding, transport or splicing before and after latently infected ACH-2 cells were induced into active replication. Panel A shows selected genes involved in RNA metabolism that were differentially expressed during active replication in the infected cells (ACH-2 cells) at 0.5–8 hr post-induction (p.i.) compared to similarly induced, parental uninfected A3.01 cells. Panel B shows gene expression profiles of a subset of DEAD-box proteins, following induction into active replication, of the genes displayed in panel A. Only genes that passed the criteria for statistical significance (P < 0.001) for the 0.5–8 hr p.i. time period (but not for other time periods) are shown. Panel C shows the expression profiles of genes encoding DDX18 and DDX39, which were up regulated during viral latency and during latency and early lytic replication. Panel D shows the expression profile of genes encoding ABC transporter proteins. The bottom of the figure shows a scale indicating the color values corresponding to the expression ratio in HIV infected/HIV uninfected cells for the differential expression of each gene shown in the figure at the different time points. Panel E is a graphical representation of the expression patterns observed in the selected DEAD-box proteins showing fold change in gene expression over corresponding controls.
Other genes encoding proteins with RNA splicing/binding and RNA transport activity were also up regulated during this period, including the methylated mRNA cap binding proteins EIF4G1 [27] and NCBP1 (CBP80) [28], which function in translation initiation and may also be involved in mediating nuclear export of RNA. In addition, genes encoding other proteins involved in nucleic acid-protein interactions were also upregulated, such as members of the SWI/SNF family of ATP-dependent chromatin remodeling factors involved in cell cycle control and the regulation of gene expression, SMARCA2 and SMARCA5 [29,30]. Another class of genes that shows differential expression during the early time period (0.5–8 hr p.i.) are the genes encoding the ATP-dependent ABC transporter proteins, which share sequence homology with members of the helicase family at the ATP binding site [31], indicating that many ATP-dependent processes may be targeted by early viral replication steps, not only as a means to facilitate viral RNA transport but also as a mechanism to shut off or divert cellular functions requiring ATP hydrolysis.
Our findings that several DEAD box protein genes are upregulated during HIV replication lend support to the published finding that two cellular DEAD-box proteins, including one (DDX3) that was identified in a broad-based screen for differentially expressed genes [7], may be important mediators for Rev-mediated RNA export. Our data also show that several other RNA binding proteins are differentially regulated during HIV-1 replication, suggesting that there may be a general involvement of these classes of genes in the HIV replication cycle, that the involvement is not limited only to DDX3 and DDX1. The additional DEAD box family members and other proteins involved in RNA metabolism may be interesting candidates for further mechanistic studies on HIV replication. For example, EIF4G1 has been shown to interact with CBP80 (NCBP1) [32], as well as with EIF4A [33], an RNA helicase with a DEAD-box motif in its sequence. The binding of EIF4G1 to EIF4A is essential for the proper function of EIF4A as an RNA helicase [34]. In our study, genes encoding EIF4G1 and CBP80 were differentially expressed during early lytic replication. Further study of the interactions of EIF4G1, CBP80 and EIF4A1 may thus be important in elucidating Rev function and viral RNA export, as well as the synthesis of viral proteins. While some of the differentially expressed DEAD box proteins, beyond DDX1 and DDX3, may play a part in Rev-dependent viral RNA export from the nucleus, it is also possible that the broad induction of the expression of DEAD box protein-encoding genes and genes encoding other RNA binding factors may indicate that such gene products are involved in other aspects of HIV replication. These aspects of HIV replication could involve activities in which the DEAD box proteins have already been implicated, such as transcription, spliceosome assembly, and translation.
In our recent publication [9], we showed that several host cell genes were differentially expressed in latently infected cell lines, even before induction of the integrated virus into active replication. In an approach analogous to our hypotheses concerning the involvement of cellular genes in active viral replication, we showed that targeting the products of some cellular genes differentially expressed in the latently infected cells could activate viral replication, ejecting the virus from latency. In our examination of the DEAD box proteins, we noted that two genes encoding DEAD box proteins, DDX18, a DEAD-box protein induced by Myc and Max [35] and DDX39, (or URH49), a DEAD-box protein induced by growth stimulation or protein synthesis inhibition thought to be involved in splicing and nuclear export, with homology to the yeast Sub2p protein [36], were differentially expressed during viral latency (DDX39, DDX18) and at early times (DDX18) after induction into active replication. Since some DEAD box proteins are important for viral RNA nuclear export and active viral replication, it may be reasonable to consider that other members of this family could have natural inhibitory activity for HIV replication, such as that seen with mutated DDX3 proteins [7]. Accordingly, certain DEAD box proteins may have roles in maintaining HIV latency. If this reasoning is correct, then the selective targeting of such DEAD box factors might offer another means of ending HIV latency and for depleting latent HIV reservoirs. The DEAD box proteins and other RNA helicases may therefore represent important cellular factors that can be manipulated to alter viral replication in several therapeutically useful ways.
Cellular genes may be differentially expressed during viral replication for many different reasons. Differential expression of cellular genes may conceivably occur because of viral actions on the host cell designed to optimize the cell for viral replication, because of cellular responses to infection aimed at inhibiting viral infection, or may be fundamentally unrelated to key aspects of viral replication. However, the findings that several DEAD box protein genes are differentially expressed during HIV replication, together with the recently published observations that two DEAD box genes, DDX3 and DDX1, exhibit differential expression during HIV replication and have important functions in HIV replication lend additional credence to the hypothesis that a careful, large scale study of differentially expressed cellular genes can provide insights into host cell factors involved in viral replication and pathogenesis. Future studies may reveal additional human co-factors for HIV replication. These cellular co-factors many represent important new therapeutic targets.
Competing Interests
The authors declare that there are no competing interests.
Authors' Contributions
VK designed and performed the experimental work and the data analysis. SZ directed and coordinated the study and participated in the data analysis. VK and SZ wrote the manuscript.
Acknowledgements
We thank Michael Lu for his help and Richard Simon for his generous advice concerning the statistical analyses.
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Nutr Metab (Lond)Nutrition & Metabolism1743-7075BioMed Central London 1743-7075-1-151558828310.1186/1743-7075-1-15ReviewThermodynamics of weight loss diets Fine Eugene J [email protected] Richard D [email protected] Department of Nuclear Medicine, Jacobi Medical Center, Bronx, NY, USA2 Department of Biochemistry, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA3 Department of Biochemistry, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA2004 8 12 2004 1 15 15 20 10 2004 8 12 2004 Copyright © 2004 Fine and Feinman; licensee BioMed Central Ltd.2004Fine and Feinman; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
It is commonly held that "a calorie is a calorie", i.e. that diets of equal caloric content will result in identical weight change independent of macronutrient composition, and appeal is frequently made to the laws of thermodynamics. We have previously shown that thermodynamics does not support such a view and that diets of different macronutrient content may be expected to induce different changes in body mass. Low carbohydrate diets in particular have claimed a "metabolic advantage" meaning more weight loss than in isocaloric diets of higher carbohydrate content. In this review, for pedagogic clarity, we reframe the theoretical discussion to directly link thermodynamic inefficiency to weight change. The problem in outline: Is metabolic advantage theoretically possible? If so, what biochemical mechanisms might plausibly explain it? Finally, what experimental evidence exists to determine whether it does or does not occur?
Results
Reduced thermodynamic efficiency will result in increased weight loss. The laws of thermodynamics are silent on the existence of variable thermodynamic efficiency in metabolic processes. Therefore such variability is permitted and can be related to differences in weight lost. The existence of variable efficiency and metabolic advantage is therefore an empiric question rather than a theoretical one, confirmed by many experimental isocaloric studies, pending a properly performed meta-analysis. Mechanisms are as yet unknown, but plausible mechanisms at the metabolic level are proposed.
Conclusions
Variable thermodynamic efficiency due to dietary manipulation is permitted by physical laws, is supported by much experimental data, and may be reasonably explained by plausible mechanisms.
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Background
Carbohydrate restriction as a general strategy for weight loss continues to gain in popularity and its utility and generally protective effect in lipid profile and glycemic control continues to be demonstrated, at least in an experimental setting [1-4]. The subject nonetheless remains controversial. Those critics who grant efficacy of low carbohydrate diets nonetheless contend that they act strictly by caloric restriction and there is no special effect of carbohydrate reduction. Beyond caloric restriction, several studies have shown increased weight loss on low carbohydrate diets compared to isocaloric low fat diets, the so-called metabolic advantage (see table 2). Although no clear experimental error has been demonstrated, critics continue to maintain that something must be wrong because the laws of thermodynamics would be violated [5], that "a calorie is a calorie" [6] We have previously shown [2,7] that this is not correct and it is our intention here to review the fundamental physics underlying the phenomenon of metabolic advantage. An outline may be described: Can metabolic advantage happen? If so, what mechanisms might account for such a phenomenon? Does it, in fact, occur?
Table 2 Isocaloric low carbohydrate (CHO) vs. higher carbohydrate investigations
Reference %CHO %CHO Wt. loss(kg) ± SEM p
Low High Low CHO arm (no. subjects) High CHO arm
Rabast et al (1978) [31] 10 68 14.0 ± 1.4 (25) 9.8 ± 1.0 (20) 0.10
Rabast et al (1981) [32] 12 70 12.5 ± 0.9 (7) 9.5 ± 0.7 (7) <0.01
Golay, Allaz et al (1996) [33] 15 45 8.9 ± 0.6 (22) 7.5 ± 0.5 (21) 0.1
Golay, Eigenheer et al (1996) [34] 25 45 10.2 ± 0.7 (31) 8.6 ± 0.8 (37) 0.13
Piatti et al (1994) [35] 35 60 4.5 ± 0.4 (10) 6.4 ± 0.9 (15) 0.3
Layman et al (2003) [36] 44 59 7.5 ± 1.4 (12) 7.0 ± 1.4 (12) 0.8
Baba et al (1999) [38] 25 68 8.3 ± 0.7 (7) 6.0 ± 0.6 (6) <0.05
Lean et al (1997) [37] 35 58 6.8 ± 0.8 (40) 5.6 ± 0.8 (42) 0.1
Young et al (1971) [39] 7 23 16.2 ± 0.9 (3) 11.9 ± 0.8 (3) <0.05
Greene et al (2003) [40] 5 55 10.4 ± 2.1 (21) 7.7 ± 1.1 (21) 0.25
Metabolic advantage: can it happen?
We have previously presented arguments that there is no violation of physical principles [2,7] and, ironically, that suggesting a change in body mass to be independent of macronutrient composition would itself be a violation of the second law of thermodynamics [7]. Here, we reframe these arguments in a more pedagogically direct way and we provide simple examples.
The misunderstanding that continues to be repeated in the expression "a calorie is a calorie" appears to be exclusive reference to the first law of thermodynamics. The difficulty with this theoretical approach is that it is only part of the relevant physics and its relationship to biologic systems. The first law says that in any transformation the total energy in the system can be accounted for by the heat added to the system, the work done by the system on its environment and the change in energy content of all the components of the system. It is important to understand, however, that the first law does not say what the relative distribution between these effects will be for any process. In fact, the first law does not even allow us to say whether the process will occur at all. To understand the progress of a physical change it is necessary to understand the second law which introduces an entity known as the entropy, S, a measure of disorder in all processes. In all real (irreversible) processes, entropy increases, usually written ΔS > 0. The most common marker of increasing entropy is heat, although it is by no means the only evidence for increased entropy.
In systems at constant temperature and pressure (i.e. biologic systems)), the first and second law are combined in the Gibbs Free Energy, ΔG, which represents the maximum useful work that can be performed by the process. The actual process however, in general derives less useful work than permitted by the theoretically available ΔG due to inefficiency in energy capture. A proper accounting of entropy and efficiency must be included if we are to understand energy utilization in biological and biochemical systems.
Biological systems and thermodynamics
It is also important in the discussion of biological systems to understand that they are open systems, i.e. they take in nutrients and oxygen and excrete carbon dioxide, water, urea and other waste products, as well as heat. The importance with respect to weight considerations is that mass and energy are conserved (the more general statement of the first law of thermodynamics), but they are not conserved entirely within the organism.
To illustrate the proper interpretation of the first law of thermodynamics consider a subject whose resting energy expenditure is met by the production of 95 moles of ATP. Since oxidation of a single mole of glucose provides 38 moles of ATP, 2.5 moles of glucose will be needed to meet this individual's resting energy requirements. It is important to note that the resultant carbon dioxide, water, and heat are not retained within the organism. The useful retained energy is in the 95 moles of ATP (Figure 1B). (Similar equations could be written for lipid or protein but we restrict our discussion to glucose for simplicity).
Figure 1 A: Oxidation of glucose in a calorimeter is completely inefficient. The products of oxidation are carbon dioxide and water, and all of the energy produced is released as heat. 1B: To illustrate the proper interpretation of the first law of thermodynamics in living organisms we must consider that conservation of matter and energy includes excretion of products into the external environment. None of the products of oxidation (CO2 and H2O) remain within the organism. There is stoichiometric balance and no net weight change. Only the ATP, representing the useful energy, is retained. The wasted heat constitutes 60% of the energy of oxidation, while the efficiency is reflected in the retained ATP, available for reactions in the organism. Body fat stores are signified as TAG (triacylglycerol) 1C. A common way of thinking of weight loss is from reduction of caloric intake. If our subject ingests 2.3 moles of glucose (or equivalent lipid and/or protein) and produces only 90 moles of ATP, then homeostasis will enlist body stores of fat (and/or lean body mass) to yield the additionally required 5 moles ATP. The additional resultant CO2 and H2O (and heat) will be excreted (and radiated) leading to weight loss. 1D: If efficiency is reduced then our subject would have to eat more (e.g. 2.9 moles of glucose, or equivalent lipid/protein) to produce 95 moles of ATP and remain at the same weight. The additional CO2 and H2O produced will be excreted maintaining constant weight. 1E: Under conditions of reduced metabolic efficiency (from 40% to about 38% in this example), 90 moles of ATP will be produced from oxidation of 2.5 moles glucose (or equivalent lipid/protein). The remaining 5 moles ATP needed for homeostasis must be made up from oxidation of body stores of lipid or lean mass. This results in weight loss, exactly as it does for the example of reduced caloric intake (Figure 1C).
The illustration above can be compared to the oxidation of glucose in a calorimeter in which no useful energy is obtained and the total energy of oxidation is measured as the heat produced. This process is completely inefficient. A traditional (Atwater) value for glucose obtained in the calorimeter is approximately 4 kilocalories of energy per gram (Figure 1A). By contrast, the living organism above metabolizes and oxidizes glucose so that approximately forty percent of the energy of oxidation is retained as useful ATP (38 moles per mole of glucose)) whereas sixty percent is released as heat, the inefficiency in this mode of oxidation. The entropy (i.e. the second law of thermodynamics) shows up in this inefficiency. The calorimeter heat can no longer be interpreted in a simple way. The energy stored in useful ATP represents the efficiency of 40% (neglecting the difference in entropy between the structures of the products and reactants). This value approximates the efficiency for oxidation of carbohydrate as well as lipid, whereas proteins are generally oxidized at a lower value of approximately 30–35% (Figure 1B).
Summary of thermodynamics in living organism
1. The second law of thermodynamics dictates that there is an inevitable metabolic inefficiency in all biological and biochemical processes with heat and high entropy molecules (carbon dioxide, water, urea) as the most common products.
2. The first law of thermodynamics is satisfied in living (open) systems by properly accounting for the mass excreted and the heat radiated and exported in high entropy molecules.
Weight loss due to reduced caloric intake
The most common example of weight loss is reduction of caloric intake. At the risk of oversimplification, if our subject ingests fewer than 2.5 moles of glucose and produces, for example, only 90 moles of ATP from food, then homeostasis would require enlisting endogenous body stores for further oxidation. This oxidation would then provide the additional 5 moles of ATP required. Oxidation of body stores (lipid or lean body mass) will result in production of additional carbon dioxide, urea, water and heat. The excretion of these products will result in weight loss. (Figure 1C).
Weight loss due to increased metabolic inefficiency
The implication of the first and second laws of thermodynamics is that reduced efficiency has precisely the same result as reduced caloric intake. One conceptually simple means of reducing efficiency involves the process of uncoupling in mitochondria. ATP is produced in a variety of cellular locations. Glycolysis produces a net of two ATP's per molecule of glucose, in the cell cytoplasm. On the other hand, we recall that 36 additional molecules of ATP are produced from glucose as a result of the mitochondrial TCA cycle and electron transport. A critical part of the process involves the development of a hydrogen ion gradient across the mitochondrial membrane. This concentration gradient provides the energy that is converted into ATP as hydrogen ions pass down the gradient through the ATP synthase particle, entirely analogous to the energy in a high-pressure gas in a cylinder with a movable piston. (The expansion of the gas is like diffusion down a gradient: It does work against the piston). In the mitochondrion the energy of moving down the gradient is captured in ATP, the medium of exchange for the performance of work within cells. This capture of energy, referred to as coupling the energy to the formation of ATP, is the essential process permitting work to be done by living systems.
There are known endogenous and pharmacologic agents, which result in uncoupling the formation of ATP from the dissipation of the gradient. Uncouplers such as 2, 4-dinitrophenol bypass ATP synthase and cause hydrogen ion gradient dissipation without ATP formation that can result in organ dysfunction causing death. More modest degrees of uncoupling may be caused by the class of endogenous compounds we know as uncoupling proteins (UCP's). Three different isoforms, UCP1, UCP2 and UCP3 have been identified thus far in mammalian tissues. While the overall and relative physiologic importance of these proteins remains incompletely understood in human tissues, UCP1 has been shown in mice [8] to result in modest degrees of uncoupling in brown fat. Elevation of fatty acid concentration has been associated with induction of UCP3 and even with pathologic reductions of myocardial efficiency in rat heart [9]. For purposes of illustration, then, we may consider that there may be physiologic triggers that result in oxidative uncoupling, reducing the overall efficiency of glucose metabolism. For example if efficiency is reduced from 40% to 35%, the result will be the production of only 34 moles of ATP instead of the usual 38. While this represents a mechanism better demonstrated in rats than humans, our subject would require more glucose to make 95 moles of ATP. Now 2.9 moles of glucose would be required to produce 95 moles ATP. Our subject would either eat more and stay at the same weight (Figure 1D) or would eat 2.5 moles of glucose, the same amount as previously, but would produce less ATP. By eating only 2.5 moles of glucose our subject's metabolism would enlist oxidation of body stores to make up the additional ATP needed for homeostasis. This would result in weight loss exactly as it did for reduced caloric intake. (Figure 1D).
The essence of the second law of thermodynamics is that it guarantees inefficiency in all metabolic processes. However, variation of efficiency is not excluded. In fact, the laws of thermodynamics are silent on the existence of variable efficiency. If efficiency can vary (as in the example of oxidative uncoupling) then "a calorie is a calorie" is no longer a true statement. The role of uncoupling proteins in humans, as indicated, is as yet incompletely defined [10]. However, thermodynamic principles permit variable efficiency, and its existence must be determined empirically.
Metabolic advantage: how could it happen?
It is possible that metabolic efficiency may be decreased by oxidative uncoupling as described above. Polymorphisms connecting uncoupling proteins with obesity or propensity to gain weight have been identified in humans [11,12] although these are not firmly established and the effect of dietary intervention is unknown. Other mechanisms are better understood and are described below.
Substrate cycling and protein turnover
Substrate or "futile" cycles refer to the dynamic process that must accompany the thermodynamic steady state [13]. In particular, increased cycling of metabolic intermediates utilizes ATP and generates heat. The simplest examples are the numerous kinase-phosphatase pairs that regulate metabolism. In addition, although not generally considered in the category of substrate cycling, inefficiency results from the repeated breakdown and re-synthesis of proteins, lipids, and carbohydrates in cycles that use ATP for no apparent net gain. Such mechanisms, however, far from futile, allow for precision in the regulation of metabolism and constitute one of the uses of ATP. Protein turnover, in particular, provides for error correction or removal of "old" or damaged proteins. The effect of metabolic path on the energetics of oxidation is illustrated in Table 1 which summarizes the analysis from our earlier paper [2]. In this example, a mole of glucose directly oxidized to CO2 and water generates 38 moles of ATP with an overall efficiency of about 38.5%. On the other hand, if glucose is first incorporated into glycogen, followed by hydrolysis of the glucose and subsequent oxidation, 2 moles of ATP are lost per mole in this cycle with overall efficiency reduced to 35%. Similarly an amino acid from an "average" protein, when directly oxidized to CO2, produces ATP with an efficiency of about 33%. If the amino acid is first incorporated into a protein and later hydrolyzed and oxidized, four ATP's per molecule are used for synthesis of the peptide bond. This reduces the efficiency to 27%. Smaller degrees of inefficiency are seen for lipid cycles (Table 1) but multiple cycles may have a cumulative effect. It is estimated, for example, that half of depot fatty acids in triacylglycerol have been through at least one cycle [14]. It should be apparent that variation in efficiency is not a thermodynamic issue but an empiric question to be determined by the requirements of metabolism.
Table 1 Effect of Path on energetics of oxidation
Macronutrient & path Mass ATP/mole Kcal/gm Efficiency (%)
Glucose → CO2 180 38 1.54 38.5
Glucose → glycogen → glucose → CO2 180 36 1.40 35
"Average" AA → CO2 1.32 33
AA → Protein → AA → CO2 -4 1.08 27
Palmitate → CO2 256 129 3.68 40.9
Palmitate → Ketone → CO2 256 121 3.45 38.3
*Adapted from Feinman, Fine: 2003 Metabolic Syndrome and Related Disorders (1): 209–219 [2]
Thyrotoxicosis
Thyroid hormone decreases efficiency possibly by mechanisms involving both uncoupling and cycling described above: oxidative uncoupling as well as increased futile cycling of intermediates [15]. It is observed in thyrotoxic mice that UCP1 decreases efficiency in brown fat at the mitochondrial level [8]. In humans, the role of UCP1 in thyrotoxicosis is less certain due to the relative paucity of brown fat. On the other hand, activation of the adrenergic system via phosphoenolpyruvate carboxykinase ultimately increases "futile" metabolic cycling of intermediates ([15]). Thyrotoxicosis is well known to result in weight loss, often with increased food intake and increased generation of heat, indicative of metabolic inefficiency. The use of thyroid hormone has even been suggested therapeutically to induce weight loss in obese individuals, although its toxicity has limited this application. Inefficiency in metabolic processes with weight loss and increased heat generation, therefore, is known to occur on clinical grounds. Even without a complete understanding of the relative importance of different underlying cellular mechanisms in humans, the potential for biochemical processes to reduce their efficiency must be considered established as a feature of mammalian metabolism.
Protein induced protein turnover
There is abundant evidence that dietary protein stimulates protein breakdown and re-synthesis. In particular, branched chain amino acids, and especially leucine, are documented to act as nutritional signals acting via both the insulin and mTOR signaling pathways [16-18]. On the macroscopic level, the energetic cost of protein turnover is demonstrable as excess heat generated during a high protein meal. Thermogenesis (thermogenic effect of feeding; old name: specific dynamic action) has been defined as the extra heat generated during a meal due to digestion or metabolism. Johnston et al [19] compared the energy expended during 9 hour intravenous feedings of a high protein meal, vs. an isocaloric high carbohydrate meal; both contrasted with a 9 hour fast. The protein meal, with 70% of its caloric value due to protein, had significantly greater thermogenesis than the high carbohydrate meal (70% of calories from carbohydrate). These data have been reproduced in numerous studies [19-22]. The overall energy costs of protein turnover and synthesis have been estimated in various animal species, including man, and tabulated by Vernon Young ([23]), based on data from other investigators [24-26]. Despite the substantial experimental difficulties involved, the cost of protein synthesis clusters at around 4–5 kcal/gram in 8 species of birds, marsupials and mammals, including man. The high energetic cost is understandable in view of the multiple ATP-requiring processes involved. The cost of protein turnover can reduce efficiency from 33% to 27%, merely in the formation and hydrolysis of a single peptide bond (requiring 4 ATP's per bond formed: Table 1). In addition, protein processes that are ATP-dependent include formation of the ribosomal initiation complex, translation and folding of the protein, and protein degradation (both ubiquitin-dependent and -independent pathways) [23]. The energy costs of protein turnover could therefore account for a metabolic advantage in high protein diets, independent of carbohydrate content. This mechanism may also contribute to inefficiency in low carbohydrate diets, often high in protein.
Gluconeogenesis-stimulated protein turnover in carbohydrate restriction
The following hypothesis is suggested from classic studies of starvation done in chronically fasted obese individuals [27,28]. The brain's metabolism requires 100 grams of glucose per day. In the early phase of starvation, glycogen stores are rapidly reduced, so the requirement for glucose, is met by gluconeogenesis. Approximately 15–20 grams are available from glycerol production due to lipolysis, but fatty acid oxidation generally cannot be used to produce glucose. Therefore, protein breakdown must supply the rest of substrate for conversion to glucose in the early phases of starvation. By 6 weeks of starvation, ketone bodies plus glycerol can replace 85% of the brain's metabolic needs, the remainder still arising from gluconeogenesis due to protein. It should be mentioned that, since the fundamental role of ketones is to spare protein, it might be expected that the reliance on protein would actually decrease with time, perhaps relating to the anecdotal observation of "hitting the wall" on weight loss diets.
Very low carbohydrate diets, in their early phases, also must supply substantial glucose to the brain from gluconeogenesis. For example, the early phase of the popular Atkins or Protein Power diet restricts dieters to about 20–30 grams of carbohydrate per day, leaving 60–65 grams to be made up from protein-originated gluconeogenesis. One hundred grams of an "average" protein can supply about 57 grams of glucose so 110 grams protein would be needed to provide 60–65 grams glucose. Increased gluconeogenesis has been directly confirmed using tracer studies on day 11 of a very low carbohydrate diet (approx 8 grams/day) [29]. If indeed, 110 grams of endogenous protein is broken down for gluconeogenesis and re-synthesized, the energy cost, at 4–5 kcal/gram could amount to as much as 400–600 kcal/day. This is a sizable metabolic advantage. Of course, the source of protein for gluconeogenesis may be dietary rather than endogenous. Whereas endogenous protein breakdown is likely to evoke energetically costly re-synthesis in an organism in homeostasis, dietary protein may conserve energy. The source of protein for the observed gluconeogenesis [29] remains an open question, but there is no a priori reason to exclude endogenous rather than dietary sources. This is therefore a hypothesis that would need to be tested. The extent to which the protein for gluconeogenesis is supplied by endogenous protein would explain very high-energy costs. It should be noted, however, that even if limited to breakdown of dietary protein sources, there would be some energy cost associated with gluconeogenesis.
Metabolic advantage: does it happen?
Having established that there is no theoretical barrier to metabolic advantage and that there are plausible mechanisms that could account for such an effect, we must ask whether it can be demonstrated experimentally, that is, whether the proposed effects are of sufficient magnitude to be a practical feature of weight reduction strategies, in particular very low carbohydrate diets. If so there will be increased weight loss for the same caloric intake, or metabolic advantage. A recent animal model provides support for greater metabolic inefficiency in rats fed carbohydrate restricted diets compared with higher carbohydrate, leading to excess weight loss [30]. Human data in Table 2 illustrates 10 clinical trials of isocaloric diets with a lower versus higher carbohydrate arm in each trial [31-40]. It can be seen that the lower carbohydrate arm in 9 of 10 studies demonstrates increased weight reduction in comparison with the higher carbohydrate arm. Three of the studies show statistical significance (p < 0.05 or better). Even without statistical significance of individual studies, however, the likelihood that the lower carbohydrate arm would have an advantage in 9 of 10 studies is equivalent to the likelihood of 9 coin toss experiments having excess heads in comparison to excess tails. The 9th binomial coefficient shows this probability to be p < 0.01.
While the above suggests the possibility of metabolic advantage, it does not prove it, nor do we know the magnitude of the effect, or the factors that control it. The studies above were chosen from among those quoted by many of the authors who have disputed the existence of metabolic advantage. Nonetheless, a formal meta-analysis would be necessary to avoid the possibility of conscious or unconscious bias in their selection. Further, it would be necessary to establish evidence that energetically costly metabolic processes are more prevalent in low carbohydrate diets than in diets of higher carbohydrate content. Whereas the proposed mechanisms are plausible, they need to be proven.
Conclusions
Thermodynamics is not the limiting factor behind the concept of metabolic advantage. On the contrary, thermodynamics guarantees inefficiency in all metabolic processes and is silent on the possibility that inefficiency may be augmented in some instances. A familiar example of inefficiency is thyrotoxicosis, with attendant weight loss and heat generation despite unchanged or increased caloric consumption. The theoretical possibility of inefficiency and metabolic advantage due to macronutrient compositional change exists, but demonstration of the phenomenon can only be resolved experimentally. Isocaloric dietary studies with a low vs. a higher carbohydrate arm support the experimental possibility of metabolic advantage. A formal meta-analysis would be required to evaluate this more objectively. Further studies, including tracer methods, would be required to establish mechanisms. The presence of high quantities of dietary protein (often a feature of low carbohydrate diets) is known to stimulate protein turnover, an energetically costly process. However, it is unclear whether this is the only factor, or whether it is necessary for metabolic advantage to occur. In particular, obligate gluconeogenesis from endogenous sources may also contribute to induction of protein turnover.
Competing interests
The author(s) declare that they have no competing interests.
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| 15588283 | PMC543577 | CC BY | 2021-01-04 16:37:45 | no | Nutr Metab (Lond). 2004 Dec 8; 1:15 | utf-8 | Nutr Metab (Lond) | 2,004 | 10.1186/1743-7075-1-15 | oa_comm |
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Lipids Health DisLipids in Health and Disease1476-511XBioMed Central London 1476-511X-3-281558832210.1186/1476-511X-3-28ResearchEffects of thyroxine and 1-methyl, 2-mercaptoimidazol on phosphoinositides synthesis in rat liver Babenko Nataliya A [email protected] Oksana A [email protected] Department of Physiology of Ontogenesis, Institute of Biology, Karazin Kharkov National University, 4, Svobody pl., Kharkov, 61077, Ukraine2004 10 12 2004 3 28 28 12 11 2004 10 12 2004 Copyright © 2004 Babenko and Krasilnikova; licensee BioMed Central Ltd.2004Babenko and Krasilnikova; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Phosphoinositides mediate one of the intracellular signal transduction pathways and produce a class of second messengers that are involved in the action of hormones and neurotransmitters on target cells. Thyroid hormones are well known regulators of lipid metabolism and modulators of signal transduction in cells. However, little is known about phosphoinositides cycle regulation by thyroid hormones. The present paper deals with phosphoinositides synthesis de novo and acylation in liver at different thyroid status of rats.
Results
The experiments were performed in either the rat liver or hepatocytes of 90- and 720-day-old rats. Myo-[3H]inositol, [14C]CH3COONa, [14C]oleic and [3H]arachidonic acids were used to investigate the phosphatidylinositol (PtdIns), phosphatidylinositol 4-phosphate and phosphatidylinositol 4,5-bisphosphate (PtdInsP2) synthesis. 1-methyl, 2-mercaptoimidazol-induced hypothyroidism was associated with the decrease of myo-[3H]inositol and [3H]arachidonic acids incorporation into liver phosphoinositides and total phospholipids, respectively. The thyroxine (L-T4) injection to hypothyroid animals increased the hormones contents in blood serum and PtdInsP2 synthesis de novo as well as [3H]arachidonic acids incorporation into the PtdIns and PtdInsP2. Under the hormone action, the [14C]oleic acid incorporation into PtdIns reduced in the liver of hypothyroid animals. A single injection of L-T4 to the euthyroid [14C]CH3COONa-pre-treated animals or addition of the hormone to a culture medium of hepatocytes was accompanied by the rapid prominent increase in the levels of the newly synthesized PtdIns and PtdInsP2 and in the mass of phosphatidic acid in the liver or the cells.
Conclusions
The data obtained have demonstrated that thyroid hormones are of vital importance in the regulation of arachidonate-containing phosphoinositides metabolism in the liver. The drug-induced malfunction of thyroid gland noticeably changed the phosphoinositides synthesis de novo. The L-T4 injection to the animals was followed by the time-dependent increase of polyphosphoinositide synthesis in the liver. The both long-term and short-term hormone effects on the newly synthesized PtdInsP2 have been determined.
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Background
The phosphoinositides is a family of lipids which members play an essential role in the receptor-mediated intracellular signaling cascades, vesicle trafficking and cytoskeletal rearrangements [for review see [1,2]] and, therefore, are crucial for the adaptation and survival of cells. Because of their importance in numerous signaling events, the phosphoinositides require an absolutely tight temporal and spatial regulation of synthesis and degradation, enabling the cell to maintain organelle identity and housekeeping functions. A rapid agonist-dependent burst of phosphoinositides biosynthesis was the first feature of the polyphosphoinositide signaling pathway to be discovered, illustrating that synthesis is tightly coupled to degradation.
The two pools of phosphoinositides are supposed to exist in the cells [3,4]. One of these is sensitive to the hormone-induced hydrolysis, and the other is hydrolysis-insensitive. The biosynthesis of phosphatidylinositol (PtdIns) is the two-component process composed of cytidyltransferase followed by a synthase [for review see [5,6]]. The first detailed study of the occurrence and localization of the PtdIns synthase activity in various tissues from guinea pig was carried out by Benjamins and Agranoff [7]. They detected the enzyme activity in all the tissues tested, including brain, liver, kidney, heart, lung and spleen. The PtdIns synthase activity was also detected in the endoplasmic reticulum, plasma membranes, Golgi apparatus and nuclei. The hormone-sensitive pool of phosphatidylinositol 4,5-bisphosphate (PtdInsP2) in the plasma [8] and nuclear [9] membranes re-synthesized in these membrane fractions. The PtdIns synthesized de novo in the endoplasmic reticulum could be converted to the glycosylPtdIns or transferred by transfer proteins to other cellular compartments where the lipid was used for the phosphatidylinositol 4-phosphate (PtdInsP), PtdInsP2 and other polyphosphoinositides production [10,11].
The regulation of the key enzymes of the PtdIns synthesis pathway: the CDP-diacylglycerol (DAG) synthase and PtdIns synthase were examined [12]. Expression of the PtdIns synthase gene caused the overproduction of the both of the PtdIns synthase and PtdIns:inositol exchange reactions, indicating that the gene encode the both enzymes. However, the overexpression of PtdIns synthase or CDP-DAG synthase alone or in combination in the COS-7 cells did not enhance the rate of the PtdIns biosynthesis and did not result in a significant proportional increase in the CDP-DAG and PtdIns cellular levels. The CDP-DAG synthase activity was inhibited by polyphosphoinositides in vitro [13]. This makes possible to suggest that these end products of the pathway may function as the feedback inhibitors of PtdIns biosynthesis in vivo. The PtdIns synthase activity has been shown to be upregulated after the hormone-induced phospholipase C (PLC) mediated hydrolysis of phosphatidylinositol-polyphosphates [14,15]. The efficiency of the PtdIns synthesis is dependent on the CDP-DAG fatty acids composition [for review see [5,6]]. In the bovine brain, the CDP-DAG has been shown to present predominantly as the 1-steroyl, 2-arachidonyl which are also the main PtdIns components.
Thyroid hormones are of vital importance in maintaining the initial level of phospholipids in cell membranes and fatty acids composition of the lipids. On the other hand, there is a little literature regarding the hormone regulation of phosphoinositides exchange. The levels of [32P]phosphoinositides and inositol 1,4,5-trisphosphate were found to be significantly lower in the hypothyroid rat hearts [16]. The effect of hypothyroidism on the insulin- and epinephrine-stimulated phosphoinositide metabolism has been investigated in the rat adipocytes [17]. The hypothyroidism enhanced the insuline-mediated phosphoinositides synthesis. The hypothyroidism caused a significant increase in both the basal and ouabain-stimulated accumulation of [3H]inositol phosphate in the hypothalamic slices, whereas the thyroxine (L-T4) completely restored the hypothalamic [3H]inositol phosphate formation [18]. The results indicate that the negative feedback action of the thyroid hormone may occur at a post-receptor site in the hypothalamus. The thyroid hormones might participate in regulating the muscarinic cholinergic neurotransmission in the adult rats striatum via the stimulatory action on the inositol phosphate formation in the [3H]inositol pre-labeled tissue [19]. Thus, it becomes evident that the thyroid gland malfunction leads to the prominent disturbances of signal transduction in the adipocytes and nervous cells via the changes of phosphoinositides metabolism.
In the paper, we examined the influence of the drug- and L-T4-altered thyroid status of animals on the phosphoinositides synthesis in the liver. Thus, it was determined that the hypothyroidism decreased considerably the levels of the newly synthesized lipids in the liver but thyroid hormones increased the synthesis of the polyphosphoinositides de novo and the arachidonic acid incorporation into the PtdIns and PtdInsP2. A single injection of the L-T4 to the euthyroid rats lead to the rapid and transient decrease of the newly synthesized PtdInsP2 followed by the increase of the PtdIns and PtdInsP2 levels in the liver.
Results and Discussion
The present paper considers the influence of thyroid functional status on the phosphoinositides synthesis in the liver. To determine the role of thyroid hormones in the regulation of phosphoinositides synthesis de novo and lipids fatty acid remodeling, the euthyroid, MMI (1-methyl, 2-mercaptoimidazol)- and MMI+L-T4-treated rats and intact animals after the single hormone injection has been studied.
It has been reported that the L-T4 induced hydrolysis of PtdInsP2 and inositol phosphates and DAG formation at the early stages of hormone action on the [14C]oleic and [14C]linoleic acid pre-labeled hepatocytes of adult 90-day-old rats [20,21]. The L-T4-mediated PLC activation was accompanied by the protein kinase C (PKC) translocation to membranes [20] and PKC dependent stimulation of mitogen-activated protein kinase [22] and acylation of phospholipids and triacylglycerol synthesis [23]. The PtdInsP2-specific PLC activation in the L-T4-stimulated cells was a short-lived event. The hormone-stimulated rise in inositol 1,4,5-trisphosphate (Ins(1,4,5)P3) was followed by its conversion into the biologically inactive inositol 1,4 -bisphosphate and inositol 1-phosphate [21].
It is known that after the receptor-triggered hydrolysis of phosphatidylinositol-polyphosphates the phospholipid must be resynthesized in order to maintain a constant level of phosphoinositides in the membranes. A single injection of L-T4 to the 90-day-old rats leads to the rapid and sustained increase in the L-T4 and triiodothyronine (L-T3) levels in blood serum (Table 1), the content of the newly synthesized PtdIns and transient decrease the PtdInsP2 level in the [14C]CH3COONa-pre-labeled liver, which was followed by the polyphosphoinositide level increase (Figure 1A). Taking into account that the PtdInsP2 suppresses the key enzymes of PtdIns synthesis [13], the transient hormone-stimulated and phospholipase C-mediated [20,21] drop of the newly synthesized polyphosphoinositide in the liver cells was supposed to be the stimulus for the PtdIns synthesis. The L-T4 administration to the old 720-day-old rats did not change the newly synthesized PtdIns and PtdInsP2 levels in the liver (Figure 1B) although increased thyroid hormones contents in blood serum (Table 1). It has been shown that the thyroid hormones are unable to stimulate rapidly the phospholipase C in the [14C]CH3COONa-pre-labeled liver slices and hepatocytes of old animals [24]. These observations, together with the earlier data [20,21], suggest that the L-T4 stimulates rapidly and nongenomicaly the phosphoinositides degradation and resynthesis in the liver of adult rats and does not act on the phosphoinositides synthesis in the cells with the disability of polyphosphoinositide signaling at old age.
Table 1 Thyroxine and triiodothyronine contents in the blood serum of the rats of different thyroid states and age.
Animals T4 T3
Adult control 128 ± 3.80 1.21 ± 0.20
Adult MMl-treated 29.5 ± 5.00** 0.83 ± 0.01*
Adult MMl+T4-treated 57.3 ± 7.30*** 1.61 ± 0.10***
Adult T4-treated 1 654 ± 50.0** 5.78 ± 0.20**
2 675 ± 98.5** 15.9 ± 4.10**
Old control 70.7 ± 11.6 1.37 ± 0.07
Old T4-treated 1 730 ± 15** 5.8 ± 1.5**
2 537 ± 128** 4.9 ± 0.0**
The T3 and T4 contents in the blood serum were determined by radioimmunoassays kits. The amount of thyroid hormones in serum was represented as nmol per liter. Treatment of the rats by MMl was performed as described in "Materials and Methods". T4 (200 mg/100 g weight) was injected to the MMl-treated rats 48 h prior to killing or normal rats 15 (1) and 60 (2) min prior to killing. Results are mean ± S.E. of 6–8 individual experiments performing in duplicate. In the Tables 1–2 and Figures 1–3 one experiment is equivalent to measurement of the parameters studied in a sample of liver of single animal. * P < 0.05 vs. control, ** P < 0.001 vs. control, *** P < 0.05 vs. MMl-treated rats.
Figure 1 Short-term effects of L-thyroxine on the newly synthesized phosphoinositides levels in liver. Panel A – 90-day-old rats; panel B – 720-day-old rats. The liver lipids were labeled by [14C]CH3COONa as described in "Materials and Methods". The L-T4 (200 μg/100 g weight) was injected to the normal rats 15 and 60 min prior to killing. The lipids were extracted and separated as described in "Materials and Methods" and the radioactivity was determined by a liquid scintillation counter. Results are mean ± S.E. of six experiments performed in duplicate.
In addition to its established role as a precursor for the signaling molecules (Ins(1,4,5)P3 and inositol 3,4,5-trisphosphate), the PtdInsP2 is now recognized as an important plasma membrane signal that activates the PLD [for review see [25]] and thus establishes the sites for vesicular trafficking, membrane movement and cytoskeletal assembly. It has been demonstrated in the mammalian cells that the PtdInsP2 is a membrane-associated cofactor of PLD [26,27]. The rapid stimulation of the PtdInsP2-specific PLC by the L-T4 in the rat hepatocytes of adult 90-day-old rats was followed by the prominent PLD activation [20]. The PLD response in the stimulated cells was reduced by the both PKC inhibitor and a high affinity ligand of PtdInsP2 – neomycin. Neomycin does not directly interact with PLC and PLD but interacts with the endogenous membrane PtdInsP2. Inclusion of the PtdInsP2 in mixed phosphatidylcholine/phosphatidylethanolamine liposomes [26] and stimulation of the phosphoinositide 5-kinase by an addition of the MgATP [27] greatly potentiates the PLD activation. Direct evidence that phosphoinositide kinase is involved in the PLD activation comes with the use of an inhibitory antibody for this enzyme [26]. The results strongly demonstrate that the PLD activation requires the enhanced PtdInsP2 synthesis and the resting cellular levels of PtdInsP2 are insufficient for enzyme stimulation. The role of PKC has been determined in the regulation of PtdInsP2synthesis in the liver [28] and other cell types [29]. The PKC translocation to membranes and enzyme activation are initial steps in the PLD [for review see [25]] and phosphoinositide 5-kinase [29] stimulation. The lack of PKC/PLD response [24] correlates with the suppressed ability of the L-T4 to stimulate the PtdInsP2 synthesis in the liver cells of the 720-day-old rats (Fig. 1B). However, in the liver cells of adult animals the L-T4 -induced PKC activation and elevation of PtdInsP2 synthesis might lead to the PLD activation.
To determine whether the altered thyroid functional status influences the phosphoinositides synthesis, we studied the myo-[3H]inositol incorporation into the PtdIns, PtdInsP and PtdInsP2 in the liver of the drug- and L-T4 -treated animals. The MMI administration to the rats was found to be accompanied by the decreased myo-[3H]inositol incorporation into the PtdIns, PtdInsP and PtdInsP2 in the liver (Figure 2A,2B) and drop of thyroid hormones levels in the blood serum (Table 1). The data obtained are consistent with the observations [30] of the drug-induced thyroid gland malfunction. The L-T4 injection to the hypothyroid rats increased the T4 and T3 contents in blood serum (Table 1), the myo-[3H]inositol incorporation into the PtdInsP2 (Figure 2B,2C) and did not changed the levels of the other newly synthesized phosphoinositides in the hypothyroid liver.
Figure 2 Phosphoinositides synthesis in the liver of rats of different thyroid status. Panel A shows myo-[3H]inositol incorporation into the lipids of control rats. Panel B shows the lipid precursor incorporation into the lipids of liver of MMI-treated animals. Panel C shows effect of L-T4 on phosphoinositides synthesis in liver of MMI-treated rats. Treatment of the 90-day-old rats by MMI was performed as described in ''Materials and Methods''. L-T4 (200 μg/100 g weight) was injected to the MMI-treated rats 48 h prior to killing. Control rats received the same volume of 0.9% NaCl. Liver slices were incubated in the presence of the myo-[3H]inositol and lipids were extracted and separated as described in ''Materials and Methods'' and the radioactivity was determined by liquid scintillation counter. Results are mean ± S.E. of 6 – 8 individual experiments performed in duplicate. *P < 0.05 vs. control, **P < 0.05 vs. MMI-treated rats.
Thyroid hormones stimulate lipogenesis in the liver by inducing the enzymes in the lipogenic pathway. The acyl-CoA-glycerol-3-phosphate acyltransferase, which is known to catalyze a rate-limiting step for the synthesis of phosphatidic acid in the rat liver, is dependent on the thyroid gland function [31]. The L-T4 administration to the euthyroid rats increased the incorporation of the [14C]palmitic acid into the phosphatidic acid and PtdInsP2 in the isolated hepatocytes [28]. The results obtained in the present work demonstrated that the L-T4 addition to the culture medium significantly increased the mass of phosphatidic acid and did not change the content of PtdIns in the hepatocytes (Table 2). The hormone addition to the culture medium caused the prominent and rapid (within 60 min of cells incubation) increase in the phosphatidic acid synthesis de novo and [14C]palmitic acid incorporation into the PtdInsP2 and did not change the PtdIns labeling in the isolated hepatocytes [28]. It can be said that the L-T4 stimulates the PtdIns precursor synthesis and accumulation in the liver cells in the both long- and short-term manner. The thyroid hormone activation of phosphoinositide synthesis in the liver cells can be supposed to go through an enhancement of the glycerol-3-phosphate acylation, phosphatidic acid accumulation and its conversion into phosphoinositides. Besides, the phosphatidic acid can activate the phosphoinositide 5-kinase [32] and thus stimulate the PtdInsP2 synthesis in the hormone-treated liver cells. Considering that the hormone could rapidly (within 60 min) stimulate the phosphatidic acid and PtdInsP2 accumulation and did not change the PtdIns content in the liver cells, it could be assumed that in such case the L-T4 increases PtdInsP2 synthesis via phosphoinositide 5-kinase activation rather than the lipid synthesis de novo.
Table 2 Rapid effect of L-T4 on phosphatidic acid and phosphatidylinositol contents in the isolated hepatocytes.
Lipid Cells:
Control L-T4 -treated
Phosphatidic acid 9,29 ± 0,71 17,7 ± 2,94 *
Phosphatidylinositol 13,6 ± 0,62 13,7 ± 1,83
Hepatocytes were isolated from the liver of adult 90-day-old rats and lipids contents were analyzed as described in "Materials and Methods" and expressed as % of total phospholipids. Hepatocytes were treated with 100 nM NaOH (control) or 10 nM of L-T4 for 30 min. Results are mean ± S.E. of six experiments. * p < 0,05 vs. control.
It is well documented that the efficiency of polyphoshoinositide derived second messanger DAG in signaling pathways is closely dependent on the degree of its unsaturation. The unsaturated fatty acids are incorporated into the sn-2 position of the phospholipids by the deacylation-reacylation reactions. Some investigations demonstrated the changes of the fatty acid composition of membraneous lipids at the hypo- and hyperthyroid state of the rats [for review see [33]]. There were generally a reciprocal changes in membranes arachidonic acid contents, namely, a decrease in the hypothyroidism and its increase after the thyroid hormone injection. The L-T3 administration to the euthyroid rats increases the saturated fatty acids and arachidonate/linoleate ratio of PtdIns in the liver cell mitochondria [34].
The hypothyroidism was associated with the decrease in the [3H]arachidonic acid incorporation into the liver total phospholipids (Figure 3A). The [14C]oleic acid labeling of the liver phospholipids was not dependent on the thyroid status of the rats (Figure 3C). The L-T4injection to the hypothyroid animals completely abolished the drug-induced changes of the [3H]arachidonic acid incorporation into the liver total phospholipids (Figure 3A). The hypothyroidism was associated with the increased arachidonic acid conversion into the prostaglandine E2 in the adult rat liver [35]. The L-T4 administration to the thyroidectomysed animals reduced the prostaglandine E2 synthesis and its content in the liver. Conceivably, the thyroid hormones regulating the arachidonic acid metabolism could maintain the initial level of polyunsaturated phospholipids in the liver cells.
Figure 3 Incorporation of [3H]arachidonic and [14C]oleic acid into liver total phospholipids and phosphoinositides of rats of different thyroid status. Panel A and B show [3H]arachidonic acid incorporation into total phospholipids and phosphoinositides of rats of different thyroid status, respectively. Panel C and D show the [14C]oleic acid incorporation into total phospholipids and phosphoinositides of rats different thyroid status, respectively. Treatment of the 90-day-old rats by MMI was performed as described in "Materials and Methods". L-T4 (200 μg/100 g weight) was injected to the MMI-treated rats 48 h prior to killing. Control rats received the same volume of 0.9% NaCl. Liver slices were incubated in the presence of the [3H]arachidonic or [14C]oleic acid and lipids were extracted and separated as described in ''Materials and Methods'' and the radioactivity was determined by liquid scintillation counter. Results are mean ± S.E. of 6 – 8 individual experiments performed in duplicate. *P < 0.05 vs. control, **P < 0.05 vs. MMI-treated rats.
The MMI did not change markedly the 14C/3H-labeling of PtdIns and PtdInsP2 in the liver slices (Figure 3B,3D). The L-T4 administration to the rats increased the incorporation of [3H]arachidonic acid into the PtdIns and PtdInsP2 in the hypothyroid liver (Table 3B) and decreased the content of the oleate-labeled PtdIns in the liver slices (Table 3D). The incorporation of the [14C]oleic acid into the PtdInsP2 did not differ between the liver slices of control, drug- and hormone-treated animals. As can be seen from the Table 1, the MMI reduced the T4 and T3 levels in the blood serum, but did not remove completely the hormones from organism. The T3 content in the serum of the drug-treated animals was relatively high as compared with control animals. However, the MMI reduced the T4 level by 77%. The T4 administration to the MMI-treated rats increased significantly T3 and T4 contents in the serum. It seems possible that the 14C/3H fatty acids incorporation into the phosphoinositides was rather regulated by the T3 than T4, although the both hormones are participated in the regulation of the other phospholipids acylation in the rat liver.
Conclusions
The present data have demonstrated that the phosphoinositides synthesis de novo and arachidonic acid incorporation into phospholipids are strongly dependent on the thyroid status of organism. The marked enrichment of animal cell phosphoinositides in arachidonate and the results obtained suggest an important role of the thyroid hormones in the regulation of polyunsaturated PtdIns and PtdInsP2 synthesis, which are the predominant substrates of PLC in the numerous signaling pathways. The both long-term and short-term effects of hormone on lipid synthesis have been determined (Figure 4). The L-T4 stimulates rapidly and nongenomicaly the PtdInsP2 degradation and resynthesis in the liver of the adult rats and does not act on the phosphoinositides metabolism in the cells with the PLC/PKC signaling disability. Besides, the L-T4 stimulates the polyphosphoinositides synthesis de novo via the long lag period of time essential to hormone interaction with nuclear receptors and stimulation of protein synthesis [36,37]. Considering that the PtdInsP2 participates in the activation of different enzymes (the PLD, protein- and lipid-kinases) involved in the signal transduction in the stimulated cells, the abnormalities of the phosphoinositides synthesis at different pathological states of thyroid gland could disturb other hormones signaling in the target cells.
Figure 4 Long- and short-term effects of thyroxine on phosphoinositides metabolism in rat liver. Hormone rapidly and non-genomicaly stimulates PtdInsP2 degradation, DAG accumulation and PKC/PLD activation. Hormone-induced PLC activation is followed by PtdInsP2 resynthesis, probably via phosphoinositide 5-kinase induction by newly synthesized- or PCh-derived PA. PA could further be converted to polyphosphoinositide precursor – arachidonate-contaning PtdIns under the long lag period of hormone action on the organism. PA – phosphatidic acid, CDP-DAG – cytidine diphosphate diacylglycerol, PAA – palmitic acid, AA – arachidonic acid, Ins – inositol, InsP – inositol 1-monophosphate, InsP2 – inositol 1,4-bisphosphate, InsP3 – inositol 1,4,5-trisphosphate, PCh – phosphatidylcholine.
Materials and Methods
Materials
myo-[3H]-inositol (58 mCi/mmol), [14C]oleic acid (58 mCi/mmol) and [3H]arachidonic acid (60 Ci/mmol) – Amersham Corp. and [14C]CH3COONa (25 mCi/mmol) – BPO Isotop (Russia); silica gel from Woelm (Germany). Phosphatidylinositol, phosphatidylinositol 4-phosphate and phosphatidylinositol 4,5-bisphosphate lipid standards were obtained from Sigma (USA). T4 and 1-methyl, 2-mercaptoimidazol were from Zdorov'e Trudyaschikhsya (Kharkov, Ukraine). T4 and T3 radioimmunoassay kits were from Minsk (Belarussia). Other chemicals used were of chemically pure grade.
Animals
90- and 720-day-old male Wistar rats, which had a free access to food and water and were kept at 24°C on a cycle of 12 h light/12 h darkness were used for experiments. The MMI was injected intraperitoneally (1 mg/100 g weight) in 0.9% NaCl to the experimental animals every day during 16 days-experiment. In some cases, the MMI-treated rats were injected intraperitoneally by T4 (200 μg/100 g weight) 48 h prior to killing. Besides, T4 (200 μg/100 g weight) was injected to the normal rats 15 and 60 min prior to killing. Control rats received 0.9% NaCl of the same volume. The animals were starved overnight prior to experiment. The thyroid state of rats was monitored by radioimmunological determination of the T4 and T3 in blood serum.
Experiments with Liver Homogenates and Slices
The 1 mCi of [14C]CH3COONa was intraperitoneally injected to rats four times every 30 minutes for 2 hours. The liver was perfused with 0.9% NaCl, then removed and washed in Krebs-Henseleit buffer, pH 7.4, containing 2 mM CaCl2 and 0.2% BSA. The pre-labeled liver was used to obtain 10% homogenates and to analyze the14C-phosphoinositides. Besides, the slices of unlabeled liver were labeled by incubation in the Eagle medium containing 10% fetal calf serum, 100 units/l streptomycin, 100 units/l penicillin, 13 mg/ml gentamycin and 0.1 μCi/ml of mio- [3H]inositol or 2.5 μCi/ml of [14C]oleic acid or 2.5 μCi/ml [3H]arachidonic acid for 1–2 h in 95% O2/5% CO2 atmosphere at 37°C. The lipids were extracted and analyzed as described below.
Cell Suspension Experiments
The hepatocytes were isolated from the liver by the method described in [38]. Preparation of hepatocytes was started between 9:00 and 10:00 a.m. The cells (107/ml) were incubated in the Eagle medium containing 10% fetal calf serum, 100 units/liter streptomycin, 100 units/liter penicillin, 13 mg/ml gentamycin and in the presence of 100 nM NaOH (control) or L-T4(10 nM) for 30 min in 95% O2/5% CO2 atmosphere at 37°C. Before lipid extraction, the cells were washed twice with a Krebs-Henseleit buffer pH 7.4, containing 2 mM CaCl2, 25 mM HEPES, 0.1% BSA. The lipids were extracted and analyzed as described below.
Extraction and Separation of Lipids
The phospholipids were extracted according to Folch et al. [39], the phosphoinositides as described in [40]. The chloroform phase was collected and dried under N2 at 37°C. The lipids were redissolved in chloroform/methanol (1:2, v/v) and applied on TLC plates. For a total phospholipids isolation, the solvent system: hexane/diethyl ether/acetic acid (80:20:2, v/v) was used, for PtdIns, PtdInsP and PtdInsP2 – chloroform/methanol/NH4OH (50:40:10, v/v). For phosphatidic acid separation the two-dimensional TLC was used. The TLC plates were developed in chloroform/methanol/ NH4OH (60:35:5, v/v) (first direction) and after – in the second direction in chloroform/methanol/acetic acid/water (80:60:7.4:1.2, v/v). The phospholipids masses were determined as described in [41]. The gel spots containing [14C/3H]lipids were scraped and transferred to the scintillation vials. Radioactivity was measured by a scintillation counter.
Authors' contributions
NAB conceived of the study and participated in its design, coordination, and manuscript preparation. OAK participated in data collection and performed the statistical analysis.
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| 15588322 | PMC543578 | CC BY | 2021-01-04 16:39:18 | no | Lipids Health Dis. 2004 Dec 10; 3:28 | utf-8 | Lipids Health Dis | 2,004 | 10.1186/1476-511X-3-28 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1871557163210.1186/1471-2105-5-187Softwareprot4EST: Translating Expressed Sequence Tags from neglected genomes Wasmuth James D [email protected] Mark L [email protected] Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, EH9 3JT, UK2004 30 11 2004 5 187 187 23 8 2004 30 11 2004 Copyright © 2004 Wasmuth and Blaxter; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The genomes of an increasing number of species are being investigated through generation of expressed sequence tags (ESTs). However, ESTs are prone to sequencing errors and typically define incomplete transcripts, making downstream annotation difficult. Annotation would be greatly improved with robust polypeptide translations. Many current solutions for EST translation require a large number of full-length gene sequences for training purposes, a resource that is not available for the majority of EST projects.
Results
As part of our ongoing EST programs investigating these "neglected" genomes, we have developed a polypeptide prediction pipeline, prot4EST. It incorporates freely available software to produce final translations that are more accurate than those derived from any single method. We show that this integrated approach goes a long way to overcoming the deficit in training data.
Conclusions
prot4EST provides a portable EST translation solution and can be usefully applied to >95% of EST projects to improve downstream annotation. It is freely available from .
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Background
The need for more sequence
Complete genome sequencing is a major investment and is unlikely to be applied to the vast majority of organisms, whatever their importance in terms of evolution, health or ecology. Complete genome sequences are available for only a few eukaryote genomes, most of which are model organisms. The focus of eukaryote genome sequencing has been on a restricted subset of known diversity, with, for example, nearly half of the completed or draft stage genomes being from vertebrates. While Arthropoda and Nematoda have two completed genomes each, with a dozen others in progress, compared to predicted diversity (over a million species each) current genome sequencing illuminates only small parts of even these phyla. The disparity between sequence data and motivation for biological study is significant. Allied to this bias in genome sequence is a bias in functional annotation for the derived proteomes: a vertebrate gene is more likely to have been assigned a function due to the focus of biomedical research on humans and closely related model species such as mouse [1].
Shotgun sample sequencing of additional genomes through expressed sequence tags (EST) or genome survey sequences (GSS) has proved to be a cost-effective and rapid method of identifying a significant proportion of the genes of a target organism. Thus many genome initiatives on non-traditional model organisms have utilised EST and GSS strategies to gain an insight into "wild" biology. An EST strategy does not yield sequence for all of the expressed genes of an organism, because some genes may not be expressed under the conditions sampled, and others may be expressed at very low levels and missed through the random sampling that underlies the strategy. However the creation of EST libraries from a range of conditions, such as different developmental stages or environmental exposures, promotes a closer examination of the biology of these species.
The well documented phylogenetic sequence deficit [2] has led us to coin the term "neglected genomes". Currently many groups are sequencing ESTs from their chosen species to perform studies in a wide-range of disciplines, from comparative ecotoxicology [3] to high-throughput detection of sequence polymorphisms [4,5]. The contribution of EST projects for neglected but biologically relevant organisms is highlighted in Figure 1. As with all sequence data, obtaining high quality annotation requires prior information and is labour intensive. The "partial genome" information that results from EST datasets presents special problems for annotation, and we are developing tools for this task.
The need for high quality translation
The PartiGene software suite [6] simplifies the analysis of partial genomes. ESTs are clustered into putative genes and consensuses determined. All the data is stored in a relational database, allowing it to be searched easily. While preliminary annotation based on BLAST analysis of nucleotide sequence can be performed, more robust methods are needed to allow high-quality analysis. The error-prone nature of ESTs makes application of most annotation tools difficult. To improve annotation, and facilitate further exploitation, a crucial step is the robust translation of the EST or consensus to yield predicted polypeptides. The polypeptide sequences present a better template for almost all annotation, including InterPro [7] and Pfam [8], as well as the construction of more accurate multiple sequence alignments, and the creation of protein-mass fingerprint libraries for proteomics exploitation. High quality polypeptide predictions can be applied to functional annotation and post-genomic study in a similar way to those available for completed genomes.
Translating Expressed Sequence Tags
Prediction of the correct polypeptide from ESTs is not trivial:
1. The inherent low quality of EST sequences may result in shifts in the reading frame (missing or inserted bases) or ambiguous bases. These errors impede the correct recognition of coding regions. The initiation site may be lost, or an erroneous stop codon introduced to the putative translation.
2. ESTs are often partial segments of a mRNA, and as most cloning technology biases representation to the internal parts of genes, the initiation methionine codon may be missed. This is a problem for some of the de novo programs which use the initiation methionine to identify the coding region (described below).
Sequence quality can be improved by clustering the sequences based on identity. For each cluster a consensus can be determined [9]. This approach, however, will not address the whole problem as poor quality EST sequences may not yield high quality consensuses and for smaller volume projects, most genes have a single EST representative. Therefore additional methods must be applied to provide accurate polypeptide predictions.
Similarity-based methods
A robust method to determine the correct encoded polypeptide is to map a nucleotide sequence onto a known protein. This concept is the basis for BLASTX [10], FASTX [11] and ProtEST [12]. BLASTX and FASTX use the six frame translation of a nucleotide sequence to seed a search of a protein database. The alignments generated for each significant hit provide an accurately translated region of the EST. BLASTX is extremely rapid, but the presence of a frameshift terminates each individual local alignment, ending the polypeptide prematurely. FASTX is able to identify possible frameshifts, but its dynamic programming approach is significantly slower than BLASTX. These methods require that the nucleotide sequence shares detectable similarity with a protein in the selected database. Many genes from both well studied and neglected genomes do not share detectable similarity to other known proteins. For example, the latest analysis of the Caenorhabditis elegans proteome shows that only ~50% of the 22000 predictions contain Pfam-annotated protein domains [8,13], and 40% share no significant similarity with non-nematode proteins in the SwissProt/trEMBL database [14]. This feature is not unique to the phylum Nematoda, and is likely perhaps to be more extreme for neglected genomes, given the phylogenetic bias of most protein databases.
ProtEST uses a slightly different similarity-based approach [12]. A protein sequence is compared to an EST database. phrap [9] is used to construct a consensus sequence from the ESTs found to have significant similarity. These consensuses are then compared to the original sequence using ESTWISE (E. Birney, unpublished [15]) giving a maximum likelihood position for possible frameshifts. The system is accurate but is not readily adaptable to the high-throughput approach necessary when dealing with very large numbers of ESTs. More crucially, an EST that does not show significant similarity to a known protein is not translated.
'de novo' predictions
To overcome the reliance upon sequence similarity, de novo approaches based on recognition of potential coding regions within poor quality sequences, reconstruction of the coding regions in their correct frame, and discrimination between ESTs with coding potential and those derived from non-coding regions have been developed [16-18].
DIANA-EST [16], combines three Artificial Neural Networks (ANN), developed to identify the transcription initiation site and the coding region with potential frameshifts. ESTScan2 [18] combines three hidden Markov models trained to be error tolerant in their representations of mRNA structure (modelling the 5' and 3' untranslated regions, initiation methionine and coding region). DECODER [17] uses an essentially rule-based method for identifying possible insertions and deletions in the nucleotide sequence, as well as the most likely initiation site, and was developed for complete cDNA sequence translation.
Each of these methods has different strengths in their attempt to identify the precise coding region; all require prior data to train their models. Published descriptions of their utility are based on training with human full length coding sequences (mRNAs), and thus tens of thousands of training sequences (many million coding nucleotides) were used to achieve optimum results. As stressed above, this amount of prior data is not available for the vast majority of EST project species (Figure 1).
New solution – prot4EST
Prior to this project, nematode ESTs available through NEMBASE [19] had been translated using DECODER, as a preliminary study had suggested that it outperformed the other available methods (DIANA-EST and ESTScan1 [20]) (Parkinson pers. com.). 7388 out of the 40000 resulting predicted polypeptides were likely to be poorly translated (<30 amino acids), and we suspected many more contained errors. This motivated the creation of a solution using several methods to enhance the quality of the polypeptide predictions, exploiting their strengths while recognising their short-comings. prot4EST is an EST translation pipeline, written in Perl, with a user-friendly interface, that links some of these described methods together. It carries out retrieval and formatting of files from online databases for the user. It has been designed to be used as a stand-alone tool, or as an integral part of the PartiGene process [6].
Implementation
DECODER
The DECODER program [17] was developed to define start codons and open reading frames in full-length cDNA sequences. It exploits the quality scores for the sequence produced from base-calling software, such as phred [21,22], and additional text-based information to identify all possible coding regions. In regions of low sequence quality up to 2 nucleotides are removed or inserted, representing possible frameshifts. A likelihood score is calculated for each possible coding sequence (CDS), and the one with the lowest score is chosen as the correct CDS. The score is computed from the probability of generating a random sequence with a better Kozak consensus (the nucleotide sequence surrounding the initiation codon of a eukaryotic mRNA), ATG position and codon usage. DECODER requires a codon bias table, which is used to determine the putative coding regions optimal codon usage. A penalty term limits the number of insertions/deletions in the corrected CDS.
ESTScan2.0
Hidden Markov models (HMM) can represent known sequence composition in a probabilistic manner [23]. This has been exploited recently in applications to find genes in genomic sequence [24,25], predict domain composition in protein sequences [26], and align multiple sequences [27]. ESTScan [18] exploits the predictive power of Hidden Markov models by combining three models:
1. Modeling mRNA structure: ESTScan separates the probable CDS from the untranslated regions (UTRs). The core of the coding sequence is represented by a 3-periodic inhomogeneous hidden Markov model. Flanking this core model are start and stop profiles for the codons observed at these positions. The profiles for untranslated regions flank the start and stop states.
2. Error tolerance: ESTScan allows insertions and deletions (indels) in the EST sequence. For example, if it is more probable that a particular nucleotide is the result of an insertion event then it is omitted from the 'corrected' sequence. Conversely, if the HMM probability scores suggest that a nucleotide has been deleted then the model inserts an X into the 'corrected' sequence to denote this prediction.
3. EST structure: ESTScan recognises that the EST may be composed of a combination of 5' UTR, CDS and 3' UTR.
ESTScan's hidden Markov models are trained using complete CDS entries from either the EMBL or RefSeq databases. Scripts included with the distribution parse the data files, extracting the necessary sequence information to produce the model files. The major issue considered at this point is redundancy. If the training data is internally redundant then the resultant model will be fully successful only in finding what is known and will have reduced power in detecting novel transcripts. Default parameters were used in ESTScan for building the HMM and in predicting polypeptides.
HSP tiling
The BLASTX program [10] allows a nucleotide sequence to be searched against a protein database. The nucleotide query is translated in all six frames and these are used as the query sequences for a BLASTP search. High scoring segment pairs (HSP) are identified that maximise a bit score derived from an amino acid similarity matrix. If a single indel occurs in the nucleotide sequence, causing a frameshift, the HSP is either terminated at this position or continues out of frame. Downstream of this frameshift the query sequence may be long enough to result in another significant HSP to the same protein sequence, this time in a different frame. Simple extraction of the best BLAST HSP will miss such features. prot4EST implements a rule-based method that considers all the HSPs for a match to a database sequence and considers whether a frameshift can be identified. Where a frameshift is identified the HSPs are joined. Where two HSPs overlap the sequence with the better bit score is used.
The prot4EST pipeline
prot4EST is an integrated pipeline utilising freely available software in a tiered, rule-based system (Figure 2).
Tier 1: Identification of ribosomal RNA (rRNA) genes
The protein databases contain (probably spurious) translations of ribosomal RNA genes and gene fragments, and thus it is important to identify and remove putative rRNA derived sequences before further processing. A BLASTN search is performed against a database of rRNA sequences obtained from the Ribosomal Database II (Table 1; [28]). A BLAST expect value cutoff of e-65 is used to identify matches. The cutoff is a conservative one to reduce the number of false positives. Those nucleotide sequences with significant matches are annotated as rRNA genes and take no further part in the translation process.
Tiers 2 and 3: Similarity search
The second and third stages are similar. First a BLASTX search is performed against proteins encoded by mitochondrial genomes. The mitochondrial protein database is obtained from the NCBI ftp site (Table 1). Any sequences with significant hits (cutoff e-8) are annotated as mitochondrion-encoded genes for the remainder of the process, and the relevant mitochondrial genetic code is used for translation. Sequences that do not have significant similarity to mitochondrial proteins are compared using BLASTX to the SwissProt database [14]. Sequences that yield no significant similarity are moved onto tier 4 of the process.
For those sequences that show significant similarity to a protein sequence from either database a HSP tile path is constructed. prot4EST then considers whether the nascent translation can be extended at either end in the same reading frame.
Tier 4: ESTScan prediction
The hidden Markov models used by ESTScan to identify the coding region are constructed from EMBL format files for complete CDS using scripts supplied with the package. Preprocessing is integrated within prot4EST, including the downloading of the EMBL files. A pair of length threshold criteria are applied to each putative polypeptide before it is accepted. The open reading frame must be at least 30 codons in length, and cover at least 10% of the input sequence. Polypeptides that satisfy these criteria undergo the extension process described above, sequences that fail any of the criteria are passed onto the next tier. The extension process is carried out on those sequences that exceed the thresholds.
Tier 5: DECODER prediction
The DECODER program is used to predict CDS and thus polypeptide translations for the remaining nucleotide sequences. For each sequence a quality file in phrap format is required. When a quality file is unavailable a file with quality values of 15 is generated for each sequence. The codon usage table required by DECODER can be specified by the user or downloaded from CUTG, the codon usage table database [29]. By default DECODER only processes the forward strand of each sequence, and therefore the reverse complement of each sequence is taken and processed through DECODER. Two putative polypeptides are generated for each nucleotide sequence. The longer polypeptide is selected as the more probable translation. The polypeptide predictions are checked using the same length threshold criteria as for ESTScan (above).
Tier 6: Longest ORF
This last attempt to provide a putative polypeptide translation determines the longest string of amino acids uninterrupted by stop codons from a six-frame translation of the sequence. If a methionine is present in this string it is flagged as a potential initiation site.
Output
The primary output from prot4EST consists of the putative polypeptides in FASTA format, complemented with files containing information describing the translated sequences. This information includes:
position of the translation with respect to the nucleotide sequence, the genetic code used for translation,
position and BLAST statistics of HSPs used in the tile path.
All this additional information is stored in two CSV format files, permitting parsing and simple insertion into a database.
Speed
This is highly dependent upon the composition and size of the dataset. As a guide, each prot4EST run carried out in the benchmarking (below), took less than an hour for a 2316-sequence input with an Athlon 1400 Mhz processor. The BLASTX searches were carried out separately and used as input to prot4EST (for details see the userguide, availabile from the program web page).
Benchmarking EST translation methods
We benchmarked five translation methods to test their relative performance. DECODER is designed to consider only the forward strand of the nucleotide sequence, as it was originally designed for full-length CDSs. When applied to ESTs it is imperative that both strands are analysed, as both 5' and 3' ESTs are generated. Therefore the reverse complement of each nucleotide consensus was also analysed. DECODER_default (1) considers only the prediction from the forward strand, whilst DECODER_best (2) uses the more accurate prediction. ESTScan (3) considers both strands of the nucleotide sequence, and was run as a stand-alone process with default settings.
Two arrangements of components within prot4EST were tested. prot4EST_ed (4) implements ESTScan before using DECODER on any remaining untranslated sequences. Conversely, prot4EST_de (5) uses DECODER first followed by ESTScan. The DECODER module in prot4EST considers translations on both the foward and reverse strands of the query sequence.
1 Data Sets
Test EST dataset for translation
We randomly selected 4000 Caenorhabditis elegans ESTs from dbEST [30]. To reduce redundancy, the ESTs were clustered using CLOBB [31]. phrap [9] was then used to derive a consensus sequence for each cluster. This resulted in 2899 nucleotide sequences. To ensure that the consensuses corresponded to a coding region, we carried out a BLASTN search for each consensus against the complete C. elegans cDNA dataset available from Wormbase (version 117) [32]. Significant matches were found for 2372 consensuses. Finally, this set was used to query the C. elegans protein dataset (Wormpep version 117), thus associating each nucleotide sequence with a corresponding reference polypeptide. A final test set of 2316 consensus sequences was produced.
Training datasets
1: Caenorhabditis elegans
Both ESTScan and DECODER require prior gene sequence. The C. elegans RefSeq collection was obtained, comprising 21033 entries (December 2003; [33]). A Perl script constructed random training sets giving differing totals of coding nucleotides from 10000 to 350000. Four sets were assembled for each level. The build_tables script (part of the ESTScan package) was used to filter out sequences [18].
We used the same training sets to build the codon usage tables required by DECODER. CUSP from EMBOSS [34] was used to build the tables, and a separate Perl script written to convert the output to that required by DECODER. For any given run of prot4EST the ESTScan HMM training set and codon usage table used were derived from the same training set of C. elegans cDNAs.
2: Prokaryote genomes
GenBank entries from 167 complete prokaryote genomes were obtained (May 2004). A Perl script was written to extract the CDS entries and construct a RefSeq-style resource for each prokaryote species (available upon request). If a taxon's genome consisted of more than one megaplasmid the sequences were combined. CDS annotation was not available for 11 genomes. We used the CDS collections for the 156 taxa to determine AT content, construct hidden Markov models and codon usage tables.
3: Arabidopsis thaliana
28960 complete CDS entries for A. thaliana were obtained from the RefSeq database [35].
4: Spirurida (Nematoda)
We queried GenBank for all complete CDS entries from species in the nematode order Spirurida.
BLAST databases
SwissProt (release 42.7) and TrEMBL (release 25.7) [14] were combined to give a SwissAll database. To recreate the situation facing neglected genome analysis, the accession numbers for all proteins from species in the nematode orderRhabditida were retrieved from the NEWT taxonomic database [36] and these entries (~23000) were removed from SwissAll.
2 Data collection and analysis
Comparison of predicted polypeptides to the 'true' polypeptide
We compared each putative polypeptide predicted from the C. elegans test dataset to its cognate reference protein using bl2seq from the NCBI distribution. Default parameters were used except for the theoretical database size (-d), set to 130000, the size of SwissProt. The blast reports were parsed using BioPerl modules [37]. Each C. elegans reference protein sequence was also compared to itself using bl2seq with default parameters. The raw and bit scores were recorded.
Calculation of comparison statistics
The raw and bit scores were normalised for length and against their theoretical maximum using equation 1, where:
BITlocal is the bit score of the local alignment between the predicted polypeptide and its cognate reference protein,
BITmax is the bit score for the alignment between the reference protein and its self,
WPlength is the length of the wormpep protein that is the reference of the nucleotide consensus translated,
ESTlength is the length of the nucleotide consensus that has been translated.
(equation 1)
Results and discussion
To measure the accuracy of translation two statistics were derived from the comparison of the predicted and reference polypeptides. The coverage is the percentage of the predicted polypeptide that aligns with the reference. The bit score represents the total of the alignment's pair-wise scores, normalised with respect to the substitution matrix used to calculate these scores. In this study the bit score was itself normalised to compensate for EST length and the maximum possible bit score for each comparison (see Methods, equation 1). The number of consensuses translated that had a significant match to their cognate reference C. elegans protein was also recorded for each run.
The influence of number of training codons
Both variants of DECODER were unable to produce robust translations for over half the nucleotide sequences no matter how many nucleotides were in the training set (Figure 3). As expected, the inclusion of the reverse complement in the DECODER analysis improved its performance. The inability of DECODER to translate more than 50% of the polypeptides can be traced to its core assumptions. One criterion used is the determination of the most likely initiation methionine. While this is almost always present in full length cDNAs (for which it was designed), the occurrence of any ATG codon in EST consensuses is less certain. We noted that DECODER will try any ATG codon to start its prediction, even if this results in a polypeptide of 2 amino acids in length.
The effect of the number of training nucleotides on ESTScan performance is pronounced. For the majority of the replicates, at each training set size the fraction of predictions that have significant matches to their reference sequence was around 75%, but the number of translations dropped significantly below 250000 training nucleotides. However, for 10000 coding nucleotides or less no robust translations are produced. Additionally, there was variance in the performance of ESTScan when there were between 20000 and 50000 training nucleotides. Examination of these training sets showed no difference in AT content compared to larger training sets, but did suggest that fluctuations in codon usage bias might be involved. The replicates that performed less well comprised sequences with shorter mean length, and had codon biases that were at the extremes of the distribution (not shown). This variation in sequence composition clearly has an effect on the probabilities that populate the HMM used by ESTScan. We suspect that the ability of ESTScan to predict robust translations when trained by datasets of 150000 to 200000 coding nucleotides is inflated as a consequence of the random selection of the training set from the complete C. elegans transcriptome. In a genuine situation, when only a small number of full-length CDS exist in the public databases, a significant number will be from highly expressed genes with atypical codon bias and structure. This bias will be evident in real-world CDS sets with fewer than 200 members (150000–200000 coding nucleotides).
When the training sets contained a large number of non-redundant coding nucleotides (> 150000), prot4EST_ed and ESTScan performed equally well (Figure 3a). When the number of coding nucleotides available for training and codon bias determination were reduced, prot4EST translations still showed significant similarity to the correct protein in at least 80% of instances.
The translations produced by prot4EST_ed were the most robust across all totals of coding nucleotides, for both coverage and bit score (Figures 3b & 3c). As the number of coding nucleotides used in training decreased, both measures showed slight reductions.
Performance of alternative prot4EST architectures
prot4EST_ed produced more robust translations for higher numbers of training sequences. However when smaller totals of training nucleotides were used the translations produced by the alternative architecture, prot4EST_de, were slightly better (Figure 3c), although a smaller proportion of translations were produced with this setup (Figure 3a).
The better performance of prot4EST_ed was examined by following the fate of individual test sequences through the prot4EST pipeline. By employing ESTScan before DECODER, larger training sets allowed the deployment of well trained HMMs (Figure 4). All predictions satisfied length and quality filters, and so were accepted as robust. The corresponding DECODER predictions, while satisfying length filters, were not as robust. As the training sets decreased in size, the ESTScan predictions failed the filters and so were ignored, and DECODER used instead.
Performance of similarity search
Seven sequences out of 2316 were identified as rRNA in tier 1. Tiers 2 and 3 of the prot4EST pipeline exploit any significant sequence similarity between the query sequence and known proteins for coding region determination. This approach identified coding regions from just under half of the consensuses, 1131. Nineteen were identified as mitochondrial genome derived. To benchmark the similarity approach against the other probabilistic methods, the accuracy of predictions from 1131 consensuses were compared. Translations derived from prot4EST tiers 2 and 3 were more robust than those from ESTScan or DECODER (Figure 5).
Given that an increase in the number of non-redundant coding nucleotides used to train ESTScan produces more robust translations, we attempted to use coding regions determined thus far to create larger training sets, with the expectation of improved translations. The results from the BLASTX search against the SwissAll database were checked for matches where the alignment included the start of the protein sequence. These results contained the information required to construct pseudo-CDS entries which can be added to the training set for populating the HMMs of ESTScan. In this study there were only six BLASTX alignments that provided suitable pseudo-CDS, failing to provide any significant increase in the level of non-redundant coding nucleotides. However other species we study have produced higher numbers of pseudo-CDS which prot4EST uses to give improved translations (data not shown).
Effect of training set and target set sequence composition
As a significant proportion of any EST set will not share similarity with known sequences, de novo translation methods need to be trained to as high a level as possible. The question is how this should be done, given the paucity of prior sequence data for individual species. Should CDS from species considered phylogenetically related be combined or should a large set from a model organism be used? A recent study of gene finding in novel genomes has shown a significant effect of sequence composition upon gene structure prediction, with more closely related model genomes providing poor training if the codon bias differs significantly from the genome of interest [25]. The performance of ESTScan was affected by even slight fluctuations in sequence composition. We examined the effect of AT content on the accuracy of translation. The complete CDS complements of 156 prokaryotes were assembled as described in the Methods. This gave a range of AT contents from 28% (Streptomyces coelicolor) to 78% (Wigglesworthia glossinidia), independent of any bias due the organisms' relatedness to C. elegans. The lowest number of non-redundant coding nucleotides was 461,299, in excess of the minimum number suggested for robust training. To explore datasets from more closely related sources all available CDS entries for the nematode order Spirurida (last common ancestor with C. elegans was 475–500 MYA [38]), and the plant Arabidopsis thaliana [39] were obtained.
There was a significant correlation between AT content of the training set and the coverage by the putative polypeptides of their reference C. elegans proteins (r = 0.49 P > 0.001) (Figure 6). The most robust predictions were produced by HMMs trained on datasets with an AT content similar to that of C. elegans. For the prokaryote training sets, the number of nucleotides used had no significant effect upon performance (data not shown). We note that some prokaryote training sets with AT contents close to C. elegans performed poorly: homogeneity of AT content is thus not a panacea. The best performance was obtained using the A. thaliana training set, with significantly better coverage than achieved with the more closely related Spirurida. As the plant dataset contained 130 times as many coding nucleotides as did the Spirurida training set, four random A. thaliana training sets of comparable size to the Spirurida were built. These smaller training sets still performed better than the Spirurida training set, though not as well as the full CDS collection.
Conclusions
prot4EST is a protein translation pipeline that utilises the advantages of a number of publicly available tools. We have shown that it produces significantly more robust translations than single methods for species with little or no prior sequence data. Around three quarters of current EST projects are associated with training sets of < 50000 coding nucleotides (Figure 1). Thus prot4EST offers significant improvement in this real world situation. Even with substantial numbers of coding nucleotides, the use of similarity searches means prot4EST is able to outperform the best de novo methods. Given the increase in protein sequences submitted to SwissProt/TrEMBL, prot4EST's ability and accuracy can only increase over time. These more accurate translations provide the platform for more rigorous down-stream annotation. Currently we are using the prot4EST pipeline to translate ~95000 nematode consensus sequences from 30 species. These translations will then be passed onto other tools we are developing for EST analysis and annotation (see ).
Availability and requirements
Project name: prot4EST
Project home page:
Operating system(s): Fully tested on Linux – Redhat9.0, Fedora2.0.
Programming language: Perl
Other requirements:
ESTScan2.0
DECODER [email protected]
BioPerl 1.4
Transeq
License: GNU GPL
Any restrictions to use by non-academics: None for prot4EST source code. DECODER requires a license. See User Guide.
Authors' contributions
JW performed all the analyses and wrote all the Perl code. MB oversaw the project and suggested additional features.
Both authors shared responsibility for writing this manuscript.
Acknowledgements
This work was funded by a BBSRC CASE PhD studentship to JW. We thank Astra Zeneca for supporting the CASE program. Work in MB's laboratory is funded by NERC, BBSRC and the Wellcome Trust. We thank Y. Fukunishi and Y. Hayashizaki of the RIKEN Institute for DECODER, C. Iselli and C. Lottaz for the ESTscan package, and our colleagues Ralf Schmid, John Parkinson, Ann Hedley and Makedonka Mitreva for support and comments on the manuscript.
Figures and Tables
Figure 1 The training set deficit for EST projects. Around 85% of species with representation in dbEST (>100 ESTs) have less than 100 complete CDS entries in the EMBL database. These species comprise ~45% of all ESTs. Sixty-six species, with 246263 dbEST sequences, have no full-length CDS. Source: dbEST and EMBL database (July 2004).
Figure 2 The prot4EST pipeline.
Figure 3 Performance of polypeptide prediction methods under different training regimes. Predicted polypeptides were compared to their reference. Four independent replicates of each training set size were used. a) Proportion of predicted polypeptide peptides having a significant BLASTP match to their reference protein. b) The mean proportion of each sequence covered by a predicted polypeptide. c) The mean relative bit score of each predicted polypeptide compared to its reference protein. The scores in b) and c) are the mean of the sequences translated by each method. The high scores shown by ESTScan at 5000 and 10000 non-redundant coding nucleotides is due to the method returning at most one polypeptide out of the 2316 nucleotides provided.
Figure 4 The relative efficiency of different organisations of DECODER and ESTScan in the prot4EST pipeline. The proportion of consensus sequences translated by each part of the pipeline for each level of training is shown. bold bars: prot4EST_ed – ESTScan translations were considered before those from DECODER. hashed bars: prot4EST_de – Robust DECODER translations were used in preference to those from ESTScan.
Figure 5 Comparison of HSP tiling, ESTScan and DECODER performance in translating the 1131 consensuses that prot4EST translated using similarity criteria.
Figure 6 Effect of AT content of training set upon translation accuracy. Each purple diamond represents a complete CDS set from a prokaryote genome. The orange box represents all CDS available from the nematode order Spirurida (~230000 non-redundant coding nucleotides). The green triangle represents the complete Arabidopsis thaliana RefSeq collection (~30000000 non-redundant coding nucleotides). The green circles are training sets of A. thaliana CDS RefSeq entries randomly selected to total ~230000 non-redundant coding nucleotides. The AT content of C. elegans is shown by the vertical dashed line.
Table 1 Description of databases used for similarity searches.
Source
Tier*
Database
Link
ribosomal RNA 1 RBP II
mitochondrial proteins 2 NCBI
protein sequences 3 SwissProt/TrEMBL
*the stage in which the database is used in prot4EST pipeline (see Figure 2).
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| 15571632 | PMC543579 | CC BY | 2021-01-04 16:02:46 | no | BMC Bioinformatics. 2004 Nov 30; 5:187 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-187 | oa_comm |
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BMC Med Res MethodolBMC Medical Research Methodology1471-2288BioMed Central London 1471-2288-4-281559601410.1186/1471-2288-4-28Research ArticleA survey of statistics in three UK general practice journal Rigby Alan S [email protected] Gillian K [email protected] Michael J [email protected] Nick [email protected] Academic Cardiology, University of Hull, Kingston-upon-Hull, UK2 NAPP Pharmaceuticals Research Limited, Cambridge, UK3 Medical Statistics Unit, University of Sheffield, Sheffield, UK4 Division of Primary Care & Psychological Medicine, University of Hull, Kingston-upon-Hull, UK2004 13 12 2004 4 28 28 15 9 2004 13 12 2004 Copyright © 2004 Rigby et al; licensee BioMed Central Ltd.2004Rigby et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Many medical specialities have reviewed the statistical content of their journals. To our knowledge this has not been done in general practice. Given the main role of a general practitioner as a diagnostician we thought it would be of interest to see whether the statistical methods reported reflect the diagnostic process.
Methods
Hand search of three UK journals of general practice namely the British Medical Journal (general practice section), British Journal of General Practice and Family Practice over a one-year period (1 January to 31 December 2000).
Results
A wide variety of statistical techniques were used. The most common methods included t-tests and Chi-squared tests. There were few articles reporting likelihood ratios and other useful diagnostic methods. There was evidence that the journals with the more thorough statistical review process reported a more complex and wider variety of statistical techniques.
Conclusions
The BMJ had a wider range and greater diversity of statistical methods than the other two journals. However, in all three journals there was a dearth of papers reflecting the diagnostic process. Across all three journals there were relatively few papers describing randomised controlled trials thus recognising the difficulty of implementing this design in general practice.
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Background
"Diagnosis is the keystone of good medical practice"[1]
General practitioners (GPs) are primarily diagnosticians [2] yet it appears that diagnosis remains their Achilles heel[3]. The problem has its origins in a misunderstanding of the differences of the five Ps (patients, pathologies, presentations, prevalences and predictive values) in hospital practice compared to primary care[4]. Decisions made by GPs are different from those made by hospital clinicians. The precise diagnostic labels may be less important than deciding on an appropriate course of action. Hence, diagnoses are often framed in terms of binary decisions; treatment versus non-treatment, disease versus non-disease, referral versus non-referral, and serious versus non-serious for example[4].
From a statistical viewpoint the binary decision making process has a lot of appeal. For example, the use of the naïve Bayes' discriminant function (and from it the derivation of likelihood ratios) is appropriate. Proponents of Bayes' argue for its simplicity and ease of interpretation[5,6]. In contrast, opponents argue that data are not used efficiently if they are simply ploughed through the "black box" of Bayes'[7,8]. Whatever the rights and wrongs of Bayes' as a technique it is time for GPs to become more familiar with statistical methods aimed at diagnosis. In relation to haematuria (blood in the urine) and the diagnosis of urological malignancy two of the authors of this paper (NS and ASR) have used Bayesian techniques in order to seek to refine diagnostic discrimination by general practitioners [9]. The results from this work have been incorporated successfully into local primary care oriented referral guidance.
Many medical journals, both generalist[10,11] and specialist [12-18], have been reviewed for their statistical content. Articles have been published in the fields of radiology, [12-14] otolaryngology, [15,16] rehabilitation medicine[17] and ophthalmology[18] to name but a few. However, general practice is under researched in this area[19]. The aim of this paper is to review three leading UK journals in general practice and to see what statistical methods are being used. It is not our intention to see if the methods are being used correctly but to look at the range of techniques reported. The outcome of this research should give pointers to the future education of GPs who wish to undertake research.
Methods
Three statisticians (MJC, ASR and GKA) (two of them holding Chartered status of the Royal Statistical Society) including one Professor, one Senior Lecturer and one Lecturer each reviewed one leading UK journal in general practice. The fourth author (NS) is a Primary Care Physician. The journals chosen were the British Medical Journal (BMJ) (general practice section), British Journal of General Practice (BJGP) and Family Practice. These three journals were chosen because they reflected the main primary care journals in the UK. The journals were hand searched for original research articles over a one-year period (1 January to 31 December 2000). Articles were classified for both their statistical content and methods of design according to criteria laid down elsewhere[10,20]. Tables 1 and 2 list the classification criteria used for both study design and statistical methods. Letters were excluded on the grounds that they are typically responses to previously published material rather than original contributions in themselves. We are aware, of course, that not all primary care research is published in these three journals alone and we comment on this later.
Table 1 Classification of design methods (after Wang and Zhang, 1988) [19]
Design method
Case report
Cross-sectional survey
Retrospective study
Prospective study
Clinical trial
basic science study
Table 2 Classification of statistical methods (after Emerson and Colditz, 1983) [10]
Category Brief description
No statistical methods or descriptive statistics No statistical content, or descriptive statistics only (e.g., percentages, means Standard deviations, standard errors, histograms
Contingency tables Chi-square tests, Fisher's test, McNemar's test
Multiway tables Mantel-Haenszel procedure, log-linear models
Epidemiological studies Relative risk, odds ratio, log odds, measures of association, sensitivity, specificity
t-tests One-sample, matched pair, and two sample t- tests
Pearson correlation Classic product-moment correlation
Simple linear regression Least-squares regression with one predictor and one response variable
Multiple regression Includes polynomial regression and stepwise regression
Analysis of variance Analysis of variance, analysis of covariance, and F-tests
Multiple comparisons Procedures for handling multiple inferences on same data sets (e.g., Bonferroni techniques, Scheffe's contrasts, Duncan's multiple range procedures, Newmann-Keuls procedure)
Non-parametric tests Sign test, Wilcoxon signed ranks test, Mann- Whitney test, Spearman's rho, Kendall's tau, test for trend
Life table Actuarial life table, Kaplan-Meier estimates of survival
Regression for survival Includes Cox regression and logistic regression
Other survival analysis Breslow's Kruskal Wallis, log rank, Cox model for comparing survival
Adjustment & standardisation Pertains to incidence rates and prevalence rates
Sensitivity analysis Examines sensitivity of outcome to small changes in assumptions
Power Loosely defined, includes use of the size of detectable (or useful) difference in determining sample size
Transformation Use of data transformation (e.g., logs) often in regression
Cost-benefit analysis The process of combining estimates of cost and health outcomes to compare policy alternatives
Other Anything not fitting the above headings includes cluster analysis, discriminant analysis, and some mathematical modelling
The main study was preceded by a pilot phase in which a random sample of 10 articles was classified both by statistical content and study design by the three statisticians. Where there were differences of opinion, consensus was reached by discussion. We met once to discuss our classification system, and to iron out differences of opinion. One problem lay in how we actually classified study design. For example, one of use used the phrase 'cross-sectional survey' while another used the phrase 'questionnaire survey' when both meant the same in terms of study design. Another problem was that we missed some of the statistical techniques (where there were many) and this required much more careful reading of the articles when we carried out the main survey. We did not carry out a formal reliability study of the pilot phase but instead relied on our experiences both as statisticians, and as journal reviewers. Similarly we chose not to carry out a formal reliability analysis in the main study.
Results
The total number of articles reviewed over a one year period was as follows: BMJ (general practice section) (n = 79), BJGP (n = 145) and Family Practice (n = 81).
Study design
The most common design was that of a cross-sectional survey being found in 24.1%, 39.3% and 35.1% of articles in the BMJ, BJGP and Family Practice respectively (Table 3). Although we classified articles by the term 'cross-sectional survey' this was not necessarily the choice term adopted by the journal. Sometimes the phrase 'questionnaire survey' was used and we assumed this was data collected cross-sectionally. We found a similar difference in nomenclature for our phrase 'cohort study' in which the phrase 'prospective survey' was also found. The highest proportion of qualitative studies was in Family Practice (21.0% compared to an average of 11.8%). Qualitative studies included those encompassing terms such as 'focus groups' and 'semi-structured interviews' for example. Figure 1 shows the proportion of papers ranked by a qualitative design. For all three journals, diagnostic studies were infrequently used. Examples of these include those based on screening (e.g., the usefulness of N-terminal brain natriuretic peptide level for screening of patients with heart failure), and calculating the sensitivity and specificity of diagnostic tests (e.g., Helicobacter pylori for the detection of peptic ulcer). Examples of more unusual study designs include those based on video recordings, literature reviews and quasi-experimental designs.
Table 3 Design methods
BMJ BJGP Family Practice Overall
Designs n (%) n (%) n (%) n (%)
Cross-sectional survey 19 (24.1) 57 (39.3) 31 (34.8) 107 (35.1)
Qualitative study 3 (3.8) 16 (11.0) 17 (21.0) 36 (11.8)
Cohort study 8 (10.1) 21 (14.5) 4 (4.9) 33 (10.8)
RCT 14 (17.7) 7 (4.8) 8 (9.9) 29 (9.5)
Reviews 4 (5.1) 8 (5.5) 2 (2.5) 14 (4.6)
Reliability/diagnostic 2 (2.5) 8 (5.5) 1 (1.2) 11 (3.6)
Case-control study 4 (5.1) 1 (0.7) 3 (3.7) 8 (2.6)
Cluster RCT 4 (5.1) 1 (0.7) 2 (2.3) 7 (2.3)
Other 21 (26.6) 26 (17.9) 13 (16.0) 60 (19.7)
Total articles 79 145 81 305
Note
RCT = randomised controlled trial.
Figure 1 Proportion of papers ranked by a qualitative design
Statistical methods
The range of statistical methods reported can be seen in Table 4. The number of methods exceeds the number of articles as some reported more than one technique. There are differences between the journals. The BMJ shows a greater range and breadth of articles than Family Practice. More sophisticated techniques are reported more often in the BMJ than either of the other two journals. In the BMJ, the two most common statistical methods used were logistic regression (n = 14, 17.7%) and the Chi-squared test (n = 13, 16.5%). The two least common were the Mantel-Haenszel statistic (n = 1, 1.3%) and Cronbach's alpha (n = 1, 1.3%). Relatively new innovations such as random effects models were seen in both the BMJ and the BJGP. The least sophisticated statistical methods appeared in Family Practice. Methods based on likelihood ratios were seldom found in either the BMJ or BJGP and not at all in Family Practice. Nonparametric tests were often unspecified but where they were included Mann-Whitney U test, Spearman's correlation coefficient and the Wilcoxon matched-pairs signed ranks test. Multiple comparisons included Bonferonni techniques and Scheffe's contrasts. Survival analysis included Kaplan-Meier curves and Cox regression.
Table 4 Statistical methods
BMJ BJGP Family Practice Overall
Methods n (%) n (%) n (%) n (%)
No statistics or simple summaries 23 (29.1) 47 (32.4) 33 (40.7) 103 (33.8)
Chi-squared tests 13 (16.5) 40 (27.6) 19 (23.5) 72 (23.6)
t-tests 7 (8.9) 22 (15.2) 17 (21.0) 46 (15.1)
Logistic regression 14 (17.7) 19 (13.1) 11 (13.6) 44 (14.4)
Nonparametric 11 (13.9) 24 (16.6) 4 (4.9) 39 (12.8)
Odds ratios/relative risks 11 (13.9) 13 (9.0) 14 (17.3) 38 (12.5)
Regression 9 (11.4) 10 (6.9) 11 (13.6) 30 (9.8)
Sample size/power 6 (7.6) 17 (11.7) 3 (3.7) 26 (8.5)
Summaries with CIs 9 (11.4) 3 (2.1) 6 (7.4) 18 (5.9)
Kappa 2 (2.5) 9 (6.2) 4 (4.9) 15 (4.9)
Sensitivity/specificity 4 (5.1) 10 (6.9) 1 (1.2) 15 (4.9)
Pearson correlation 2 (2.5) 6 (4.1) 6 (7.4) 14 (4.6)
Multiple comparisons 2 (2.5) 4 (2.8) 4 (4.9) 10 (3.3)
ANOVA 5 (6.3) 4 (2.8) 9 (3.0)
Mantel-Haenszel 1 (1.3) 5 (3.4) 2 (2.5) 8 (2.6)
Random effects models 4 (5.1) 4 (2.8) 8 (2.6)
Cronbach's alpha 1 (1.3) 5 (3.4) 1 (1.2) 7 (2.3)
Fisher's exact test 7 (4.8) 7 (2.3)
Likelihood ratio 3 (3.8) 3 (2.1) 6 (2.0)
Survival analysis 6 (7.6) 6 (2.0)
Other 4 (5.1) 37 (25.2) 10 (12.3) 51 (16.7)
Total articles 79 145 81 305
Notes
CIs = confidence intervals.
ANOVA = analysis of variance.
One-third of all articles reported no statistics or simple summaries (for example, mean, median, percentage, standard deviation, interquartile range). No journal article with a qualitative design had any statistical content.
A large number of articles reported other statistical methods, in particular the BJGP. This was due to a wide range of statistical methods being reported only once. Examples include time series, multilevel modelling and factor analysis. In others, we could not decipher which statistical techniques had been used.
Table 5 shows the rank order of the statistical methods by each journal. Differences between the journals can be seen more clearly.
Table 5 Ranking of statistical techniques
BMJ BJGP Family Practice
Methods Rank Rank Rank
Chi-squared tests 2 1 1
t-tests 7 3 2
Logistic regression 1 4 4.5
Nonparametric 3.5 2 9
Odds ratios/relative risks 3.5 6 3
Regression 5.5 7.5 4.5
Sample size/power 8.5 5 11
Summaries with CIs 5.5 17 6.5
Kappa 15 9 9
Sensitivity/specificity 11.5 7.5 13.5
Pearson correlation 15 11 6.5
Multiple comparisons 15 15 9
ANOVA 10 15
Mantel-Haenszel 17.5 12.5 12
Random effects models 11.5 15
Cronbach's alpha 17.5 12.5 13.5
Fisher's exact test 10
Likelihood ratio 13 17
Survival analysis 8.5
Notes
Excluding other methods and no statistics/simple summaries.
CI = confidence interval.
ANOVA = analysis of variance.
Discussion
Two-thirds of all journal articles relied on some type of statistical analysis beyond descriptive statistics (Table 4). The Chi-squared test and t-tests were commonly used in the BJGP and Family Practice. Papers in the BMJ and the BJGP used more sophisticated statistical methods than Family Practice (Table 4). While both the BMJ and the BJGP used sophisticated methods, the BMJ used them more often. Why might this be so? The sophistication of methods used is influenced by three factors. First, issuing instructions to authors of a statistical nature. This requires a bank of statisticians available for review to which the BMJ has access. Second, general articles on statistical aspects of writing papers. Third, tutorial type articles explaining specific techniques. The BMJ continues to take a lead in the latter two areas and indeed published statistical guidelines for contributions to medical journals over 20 years ago[21]. Despite the lack of sophistication in Family Practice, there has been a trend of using more advanced statistics elsewhere,[14,15,17,20] and this has been linked to the increasing availability of computer packages[14]. The BJGP is currently struggling to find statistical reviewers (personal communication by Editor to ASR). It is perhaps too easy for us to lay blame at the Editors door for this lack of sophistication. Statisticians are relatively rare, and review, for the most part, is unpaid.
Although these three journals publish a large proportion of the research in general practice within the UK, they by no means represent 100% of it. To look at this further we examined the year 2000 and undertook a MEDLINE search using the key indexing phrase 'General Practice'. We found over 800 articles in a diversity of journals. Articles were published in the fields of rheumatology, medical ethics, obstetrics, public health, clinical pharmacology, clinical neurology and telemedicine to name but a few.
We chose to look at the year 2000. Would our results be different had we selected a different year? The published literature suggests otherwise. In a 20 year old study, Emerson and Colditz[10] found t-tests (44%) and Chi-squared tests (27%) were the most common statistical methods reported although now Chi-squared tests are more common than t-tests (Table 5). Given the emphasis on statistical computing today we might have expected less reliance on these two methods. What lies behind this lack of progress? Altman and Goodman[22] looked at the speed of the transfer of technology of new statistical methods into the medical literature. They concluded that many methodological innovations of the 1980s had still not made their way into the medical literature of the 1990s suggesting a typical lag-time of 4–6 years. Lag-time is likely to be related to quality statistical review and this may be longer in journals with less impact. It is also worth reporting that since we carried out this survey (year 2000) there will have been a modest increase in the use of newer, more sophisticated statistical techniques.
Now let us turn to study design. The gold standard research design is considered to be the randomised controlled trial (RCT). It has been acknowledged that carrying out RCTs in general practice are difficult[23,24]. In our survey we found few RCTs (Table 3). There are particular problems of recruitment with respect to primary care. Many issues have been discussed. For example, most practices have no formal contractual arrangement to participate in research and may be unwilling to participate unless there is immediate benefit to their patients. It is known that motivating practices for long-term follow-up studies particularly is not easy[25]. Practices may feel uncomfortable about randomising their patients[26] but, delegation of this duty to another may lead to a breakdown of the special doctor/patient relationship. There are statistical and sample size concerns also. Randomisation by practice (so-called cluster randomisation) leads to larger sample sizes being required[27,28].
What are the issues here? Are they really that different from secondary care? A recent publication posed the question 'What do residents really need to know about statistics?'[29]. The authors surveyed six journals and catalogued them for their statistical and methodological content. The most popular statistical tests across the whole range of journals were the Chi-squared test followed by the t-test. The authors concluded that with knowledge of each of these two tests clinicians should be able to interpret up to 70% of the medical literature.
Conclusions
For all three journals there was a dearth of articles reflecting the diagnostic process. Why is this? It has already said that diagnosis is the Achilles Heel of GPs[3]. If it is not to remain this way we must start to educate doctors. The question is how. The latest "Tomorrow's Doctors"[30] states that students must have "Adequate knowledge of the sciences on which medicine is based and a good understanding of the scientific methods including principles of measuring biological functions, the evaluation of scientifically established facts and the analysis of data". Clearly, there is a role for teaching statistics in the education of doctors who wish to undertake research. The much greater prevalence of methods concerning binary data (Chi-squared test, logistic regression, odds ratios/relative risks) over methods concerned with continuous data should be reflected in our (statistical) teaching. Initial training in means, medians and modes should be replaced by relative risk, absolute risk and numbers needed to treat.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
Three authors (ASR, GKA and MJC) carried out the literature review while all four authors contributed to the writing.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We wish to thank the referees for their constructive comments.
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| 15596014 | PMC543580 | CC BY | 2021-01-04 16:32:50 | no | BMC Med Res Methodol. 2004 Dec 13; 4:28 | utf-8 | BMC Med Res Methodol | 2,004 | 10.1186/1471-2288-4-28 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1821556337110.1186/1471-2105-5-182Methodology ArticleA new mixture model approach to analyzing allelic-loss data using Bayes factors Desai Manisha [email protected] Mary J [email protected] Department of Biostatistics, Columbia University, 722 West 168th Street, R629, New York, NY 10032, USA2 Department of Biostatistics, University of Washington, Box 357232, Seattle, Washington 98195, USA2004 24 11 2004 5 182 182 9 4 2004 24 11 2004 Copyright © 2004 Desai and Emond; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Allelic-loss studies record data on the loss of genetic material in tumor tissue relative to normal tissue at various loci along the genome. As the deletion of a tumor suppressor gene can lead to tumor development, one objective of these studies is to determine which, if any, chromosome arms harbor tumor suppressor genes.
Results
We propose a large class of mixture models for describing the data, and we suggest using Bayes factors to select a reasonable model from the class in order to classify the chromosome arms. Bayes factors are especially useful in the case of testing that the number of components in a mixture model is n0 versus n1. In these cases, frequentist test statistics based on the likelihood ratio statistic have unknown distributions and are therefore not applicable. Our simulation study shows that Bayes factors favor the right model most of the time when tumor suppressor genes are present. When no tumor suppressor genes are present and background allelic-loss varies, the Bayes factors are often inconclusive, although this results in a markedly reduced false-positive rate compared to that of standard frequentist approaches. Application of our methods to three data sets of esophageal adenocarcinomas yields interesting differences from those results previously published.
Conclusions
Our results indicate that Bayes factors are useful for analyzing allelic-loss data.
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Background
Allelic-loss data
The goal of studies of allelic loss is to determine those loci in tumor tissue where genetic material has been lost. A tumor suppressor gene (TSG) is much more likely to lie on a chromosome arm where there has been significant allelic loss than elsewhere [1,2]. The statistical challenge lies in distinguishing between "random" allelic loss that is expected in a tumor cell population and "nonrandom" loss that may be biologically meaningful. This corresponds to determining whether there is one group of arms with background allelic loss versus two groups of arms, one with background loss rates and one with elevated loss rates.
Three allelic-loss data sets on esophageal adenocarcinomas
Esophageal adenocarcinoma is a form of cancer involving the cells along the lining of the esophagus. The cause of esophageal adenocarcinoma is not well understood. The incidence of this cancer has been increasing rapidly. In fact, it is one of the fastest growing cancers in the United States over the past 20 years [1,3,4]. A strong association has been established between the pre-malignant condition known as Barretts esophagus and the development of adenocarcinomas of the esophagus. Barretts esophagus is a condition that develops in 10–20% of patients with chronic gastroesophageal reflux disease. The condition is characterized by the metaplastic change from normal squamous to columnar epithelium in the esophagus [1,4]. Approximately 1% of patients with Barretts esophagus progress to esophageal cancer [3]. Of those who develop the cancer about 90% will die as a result of the disease [1].
We examine three data sets of allelic-loss on esophageal adenocarcinomas that attempt to identify the tumor suppressor genes (TSGs) involved in the development of this disease. These data sets have been previously analyzed and published. We refer to each data set by the last name of the first author of the publication. Some of the data sets record allelic loss on multiple loci per chromosome arm for some of the arms. However, because the number of loci evaluated per chromosome arm is not random (i.e., chromosome arms suspected of harboring a TSG will be assessed at more loci than others), we consider only one locus per chromosome arm. In these cases, we choose data from the most informative locus for that chromosome arm.
Our approach
Our general approach to analyzing allelic-loss data can be described in two main steps. The first step is to choose an appropriate model for the data using Bayes factors. The second step is to classify the chromosome arms as harboring TSGs or not according to the selected model. The details involved in these two steps are described below.
Results and Discussion
Proposed class of models
A natural way to model allelic-loss data is in terms of a mixture of two distributions: one distribution corresponds to chromosome arms that harbor TSGs and the other corresponds to arms that do not. It is reasonable to expect considerable variability in the loss rates of arms that harbor TSGs due to the existence of multiple pathways leading to the same tumor type [5]. For example, deletion of a particular TSG may be in the causal pathway for 60% of tumors of a particular type while another TSG (or other TSGs) may account for the remaining 40% of the cases. In addition, it is conceivable that various factors play a role in background loss rates. For example, factors such as cell viability, fragility of the chromosome arm, and the length of telomeres are believed to influence background loss rates [6]. It is plausible that the non-TSG loci that contribute to the background loss rate are in fact composed of two biologically different groups of loci. This group includes loci that are essential for cell viability and those that are not essential. The essential loci would be expected to exhibit loss rates considerably lower than that of the non-essential loci as their function controls the cell's survival.
We propose a class of mixture models that account for the variation inherent in this type of data. Specifically, the class of models we propose is a mixture of two beta-binomial distributions. Let Xi be the number of tumors with allelic-loss for the ith chromosome arm, and let ni be the number of informative tumors for the ith chromosome arm, for i = 1, 2,...,N, where N is the number of chromosome arms in the study. The density function for Xi is written as follows:
where θ ≡ (η, π1, ω1, π0, ω0) is a vector of unknown parameters, η is the mixing probability, πj is the average loss rate, and ωj is the dispersion parameter for j = 0,1.
The distribution converges to a mixture of two binomial distributions as both dispersion parameters go to 0 (ω0 → 0 and ω1 → 0). If only one of the dispersion parameters goes to 0 (ω0 → 0 or ω1 → 0), the distribution reduces to a mixture of a beta-binomial and a binomial distribution. Note that the model has only one component when the mixing parameter is zero (η = 0).
Model selection using Bayes factors
Bayes factors are measures used to compare the fit of two competing models. We suggest using Bayes factors to select an appropriate model for the data from the proposed class of mixture models. Let H0 and H1 represent the models under the null and alternative hypotheses, respectively. When comparing two models, it is of interest to examine the posterior odds of one model to another. It is easy to show that the posterior odds of one model to another is
Equation (1) shows that the posterior odds is calculated as the product of a term known as the Bayes factor and the prior odds. The Bayes factor is the marginal likelihood of the data under H1 divided by the marginal likelihood of the data under H0, or B10 ≡ Pr(X |H1)/Pr(X|H0). Thus, as Bayes factors are proportional to the posterior odds of one model to another, they are desirable measures to use for model selection. Note that if the prior odds are assumed to be 1, then the Bayes factor is equivalent to the posterior odds.
One can think of the Bayes factor as a Bayesian likelihood ratio statistic. Like the likelihood ratio statistic, the Bayes factor is a ratio of likelihoods under two models being considered. However, while the likelihood ratio statistic is the ratio of two maximized likelihoods for two competing, nested models, the Bayes factor is the ratio of two likelihoods integrated or averaged over the entire parameter space and the models need not be nested. An important consideration with a Bayesian approach is that a prior distribution is assumed for all of the parameters in the model. The advantage to this is that one can incorporate prior information into determining which model is more appropriate. This is a disadvantage, however, if the Bayes factor is sensitive to the prior and if the prior has been chosen incorrectly.
Large Bayes factors are evidence in favor of the alternative hypothesis. Kass and Raftery (1995) discuss guidelines for interpreting the measure [7]. Following the authors' suggestion, we transform the Bayes factor to the same scale as that of the likelihood ratio statistic and use the criterion that 2lnB10 > 2 implies positive evidence in favor of the alternative model.
Comparing a uni-component model to a two-component model would address the question of whether there is one versus two groups of chromosome arms. Further, comparing a two-component beta-binomial model to a two-component binomial model would address whether there is overdispersion in either group. The advantage of this is that it provides insight into the number of chromosome arm groups, whereas standard applicable frequentist tests will only indicate whether there is one or more groups [8,9].
Classification
Provided there is sufficient evidence to indicate that there are two groups of chromosome arms, it is desirable to identify which chromosome arms belong in which group. Classification of the chromosome arms can be done by calculating the conditional probability of group membership of each arm under a given model. If Xi ~ ηf1(xi, ni, θ1) + (1 - η)f0(xi, ni, θ0), then it can be shown using Bayes' rule that
where is the maximum likelihood estimate (MLE) of θ, Zi is the group membership of the ith chromosome arm and Zi = 1 implies that the ith chromosome arm is in the TSG group. For the analyses here, chromosome arms with conditional probabilities exceeding 0.5 are classified in the TSG group. Also note that MLEs are computed using the nlminb function in S-Plus.
Performance of the Bayes factors
Table 1 presents a description of simulated data sets used to evaluate the performance of the Bayes factors. One hundred data sets are generated under each scenario. All parameters chosen to generate the data are based on the Barrett esophageal cancer data set discussed later [1]. Under the first scenario, data are generated from a two-component binomial mixture model, where each group has a constant loss rate. The two groups are fairly well-separated with the TSG group's loss rate considerably higher than the background loss rate. We specify only five chromosome arms to harbor TSGs, which is believed to be typical. The second scenario is one where there are no TSGs and the background loss rate follows a beta distribution. The distributional parameters are chosen by examining the Barrett data set after removal of the five chromosome arms with the highest rates of allelic loss (these arms are implicated by Barrett et al. (1996) [1] as potentially harboring TSGs). This gives an expected loss rate of 0.26 and a dispersion parameter of 0.07. Under the third scenario there are two groups of chromosome arms with one group exhibiting a constant background loss rate of 0.22 and the second group of five chromosome arms exhibiting varying and higher rates of allelic loss. In the last scenario, both groups of chromosome arms have varying loss rates. The TSG loss rate distribution follows that of Scenario 3 and the non-TSG loss rate distribution follows that of Scenario 2.
Table 2 presents the percentage of time one model is favored over the other based on 2ln(Bayes factor) for data generated under each of the scenarios described in Table 1. For each scenario, a 5 × 5 matrix of pairwise comparisons is presented. The rows of the matrix correspond to models considered under H1 (models appearing in the numerator of the Bayes factor). The columns of the matrix correspond to models considered under H0 (models appearing in the denominator of the Bayes factor).
For data generated from a two-component binomial model (Scenario 1), the true model is mostly favored over the uni-component models. In fact, when comparing the true model to a uni-component beta-binomial model, the latter model is only favored 5% of the time. This can be viewed as a false-negative rate. Note that the Bayes factors never provide evidence in favor of a uni-component model in comparisons with either of the other two-component models for data from this scenario. Furthermore, the true model is selected 75% of the time over the two-component beta-binomial model. The Bayes factors are ambiguous, however, when comparing the true model to a two-component beta-binomial/binomial model, where neither is favored 69% of the time.
For data that follow a uni-component beta-binomial distribution (Scenario 2), the results are inconclusive 62% of the time when comparing the true model to the two-component binomial model. For twenty-two percent of the data sets the right model is favored, but 16% of the time, the two-component model is selected. Thus, this comparison results in a 16% false-positive rate. Similar results are found when comparing the true model to a two-component beta-binomial/binomial model. The Bayes factors favor the correct model over the two-component beta-binomial model roughly half the time and favor neither model the other half. Comparisons between the two-component models and the one-component binomial model not surprisingly show a strong preference for the two-component models, as they better accommodate the variability of the data.
The third quarter of Table 2 presents results for data generated under Scenario 3. The two-component beta-binomial/binomial model is favored in the majority of the cases over the other models within the class, which makes sense as this model is most similar to the data-generated model. Only once is an alternative hypothesis favored when compared to this model and this is the two-component beta-binomial model. When comparing the two-component beta-binomial/binomial model to the other two-component models, the Bayes factors do not favor either of the models being compared about 20 percent of the time. In general, the two-component models were mostly favored over the one-component models.
For data generated under Scenario 4, we expect the two-component beta-binomial model to be chosen over the other models in the class as this model is closest to the truth. The results show that when this model is compared to the two-component binomial or the one-component beta-binomial, it is mostly favored, and these models are never selected. As the two-component beta-binomial model is fairly similar to the two-component beta-binomial/binomial model, however, most of the time neither model is chosen over the other. The two-component beta-binomial is favored only 35% of the time, while the two-component beta-binomial/binomial is favored 9% of the time. Interestingly, when comparing the one-component beta-binomial to the two-component binomial, the one-component model is chosen 72% of the time and the two-component binomial model is chosen only 5% of the time. This suggests that the measure is fairly sensitive to the overdispersion in the two groups. Another example of this is a comparison between the two-component beta-binomial/binomial model and the one-component beta-binomial model. In this case, the two-component model is only favored 54% of the time, where the uni-component model is a better fit to 5% of the data sets, and both models are equally good fits to the data 41% of the time.
This simulation study demonstrates that the Bayes factors are an appropriate method of model selection. They perform particularly well for data generated from the two-component models. In particular, most of the time, the correct model is chosen, and furthermore, reasonable false-negative rates are observed for comparisons made on data generated from the two-component binomial model as well as the two-component beta-binomial/binomial model. Data generated from a one-component beta-binomial model produces interesting results. Although the false-positive rates are reasonable when comparing the one-component beta-binomial model to the other two-component models (16%, 7% and 0% for the two-component binomial, two-component beta-binomial/binomial, and two-component beta-binomial, respectively), there is a large percentage of time, when neither model is favored (62%, 69% and 50%). Since both models are often good fits to the data, it would be difficult to decide with confidence whether or not there is a second group of arms in these cases.
Application of methods to data sets
In this section, we apply the methods discussed to three allelic-loss data sets. Specifically, we use Bayes factors to choose a reasonable model or set of models for the data in order to address whether TSGs exist on any of the chromosome arms, and we classify the chromosome arms as harboring TSGs or not based on the selected model(s).
Table 3 presents a summary of the results for the three data sets. The set of models chosen by the Bayes factors is provided along with the individual chromosome arms that were identified as having TSGs based on these models. The set of chosen models was comprised of those with 2ln(Bayes factors) exceeding 2 when compared to models outside the set and with 2ln(Bayes factors) less than 2 when compared to models within the set. Details of the analysis for each data set are described below, with slightly more emphasis placed on the first data set.
The Barrett data set
The Barrett data set records allelic loss on 20 esophageal adenocarcinomas and two high-grade dysplasias. Figure 1 presents a histogram of the proportion of tumors with allelic loss for each of the forty chromosome arms studied (markers were not placed on the short arms of chromosomes 13, 14, 15 and 22 as these are too small to study). Two of the chromosome arms examined do not exhibit allelic loss (arms 20q and 21p) for any of the tumors observed. The mean allelic-loss rate for all arms exhibiting loss is 0.27 and the median allelic-loss rate is 0.24. From the figure, three chromosome arms appear to stand apart from the others in exhibiting considerably higher allelic-loss rates: 9p, 5q, and 17p.
Table 4 presents 2ln(Bayes factors) for the pairwise comparisons of the models for each of the three data sets. In addition, the posterior probability of each model is presented assuming a prior probability for the models such that
P(2 Component Model) = P(1 Component Model) = 1/2
This gives
P(2 bb) = P(2 bb/bin) = P(2 bin) = 1/6
P(1 bb) = P(1 bin) = 1/4.
For the Barrett data set, the two-component models are strongly favored over the one-component models, clearly indicating a group of arms that exhibit higher than background loss rates. In particular, the Bayes factors demonstrate that the two-component beta-binomial/binomial model provides the best fit. Note that the posterior probability of this model is considerably higher than that of the others, providing further evidence of its superiority.
Table 5 presents the MLEs of the parameters for the two-component models listed in order of posterior probability (largest to smallest). First note that = 0, reducing the two-component beta-binomial model to a two-component beta-binomial/binomial model. The parameter estimates for these two models are identical and imply that the beta-binomial distribution corresponds to the TSG loss and the binomial distribution corresponds to the background loss. The estimate of the probability that a chromosome arm is in the TSG group is 0.097. The estimated background loss rate is 0.228, and the expected background loss rate for arms with TSGs is estimated at 0.708 with a loss rate variance of 0.07. The fit from the two-component binomial model gives a slightly lower mixing parameter estimate and a slightly higher estimate of the TSG loss rate.
The conditional probabilities of group membership based on the two-component beta-binomial/binomial model yield the same classification rule as that based on the other two-component models. Chromosome arms 5q, 9p, and 17p are classified in the TSG group. The conditional probabilities of group membership for these chromosome arms are quite similar across the three models.
The Gleeson data set
The Gleeson data set consists of 38 esophageal adenocarcinomas. Allelic-loss data were recorded on 39 chromosome arms (as in the Barrett data set, the short arms of chromosomes 13, 14, 15, 21, and 22 were not included in the study). A histogram of the proportion of tumors with allelic loss is presented in Figure 2. The mean allelic-loss rate is 0.36 and the median allelic-loss rate is 0.32. By simply viewing the histogram, four of the chromosome arms have been identified as having suspiciously high allelic-loss rates. These are chromosome arms 4q, 9p, 18q, and 17p.
For the Gleeson data set, the two-component beta-binomial/binomial model, the two-component binomial model and the uni-component beta-binomial model are all favored over the two-component beta-binomial model and the uni-component binomial model (See Table 4). Because two of the two-component models as well as the uni-component beta-binomial model are comparable fits to the data, this may imply there is not strong enough evidence of more than one group of chromosome arms. However, while the uni-component beta-binomial model and the two-component beta-binomial/binomial model appear to fit similarly, the two-component binomial model appears to be a slightly better fit than these two as shown by the corresponding posterior probabilities.
Maximum likelihood estimates obtained from fitting both the two-component beta-binomial and the beta-binomial/binomial model imply both components follow a binomial distribution as the dispersion parameter estimates are 0. Fits of all three two-component models yield identical parameter estimates, and therefore the rule obtained from the two-component binomial model which has the highest posterior probability is equivalent to that obtained from the other two-component models. Classification using this model places six chromosome arms in the TSG group. These are identified as chromosome arms 4q, 9p, 9q, 12q, 17p, and 18q. Note that three of these chromosome arms (4q, 9q and 12q) exhibit lower than the average background loss rate in the Barrett data set. However, 9p and 17p are categorized along with 5q in the TSG group. Furthermore, although not classified in the TSG group, chromosome arm 18q exhibits the fourth highest allelic-loss rate in the Barrett data set.
The Hammoud data set
The Hammoud data set consists of 30 esophageal adenocarcinomas on 39 chromosome arms (the same arms included in the Gleeson data set). A histogram of the Hammoud data set is presented in Figure 3. Chromosome arms 4q and 17p have been identified on the plot as they appear to stand out from the others as having relatively high allelic-loss rates. The mean allelic-loss rate is 0.20 and the median allelic-loss rate is 0.18.
The pairwise comparisons using the Bayes factors for the Hammoud data set (See Table 4) demonstrate that both the two-component beta-binomial/binomial model and the two-component binomial model give the best fits to the data. Note that the posterior probabilities of these models are practically the same indicating these models are equally good fits to the data. As only two-component models are selected from the class, there is strong evidence to suggest that a second group of chromosome arms with TSGs exists. Classification using both the two-component beta-binomial/binomlal model and the two-component binomial model places chromosome arms 4q and 17p in the TSG group. Both models yield similar conditional probabilities of group membership for the arms, and as in the other data sets, both models yield the same classification rule. Note that chromosome arm 4q is implicated by our analysis of the Gleeson data set and 17p is implicated by our analyses of all three previous data sets.
Conclusions
Testing of one versus two components in a mixture model is problematic as the likelihood ratio test is not applicable. Bayes factors provide a natural solution to this problem. Although we make only crude comparisons using the Bayes factors, the results favor the right model most of the time for data arising from a two-component model. More importantly, when comparing a two-component model versus a one-component model for these data, the two-component model is generally chosen.
For data that arise from a one-component beta-binomial model, the Bayes factors were not able to choose as well between the true model and a two-component model. Specifically, when comparing the true model to the two-component binomial, the false-positive rate was 16%. On the other hand, the Bayes factors are inconclusive for 62% of the data sets when making this comparison. This is actually encouraging when considering some frequentist options. Standard applicable frequentist methods such as an exact Monte Carlo test and the dispersion score test are limited to testing for one versus more than one group of chromosome arms [8,9]. Simulation studies examining these methods for these data reject the hypothesis of one group 93 and 89 percent of the time, respectively [10]. Based on this, one might conclude that a model with two (or more) groups would be appropriate. The results presented here would not support such a conclusion, at least most of the time. However, it is important to note that if such variability exists in the data as is expected and is ignored, the false-positive rate can be quite high. For example, if comparing a two-component binomial model and a one-component binomial model when there is only one group of chromosome arms exhibiting background loss, the two-component model would likely be favored. Thus, in practice it is recommended that several comparisons are made before selecting a model. In addition, it may be desirable to consider the posterior probabilities of all models jointly. When examining the posterior probabilities of each of the models for the four scenarios considered here, we found that the true model had the highest median posterior probability.
Table 3 summarizes the results of applying our approach to three esophageal adenocarcinoma data sets. It is important to note that a common locus on a chromosome arm was rarely chosen across the three studies. In fact, there were only a handful of loci that were investigated by at least two of the three data sets. Not surprisingly, chromosome arm 17p is chosen by the two-component models for all data sets as being in the TSG group. Chromosome arm 17p harbors a well known TSG called p53, which has been implicated in several cancers, including colon cancer, breast cancer and non-small cell lung cancer to name a few [1]. Also note that chromosome arm 9p is placed in the TSG group for the Barrett data set as well as the Gleeson data set. Similarly, chromosome arm 4q has been identified in both the Gleeson and Hammoud data sets. The Barrett data set also characterizes chromosome arm 5q as harboring a TSG, which has been previously identified in other studies as having a high frequency of allelic loss in colon cancer, non-small cell lung cancer, as well as renal cancer [1]. Similarly, 18q, identified in the Gleeson data set, is suspected of playing a causal role in colon cancer and osteosarcoma based on high allelic-loss frequencies there [1]. Also, chromosome arm 3p has been identified as having high loss in renal and non-small cell lung cancer [1]. The results from applying our methods to the three data sets differ somewhat from those of the previously published analyses. First a potential bias exists in the design of current allelic-loss studies, and is seen in the design of the Barrett and Gleeson studies. Chromosome arms suspected of harboring TSGs are evaluated at more loci than other arms. The proportion of tumors with allelic loss on an arm is then defined as the number of tumors with allelic loss at at least one of the informative loci divided by the number of tumors informative at at least one of the loci. For example in the Barrett study, one locus is investigated for most chromosome arms, but two loci are assessed for loss on arms 13q, 17p, and 18q. This increases the probability that allelic loss will be observed at those arms examined at two loci than at those examined at only one. To address this issue, our analysis considers only one locus (the most informative) per chromosome arm.
In the analysis presented by Barrett et al. (1996), the authors consider a uni-component binomial distribution for the background loss [1]. Frequencies falling far out in the tails of the binomial distribution, assuming a background loss rate of 0.23, correspond to chromosome arms with potential TSGs. However, it should be noted that the model upon which we base our results (two-component beta-binomial/binomial model) is selected over that assumed by Barrett et al. (1996), where our model has a corresponding posterior probability of 0.814 and the uni-component binomial has a posterior probability < 0.001 [1]. The results from Barrett et al. (1996) indicate that chromosome arms with significantly high loss rates are 5q, 9p, 13q, and 17p (with corresponding p-values < 0.05) [1]. Our approach also yields classification of 5q, 9p, and 17p in the TSG group. Although the fourth highest conditional probability corresponds to arm 13q, assuming a two-component beta-binomial/binomial model, the probability that it is in the TSG group is estimated to be quite low (0.084) with our approach. Barrett et al. (1996) also implicate chromosome arms 1p and 18q as potentially harboring TSGs (p-values < 0.10 and > 0.05) [1]. Our analysis demonstrates that these arms are not likely to be classified in the TSG group with conditional probabilities of 0.077 and 0.123, respectively.
The analytic approach employed by Gleeson et al. (1997) is to select a chromosome arm with a corresponding allelic-loss rate above an arbitrarily chosen cut-off of 50% as criterion for potentially harboring a TSG [11]. With this approach, Gleeson et al. (1997) implicate the following 10 chromosome arms; 3p, 4q, 5q, 8p, 9p, 9q, 12q, 13q, 17p, and 18q [11]. Our method gives the following conditional probabilities of harboring a TSG for these arms respectively: 0.003, 0.982, 0.327, 0.012, 0.916, 0.813, 0.859, 0.121, and 0.998. While our method also selects six of these arms, the conditional probability of the unselected four are estimated to be fairly low. Interestingly our conclusions regarding the Hammoud analysis correspond well to those of the authors. The criterion the authors used for selection of a chromosome arm into the TSG group was that the chromosome arm's allelic-loss rate should exceed two standard deviations above the observed mean allelic-loss rate. This approach is similar to that of Barrett et al. (1996) and more sound than that employed by Gleeson et al. (1997) as it assumes a reasonable model for the allelic-loss rate (in this case a normal distribution) and selects those outliers to the right of the distribution as suspicious [1,11]. Our approach, however, is more flexible in that multiple models consistent with the biological nature of the data are considered and compared and further, conditional probabilities of harboring a TSG are provided for each chromosome arm. For the arms selected by both us and Hammoud et al. (1996), the two arms selected, 4q and 17p, have conditional probabilities of 0.968 and 0.994 for harboring TSGs, respectively [4].
Results from the Bayes factors for the Gleeson data set are not completely clear. They cast doubt on whether the true underlying distribution really has two components or whether the two-component models chosen also provide a reasonable fit (relative to all the models considered) to overdispersed data exhibiting only background loss. Recall the simulation study where we demonstrate that for data arising from a uni-component beta-binomial model, the Bayes factors indicate that both the true model and the two-component binomial model are often both reasonable fits to the data. This motivates incorporating Bayesian model averaging (BMA) into the inference process [12]. An alternative would be to compute the posterior odds of a second component. First, the posterior probability of a two-component model could be obtained by averaging over the three two-component models. Second, the posterior probability of a uni-component model could be computed by averaging over the relevant uni-component models. The averaged Bayes factor would then be a ratio of the posterior probability of a two-component model to the posterior probability of a one-component model.
Furthermore, one could use Bayesian model averaging when estimating the conditional probability of group membership for each of the chromosome arms. Maximum likelihood estimates from different high probability models could lead to different inferences about parameters. Thus, this approach of averaging the conditional probability over the various models to classify the arms or weighting the parameter estimates by the posterior probability of a given model may be more desirable than choosing a single best model from which to make inference. Specifically, one could weight estimates by P(Hj|X). For example, suppose chromosome arm 13q is suspected of harboring a TSG from past experiments and we desire a probability that Z13q = 1 based on these data. Because of model uncertainty we may be hesitant to compute the probability based solely on one model. Instead, we could estimate this probability as:
where j indexes over all of the models considered. This is a potential alternative to classifying the chromosome arms using the classical maximum likelihood approach that needs to be further explored. It is interesting to note that the two-component beta-binomial mixture model was never chosen for any of the data sets. Although it was certainly favored over the one-component binomial model in all data sets and over the uni-component beta-binomial model in the Barrett data set, it was never chosen to be in the set of candidate models. The class of models considered here is based on our beliefs of the biology of the data. However, the ability to screen the tumor cell genome for chromosome arms which harbor TSGs lies in a better understanding of the background distribution. Characterizing the background distribution would allow a more definitive identification of arms exhibiting abnormal loss.
Methods
Data
The three data sets to which we apply our methods were previously published and analyzed using other techniques [1,4,11].
Computing Bayes factors for the proposed class of mixture models
Computing Bayes factors can be challenging as non-trivial integration is often required to estimate the marginal probabilities under each model considered. Specifically, calculating Bayes factors involves integrating the likelihood over the entire parameter space for each model considered. Thus, the integrals tend to be high-dimensional. In general, we need to compute
I = ∫ Pr(X|λ, H)π(λ|H)dλ.
This can be quite computationally intensive. When the integral is of high dimension (> 6), quadrature methods can be unreliable [13]. In addition, and more relevant to our situation, for moderate to large sample sizes (> 35), numerical methods can be both inefficient and unreliable [7,14]. An alternative approach is to use Gibbs sampling techniques. However, for mixture models, these methods often miss important mass as the chain tends to get stuck near one mode resulting in an underestimate of the integral [14]. Furthermore, because the sampling is not independent, there is no simple way of self-monitoring convergence.
Another method of estimating integrals is simple Monte Carlo, that involves sampling from the prior distribution, π(λ). The simple Monte Carlo estimate of the integral is the averaged likelihood at the sampled parameter values or
This has been shown to be a good estimate for likelihoods that are relatively flat. However, if the posterior is concentrated relative to the prior, the variance of the estimate will be large, and convergence to a Gaussian will be slow [7]. Thus, sampling from the prior distribution is often not very efficient. A potential solution to this problem is to do importance sampling that involves sampling from π*(λ), the importance sampling function [7,14]. The estimate then becomes
where is known as the importance sampling ratio. The simple Monte Carlo estimate is a special case of importance sampling where π*(·) is chosen to be the prior distribution. However, the importance sampling estimate can be an improvement over the simple Monte Carlo estimate if π*(·) is chosen such that the sampling is more efficient, e.g., if π*(·) is centered around the mass. There has been some success with importance sampling in a non-mixture model setting [14].
Our solution is to first write the likelihood in its complete-data form. The likelihood for the mixture of two beta-binomial distributions is written as follows:
where z = (z1, z2,...,zN)T and the zis are unobserved group membership indicators such that zi = 0 if xi is from the background component and zi = 1 if xi is from the TSG component. Then the marginal probability of X becomes
where I denotes the marginal probability of the data (or integrated likelihood) and where g is the prior distribution of θ.
We then estimate this integral using a method we developed called the Uniform Distance Method (UDM). This method is a variant on importance sampling and involves a combination of either quadrature or exact integration and sampling of the membership vectors, Z. The idea behind the method is to use P(Z|θ = , x) where is the MLE of θ to provide information on the important groupings, i.e., which chromosome arms are likely to be clustered together. While the membership vectors are sampled independently, the membership values within a group are sampled dependently, making these groupings more likely to be maintained than if the values were sampled independently.
The development and assessment of UDM is discussed in detail in Desai (2000) and demonstrates solid performance in estimating these integrals [10]. Software for implementing the method is available by contacting the first author. Note that for all analyses presented in this paper, uniform priors are assumed for the unknown parameters.
Abbreviations
TSG, tumor suppressor gene; MLE, maximum likelihood estimate; 2 bb, two-component beta-binomial model; 2 bb/bin, two-component beta-binomial/binomial model; 2 bin, two-component binomial model; 1 bb, uni-coniponent beta-binomial model; 1 bin, uni-component binomial model; BMA, Bayesian model averaging; UDM, uniform distance method
Authors contributions
Both MD and MJE contributed substantially to the development of the models and the methodology. MD performed the simulation study and analysis of the three data sets. Both authors have read and approved the final version of the manuscript.
Acknowledgements
This work was developed as part of the first author's doctoral dissertation in the Department of Biostatistics at the University of Washington in Seattle, Washington. The research was partially supported by the National Institute of Health grants 5R29CA77607 and 5T32CA0916825.
Figures and Tables
Figure 1 Histogram of allelic loss for the Barrett data set
Figure 2 Histogram of allelic loss for the Gleeson data set
Figure 3 Histogram of allelic loss for the Hammoud data set
Table 1 Description of scenarios used in simulation study
Loss Rates
Scenario Model* Non-TSG** group TSG group
1 Two-component binomial mixture = 0.22 (33 arms) α1 = 0.66 (5 arms)
2 Uni-component beta-binomial α0 ~ β(0.26, 0.07) (38 arms) -
3 Two-component multi-binomial/binomial mixture α0 = 0.22 (33 arms) α1 = (1, 0.80, 0.64, 0.43, 0.43) (5 arms)
4 Two-component multi-binomial/beta-binomial α0 ~ β(0.26, 0.07) (33 arms) α1 = (1, 0.80, 0.64, 0.43, 0.43) (5 arms)
* Model from which data were generated
** TSG: Tumor suppressor gene
† α0: loss rate for non-TSG group. α1: loss rate for TSG group
Table 2 Percentage of time model under H1 is favored over model under H0 for different scenarios For a given scenario, the rows indicate the model under H1 while the columns indicate the model under H0. The (i, j)th element in the matrix represents the percentage of time the model in the ith row is favored over that in the jth column.
Scenario 1 (α0 = 0.22, α1 = 0.66)
H1/H0 2 bin* 2 bb/bin 2 bb 1 bb 1 bin
2 bin 0 21 75 81 100
2 bb/bin 10 0 80 80 100
2 bb 5 0 0 50 98
1 bb 5 0 0 0 100
1 bin 0 0 0 0 0
Scenario 2 (α 0 ~ β (0.26,0.07))
H1/H0 1 bb 2 bin 2 bb/bin 2 bb 1 bin
1 bb 0 22 21 49 75
2 bin 16 0 24 44 72
2 bb/bin 7 14 0 26 74
2 bb 0 12 0 0 68
1 bin 7 0 7 18 0
Scenario 3 (α0 = 0.22, α1 = (1, 0.80, 0.64, 0.43, 0.43))
H1/H0 2 bb/bin 2 bb 2 bin 1 bb 1 bin
2 bb/bin 0 78 79 98 100
2 bb 1 0 31 100 100
2 bin 0 28 0 87 100
1 bb 0 0 5 0 100
1 bin 0 0 0 0 0
Scenario 4 (α0 ~ β (0.26, 0.07), α1 = (1, 0.80, 0.64, 0.43, 0.43))
H1/H0 2 bb 2 bb/bin 1 bb 2 bin 1 bin
2 bb 0 35 75 97 100
2 bb/bin 9 0 54 99 100
1 bb 0 5 0 72 100
2 bin 0 0 9 0 100
1 bin 0 0 0 0 0
*2 bb: Two-component beta-binomial.
2 bb/bin: Two-component beta-binomial/binomial.
2 bin: Two-component binomial.
1 bb: One-component beta-binomial.
1 bin: One-component binomial.
Table 3 Summary of results after applying methods to three data sets For each data set, the selected model(s) with the chromosome arms classified in the tumor suppressor gene group and corresponding conditional probabilities of harboring a tumor suppressor gene are provided. A set of models was chosen such that models in the set had 2ln(Bayes factors) exceeding 2 when compared to models outside the set and 2ln(Bayes factors) less than 2 when compared to models within the set. A chromosome arm is in bold print if it has been identified in more than one data set.
Data Set Model Chosen Chromosome Arms Classified in TSG* Group (conditional probability)
Barrett 2 bb/bin 5q (1), 9p(0.962), 17p(1)
Gleeson 2 bb/bin 4q(0.982), 9p(0.916), 9q (0.813), 12q (0.859), 17p (0.998), 18q (0.954)
2 bin 4q(0.982), 9p(0.916), 9q (0.813), 12q (0.859), 17p (0.998), 18q (0.954)
1 bb none
Hammoud 2 bb/bin 4q (0.968), 17p (0.994)
2 bin 4q (0.989), 17p (0.998)
* TSG: tumor suppressor gene
2 bb: Two-component beta-binomial
2 bb/bin: Two-component beta-binomial/binomial
2 bin: Two-component binomial
1 bb: One-component beta-binomial
1 bin: One-component binomial
Table 4 2ln(Bayes Factors) and posterior probabilities of each model considered for the three data sets For a given data set, the first five rows of data correspond to the model under H1 while the first five columns correspond to the model under H0. The (i, j)th element in the matrix represents the value of 2ln(Bayes Factors) for the model corresponding to the ith row versus the model corresponding to the jth column. Values of 2ln(Bayes Factors) are in bold print if they exceed 2. The last column provides values of the posterior probability of the model in the ith row. Those values corresponding to selected models are in bold print.
Barrett data set
H1/H0 2 bb* 2 bb/bin 2 bin 1 bb 1 bin Post.Prob**
2 bb 0 -4.398 -0.114 12.144 45.281 0.090
2 bb/bin 4.398 0 4.284 16.542 49.679 0.814
2 bin 0.114 -4.284 0 12.258 45.395 0.096
1 bb -12.144 -16.542 -12.258 0 33.137 < 0.001
1 bin -45.281 -49.679 -45.395 -33.137 0 < 0.001
Gleeson data set
H1/H0 2 bb 2 bb/bin 2 bin 1 bb 1 bin Post.Prob.
2 bb 0 -2.173 -3.390 -2.065 6.705 0.066
2 bb/bin 2.173 0 -1.724 0.108 8.878 0.194
2 bin 3.390 1.724 0 1.832 10.601 0.460
1 bb 2.065 -0.108 -1.832 0 8.770 0.276
1 bin -6.705 -8.878 -10.601 -8.770 0 0.003
Hammoud data set
H1/H0 2 bb 2 bb/bin 2 bin 1 bb 1 bin Post.Prob.
2 bb 0 -3.514 -3.513 -1.114 5.951 0.070
2 bb/bin 3.514 0 0.020 2.400 9.465 0.404
2 bin 3.513 -0.020 0 2.380 9.444 0.400
1 bb 1.114 -2.400 -2.380 0 7.064 0.122
1 bin -5.951 -9.465 -7.064 -7.064 0 0.004
*2 bb: Two-component beta-binomial
2 bb/bin: Two-component beta-binomial/binomial
2 bin: Two-component binomial
1 bb: One-component beta-binomial
1 bin: One-component binomial
** Post.Prob.:Posterior probability
Table 5 Results from fitting two-component models to the Barrett data set Maximum likelihood estimates along with selected chromosome arms and corresponding conditional probabilities of harboring a tumor suppressor gene for the two-component models for the Barrett data set.
Model Arms classified in TSG† group (conditional probability)
2 bb/bin* 0.097 0.708 0.487 0.228 - 5q (1); 9p(0.962); 17p(1)
2 bin 0.073 0.827 - 0.230 - 5q (1); 9p(0.93); 17p(1)
2 bb 0.097 0.708 0.487 0.228 0.000 5q (1); 9p(0.962); 17p(1)
†TSG: tumor suppressor gene
*2 bb; Two-component beta-binomial
2 bb/bin: Two-component beta-binomial/binomial
2 bin: Two-component binomial
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| 15563371 | PMC544187 | CC BY | 2021-01-04 16:02:47 | no | BMC Bioinformatics. 2004 Nov 24; 5:182 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-182 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1881557420210.1186/1471-2105-5-188Research ArticleHaplotype frequency estimation error analysis in the presence of missing genotype data Kelly Enda D [email protected] Fabian [email protected] Ross [email protected] Hitachi Dublin Lab., Hitachi Europe Ltd., O'Reilly Institute, Trinity College, Dublin 2, Ireland2 Dept. of Clinical Medicine, Trinity College Dublin and Dublin Molecular Medicine Centre at St. James's Hospital, Dublin, Ireland2004 1 12 2004 5 188 188 29 7 2004 1 12 2004 Copyright © 2004 Kelly et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Increasingly researchers are turning to the use of haplotype analysis as a tool in population studies, the investigation of linkage disequilibrium, and candidate gene analysis. When the phase of the data is unknown, computational methods, in particular those employing the Expectation-Maximisation (EM) algorithm, are frequently used for estimating the phase and frequency of the underlying haplotypes. These methods have proved very successful, predicting the phase-known frequencies from data for which the phase is unknown with a high degree of accuracy. Recently there has been much speculation as to the effect of unknown, or missing allelic data – a common phenomenon even with modern automated DNA analysis techniques – on the performance of EM-based methods. To this end an EM-based program, modified to accommodate missing data, has been developed, incorporating non-parametric bootstrapping for the calculation of accurate confidence intervals.
Results
Here we present the results of the analyses of various data sets in which randomly selected known alleles have been relabelled as missing. Remarkably, we find that the absence of up to 30% of the data in both biallelic and multiallelic data sets with moderate to strong levels of linkage disequilibrium can be tolerated. Additionally, the frequencies of haplotypes which predominate in the complete data analysis remain essentially the same after the addition of the random noise caused by missing data.
Conclusions
These findings have important implications for the area of data gathering. It may be concluded that small levels of drop out in the data do not affect the overall accuracy of haplotype analysis perceptibly, and that, given recent findings on the effect of inaccurate data, ambiguous data points are best treated as unknown.
==== Body
Background
Haplotype analysis has become a valuable tool for researchers in population genetics. In particular, the value attached to the prediction of the constituent haplotypes of a given sample and their frequency of occurrence is such that a variety of methods have been developed for this purpose. Many of these methods, however, depend on knowledge of the phase of the data supplied. In general, genotypic data from polymorphic loci are ascertained phase-unknown. Various methods for determining the gametic phase exist. With sufficient data from the genotyping of family members, definitive haplotypes may be inferred. However, in particular for late-onset disorders, these data may be difficult or even impossible to obtain. At the laboratory level, techniques such as chromosomal isolation or long-range PCR [1] may be utilised in the prediction of haplotypes, but they suffer the dual drawbacks of being both technologically demanding and in many cases prohibitively expensive in practice. Thus researchers have moved towards computational solutions to this problem. Prominent among the techniques employed for the estimation of the true haplotype frequencies of a phase-unknown sample are those based on the Expectation-Maximisation (EM) algorithm. Hill [2] originally proposed the use of the EM algorithm in genetics, and three years later the term was first coined by Dempster et al. [3] and the method put on a more formal footing. A number of EM-based methods for haplotype frequency estimation (HFE) have been produced [4,5]. Excoffier and Slatkin [6] provide a thorough outline of the implementation of the EM algorithm as applied to the problem of HFE.
Reliable computational techniques for the estimation of haplotype frequencies have been around for some time, and extensive studies of the accuracy of the EM-based methods have been carried out [7,8], but until recently there has been little investigation of the effect of missing data on these techniques. This is surprising considering that, even with modern automated DNA analysis methods, the problem of missing data is not uncommon, whether due to the failure of amplification or insufficient DNA. Zhao et al. [9] have developed the GENECOUNTING software specifically to take into account missing data in a sample, but have not produced any validation of the method. The HAPLO [5] program is also capable of analysing multiallelic data with missing alleles, using jackknife techniques for error analysis. The SNPHAP [10] algorithm can handle large numbers of loci and unknown alleles, but is restricted to the analysis of biallelic loci. In order to carry out an investigation of the effect of missing data on HFE, a program, based on the algorithm outlined in [6], has been developed which can accommodate multiallelic loci and a significant percentage of unknown alleles. The necessary alterations to the existing implementation of the EM algorithm are outlined in the Methods section. Following this, biallelic and multiallelic data sets were analysed with varying quantities of unknown alleles randomly substituted. The analysis is similar to previous work by Kirk and Cardon [11], which described the effect of genotyping error on HFE. Here we investigate the effect of missing data on the sizes of the confidence intervals (CIs) about the haplotype frequency point estimates (or simply "point estimates"). Surprisingly, the loss of as much as 30% of the allelic data did not have a significantly detrimental effect on the quality of the results. The frequencies of haplotypes which predominate in the complete data analysis remain essentially the same after randomly selected data have been relabelled as missing. The error estimates associated with the predicted frequencies, which are generated via a bootstrap method, are also quite stable, but increase as the proportion of missing data increases.
Results
Source of data
Two sources of data were used for the principal part of this study. The first is real single nucleotide polymorphism (SNP) data; the second is multiallelic data generated via population generation software. Three additional sets of data containing 10%, 20% and 30% missing alleles respectively were generated from each of the two original sets. The process of generation is described in the Methods section. HFE was carried out on the eight data sets listed above. In each case 1,000 bootstrap iterations were performed for each HFE analysis and the 95% CIs about the point results were selected. For the sake of clarity the results from analyses of the 20% unknown alleles data sets have been omitted from the displayed graphs. Further tests were performed to investigate the effect of sample size upon the quality of the results. To this end two sets of progressively smaller data sets, with and without missing alleles, were generated from the SNP and multiallelic data sets, and HFE was carried out. The method of selecting these data is outlined in the Methods section.
An additional data set, unrelated to those previously described, consisting of data from five SNP loci was generated for the purposes of performing tests on data with weak LD between the loci. A further data set with 10% missing alleles was generated from these additional data.
Seven loci biallelic data sets
Figure 1 displays point estimate results from the analyses of the seven loci SNP data sets with 536 sample points. Figure 1 is a comparison of the frequencies of the 26 haplotypes present in the phase-known data, and their predicted frequencies when the phase is assumed unknown and data are missing. The percentage of missing alleles varies from zero (labelled "complete data") to 30. The haplotypes derived from the phase-known data were labelled from 1 to 26 in non-increasing order of the magnitude of their frequency, hence the "haplotype label" of the x-axis. For a quantitative measure of the discrepancies in the frequencies between the phase-known and phase-unknown frequency predictions we use the measure D (h, ) [6,11] given by
where hi and are the haplotype frequencies derived from the phase-known and phase-unknown data respectively, and N is the number of possible haplotypes in the sample. As these data are from seven biallelic loci, N = 27 = 128 in this case. The results are displayed in Table 1. Also recorded in Table 1 is the percentage increase in D (h, ) as the percentage of unknown alleles in the sample increases. In each case it is the percentage increase relative to the complete data value that is measured. Three haplotypes absent from the phase-known data set appear in the results of the HFE analysis of the complete data. Their frequencies are 2.3 × 10-3, 1.4 × 10-3, and 1.1 × 10-3. Of the haplotypes present in the phase-known data, only one haplotype appears with a frequency less than these, the given frequency being 9.3 × 10-4. Figure 1 and Table 1 offer complementary illustrations of the effect of missing data. Table 1 provides a good overall picture of how the accuracy of the HFE method deteriorates with inferior data quality. The effect is most marked in the initial jump from complete to 10% missing data, where a 35% increase in D (h, ) is recorded. The subsequent percentage increases going from 10% to 20% and 20% to 30% unknown alleles are 22% and 16%, respectively, of the value of D (h, ) for the complete data. Figure 1 allows us to view specifically where this deterioration is most evident, in the mid-range frequency haplotypes. Figures 2 and 3 display the effect of increasing quantities of missing data on the 95% CIs of the haplotype frequencies estimated from the phase-unknown data. In an attempt to quantify this effect, the spread of the CIs for each haplotype (the difference between the two bootstrap haplotype frequencies which give the limits of the 95% CI) was summed for each data set. The sum for each data set containing missing data was compared with the sum for the complete data set (no missing data). The ratio of the two values (the ratio of the extent of the CIs or RCI) for each comparison is displayed in Table 2. Here we see a superlinear increase in the RCI with increasing proportions of missing data. Despite this, we note from Figures 2 and 3 that, even for the 30% missing data case, the CIs for the complete data are not entirely contained within the CIs for the data with unknown alleles for many of the haplotypes.
Multiallelic data sets
Similar computations to those carried out for the SNP data sets were carried out for the four multiallelic data sets. Figure 4 is a comparison of the frequencies of the most prominent haplotypes in the phase-known data, and their predicted frequencies when the phase is assumed unknown and data are missing. As with the seven loci SNP data sets, the percentage of missing data varies from zero to 30. The haplotypes are labelled as before. However, as 118 distinct haplotypes appear in the phase-known data, only the frequencies for the 40 most common are illustrated in Figures 4 to 6 for reasons of clarity. No haplotype with a frequency greater than 0.005, as given by the phase-known data, was excluded from the graphs by this trimming. As with the biallelic data, the discrepancy between the phase-known and phase-unknown frequency predictions, D (h, ), was measured. As the allele counts at each of the seven loci are 8, 2, 2, 9, 2, 5, and 2 respectively, the sum in Equation 1 is over the N = 5760 possible haplotypes in the sample. The results are displayed in Table 3. As in Table 1, the percentage increase in D (h, ) as the percentage of unknown alleles in the sample increases is also recorded.
129 distinct haplotypes were estimated to have a frequency of greater than 10-6 as a result of the HFE analysis. 29 of these do not appear in the phase-known data, with the most common of these having a frequency of 2.187 × 10-3. 68 haplotypes in the phase-known data display a frequency greater than this. As with the SNP case, Figure 4 and Table 3 together provide a good overall picture of the effect of missing data on the accuracy of the HFE method. Table 3 displays similar percentage increases in D (h, ) with the 10% and 20% missing data cases to those of Table 1 (42% and 18% respectively), though there the similarity ends, as the jump in D (h, ) going from 20% to 30% unknown alleles comes to 40% of the value of D (h, ) for the complete data. In Figure 4 we see how the phase-unknown frequency predictions match well the observed phase-known frequencies for the more prominent haplotypes, but less well for the less common haplotypes, particularly for the 30% missing data case.
Similarly to the SNP case, Figures 5 and 6 display the effect of increasing quantities of missing data on the 95% CIs of the haplotype frequencies estimated from the phase-unknown data. As before, measurement of this effect was made by observing the relative increase in the sizes of the CIs. The results are displayed in Table 4. In contrast to the SNP case, we see a linear increase in the RCI with increasing proportions of missing data. This contrast is further marked by Figures 5 and 6 where we note that the CIs for the complete data are, in the case of most haplotypes, entirely contained within the CIs for the data with unknown alleles.
Sample sizes
Investigations were made into the effect of the sample size on the performance of the HFE method when 10% of the data was missing. Three further data sets of sizes 300, 100 and 50 individuals were generated by random selection from the original seven loci SNP and multiallelic sets. From these data, six additional sets with 10% missing alleles were created. HFE was performed upon these additional data, and the D (h, ) results for each were displayed in Table 5. In each case the phase-known haplotype frequencies used in the computation of D (h, ) were those derived from the respective smaller samples (e.g. the accuracy of the HFE method as applied to the SNP sample with 300 individuals was calculated relative to the haplotype frequencies observed in the phase-known sample with 300 individuals, and not those observed in the original data). As may be expected, in all cases we see an increase in D (h, ) as we move from the complete data to the data sets with missing alleles. D (h, ) also is seen to increase as the sample size decreases. However, what is of note is the pattern involved. For the seven loci SNP case, the percentage increase in D (h, ) from complete to missing data itself increases monotonically as the sample size is reduced. A similar pattern is not observed in the multiallelic data.
Performance at low LD levels
Fallin and Schork [7] illustrate how the performance of the EM-based HFE method diminishes with falling LD strength. Here we investigated how the accuracy of our implementation behaves on a data set exhibiting weak LD when 10% of the alleles are missing. A population of 500 individuals with data at five SNP loci was generated specifically for this part of the study. Lewontin's D' [12] was found to range between 0.117 and 0.014 for all adjacent loci. Table 6 displays D (h, ) readings for this particular case. Here we see a large percentage increase of 60% in D (h, ) as we move from the complete data to 10% missing data.
Discussion
The results displayed here show the impact of the addition of increasing quantities of missing alleles on the quality of haplotype frequency estimates. Studying Figure 1 in tandem with Table 1, and Figure 4 in tandem with Table 3, we see a loss of accuracy of the HFE method as the quality of the data degrades. This is particularly true for the multiallelic data set with 30% missing alleles. Here the loss of accuracy is most apparent with the rarer haplotypes as may be seen in Figure 4, whereas for the seven loci SNP case, Figure 1 illustrates that the low frequency haplotypes are dealt with remarkably well, even at high missing data proportions. For both sets of data the ability of the method to predict the frequencies of the most prominent haplotypes in the samples holds up well as the percentage of unknown alleles increases. Figures 2 and 3 and Figures 5 and 6 display a similar behaviour in the bootstrap generated CIs. To summarise, there are two significant aspects of the analysis of genotypic data containing incompletely typed individuals evident here. Firstly, that the HFE algorithm, given phase-unknown data with moderate to high levels of LD, predicts the frequencies of the underlying haplotypes with a high degree of accuracy, as is evident from the point estimate graphs, Figures 1 and 4. Tables 1 and 3 quantify how the quality of the frequency predictions behave with increasing percentages of missing data. For the multiallelic case where 30% of the alleles are unknown, Table 3 shows that the discrepancy between the phase-known and phase-unknown predicted frequencies has doubled when compared with the complete data case, though from the study of Figure 4 the bulk of this discrepancy would appear to originate from the lower frequency haplotypes. The second aspect is the extent of the 95% CIs. We see a steady increase in the spread of the CIs with the addition of missing alleles, reflecting the growing uncertainty in the data. However, the most prominent haplotypes in both the SNP and multiallelic data sets maintain their distinctiveness, even at the 30% unknown alleles level. These data show that, in particular for the SNP data set, the effect of relabelling significant proportions of the data as unknown on the performance of the HFE algorithm is minor.
Although study of the illustrated graphs suggests that the impact of missing data is more pronounced with the more complex multiallelic data sets, Tables 2 and 4 demonstrate that the relative increase in the size of the CIs is similar across the biallelic and multiallelic data sets, and is almost identical for the 30% missing data sets. There appears to be a discrepancy between the two measures, namely D (h, ) and the RCI, used here to quantify the degradation in the quality of the results with increasing percentages of unknown alleles. Tables 1 and 3 imply that the HFE method works significantly better for biallelic data than for multiallelic data, whereas this phenomenon is much less evident in Tables 2 and 4. This may be explained by the fact that D (h, ) is an absolute measure of the performance of the algorithm, as the phase-known data are available for each data set and thus the exact sample haplotype frequencies are known. This discrepancy is to be expected; D (h, ) is a sum over all possible haplotypes and there exist only 128 (27) possible haplotypes for the seven loci SNP data, whereas the multiallelic data, as noted in the Results section, have 5760 possible haplotypes. Also, it is not surprising that haplotype frequencies estimated from the multiallelic data set are found to be less accurate than those estimated from SNPs, given the more complex nature of the data. The RCI is a relative measure, and illustrates not so much the accuracy of the algorithm, rather the effect of additional missing data. The results displayed in Tables 2 and 4 show that the algorithm handles the increase in the proportion of unknown alleles equally well for both SNPs and multiallelic data, although it should be pointed out that the RCI measure gives no indication of the accuracy of the point estimates, and should generally be considered in tandem with a measure such as D (h, ). Interestingly, the results for the multiallelic data set were achieved despite departure from Hardy-Weinberg equilibrium (HWE) at two of the seven loci (see Methods section). Although this technique relies on the assumption of HWE, Niu et al. [13] have demonstrated it to be reliable and robust even when the HWE assumption has been violated. Fallin and Schork [7] have shown that HWE violation which results in an excess of heterozygosity leads to an increase in HFE error, though their results are based on a five-locus system, and the observed error increase when two of the five loci were found to be in disequilibrium was minimal. As we are dealing here with a seven-locus system, the effect on the error was likely to have been even less apparent.
The investigation into the effect of smaller sample sizes has produced some surprising results. Comparing Table 1 with Table 5, we see that the relative increase in D (h, ) observed when 10% of the seven loci SNP data is relabelled as missing does not change substantially as the size of the sample reduces. For the full sample of 536 individuals, the percentage jump in D (h, ) moving from the complete data to 10% missing data is approximately 35%. For the sample of size 300, this increase is 37%. Likewise for the samples of size 100 and 50, the increases are 41% and 47% respectively. However, for the multiallelic data, we see a contrasting trend. The percentage jump in D (h, ) decreases rather than increases with increasing missing data proportions. Inspection of Tables 3 and 5 shows us that the percentage increase in D (h, ) when moving from the complete data to 10% missing data for the full sample of 500 individuals is approximately 42%, whereas for the sample of size 300 this drops to 32%. The recorded increase for the sample of size 100, 5%, is even more striking. (The sample of size 50 is not considered here, as the matching observed between the phase-known and phase-unknown frequencies was of poor quality (figure not shown), and any conclusions drawn from analysis of this case would be highly suspect). Thus no definitive conclusions may be made as to the effect of missing data as the sample size is reduced, other that to say that the matching between the phase-known and phase-unknown frequencies deteriorates with falling sample size, as would be expected.
Table 6 underlines the relationship between strong LD and superior performance of the EM method [7]. For the weak LD data set, we see that D (h, ) for the complete data is comparable to that of the seven loci SNP data with 30% missing alleles. It should also be borne in mind that, as the weak LD data set features only five SNP loci, the sum for D (h, ) is over a mere 32 possible haplotypes, as compared to 128 for the seven loci SNP data, emphasising the fall-off in accuracy. Also of note is the similarity in the sample sizes -500 in the weak LD case, and 536 in the moderate to strong LD case. Moving to the 10% missing allele case, we witness a further 60% drop in accuracy, a considerably greater percentage that was observed for the medium to high LD data sets, a result which again calls into question the reliability of the method in the presence of weak LD.
Conclusions
Here we show that the EM method, with the modifications to the implementation for complete data detailed here, can generate accurate estimates of haplotype frequencies even when large amounts of data are missing, in this case up to 30%. Moreover, using this method, the degree of accuracy can easily be estimated using conventional bootstrapping approaches. This is of considerable importance in the design of experiments, as it is therefore obvious that small levels of drop out in the data for whatever reason do not affect the overall accuracy of the approach perceptibly. Furthermore, considering the strongly deleterious effects of even small amounts of inaccurate data [11], this analysis shows that large amounts of missing data are much less detrimental to the overall quality of the results than incorrectly typed sites. Thus from a practical standpoint it is clearly preferable that if any doubt exists as to a genotype's identity, it should be excluded rather than included using a "best guess".
Methods
Seven loci biallelic data
The data used in this part of the study are derived from a genetic investigation of cystic fibrosis sufferers [14]. The haplotypes used here are actual haplotypes composed of a subset of the markers typed in the vicinity of the CFTR gene locus. The haplotypes comprise seven biallelic loci. From these haplotypes 536 phase-known genotypes were constructed via random resampling. Thus the data set comprised of 536 individuals each with seven SNP loci. In common with Kirk and Cardon [11] a linkage disequilibrium (LD) analysis was carried out on the data. For adjacent loci, D' was found to be ≥ 0.9 for all intervals but the third and fifth, where D' ≤ 0.25. As HWE is assumed for HFE, each locus was tested and found to be in HWE.
Multiallelic data
An initial population of fifty individuals with data from seven loci spaced 1 cM apart was generated in silico. The number of distinct alleles at each locus ranged from two to nine. A trait marker was introduced between the 3rd and 4th loci for 10 of the 50 founders. The population was evolved for thirty generations as an isolated group with random mating. The birth rate per couple was binomially distributed, with a range of zero to ten offspring and a mean of 2.5. 500 individuals bearing the trait were randomly selected from the final generation for analysis. As with the SNP data, the level of LD across the interval was measured. D' was found to lie between 0.5 and 0.8 for all adjacent loci except between the second and third loci where D' = 1.0 and the fifth and sixth where D' = 0.24. A test for HWE [15] was performed, and it was found that the fourth and fifth loci were not in HWE (P-values < 0.001 and 0.003 respectively). In both cases an excess of heterozygosity was evident (observed heterozygosities of 0.848 and 0.232, respectively compared with expected heterozygosities of 0.729 and 0.205, respectively).
Smaller sample sizes
The data sets of reduced size used in this analysis were generated from the original seven loci SNP and multiallelic data sets via a random sampling process. The process was identical for both. Initially 300 individuals were chosen from the original data. Following this, 100 individuals were chosen from the newly created set of size 300. Finally 50 individuals were chosen from the set of size 100. In each case the selection process was random and done without replacement. From each of these six smaller data sets, six additional sets of data with 10% missing alleles were generated by the process outlined below.
Low LD data
A population of 500 individuals with data from five SNP loci was generated in silico specifically for the testing of the performance of the HFE algorithm in low LD circumstances. D' was found to range between 0.117 and 0.014 for all adjacent loci. The data were also tested for HWE. The first locus was found to be marginally not in HWE (P = 0.0465), with excess homozygosity in evidence. All other loci were found to be in HWE.
Phase-unknown data
The HFE algorithm assumes that the input data are phase-unknown, and thus no alteration was necessary to the sample data sets which were phase-known before input. Comparison tests on the phase-known data, and phase-unknown data generated from the phase-known data via a process of phase-randomisation have confirmed that no bias is introduced by the use of phase-known data (results not shown).
Generation of missing data
Data sets containing unknown alleles were generated from the original data via the following procedure:
1. Each individual is selected in turn.
2. For each locus a random number between 0 and 100 is generated.
3. If this random number falls below the desired percentage of unknowns, both of the individual's alleles at the locus in question are redefined as unknown. This ensures that all unknowns appear in homologous pairs.
4. The process is repeated until all loci for all individuals are exhausted.
Thus the desired percentage of unknown alleles is achieved globally, and the percentage of missing data at each locus may vary. Three additional sets of data were generated from each of the two original sets in this way, with 10%, 20% and 30% missing data respectively, giving eight data sets in all for the principal component of the study.
Expectation-Maximisation algorithm
For known gametic phase, HFE is a straightforward process of counting the constituent haplotypes in the sample. For the case where the gametic phase is unknown, maximum-likelihood haplotype frequencies are computed using the EM algorithm. The particular implementation used here for the finding of the haplotype frequencies is similar to that outlined by Excoffier and Slatkin [6]. The operation of the algorithm is based on the assumption of HWE, though as mentioned above, the method has been found to be quite robust in the presence of deviations from HWE [13].
Implementation of the EM algorithm
Missing data in a sample necessitate alterations to the implementation for complete data of the EM-based algorithm. When all alleles in an individual are known, there exist cj possible genotypes consistent with this phenotype where
and sj is the number of heterozygous loci in phenotype j. However, when unknown alleles appear at a locus, the situation is considerably more complex. In this case each unknown allele may take on the identity of any of the alleles observed at that locus. We require that unknown alleles always appear in pairs – the amplification of one allele only would result in the appearance of a homozygote which may bias results. Thus if there are Ni distinct alleles (forms) observed at locus i in the entire sample, the number of possible complete phenotypes consistent with the observed phenotype is increased by a factor of Ni(Ni + 1)/2 by the presence of an unknown site. This factor is the number of ways of selecting two alleles from a pool of Ni distinct alleles when repetition is allowed. Thus the number of possible complete phenotypes given by phenotype j is given by
where M is the number of loci in the sample and
where Ni is the number of distinct alleles observed in the sample at locus i. For each possible complete phenotype i of the κj complete phenotypes possible for individual j, there exist ci possible genotypes, as given by Equation 2. Thus the number possible complete genotypes for phenotype j is given by
Then, following [6], the probability Pj of the jth phenotype, assuming random mating, is given by:
where Pi(hkhl)is the probability of the ith genotype made up of haplotypes k and l, and
where pk and pl are the population frequencies of the kth and lth haplotypes.
Expectation step
At the tth step of the EM iterative process, the probability of resolving each phenotype into the different possible genotypes is given by:
where nj is the number of individuals with phenotype j, and n is the total number of individuals in the sample. Thus nj/n is the proportion of the total sample that has phenotype j, and Pj(hkhl)/Pj is the conditional probability of the particular genotype given the phenotype.
Maximisation step
The haplotype frequencies are then computed using a form of gene-counting [16,17] :
where N is the number of globally distinct haplotypes (the number of different possible haplotypes in the sample), is the frequency of haplotype v, m is the number of distinct phenotypes in the sample, and εiv is equal to the number of times haplotype v appears in genotype i.
Generation of confidence intervals
The technique of bootstrapping [18] was used to generate CIs about the point haplotype frequencies estimated from the phase-unknown data. Specifically, the percentile bootstrap approach was used.
Authors' contributions
EDK carried out the main programming work, performed the tests and drafted the manuscript. FS designed the population generation tool and assisted in the programming effort. RM assisted in the drafting of the manuscript and provided the SNP data. All authors read and approved the final manuscript.
Figures and Tables
Figure 1 Point estimates for seven loci SNP data. Point estimate haplotype frequencies for seven loci SNP data set for phase-known and phase-unknown data with 0%, 10%, and 30% missing data.
Figure 2 Seven loci SNP data with 10% missing alleles. 95% CIs for haplotype frequencies estimated from seven loci SNP phase-unknown data with 0% and 10% missing data. Frequencies derived from phase-known data also shown.
Figure 3 Seven loci SNP data with 30% missing alleles. 95% CIs for haplotype frequencies estimated from seven loci SNP phase-unknown data with 0% and 30% missing data. Frequencies derived from phase-known data also shown.
Figure 4 Point estimates for multiallelic data. Point estimate haplotype frequencies for multiallelic data set for phase-known and phase-unknown data with 0%, 10%, and 30% missing data.
Figure 5 Multiallelic data with 10% missing alleles. 95% CIs for haplotype frequencies estimated from multiallelic phase-unknown data with 0% and 10% missing data. Frequencies derived from phase-known data also shown.
Figure 6 Multiallelic data with 30% missing alleles. 95% CIs for haplotype frequencies estimated from multiallelic phase-unknown data with 0% and 30% missing data. Frequencies derived from phase-known data also shown.
Table 1 D (h, ) for 7 loci SNP data. Measure of discrepancy between phase-known and phase-unknown frequency predictions for seven loci biallelic data, 536 individuals.
% missing alleles D (h, ) % increase from complete data
0 0.043822 0
10 0.059222 35
20 0.069017 57
30 0.075998 73
Table 2 RCI for 7 loci SNP data. Ratio of extent of 95% CI for missing data sets compared to complete data set for seven loci biallelic case, 536 individuals.
% missing alleles RCI
0 1.0
10 1.102024
20 1.243451
30 1.469974
Table 3 D (h, ) for multiallelic data. Measure of discrepancy between phase-known and phase-unknown frequency predictions for multiallelic data.
% missing alleles D (h, ) % increase from complete data
0 0.106954 0
10 0.151494 42
20 0.170971 60
30 0.213888 100
Table 4 RCI for multiallelic data. Ratio of extent of 95% CI for missing data sets compared to complete data set for multiallelic case.
% missing alleles RCI
0 1.0
10 1.162630
20 1.315285
30 1.470215
Table 5 D (h, ) for smaller samples. Measure of discrepancy between phase-known and phase-unknown frequency predictions for smaller samples randomly selected from larger data sets.
Data set # individuals % missing alleles D (h, ) % increase from complete data
7 loci SNP 300 0 0.049354 0
7 loci SNP 300 10 0.067410 37
7 loci SNP 100 0 0.104105 0
7 loci SNP 100 10 0.147034 41
7 loci SNP 50 0 0.155912 0
7 loci SNP 50 10 0.229097 47
multiallelic 300 0 0.153865 0
multiallelic 300 10 0.202678 32
multiallelic 100 0 0.227170 0
multiallelic 100 10 0.238658 5
multiallelic 50 0 0.320917 0
multiallelic 50 10 0.372827 16
Table 6 D (h, ) for low LD data. Measure of discrepancy between phase-known and phase-unknown frequency predictions for low LD data.
% missing alleles D (h, ) % increase from complete data
0 0.090178 0
10 0.144290 60
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| 15574202 | PMC544188 | CC BY | 2021-01-04 16:02:47 | no | BMC Bioinformatics. 2004 Dec 1; 5:188 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-188 | oa_comm |
==== Front
BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1981560146510.1186/1471-2105-5-198SoftwareGenomeViz: visualizing microbial genomes Ghai Rohit [email protected] Torsten [email protected] Trinad [email protected] Institute of Medical Microbiology, Faculty of Medicine, Justus-Liebig-University, Frankfurter Strasse 107, D-35392 Giessen, Germany2004 15 12 2004 5 198 198 18 6 2004 15 12 2004 Copyright © 2004 Ghai et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
An increasing number of microbial genomes are being sequenced and deposited in public databases. In addition, several closely related strains are also being sequenced in order to understand the genetic basis of diversity and mechanisms that lead to the acquisition of new genetic traits. These exercises have necessitated the requirement for visualizing microbial genomes and performing genome comparisons on a finer scale. We have developed GenomeViz to enable rapid visualization and subsequent comparisons of several microbial genomes in an interactive environment.
Results
Here we describe a program that allows visualization of both qualitative and quantitative information from complete and partially sequenced microbial genomes. Using GenomeViz, data deriving from studies on genomic islands, gene/protein classifications, GC content, GC skew, whole genome alignments, microarrays and proteomics may be plotted. Several genomes can be visualized interactively at the same time from a comparative genomic perspective and publication quality circular genome plots can be created.
Conclusions
GenomeViz should allow researchers to perform visualization and comparative analysis of up to eight different microbial genomes simultaneously.
==== Body
Background
Current efforts in genome sequencing have led to a rapid increase in the number of microbial genome sequences. A total of 522 ongoing microbial genome projects are listed in the GOLD database [1] and while 167 microbes have been completely sequenced. These sequencing projects now include several bacterial pathogens and different isolates of the same bacterial species that differ with respect to virulence and physiology. Thus, complete and partial genome sequences of a number of closely related species/strains from various genera such as Escherichia, Bacillus, Helicobacter, Mycobacterium, Streptococcus, Staphylococcus and Listeria are available. Genomic data too, is diverse, ranging from COG functional classification data [2], genomic islands [3,4], expression data from microarrays and proteomics, GC skew, AT skew, GC%, to whole genome alignments. Such rapid increase in genomic information necessitates the development of tools that offer rapid and convenient visualization capabilities. Furthermore, it is important to contrast and compare data deriving from several different sources (computational, genomic, proteomic) to have a better understanding of genome function.
Several genome visualization tools have been developed in the last few years. The Microbial Genomes Viewer [5] offers a good online solution to genomic visualization, allowing flexibility in using one's own data. However, the plot is not very interactive and provides no undo facility as once a mistake is made one has to recreate the entire plot. GenoMap [6] can be used to create circular genome plots. Although the visualization is helpful, only limited interaction is possible with the resulting plot. GenomeAtlas [7] provides picture-based structural DNA analysis for a large number of genomes via a web-interface. GenomePlot [8] also provides a method to render chromosome wheel plots using tab-delimited input files, although it lacks the interactivity with the pictures and requires a rather specific input file format that may have to be customized for each genome. BugView [9] is another application that allows comparative analysis of microbial genomes, however it allows only two genomes to be viewed and compared simultaneously. The linear plots are useful and offer much flexibility but the circular plots are static. Genome2D [10] offers useful visualization options for data visualization and integration of several algorithms for a single genome at a time. Artemis [11] and ACT [12] are convenient programs for visualizing single genomes or comparing multiple genomes on linear scales.
Implementation
GenomeViz has been programmed in ActiveTcl. ActiveTcl [13] is available freely from ActiveState. PERL [14] is needed to run the scripts available with GenomeViz. This is usually installed on Linux and Solaris systems but can also be freely downloaded. GenomeViz works only on Unix-based platforms and has been tested on Linux and Solaris operating systems. Currently, it does not work on Windows because of a bug in the Tcl library on Windows which causes narrow arcs on a canvas to be drawn incorrectly. We recommend a minimum of 512 MB RAM to run the program.
Results and discussion
GenomeViz uses the concept of "tags" which may be applied to groups of genes for classification-type data. A tag file is tab-delimited text file of three columns. It has the "tags", their colors, and their brief descriptions. A pre-prepared tag file for the COG functional categories is available for immediate use. The map file has all the information required to create the plot (gene name, strand, start and end in genome, annotation, and the tag for the gene). It is also a tab-delimited text file. Both file formats (tag and map) are easy to manipulate in a spreadsheet application like Microsoft Excel. However, care must be taken while manipulating data in such applications since errors may creep in the data as demonstrated by Zeeberg et al. [15]. The map file alone is sufficient for plotting numerical data, but both the map and tag files are needed to plot classification-type data. Data type (qualitative or quantitative) is automatically detected from the map file. A PERL script "tagit" is also available for "tagging" a particular set of genes with user-defined tags. Another script, "avid2viz" is also available which reformats whole genome alignments created by the AVID program [16] to a map file format that can be visualized in GenomeViz (Figure 1).
In order to minimize initial difficulty that users may encounter in creating their own map files, we provide pre-prepared map files for over a hundred genomes. Of these nearly seventy genomes are loaded with the COG classification scheme and may be used immediately. The program also performs checks on the input map file for possible formatting errors and attempts to indicate location of errors (if any) before creating the plot. Several types of plots may be created; on either single or double strands and color gradients and line-graphs are available for numerical data. Once the plots are done, mouse-over on any gene immediately displays associated information from the map file in a display area.
Using GenomeViz, it is also possible to search, highlight and retrieve genes of interest. Each loaded genome may be queried separately. Regular expression searches are fully supported and results are highlighted in the genome. For instance, the simple expression ribosomal|ribosome will mark in color all ribosomal proteins in any genome and retrieve all the information for these genes from the map file. The "|" operator is the standard OR operator in Tcl expressions. Genes involved in iron metabolism/regulation which are usually annotated with keywords like ferrous, ferric or iron may be retrieved with the expression "ferric|ferrous|iron". The results can be saved as a text file. Users can also use their own annotations to visualize and query their genome of interest provided these annotations are available in the map file format. It is also possible to display, in different colors, the results of different queries on the same genome by changing the search color before performing a search. This will enable visualization of, for instance, the distribution of genes/operons involved in iron and zinc metabolism/regulation separately.
A 'Select COGs' option enables one to retrieve all genes from a particular COG category, e.g. "Cell division and Chromosome partitioning" or "Transcription". Each loaded genome can thus be queried separately. Usually, this is more useful when using a special tag file (CogsGrayScale.tag) that colors all genes as "grey", so that a neutral background is available for highlighting the distribution of genes of a particular COG category over the entire genome. Categories of interest can be highlighted in different colors simply by changing the selection color before selecting the category. Results of each query are also displayed in a text box from where they may be saved as a text file. Thus, GenomeViz allows a rapid overview of the similarities in distribution of various functional categories in closely related genomes (Figure 2). It is also possible to visualize differences/similarities in data derived from various different sources e.g. horizontally transferred genes (Figure 3).
Several options are available for printing the circular plot. The graphics can be directly sent to the printer or saved to a PostScript file and read by standard graphics programs. A number of page size options are available and extra large plots spanning many pages may also be printed. A detailed program manual is available with notes on installation, usage and examples.
Conclusions
We describe a rapid and convenient application GenomeViz for simultaneous visualization and comparison of varied genomic data from several microbial genomes. Future updates for software and data will be available from the project home page.
Availability and requirements
• Project name: GenomeViz
• Project home page:
• Operating system(s): Linux, Solaris, Unix
• Programming language: Tcl/Tk
• Other requirements: ActiveTcl, PERL
• License: Free for academic use
• Any restrictions on use by non-academics: Contact corresponding author for a license.
List of abbreviations used
COG: Clusters of Orthologous Groups
SIGI: Score-based Identification of Genomic Islands
Authors' contributions
RG conceived the program, wrote and tested it, prepared the manuals and the website. TC oversaw the entire development process. TH offered suggestions on program features. RG and TC prepared the manuscript. All authors read and approved of the final manuscript.
Acknowledgements
The authors thank Dr. Uday Kishore for helpful suggestions on the manuscript. The work reported herein is supported by grants from the Deutsche Forschungsgemeinschaft and the BMBF Network Program Pathogenomics to TC. RG is supported by the Graduate College of Biochemistry of Nucleoprotein Complexes, Justus Liebig University, Giessen, Germany
Figures and Tables
Figure 1 Whole genome alignments of five Listeria strains/species. From outside to inside: L. monocytogenes EGDe serovar 1/2a COG categories (outer two circles), L. monocytogenes F6854 serovar 1/2a (blue, 133 contigs), L. monocytogenes F2365 serovar 4b (red, whole genome), L. monocytogenes H7858 serovar 4b (orange, 180 contigs) and L. innocua (innermost, green, whole genome). All genomes were aligned separately to L. monocytogenes EGDe with AVID. Sequence data for strains L. monocytogenes strains F6854 and H7858 was obtained from The Institute for Genomic Research [17].
Figure 2 A typical image generated by GenomeViz. From outside to inside: Listeria monocytogenes COG categories (two circles), horizontally transferred gene categories in L. monocytogenes identified using SIGI (two circles), mean centered GC% of L. monocytogenes genes (red-above mean, blue-below mean, one circle), GC% gradient (red-high GC%, green-low GC%, one circle), Listeria innocua COG categories (two circles), horizontally transferred gene categories in L. innocua identified using SIGI (two circles), mean centered GC% of L. innocua genes (red-above mean, blue-below mean, one circle), GC% of L. innocua genes shown as a line graph (innermost circle). When created in GenomeViz, this image is fully interactive and any plotted circle may be queried. It also shows the different ways in which qualitative or numerical data may be plotted. Differences in the horizontally transferred genes in the two Listeria species may be examined and related to GC content in the region.
Figure 3 Comparing data from different sources using GenomeViz.The figure shows a comparison of the distribution of horizontally transferred genes in Escherichia coli K12 compiled from three different sources. From outside to inside: Escherichia coli K12 COG categories (two circles), genes identified by SIGI (two circles), genes listed in the Horizontal Gene Transfer Database [18] (two circles), standard deviations of genes identified by IslandPath (single circle, red +ve, green -ve), mean centered GC content of the genome (red: above mean, blue: below mean), GC content of the genome again as a single-sided line plot (green).
==== Refs
Bernal A Ear U Kyrpides N Genomes OnLine Database (GOLD): a monitor of genome projects world-wide Nucleic Acids Res 2001 29 126 127 11125068 10.1093/nar/29.1.126
Tatusov RL Fedorova ND Jackson JD Jacobs AR Kiryutin B Koonin EV Krylov DM Mazumder R Mekhedov SL Nikolskaya AN Rao BS Smirnov S Sverdlov AV Vasudevan S Wolf YI Yin JJ Natale DA The COG database: an updated version includes eukaryotes BMC Bioinformatics 2003 4 41 12969510 10.1186/1471-2105-4-41
Hsiao W Wan I Jones SJ Brinkman FS IslandPath: aiding detection of genomic islands in prokaryotes Bioinformatics 2003 19 418 420 12584130 10.1093/bioinformatics/btg004
Merkl R SIGI: score-based identification of genomic islands BMC Bioinformatics 2004 5 22 15113412 10.1186/1471-2105-5-22
Kerkhoven R Van Enckevort FH Boekhorst J Molenaar D Siezen RJ Visualization for genomics: the Microbial Genome Viewer Bioinformatics 2004 20 1812 1814 14988111 10.1093/bioinformatics/bth159
Sato N Ehira S GenoMap, a circular genome data viewer Bioinformatics 2003 19 1583 1584 12912843 10.1093/bioinformatics/btg195
Pedersen AG Jensen LJ Brunak S Staerfeldt HH Ussery DW A DNA structural atlas for Escherichia coli J Mol Biol 2000 299 907 930 10843847 10.1006/jmbi.2000.3787
Gibson R Smith DR Genome visualization made fast and simple Bioinformatics 2003 19 1449 1450 12874063 10.1093/bioinformatics/btg152
Leader DP BugView: a browser for comparing genomes Bioinformatics 2004 20 129 130 14693822 10.1093/bioinformatics/btg383
Baerends RJ Smits WK De Jong A Hamoen LW Kok J Kuipers OP Genome2D: a visualization tool for the rapid analysis of bacterial transcriptome data Genome Biol 2004 5 R37 15128451 10.1186/gb-2004-5-5-r37
Rutherford K Parkhill J Crook J Horsnell T Rice P Rajandream MA Barrell B Artemis: Sequence visualization and annotation Bioinformatics 2000 16 944 945 11120685 10.1093/bioinformatics/16.10.944
ACT Home page
ActiveTcl download site
PERL Home page
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Bray N Dubchak I Pachter L AVID: A global alignment program Genome Res 2003 13 97 102 12529311 10.1101/gr.789803
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| 15601465 | PMC544189 | CC BY | 2021-01-04 16:02:47 | no | BMC Bioinformatics. 2004 Dec 15; 5:198 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-198 | oa_comm |
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BMC GenetBMC Genetics1471-2156BioMed Central London 1471-2156-5-331559834310.1186/1471-2156-5-33Methodology ArticleMethod for determination of (-102C>T) single nucleotide polymorphism in the human manganese superoxide dismutase promoter Martin Robert CG [email protected] Kalista [email protected] Mark A [email protected] Qing [email protected] Benjamin D [email protected] Jolanta [email protected] Nathaniel [email protected] David W [email protected] Departments of Surgery, University of Louisville School of Medicine, Louisville, KY, USA2 Pharmacology & Toxicology, University of Louisville School of Medicine, Louisville, KY, USA3 James Graham Brown Cancer Center, University of Louisville School of Medicine, Louisville, KY, USA4 Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Bethesda, MD, USA5 Division of Cancer Epidemiology and Prevention, Cancer Center and M. Slodowska-Curie Institute of Oncology, Warsaw, Poland2004 14 12 2004 5 33 33 14 4 2004 14 12 2004 Copyright © 2004 Martin et al; licensee BioMed Central Ltd.2004Martin et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Manganese superoxide dismutase (MnSOD) plays a critical role in the detoxification of mitochondrial reactive oxygen species constituting a major cellular defense mechanism against agents that induce oxidative stress. The MnSOD promoter contains an activator protein-2 (AP-2) binding site that modifies transcription of MnSOD. Mutations have been identified in the proximal region of the promoter in human tumor cell lines. One of these mutations (-102C>T) has been shown to change the binding pattern of AP-2 leading to a reduction in transcriptional activity. The aim of our study was to develop a method to identify and determine the frequency of this (-102C>T) polymorphism in human tissues.
Results
A new TaqMan allelic discrimination genotype method was successfully applied to genomic DNA samples derived from blood, buccal swabs, snap frozen tissue and paraffin blocks. The polymorphism was shown to be in Hardy-Weinberg Equilibrium in an evaluation of 130 Caucasians from Warsaw, Poland: 44 (33.8%) were heterozygous and 6 (4.6%) were homozygous for -102T.
Conclusion
This report represents the first description of the MnSOD -102C>T polymorphism in human subjects by a novel Taqman allelic discrimination assay. This method should enable molecular epidemiological studies to evaluate possible associations of this polymorphism with malignancies and other diseases related to reactive oxygen species.
MnSODHuman manganese superoxide dismutasegenotyping
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Background
Antioxidant enzymes such as superoxide dismutase (SOD) protect cells from oxidative stress. Generation of reactive oxygen species (ROS) has been implicated in the etiology of a diversity of human diseases, including cancer[1], aging[2], atherosclerosis[3] and neurodegenerative diseases[4,5]. Superoxide dismutase catalyzes the dismutation of superoxide radical (O2-) to H2O2 and O2. Three distinct types of SODs have been identified in human cells: 1) a homodimeric cytosolic CuZnSOD [6], 2) an extracellular homotetrameric glycosylated SOD [7], and 3) a mitochondrial matrix homotetrameric manganese superoxide dismutase (MnSOD) [8].
Numerous reports indicate a relative deficiency of superoxide dismutase catalytic activity, including mitochondrial MnSOD, in many types of solid tumors [9,10]. Interest in this relative deficiency of SOD activity has been greatly increased by observations that over-expression of SOD in tumor cells will suppress cell division in culture and tumor growth in vivo[11]. In addition recent reports have suggested a possible association between decreased SOD activity and malignant phenotype[12]. While the precise reasons for this relationship between tumor cell growth rate and intracellular SOD activity are not known, these findings support the general idea that decreased expression of SOD may promote tumor growth. In fact, as a result of these and other observations, MnSOD is considered a tumor suppressor gene[1].
Further evaluation of MnSOD suggests that it is critically important in maintenance of mitochondrial function. Mice with deficiency of this enzyme exhibit progressive cardiomyopathy, neurodegeneration and perinatal death[13]. These studies went on to confirm that transgenic mice that express human MnSOD in the mitochondria are protected from environmental oxygen-induced lung injury [14] and adriamycin-induced cardiac toxicity[15]. In contrast, disruption of the other two SODs yielded viable mice which were normal in non-stressful conditions [16]. Thus the mitochondrial MnSOD represents a major cellular defense against oxidative stress.
Genetic polymorphism in the MnSOD mitochondrial targeting sequence has been associated with risks to various diseases including breast cancer[17,18], lung cancer[19], cardiomyopathy[20] and Parkinson's disease[21].
A reduction of MnSOD activity has been shown to exist in many types of human cancer cells when compared to normal cells [22]. A recent report has also demonstrated the possible association between decreased SOD activity and malignant phenotype[12]. A recent report demonstrated a new mutation L60F, in exon 3 of the mature protein in the Jurkat human T-cell leukemia-derived cell line that reduced MnSOD [12]. Thus, it appears that reduced levels of MnSOD activity in human cancer cells can be associated with coding region mutations that alter protein sequence as well as promoter region mutations that alter gene expression [23].
The human MnSOD gene is localized to chromosome 6 (6q25). The MnSOD promoter region is characterized by a lack of TATA or CAAT boxes but the presence of a GC rich region containing multiple SP-1 and AP-2 binding sites [24]. Further work identified one cause for the reduced expression of MnSOD in some human tumor lines; the occurrence of three heterozygous mutations in the upstream promoter region of this gene [25]. One of these mutations in the MnSOD promoter sequence (MnSOD -102C>T) has been shown to change the binding pattern of AP-2 leading to a reduction in transcriptional activity. However the presence of this polymorphism has not been reported in human tissue.
In this study we developed a TaqMan allelic discrimination assay to reliably genotype DNA from many tissues (i.e. blood, buccal swabs, paraffin blocks, and snap frozen tissue) for the -102C>T polymorphism in the MnSOD promoter.
Results
We confirmed the presence of the -102C>T single nucleotide polymorphism in human subjects and submitted the sequence variant to Genbank[26]. The genotyping success rate with this technique in the Polish Caucasian population was 85%. An evaluation of 130 DNA samples successfully genotyped from Polish Caucasians not known to have cancer, demonstrated 80 (61.5%) were homozygous (-102C), 44 (33.8%) were heterozygous (-102CT) and 6 (4.6%) were homozygous (-102TT). This distribution is consistent with the Hardy-Weinberg Law. The success rate with this technique in an additional American control population was blood (95%), buccal swabs (90%), snap frozen tissue (80%) and paraffin-embedded samples (75%). The success rates were influenced by DNA quality, DNA extraction technique, and the ability to acquire enough DNA from the buccal swab.
Discussion
Reactive oxygen species in the form of superoxide radicals, hydrogen peroxide, and hydroxy-radicals are formed during incomplete reduction of molecular oxygen during normal respirations. The production of reactive oxygen species remains relatively stable during normal physiologic respirations. A significant increase in the production of reactive oxygen species such as superoxide radicals can be greatly increased as a result of metabolic disorders or more commonly from exposure to toxins such as cigarette smoke, well-cooked meat, urban residency, and excessive alcohol consumption.
Under normal physiologic conditions, superoxide radicals are detoxified by superoxide dismutase. Among the three SODs, MnSOD has been demonstrated to be the only form that has been essential for survival of aerobic life [27]. Inactivation of the MnSOD gene in E. coli significantly increased mutation frequency and cell death when bacteria were grown under aerobic conditions [28]. This has been further demonstrated in the evaluation in mammals in which the inactivation of MnSOD gene has led to detrimental effects. Polymorphisms of the human MnSOD gene have been found in the promoter region, the sequence coding for mature protein, and the mitochondrial targeting sequence. Initial evaluation of the five prime flanking regions from human tumor cell lines indicated that there were no major additions or deletions in the five prime flanking regions of the human MnSOD gene [29]. However, there were three mutations that were identified in these tumor cell lines: a C to a T at the – 102 position; a C to a G at the – 38 and an insertion of an A in 11 straight Gs at the – 93 position in relation to the transcription initiation site. The significance of these mutations was felt to be important because this region includes multiple binding sites for SP-1 proteins as well as AP-2 binding sites. Further evaluation of these mutations identified that the C to T change at the – 102 position effected the overall transcription of the MnSOD gene [30]. This change in transcription may result from an effect on the AP-2 binding site. Although the -102 C to T mutation was reported in human tumor cell lines [25], no evaluation has been assessed in human subjects.
Evaluation of the -102C>T polymorphism is complicated by difficulty in PCR because of the excessive GC rich region in which this polymorphism exists. This location, upstream from the transcription start site was extremely difficult to identify through multiple PCR-restriction fragment length polymorphism (RFLP) assays, which failed to adequately digest at this polymorphism site, and led to multiple false negative results. We found only the highest quality DNA (i.e. blood) was able to be evaluated using a PCR RFLP assay with only 50% genotyping success. This failure to accurately reproduce the PCR-RFLP assay [31], led us to the development of this TaqMan allelic discrimination assay.
The TaqMan allelic discrimination assay provided results that were confirmed by automated DNA sequencing and blind repeat genotyping. Although we did not test it use on DNA from multiple tissues from the same individual, it was successful for DNA samples derived from buccal swabs and paraffin blocks. It has significant advantages over RFLP analysis, allele-specific amplification, allele-specific hybridization, and oligo-nucleotide ligation assay techniques. The reasons for this advantage come from the reduction in labor intensive work up, the lack of need for special handling of radioactive probes, and the ability to modify this technique to evaluate multiple polymorphisms in this gene. In addition as more significant polymorphisms within the MnSOD gene are discovered, this technique will facilitate detection within the MnSOD gene.
The limitations of this technique ultimately come from the quality of DNA that is available and the significant initial expense that is required for a TaqMan assay instrumentation.
Conclusions
This report represents the first description of the MnSOD -102C>T polymorphism in human subjects by a novel Taqman allelic discrimination assay. This method should enable molecular epidemiological studies to evaluate possible associations of this polymorphism with malignancies and other diseases related to reactive oxygen species.
Methods
DNA sources
Most DNA samples (130) were isolated from buffy coats of Caucasian controls derived from a population-based case-control study of stomach cancer carried out in Warsaw, Poland as previously described [32]. To test the utility of the method to genotype DNA from various tissue sources, peripheral blood (20 samples), buccal swabs (40 samples), paraffin blocks (40 samples), and snap frozen tissues (15 samples) were collected from research subjects in the USA (Louisville, Kentucky).
DNA extraction from paraffin sections was performed after tissue sections (10 sections, 10 μm thick) were cut from paraffin blocks. Samples were removed from paraffin through a sequential extraction with histaclear, 100% ethanol and acetone, and dried under vacuum. The pellet was incubated overnight with proteinase K (200 μg/ml in 50 mM Tris-HCL pH 8.5, 1 mM EDTA and 0.5% Tween-20) at 55°C. After heating at 100°C for 10 min, digestion was sequentially extracted with phenol, phenol/chloroform and chloroform. DNA was precipitated with the addition of 3X volume 95% ethanol.
Primer design
SNP-specific polymerase chain reaction (PCR) primers and fluorogenic probes (Table 1) were designed using Primer Express (Version 1.5; Applied Biosystems, Foster City, CA). This technique has been utilized extensively in genotyping other candidate genes with multiple single nucleotide polymorphisms[33,34]. The fluorogenic probes were labeled with a reporter dye (either FAM or VIC) and are specific for one of the two possible bases (-102 C or T) in the MnSOD promoter region. A MGB quencher probe was utilized on the 3' end by a linker arm. TaqMan Universal PCR Master Mix (Applied Biosystems) was used to prepare the PCR. The 2X mix was optimized for TaqMan reactions and contained AmpliTaq-Gold DNA polymerase, AmpErase, UNG, dNTPs with UTP and a Passive Reference. Primers, probes and genomic DNA were added to final concentrations of 300 nM, 100 nM, and 0.5–2.5 ng/μl respectively. Controls (no DNA template) were run to ensure there was no amplification of contaminating DNA. Reference control DNA was also utilized to verify the polymorphisms identified. The amplification reactions were carried out in an ABI Prism 7700 Sequence Detection System (Applied Biosystems) with two initial hold steps (50°C for 2 min, followed by 95°C for 10 min) and 50 cycles of a two step PCR (95°C for 15 sec, 60°C for 1 min). The fluorescence intensity of each sample was measured at each temperature change to monitor amplification of the 278 base pair MnSOD promoter region. The -102 nucleotide was determined by the fluorescence ratio of the two SNP-specific fluorogenic probes. The fluorescence signal increases when the probe with the exact sequence match binds to the single stranded template DNA and is digested by the 5'-3' exonuclease activity of AmpliTaq-Gold DNA polymerase (Applied Biosystems). Digestion of the probe releases the fluorescent reporter dye (either FAM or VIC) from the quencher dye. As shown in figure 1, the method readily distinguishes between at C or T at -102 in the MnSOD promoter region.
Table 1 Primers and Fluorgenic Probes for -102C>T MnSOD Allelic Discrimination
Primers
-102-Forward Primer (-252 to -234) 5'-gcagacaggcagcgaggtt-3'
-102-Reverse Primer (35 to 19) [287 bp] 5'-ctgaagccgctgccgaa-3'
Probes
-102C-Taqman Probe (-97 to -107) fam-ccgcgggcccc
-102T-Taqman Probe (-97 to -107) vic-ccgcgagcccc
Figure 1 Fluorescence ratios of FAM-labeled/VIC-labeled fluorogenic probes specific for -102C>T polymorphism in MnSOD. Each bar represents mean standard error for determinations in DNA from 3 human subjects. Open bar represents DNA samples homozygous for -102C. Solid bars represent DNA samples homozygous for -102T. Crossed bars represent DNA samples heterozygous for the SNP. The fluorescence ratios differed significantly (p < 0.05) among homozygous and heterozygous genotypes.
Twenty samples with genotypes C/T (4 samples), T/T (3 samples), and C/C (13 samples), some of which were derived from paraffin-embedded tissues, were all confirmed by automated DNA sequencing. These sequence-confirmed samples served as reference standards for the remaining samples. In addition, 10% of the samples were genotyped blind a second time with identical results obtained.
Author contributions
RM: Participated in design of study and manuscript preparation
KH: Participated in genotyping samples
MD: Participated in design of methods of assay
QL: Participated in statistical analysis
BM: Participated in design of methods of assay
JL: Participated in sample collection
NR: Contributed to the study design and the analysis and interpretation of the data
DH: Participated in design of study and manuscript preparation
Acknowledgements
This study was partially supported by USPHS grants CA34627 and CA97942 from the National Cancer Institute.
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| 15598343 | PMC544190 | CC BY | 2021-01-04 16:30:33 | no | BMC Genet. 2004 Dec 14; 5:33 | utf-8 | BMC Genet | 2,004 | 10.1186/1471-2156-5-33 | oa_comm |
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BMC Med Res MethodolBMC Medical Research Methodology1471-2288BioMed Central London 1471-2288-4-291559601710.1186/1471-2288-4-29DatabaseA suite of web applications to streamline the interdisciplinary collaboration in secondary data analyses Pietrobon Ricardo [email protected] Ulrich [email protected] Henrique [email protected] Andreia P [email protected] Laurence D [email protected] Danny O [email protected] Center for Excellence in Surgical Outcomes, Duke University Medical Center, Duke University, Durham, NC, USA2 Center for Excellence in Surgical Outcomes, University Hospital Basel, Department of General Surgery and Surgical Research, Basel, Switzerland3 Duke University Health System, Rio Claro/Sao Paulo, Brazil4 Reliable International Research, Center for Excellence in Surgical Outcomes, Campina Grande do Sul/ Parana – Brazil5 Department of Surgery, Duke University Medical Center, Durham, NC, USA6 Department of Internal Medicine, University of North Carolina at Chapel Hill, NC, USA2004 14 12 2004 4 29 29 10 5 2004 14 12 2004 Copyright © 2004 Pietrobon et al; licensee BioMed Central Ltd.2004Pietrobon et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
We describe a system of web applications designed to streamline the interdisciplinary collaboration in outcomes research.
Description
The outcomes research process can be described as a set of three interrelated phases: design and selection of data sources, analysis, and output. Each of these phases has inherent challenges that can be addressed by a group of five web applications developed by our group. QuestForm allows for the formulation of relevant and well-structured outcomes research questions; Research Manager facilitates the project management and electronic file exchange among researchers; Analysis Charts facilitate the communication of complex statistical techniques to clinicians with varying previous levels of statistical knowledge; Literature Matrices improve the efficiency of literature reviews. An outcomes research question is used to illustrate the use of the system.
Conclusions
The system presents an alternative to streamline the interdisciplinary collaboration of clinicians, statisticians, programmers, and graduate students.
biomedical researchstatistical data interpretationresearch designplanning techniquesInternetcomputer-assisted instruction
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Background
In the last decade, the number of relevant data sources available for outcomes research has grown exponentially. In contrast, the number of individual researchers with clinical and statistical expertise required to explore these data sets increase at a much slower pace. As a result, an immense quantity of valuable clinical data are left untouched, never becoming clinical publications that could potentially improve health care.
The disproportion between data volume and number of qualified researchers can be explained by the growing complexity involved in outcomes research projects using secondary data analyses. Researchers have to formulate of a clinically relevant and methodologically sound research question, find appropriate data sources, perform statistical analyses, and generate a final manuscript that will be submitted for peer-review. Frequently, individual researchers have the training and time to perform a few of these steps, but the integration of all tasks calls for an interdisciplinary systems approach [1,2]. This interdisciplinary effort, however, is often challenged by communication problems among researchers with different backgrounds, particularly when physicians with an exclusive clinical education attempt to work in collaboration with quantitative researchers such as statisticians [3]. As a consequence, the output of such collaboration is either scarce or absent.
This article describes a suite of web applications developed to facilitate the process of converting outcome databases into clinical manuscripts, to streamline the interdisciplinary collaboration of researchers, and to connect all different steps of the outcomes research process. To illustrate its use, we will describe how a research project has been conducted using this system from its early phase of research question formulation to the completion of the final manuscript.
Construction and content
The system of Web applications is composed by five different tools: QuestForm, Research Manager, Analysis Charts, and Literature Matrices. These tools were designed to assist researchers in each of the phases encountered in an outcomes research project involving secondary data analysis (Figure 1). All tools are freely available at a designated web site . The following sections will describe each of the Web applications and their application in the answer of a real outcomes research question.
Figure 1 Research phases, challenges, and respective tools
QuestForm
General description
QuestForm, an acronym for "Question Formulation", is an application designed to assist researchers in the location of clinical databases and formulation of outcome research questions (Figure 2). Clinical databases contain raw data (observations from individual patients) from national administrative claim data, cohort studies, clinical trials, and registries (see for an updated list). All databases have been de-identified and do not contain protected health information as specified by the Health Insurance Portability and Accountability Act (, accessed on Aug/04/2004). The application The application is built using Extensible HyperText Markup Language 1.0 (XHTML) [4], Java, and a relational database (MySQL 4.0)[5].
Figure 2 QuestForm application – Database search engine
QuestForm starts by presenting researchers with three main strategies to find research databases: use of pre-determined key words that describe the database as a unit (Figure 2), user-defined key words to describe variables present in the data dictionary of each database, and the presentation of a complete list of all databases. Once databases are located, researchers can read an overall summary about the database including details about number of subjects, sampling strategy, data ownership, and overall characteristics of the study population and associated procedures (Figure 3). Researchers then determine that the database most appropriate to answer the research question at hand, a JAVA screen is displayed for research question formulation (Figure 4). This screen presents all variables displayed in hierarchical categories. Variables are presented with the corresponding question and alternative responses. All variables can be inserted into a research question (Question Diagram) divided into the classical categories for an epidemiological question: Outcomes, Predictors, Confounders, Inclusion and Exclusion Criteria. Search engines are provided for ICD9-CM diagnosis and procedure codes (Figure 5), which can also be inserted into the Question Diagram. Finally, previously formulated Question Diagrams can be shared among researchers. This latter functionality allows researchers to both share Question Diagrams among members of the ongoing project as well as share previously formulated Question Diagrams with researchers from other teams. Once the question is fully formulated, researchers can save the question as in a graphical format known as Question Diagram.
Figure 3 QuestForm application – Overall description of the database
Figure 4 QuestForm application – Formulation of a Question Diagram
Figure 5 QuestForm application – Search engine for ICD9 codes
Outcomes research application
Dr. Guller initiated the project searching for an existing database that would allow him to compare surgical outcomes between laparoscopic and open appendectomy procedures in the treatment of acute appendicitis. The outcomes were pre-specified as mortality and infection, although other existing outcomes would also be of interest. Although there are multiple single-institution studies attempting to answer this question [6,7], few studies have taken a population approach to test whether one procedure is superior to the other.
As a first step, Dr. Guller searched across previously formulated Question Diagrams to evaluate whether other studies could have used a similar research design. Since none was found in QuestForm, Dr. Guller searched across more than forty different databases for an existing database that would have the variables to answer his research question. After navigating through multiple data dictionaries, Dr. Guller found that the Nationwide Inpatient Sample (NIS) Release 6, 1997 [8] presented the variables and an adequate number of patients to answer his question.
Once the database was located, Dr. Guller selected the outcomes of interest (length of hospital stay, in-hospital complications, in-hospital mortality, rate of routine discharge), main predictors (laparoscopic versus open procedures), and confounders (age, gender, race, household income, comorbidity, hospital volume, location of the hospital, teaching status of hospital, and appendix perforation), inserting each of them into the research question fields in QuestForm. Using built-in search engines for ICD9 codes, Dr. Guller created the definition for each of the above-mentioned variables and defined the inclusion and exclusion criteria. The final research question was then saved as a Question Diagram and immediately submitted to Dr. Pietrobon for feasibility evaluation. Dr. Pietrobon judged that the project was feasible and could be completed using the database indicated by Dr. Guller. At this point, the project was initiated and a detailed project management plan was established using Research Manager.
Research manager
Research Manager is a Web application developed by our group designed to facilitate the project management of clinical research projects. Similar to QuestForm, Research Manager is licensed under the GNU Public License [10], which allows individuals to copy, modify, and freely distribute the software as long as the source code is provided.
Research Manager provides multiple features to facilitate project management of clinical research projects. All projects are displayed by category (e.g., cardiology, general surgery, etc) with a brief description. The internal content of all projects is password protected. All internal tasks within a project are assigned to individual researchers. Project administrators initially assign deadlines that can be modified by task leaders within three days. All participating members of the project receive weekly reports containing details about the activity and the latest electronic file within each task (Figure 6). These files can include research questions, data analysis files, synthesis of a literature review, and manuscript drafts. Project members can also customize the application to receive updates for every single file uploaded to Research Manager in real time if they decide to closely track the project. Expired tasks are marked in the weekly report sent to the entire team, thus providing an incentive for investigators to keep tasks within planned deadlines.
Figure 6 Research Manager
Research Manager helps identifying such problems and enables their early elimination, thus avoiding delays in the project completion. Finally, since weekly reports are generated to all participating members, Research Manager also provides peer-incentive for project members to complete their tasks in a timely manner.
Outcomes research application
Once the Question Diagram was evaluated by the clinical epidemiologist, this project was transferred to Research Manager. Contact information for each of the researchers involved and deadlines for completion of the main phases of the research process were set, including data extraction and cleaning, data analysis, literature review, and manuscript writing. Weekly reports were generated to update investigators on all tasks of the project, including different versions of the statistical analysis, modifications in the research question, and manuscript sections.
With an established project plan, the statistician in charge selected the best methods for analysis. Since the database is a random sample of the United States and requires special survey analysis methods, it was necessary that all involved researchers understood the statistical approach by using Analysis Charts.
Analysis charts
Analysis Chart is a tool designed to enhance the understanding of statistical methods to a format that is understandable by clinical researchers with different previous levels of statistical knowledge. As such, it is important in the design as well as the analysis phases of a project (Figure 7). The application was built using Extensible HyperText Markup Language (XHTML 1.0) [4] in combination with Cascading Style Sheets [11].
Figure 7 Analysis Chart
Analysis charts are composed by cascading links that display information about quantitative methods in progressive levels of complexity called "layers of information". Each layer explains the statistical method with an increasing level of complexity. In this manner, clinicians interested in simply understanding the method to evaluate whether it can be applied to a research project can simply read the first three layers. In contrast, researchers interested in a direct application of the method to available research data can follow all layers and their respective references. Most commonly, complex techniques are presented using five layers of information. Layer 1 summarizes the general goal of the method. Layer 2 presents previous clinical applications so that the researcher can visualize situations in which the method may be realistically applied. Layer 3 describes the data requirements for the application of the statistical technique. Layer 4 describes the basic statistical underpinnings of the method, initially breaking down equations and then reassembling them. Finally, layer 5 presents a list of available software packages for the implementation of the technique as well as cases studies where all previous layers are applied to real data sets. Each layer ends with a section containing selected references that explain the topic in more detail.
Outcomes research application
While deciding on the most appropriate analysis strategy for the Question Diagram, Drs. Pietrobon and Guller consulted the Analysis Chart searching for the most appropriate statistical methods of analysis. Given the nature of the research question and that the NIS database has a sample design, Drs. Guller and Pietrobon opted for an approach involving multiple and logistic regression models while adjusting for sampling weights, strata, and clusters. With a defined analysis protocol, the research question was then transferred to a statistical programmer trained in the translation of Question Diagrams into statistical code. This process was closely evaluated by Drs. Pietrobon and Guller, who scrutinized the statistical code and results from a clinical and statistical perspectives. Once the results were deemed to be accurate, Dr. Pietrobon started the literature review using a Literature Matrix.
Literature matrices
Literature matrices consist of a comprehensive but not necessarily exhaustive review of the literature focused on a narrow clinical topic (Figure 8). Each article is analyzed using the following criteria: study objectives, data sources, outcome variables, primary predictor variables, confounders, statistical analysis, results, established knowledge, and shortcomings. Each literature matrix is saved as an XHTML file that can be visualized in web browsers as well as imported in any commercial or open source spreadsheet applications such as Microsoft Excel® or Open Office Calc [12].
Figure 8 Literature Matrix
The advantage of the Literature Matrix as implemented in the Web suite is its availability over the web to the whole outcomes research community. This allows Literature Matrices to be constantly updated, with authors receiving their due credit in a list of contributors. Literature Matrices also enable researchers to obtain a complete summary of the literature without going through the cumbersome process of copying and reading a manuscript for the first time. In contrast, researchers' time can be spent more efficiently in reviewing what has already been compiled and attempting to expand the Literature Matrix with other relevant bibliographic references.
Outcomes research application
In order to evaluate the literature, a graduate student performed a thorough literature search. All relevant articles were copied, read, and the data extracted according to the established categories. Once the Literature Matrix had been completed, Drs. Guller compared the current project results with results published in the literature. The structured information in Literature Matrices also allowed Dr. Guller to compare the strengths and weaknesses of the current project in relation to previous publications. Once this phase was completed, Dr. Guller proceeded to the final writing of the manuscript using Output Templates.
Outcomes research application
At the end of the project, Dr. Guller combined the Question Diagram, Analysis Chart, and Literature Matrices to write the final manuscript. While Analysis Charts provided the information concerning the statistical techniques used in this study, Literature Matrices provided the basis for comparison of the study results against previous publications. Although not included in the final manuscript, Dr. Guller also had access to multiple analysis files through Research Manager to orient him in each of the steps taken during the research question formulation, data analysis, and literature review.
Use of web application by clinical researchers
The use of web applications by individual clinical researchers can be summarized in Figure 9.
Figure 9 Integration between UR tools and clinical researchers
Utility
Since the concept of streamlining the interdisciplinary collaboration preceded the existence of the current suite of web applications, different versions of the central idea have been gradually applied since the second half of 2002. Although our usability, qualitative, and economic studies to evaluate this application are still ongoing, we have noticed a significant improvement in the number and quality of our publications as evidenced by the increasing acceptance rates of our manuscripts for publication in peer-reviewed journals. The Web applications are currently used in research projects involving Duke University and other universities in the United States and abroad, where they have been shown to facilitate inter-institutional collaborations.
Discussion
Improving the efficiency of an interdisciplinary approach to secondary data analyses has multiple potential benefits. These include an increase in the overall clinical significance of the final publication, decrease in the number of failed projects, decrease in the time for completion of individual projects, improvement in the education of future outcomes researchers, decrease in the cost-benefit ratio for individual outcomes research projects, and, perhaps most importantly, an automation of repetitive tasks. This last factor is crucial since eliminating repetitive tasks will allow researchers to concentrate on the design of innovative projects [2].
Surprisingly, few systems and applications have been described to solve the problem of complex interdisciplinary collaboration between clinicians and statisticians. Isolated approaches have usually focused on specific portions of the outcomes research process, without attempting to integrate them into a cohesive system. For example, Marshall [13] has proposed the use of a secure Internet web site for collaborative medical research and data collection. While this system seems to achieve its proposed objectives, it does not improve the process of guiding teams in the translation of data into useful clinical information. Other systems have approached the process in a more comprehensive manner. The Research Toolbox [14], for example, is a software application that combines databases for literature searches in addition to providing templates for the scientific output. The system is applicable to any type of research, but lacks the ability to connect researchers over the World Wide Web. It also does not address the formulation of research questions from existing databases, selection of statistical techniques, exchange of manuscripts, or project management. Finally, the Web-based Medical Information Retrieval System (WebMIRS) project, funded by the National Library of Medicine [15], allows researchers not only to evaluate the database content but also to perform the data extraction of specific subsets of the data set. In spite of its high performance as a research tool, WebMIRS is currently restricted to one single publicly available database (National Health and Examination Survey – NHANES), and does not contribute to other phases of the outcomes research process.
Although this newly developed system of applications provides a significant improvement in the way secondary data analyses are conducted, it still has limitations. First, because of the lack of a formal evaluation of the effectiveness of this system, we are unable to quantify its real time and cost saving benefits. Second, the system is currently restricted to secondary analyses and does not allow for the planning of prospective data collection. Although one of the main advantages of formulating a research question based on existing data sets is the bounded nature of the process, future applications should attempt to create rules and algorithms that may guide prospective data collection.
Conclusion
In summary, we have experienced that this system has significant advantages over the traditional manner of conducting outcomes research based on secondary data analyses. This tool may have important applications, not only resulting in an improvement in the overall efficiency of the outcomes research process, but also affecting the way new outcomes researchers are trained and introduced to a research environment.
Availability and requirements
The Web application is available at
Abbreviations
XHTML Extensible HyperText Markup Language 1.0
JAVA: by Sun Microsystems
ICD9-CM: International Classification of Diseases, Ninth Revision – Clinical Modification
NIS: Nationwide Inpatient Sample
GNU: GNU's Not linux General Public License
WebMIRS: Web-based Medical Information Retrieval System
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Competing interests
The author(s) declare that they have no competing interests
Authors' contributions
Ricardo Pietrobon – design, manuscript drafting
Ulrich Guller – design, manuscript revision for important intellectual content
Henrique Martins – design, manuscript revision for important intellectual content, software programming
Andreia P Menezes – design, manuscript revision for important intellectual content
Laurence D. Higgins – design, manuscript revision for important intellectual content
Danny O. Jacobs – design, manuscript revision for important intellectual content
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank Juliana and Guilherme Serzedello for assistance with manuscript formatting.
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Web-based Medical Information Retrieval System
| 15596017 | PMC544191 | CC BY | 2021-01-04 16:32:50 | no | BMC Med Res Methodol. 2004 Dec 14; 4:29 | utf-8 | BMC Med Res Methodol | 2,004 | 10.1186/1471-2288-4-29 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-861556656510.1186/1471-2407-4-86Research ArticleImmunodominant PstS1 antigen of mycobacterium tuberculosis is a potent biological response modifier for the treatment of bladder cancer Sänger Christian [email protected] Andreas [email protected] Gabriele [email protected] Ralf [email protected] Fatima [email protected]öhle Andreas [email protected] Mahavir [email protected] Sven [email protected] Division of Immunotherapy, Research Center Borstel, Parkallee 26b, 23845 Borstel, Germany2 LIONEX Diagnostics & Therapeutics GmbH, Mascheroder Weg 1 b, 38124 Braunschweig, Germany3 Helios Agnes Karll Hospital, Lübecker Str. 18–20, 23611 Bad Schwartau, Germany2004 26 11 2004 4 86 86 18 2 2004 26 11 2004 Copyright © 2004 Sänger et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Bacillus Calmette Guérin (BCG)-immunotherapy has a well-documented and successful clinical history in the treatment of bladder cancer. However, regularly observed side effects, a certain degree of nonresponders and restriction to superficial cancers remain a major obstacle. Therefore, alternative treatment strategies are intensively being explored.
We report a novel approach of using a well defined immunostimulatory component of Mycobacterium tuberculosis for the treatment of bladder cancer. The phosphate transport protein PstS1 which represents the phosphate binding component of a mycobacterial phosphate uptake system is known to be a potent immunostimulatory antigen of M. tuberculosis. This preclinical study was designed to test the potential of recombinant PstS1 to serve as a non-viable and defined immunotherapeutic agent for intravesical bladder cancer therapy.
Methods
Mononuclear cells (PBMCs) were isolated from human peripheral blood and stimulated with PstS1 for seven days. The activation of PBMCs was determined by chromium release assay, IFN-γ ELISA and measurement of lymphocyte proliferation. The potential of PstS1 to activate monocyte-derived human dendritic cells (DC) was determined by flow cytometric analysis of the marker molecules CD83 and CD86 as well as the release of the cytokines TNF-α and IL-12. Survival of presensitized and intravesically treated, tumor-bearing mice was analyzed by Kaplan-Meier curve and log rank test. Local and systemic immune response in PstS1-immunotherapy was investigated by anti-PstS1-specific ELISA, splenocyte proliferation assay and immunohistochemistry.
Results
Our in vitro experiments showed that PstS1 is able to stimulate cytotoxicity, IFN-γ release and proliferation of PBMCs. Further investigations showed the potential of PstS1 to activate monocyte-derived human dendritic cells (DC). In vivo studies in an orthotopic murine bladder cancer model demonstrated the therapeutic potential of intravesically applied PstS1. Immunohistochemical analysis and splenocyte restimulation assay revealed that local and systemic immune responses were triggered by intravesical PstS1-immunotherapy.
Conclusion
Our results demonstrate profound in vitro activation of human immune cells by recombinant PstS1. In addition, intravesical PstS1 immunotherapy induced strong local and systemic immune responses together with substantial anti-tumor activity in a preclinical mouse model. Thus, we have identified recombinant PstS1 antigen as a potent immunotherapeutic drug for cancer therapy.
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Background
Urothelial carcinoma of the bladder accounts for about 4% of all cancer related death in man. The large majority of tumors (70–80%) is superficial at diagnosis and has a high rate of local recurrence (70%) and progression (30%) after local surgical therapy. Therefore, patients require lifelong medical follow-up examinations and effective prophylactic treatment to prevent recurrences and progression of the tumor.
In this type of cancer, the immunotherapeutic use of Mycobacteria – specifically Bacillus Calmette-Guerin (BCG), a non-pathogenic strain of Mycobacterium bovis – has a well-documented and successful clinical history. Immunotherapy with BCG is performed by six weekly instillations of viable Mycobacteria (for induction course) into the bladder of patients after initial transurethral resection of the tumor. Until now various clinical trials have shown that this type of therapy is superior to topical chemotherapy and transurethral resection of the tumor alone to prevent recurrences and local progression especially in patients with high risk tumors [1-3]. Despite several clear advantages of BCG immunotherapy for the treatment of bladder cancer, several problems and limitations compromise its use. Although BCG is the most effective agent against superficial transitional cell carcinoma (TCC), currently there are still 30 to 40% of patients not responding to the therapy [4]. Furthermore, in the case of muscle invasive bladder cancer, BCG has not been shown to be effective [5]. BCG's activity appears to be strictly localized and as a living organism, BCG poses unique toxicity problems associated with its use. Although only 5% of these problems are severe, most if not all patients experience some irritable bladder symptoms (cystitis) during BCG therapy [6]. Roughly 40% develop hematuria and 30% experience flu-like symptoms including fever, malaise and nausea or vomiting. Actual BCG sepsis is a rare event and has been reported in only 0.4% of all cases. In addition, some of these cases have been fatal [7,8]. Thus, the reported side effects limit the clinical applicability and acceptance of this effective immunotherapy and underscore the need for alternative forms of treatment.
In order to limit toxicity recent endeavors are therefore focused on the development of alternative non-viable products and some of those have already been tested in preclinical and clinical studies [9-12]. The mycobacterial antigen PstS1 is known as a highly immunogenic and immunostimulatory component of the mycobacterial cell membrane [13]. PstS1 is the phosphate binding subunit of the inorganic phosphate uptake system from M. tuberculosis belonging to the family of ABC (ATP-binding cassette) transporters [14,15]. It is a glycosylated lipoprotein which can be found both, intracellularly and secreted into the extracellular culture supernatant [16,17]. Moreover PstS1 represents one of the most immunogenic antigens in active multibacillary tuberculosis [18].
We hypothesized that this highly immunogenic protein antigen could function as an effective biological response modifier in immunotherapy of bladder cancer. To test the tumortherapeutic potential of recombinantly expressed PstS1 [19] we performed a detailed analysis of the immunostimulatory capacity of this antigen in a well-defined human in vitro system and in a previously described murine model of experimental bladder cancer therapy [20]. Because the role of prior exposure of bladder cancer patients to mycobacterial antigens for the effectiveness of BCG therapy is controversely discussed [21-23], we performed intravesical PstS1-immunotherapy with and without prior sensitization of mice.
The data reported herein demonstrate that PstS1 is a potent activator of human tumor-cytotoxic MNCs, induces maturation and activation of human dendritic cells and most importantly is very effective in the treatment of experimental orthotopic bladder cancer. While local and systemic immune responses were observed in sensitized and non-sensitized mice, immunotherapy of cancer was only successful in non-sensitized animals.
Methods
Cell culture
The human bladder tumor cell line T-24 was cultured at 37°C and 5% CO2 in RPMI 1640 (PAA Laboratories, Linz/Austria) containing 10% FCS (Linaris, Bettingen/Germany), 1% glutamine, 100 U/ml penicillin and 100 μg/ml streptomycin. The murine bladder tumor cell line MB-49 was cultured at 37°C and 5% CO2 in DMEM (PAA Laboratories, Linz/Austria) containing 10% FCS (Linaris, Bettingen/Germany), 1% L-glutamine, 100 U/ml penicillin and 100 μg/ml streptomycin.
Stimulation of human PBMCs
MNCs from heparinized blood of healthy human donors were obtained by discontinuous gradient centrifugation using Biocoll Separating Solution (Biochrom, Berlin/Germany) and adjusted to a concentration of 1 × 106/ml in RPMI-1640 medium containing 5% human serum, 100 U/ml penicillin and 100 μg/ml streptomycin. Recombinant PstS1 in concentrations from 0.1 μg/ml – 100 μg/ml, BCG (Connaught substrain, Immucyst, 4 × 104 cfu/ml), or PBS (100 μl) was added and the cells were cultured for 7 days in 6-well microtiter plates at 37°C and 5% CO2.
Chromium release assay
Cytotoxicity was determined in a standard 4-hour chromium release assay. Target cells were labeled with Na251CrO4 (ICN, Irvine/USA) for 1 h at 37°C, washed and resuspended at 5 × 104 cells/ml. Effector cells were added to a total of 100 μl of target cells at an effector:target ratio of 40:1. The radioactive content of the supernatant was measured in a gamma-counter (Berthold, Wildbad/Germany). The specific lysis was determined according to the formula: spec. lysis (%) = 100 × (Exp – Spo)/ (Max – Spo) where Exp is the experimental release, Spo the spontaneous release and Max the maximum release.
Human IFN-γ ELISA
Supernatants of PstS1, BCG and PBS stimulated PBMCs were recovered at days 2, 5 or 7 and examined for the presence of IFN-γ. Each experiment was carried out several times with different donors. Detection was performed with an anti human IFN-γ ELISA-Kit according to the manufacturers' instructions (eBioscience, San Diego/USA).
Proliferation of human PBMC
PBMCs were stimulated for 2 to 7 days with PstS1, BCG, PBS or PHA (Sigma). 2 × 105 cells / well of the stimulated PBMCs were cultured in a microwell plate and 1 μCi 3H-Thymidine (five wells per sample) (Amersham, Freiburg/Germany) was added during the last 18 h of stimulation. DNA was harvested on filter membranes and thymidine incorporation was measured by liquid scintillation counting (counter: LKB Wallace 1205 beta-plate, Turku/Finland).
Generation and stimulation of human monocyte derived dendritic cells
After separating peripheral blood mononuclear cells (PBMCs) from heparinized blood of healthy donors by Ficoll-Paque centrifugation, monocytes were elutriated by counterflow centrifugation. For generation of immature DCs, 2 × 106 monocytes were cultured for seven days with 2 ml RPMI 1640, 10% FCS (Linaris, Bettingen/Germany) 1% penicillin/streptomycin and IL-4/GM-CSF (500 U/ml each) (TEBU/PeproTech, Offenbach/Germany) in 24-well cell culture plates (Nunc, Wiesbaden/Germany). Exchange of medium was carried out at day three and day five. This procedure resulted in full differentiation of monocytes with no undifferentiated monocytes present in the cultures after seven days. Immature DCs were stimulated for three days with 10 μg/ml PstS1 or BCG (MOI = 0.01) and subjected to flow cytometry. Debris was excluded from the analysis according to FSC/SSC gating.
Flow cytometry
Expression of cell surface molecules was analyzed by flow cytometry (FACSCalibur, Becton Dickinson, Franklin Lakes/USA) using phycoerythrin (PE) conjugated monoclonal antibodies (mAbs): anti-CD1a (Biosource, Camarillo/USA), anti-CD83 (Pharmingen, San Diego/USA), anti-CD14 and anti-CD86 (Dianova, Hamburg/Germany).
1 × 105 DCs suspended in PBS containing 5% human serum and 0.1 % sodium acide were incubated with mAbs for 30 minutes on ice. Then cells were washed with PBS and resuspended in 400 μl 1.5 % paraformaldehyde in PBS.
human TNF-α and IL-12 ELISA
Supernatants of PstS1, BCG and PBS stimulated DCs were recovered at day 3 after stimulation and examined for the presence of TNF-α and IL-12. Each experiment was carried out several times with different donors. TNF-α detection was performed with a quantitative ELISA, provided by Dr. H. Gallati (Intex, Muttenz/Switzerland).
IL-12 detection was carried out with an ELISA-Kit from eBioscience (San Diego/USA).
PLG-Particle preparation and protein loading
PLG-particles (10 mg/ml) (Lionex, Braunschweig/Germany) were diluted 4:1 with PBS and the pH was adjusted to 7.0 with NaOH. PstS1 was recombinantly expressed in E. coli and purified by standard chromatography.
250 μg of PLG-particles were loaded with PstS1 or BSA by coincubation with 60 μg of the respective protein for 15 h at RT on a shaker (overall volume 100 μl). Afterwards the particles were spun down and washed for 5 min with 100 μl PBS. The supernatant was removed and the particles were diluted in 100 μl of PBS. 10 μl of loaded particle solution were centrifuged and the pellet was treated for 30 min at RT with 0.1 M NaOH/ 10% SDS. After centrifugation in a minifuge, supernatant was removed and analyzed on a 10% SDS page. Using recombinant PstS1 as a calibration standard it was determined that 250 μg PLG-particles bound approximately 50 μg of PstS1 protein.
PLG-particle vaccination and immunotherapy of experimental bladder cancer
Female C57BL/6 mice were purchased from Charles-River Laboratories (Sülzfeld/ Germany) at the age of 6–8 weeks. To test efficacy of PstS1 therapy in C57/BL6 mice a published syngeneic, orthotopic bladder cancer model was used [10]. Trial I: 15 animals per group were subcutaneously injected with 250 μg PLG-particles coated with 50 μg PstS1 or 250μg PLG-particles alone. Ten days later the mice were anesthetized by intraperitoneal treatment with Pentobarbital (0.067 mg/g body weight). After insertion of a 24-gauge teflon intravenous catheter (Insyte-W, Becton Dickinson, Franklin Lakes/USA) transurethrally into the bladder, electrocoagulation was performed with a guide wire. Thereafter 6 × 104 MB-49 cells were instilled into the bladder. Intravesical immunotherapy with 100 μg PstS1 in 100 μl PBS was performed on days 1, 8, 15 and 22 after tumor implantation. Control groups were instilled with PBS (100 μl) alone. The viability status of the mice was checked daily. Surviving mice were sacrificed on day 70. Survival of mice was compared using Kaplan-Meier analysis and log-rank test. Trial II: Performance similar to trial I with the following differences. 11 animals per group were subcutaneously injected with a) 250 μg empty PLG-particles b) 250 μg PLG-particles coated with 50 μg BSA c) 100μl of PBS. Intravesical treatments were performed with 100 μg PstS1 or PBS only.
Immunohistochemistry of murine bladders
C57/BL6 mice (5 per group) were treated as described in table I. One day after the fourth instillation animals were sacrificed. Bladders were dissected immediately, shock-frozen in liquid nitrogen and stored at -80°C. The immunohistochemistry and analysis of cellular influx of different leukocyte subsets was performed on 5μm frozen sections as described previously [24].
anti-PstS1-IgG ELISA from murine blood
Murine blood was obtained immediately after sacrificing mice by heart punctation and centrifuged in a microcentrifuge to obtain the serum for antibody analysis which was performed with the PstS1-ELISA-kit (Lionex GmbH, Braunschweig/Germany) according to the manufacturer's instructions.
Splenocyte restimulation assay
Splenocytes were isolated, washed with PBS and erythrocytes were lysed with H2O. Afterwards 2 × 105 cells / well were cultured in a microwell plate and stimulated with 10 μg/ml PstS1 for a period of five days (five wells per sample). Then 1μCi 3H-Thymidine (Amersham, Freiburg/Germany) was added and cells were incubated for 15 h at 37°C and 5% CO2. Finally the DNA was harvested on filter membranes and 3H-Thymidine incorporation was measured by liquid scintillation counting (counter: LKB Wallace 1205 beta-plate, Turku/Finland).
Results
PstS1 activates human PBMCs
To assess the immunostimulatory properties of our PstS1-preparation we analyzed several human PBMC in vitro systems. As in these systems the immunostimulatory mechanisms of whole BCG bacteria have been thoroughly studied in the past [9,25-27] we used BCG as a positive control and reference stimulus.
In a first set of experiments the optimal concentration of PstS1 to stimulate human PBMC cytotoxicity (Fig. 1A), IFN-γ release (Fig. 1B) and proliferation (Fig. 1C) was determined. Using a concentration range from 0.1 μg/ml to 100 μg/ml PstS1 showed a typical bell-shaped dose response curve. A concentration of 10 μg/ml was found to be optimal for stimulation of human PBMC in all three readout systems. In order to analyze the time course of human PBMC activation, a time kinetic study of PBMC stimulation was performed. BCG, which has been previously described to induce potent anti-tumor cytotoxicity in human PBMCs [27] was used as a reference stimulus and positive control.
As depicted in figure 2A,2B,2C activation of human PBMC by PstS1 was time-dependent with the strongest activation on day 7 and only marginal activation at early time points (day 2). Overall, in this study we have tested the activation of PBMC of ten different human donors in response to PStS1 stimulation. As expected, we observed a certain degree of donor variability in this assays with e.g. IFN-γ induction ranging from 500 pg/ml to 5 ng/ml. Comparison with BCG consistently indicates a similar time-kinetic of PBMC activation between these two biological response modifiers albeit with higher absolute levels of activation induced by BCG. This kinetic of activation is contrasted by mitogenic stimulation with PHA (figure 2c) or ConA (not shown) which show an expected peak of PBMC stimulation at early time points with a subsequent dramatic decrease.
Dendritic cell activation by PstS1
Dendritic cells are central cellular mediators for the induction of anti-tumor immune responses. We tested the potential of PstS1 to induce activation and maturation of human monocyte-derived DCs.
After 7 days of differentiation human monocyte-derived dendritic cells showed the typical immature phenotype with low or absent expression of the monocyte marker CD14 and the maturation markers CD83 and CD86 (Fig. 3A). At the same time immature DCs expressed high amounts of CD1a. Stimulation of dendritic cells with either PstS1 or BCG induced strong upregulation of CD83 and CD86 indicating phenotypical maturation of DC after challenge with PstS1. In parallel, the cytokine response of DC was assessed and PstS1 was found to induce substantial amounts of TNF-α and IL-12 p70, two key cytokines in dendritic cell biology. Interestingly, PstS1 reproduceably induces IL-12p70 in the ng range, while BCG only induced relatively low levels of IL-12p70 (50–500 pg/ml) (Fig. 3B).
Intravesical immunotherapy with PstS1 in sensitized and non-sensitized mice
After we have shown profound immunostimulatory properties of PstS1 in vitro we conducted a series of studies to test its immunotherapeutic potential in vivo. Prompted by the controversy about the role of prior exposure to mycobacterial antigens in BCG immunotherapy we wanted to assess the in vivo activity of PstS1 in sensitized and non-sensitized mice. For this purpose two independent in vivo experiments were carried out. PLG-particles have previously been described as useful agents for sensitization of mice to mycobacterial antigens and were used as such in our study [13].
In a first series of experiments we analyzed mice which had been s.c. sensitized with empty control particles or with particles loaded with PstS1 antigen. Ten days later mice received inoculation of tumor cells and subsequent intravesical treatment with either PstS1 or PBS control solution (four weekly instillations). Mice which received s.c. PBS followed by intravesical PBS served as negative controls. Using this experimental set up we could show that pre-vaccination with empty particles and intravesical treatment with PstS1 protein significantly prolonged survival of mice and thus provided a clear therapeutic benefit in this model of experimental bladder cancer (Fig. 4a). When mice received s.c. injections of PLG-particles followed by intravesical control PBS, survival of mice was marginally increased suggesting a possible minor non-specific effect of the sensitization procedure (Fig. 4b). Unexpectedly, antigen-specific sensitization with PstS1-loaded particles prior to intravesical PstS1 inoculation completely abrogated the therapeutic effect (Fig. 4c). To further substantiate the findings of trial one a second trial with a modified setup was conducted. In this second trial we compared the effect of s.c. PBS injections, injections of empty PLG-particles and injections of PLG-particles loaded with an irrelevant antigen (BSA) on the effect of intravesical PstS1 treatment. This experimental set up revealed a modest therapeutic benefit of intravesical PstS1 in the absence of prior sensitization (Fig. 5a). The number of mice in this experiment was, however, too small to achieve statistical significance using a stringent log-rank test (p = 0.1052). When intravesical PstS1 instillations were combined with pre-vaccination with empty PLG-particles, the result from the previously described trial was confirmed and again a clearly prolonged survival of mice became evident (Fig. 5b). Based on the hypothesis that the sensitization by itself, irrespective of the antigen used for sensitization, might have impeded the effect of intravesical PstS1 in trial one, we tested the effect of particles loaded with irrelevant BSA protein. However, as shown in Fig. 5c, the therapeutic effect of PstS1 remained virtually unchanged suggesting that the inhibition was not due to the sensitization by itself but rather PstS1 specific.
Local and systemic immune response in PstS1-immunotherapy
After we had demonstrated successful immunotherapy of bladder cancer by local instillation of PstS1 we next analyzed the local and systemic immune response of the various treatment groups to obtain initial insight into the immunological basis of this novel immunotherapy. To achieve this we analyzed the systemic antibody response by a semiquantitative anti-PstS1 IgG ELISA, the systemic lymphocyte response by splenocyte restimulation assays and the local immune response in the bladder by immunohistology.
As expected no anti-PstS1 IgG response was detected in the two groups which neither had been sensitized by PstS1 antigen s.c. nor received intravesical inoculations of PstS1. On the other hand, both s.c. and intravesical challenge with PstS1 resulted in positive antibody responses in every animal of the respective groups. This is an interesting finding as it indicates that systemic anti-PstS1 immune responses cannot only be induced by the well established s.c. route but also by intravesical instillations of antigen into the murine bladder (Table 1). Positive antibody responses were also observed in the group which received a combination of s.c. PstS1-loaded particles and intravesical PstS1 and as such failed therapy. A similar picture was observed with regard to the response of restimulated splenocytes (Fig. 6). The two groups of mice with no prior contact to PstS1 only minimally responded to in vitro stimulation with PstS1. In contrast, the four groups of mice which had been exposed to PstS1 either s.c. or intravesically or via both routes strongly responded to specific restimulation of their splenocytes. Again these data indicate that systemic immune responses to PstS1 can be achieved by both s.c. and intravesical challenge. While a s.c. sensitization of mice with PstS1 abrogated the anti-tumor effect of intravesical PstS1 (see Fig. 4) the systemic immune response was not negatively affected but even enhanced in some animals (see Fig. 6).
After the analysis of the systemic immune response to PstS1 we continued our experiments with an immunohistological study of the local immune response. To this end we specifically looked at the influx of lymphocytes, dendritic cells, macrophages and granulocytes (table 2). As expected, bladders of control mice with no PstS1 injections (groups 1–2) were only minimally infiltrated by granulocytes (Gr-1 antigen) or CD11b-positive cells (activation marker for granulocytes and macrophages). On the other hand, after local instillation of PstS1 a distinct influx of granulocytes and macrophages was observed (groups 4–5, table 2). This cellular infiltration of the bladder with granulocytes and macrophages was not substantially altered by additional sensitization of the mice with PstS1-loaded particles (group 6, table 2). In contrast to what is known about classical immunotherapy with BCG [24], challenge of mice with PstS1 only induced a moderate infiltration of the bladder with CD4-positive lymphocytes (groups 4–5, table 2). A slight increase in the number of CD8-positive cells was noted in mice which received s.c. PstS1 sensitization in addition to intravesical PstS1 (group 6, table 2). Dendritic cells (CD11c) were a rare cell population in control mice without PstS1 injections (groups 1–2). A slightly increased number of dendritic cells was noted in the bladders of mice after intravesical instillation of PstS1. Nonetheless, the overall local immune response in the bladder during PstS1 immunotherapy is clearly dominated by the influx of granulocytes and macrophages. A certain induction of local bladder cellularity was also noted in mice which received s.c. injections of PstS1 but no intravesical instillation of the antigen (group 3). The reason for this effect of s.c. PstS1 injection on bladder cellularity is unclear.
Discussion
BCG therapy is a clinically successful therapy in the treatment of bladder cancer but its acceptance is hampered by hazards and side effects related to the use of viable mycobacteria. The aim of this study was to evaluate the anti-tumor potential of the well-defined, non-viable mycobacterial antigen PstS1.
To this end we first tested the potential of recombinant PstS1 to activate human PBMC cultures. In this series of experiments PstS1 induced strong cytotoxicity, proliferation and IFN-γ production in human PBMC. Ten μg/ml PstS1 were found to be optimal for activation of human PBMC. We used BCG lyophilisate as a reference stimulus in these experiments as we have previously described BCG as a potent activator of human PBMC functions [27]. PstS1 followed a similar time kinetic as BCG in the stimulation of human PBMC, albeit activation of PBMC functions was somewhat lower with PstS1 compared with BCG. PstS1 was previously shown to induce profound cellular immunity in different vaccination studies [28-30]. Therefore, our results further confirm the potential of PstS1 to function as a potent inducer of T-helper 1 and cell-mediated immune responses.
While IFN-γ is a key cytokine in TH1 immune responses produced by activated lymphocytes, IL-12 is mainly produced by monocytes/macrophages as well as dendritic cells and important in the early phase of cellular immunity [31]. IL-12 is a potent inducer of anti-tumor immunity [32] and acts together with IFN-γ and TNF-α in a positive feedback loop [25,33,34]. Although BCG mycobacteria are relatively weak inducers of bioactive IL-12p70 in human dendritic cells in the absence of additional costimulation like CD154 or IFN-γ (figure 3b), IL-12 has been shown to be essential for therapeutic efficacy in experimental BCG immunotherapy of bladder cancer [24]. Given the importance of dendritic cells for the initiation of anti-tumor immune responses [35] and as a source of IL-12 and TNF-α we tested the capacity of recombinant PstS1 to activate human monocyte-derived dendritic cells. Our results clearly identified PstS1 as a potent stimulator of human DCs as it provoked upregulation of CD83 and CD86 surface expression as well as induction of cytokines. Of interest is the expression of high amounts of bioactive IL-12 after stimulation of DCs with PstS1 as only small amounts of this cytokine are produced after challenge with BCG or other mycobacterial antigens (figure 3 and unpublished observations).
The encouraging in vitro experiments then prompted us to evaluate the immunotherapeutic potential of PstS1 in a well-established murine model of experimental bladder cancer [20]. The effect of prior sensitization or responsiveness to mycobacterial antigens for the effectiveness and outcome of subsequent immunotherapy with BCG is still a matter of intensive debate [21-23]. Taking this into account we took advantage of the fact that we had in hand a well-defined, recombinant antigen already evaluated in vaccination studies against mycobacterial infections and included a prime-boost treatment regimen into our experimental immunotherapy protocol. The main objective of our series of in vivo experiments was to determine the anti-tumor potential of local instillations of recombinant PstS1 and to compare the effect of intravesical PstS1 in sensitized and non-sensitized mice. Because adsorption of PstS1 to L-particles has previously been described as very efficient in inducing specific cellular immunity to this antigen [29], we adopted this method of sensitization for our treatment protocol. Using a protocol of four weekly instillations of PstS1 into the bladder (adopted from the treatment schedule in use for immunotherapy with BCG) we observed a strong therapeutic effect of PstS1 instillations. Intravesical PstS1 significantly prolonged survival of mice and induced systemic immune responses and cellular infiltration of the bladder with different subpopulations of leukocytes. The anti-tumor effect of PstS1 was already evident after intravesical instillation of PstS1 only but was further enhanced after s.c. injection of empty L-particles (Fig. 5). Unexpectedly, sensitization of mice with PstS1-loaded particles almost completely abrogated the therapeutic effect of intravesical PstS1 (Fig. 4). In order to test whether the negative effect of prior sensitization was specific for PstS1 or could also be induced by s.c. injection of L-particles loaded with an irrelevant antigen, we compared the therapeutic effect of intravesical PstS1 combined with a) s.c. sensitization with BSA-loaded L-particles, b) s.c. injection of empty L-particles and c) s.c. injection of PBS only (Fig. 5). Clearly, the injection of BSA-loaded particles did not influence the therapeutic effect of intravesical PstS1 indicating that only PstS1-specific previous sensitization is detrimental (Fig. 5a and 5c). We analyzed the systemic serum antibody response, activation of splenocytes and the local cellular infiltration of the bladder wall in sensitized and non-sensitized mice (Fig. 6) in order to understand and explain the remarkable therapeutic potential of intravesical PstS1 as well as the negative effect of prior specific sensitization. Interestingly, even without sensitization, instillation of PstS1 into the bladder provoked a systemic anti-PstS1 response visualized by murine anti-PstS1 serum antibodies and splenocyte proliferation after rechallenge in vitro. In addition to this systemic immune activation, intravesical PstS1 also induced the local influx of lymphocytes, macrophages and granulocytes into the bladder. While only a minimal influx of CD8-cells could be observed after instillation of PstS1, a considerable infiltration of the bladder with granulocytes, macrophages and CD4-cells was noted. Although prior specific sensitization with PstS1 completely abrogated the anti-tumor effect of intravesical PstS1, the cellular infiltration of the bladder wall remained essentially unchanged when comparing sensitized and non-sensitized animals. In the splenocyte restimulation assay an enhanced response of two out of four mice was noted in the group of mice which received s.c. PstS1 followed by intravesical PstS1 compared with the two groups which received either treatment alone (Fig. 6). This indicates that the combination of s.c. sensitization and intravesical treatment indeed augmented the systemic immune response at least in some animals. Surprisingly, this enhanced systemic immune response coincided with an abrogation of tumor-therapeutic efficacy. A possible explanation for this phenomenon could be that the anti-PstS1 antibodies, which were induced after s.c. priming would bind to the recombinant PstS1 shortly after instillation into the bladder and thereby neutralize its function and anti-tumor effect. However, as mentioned earlier, even in the PstS1-specific prime-boost treatment regimen an unchanged cellular infiltration of the bladder wall was noted. Alternatively, the sensitization might render the subsequent local immunotherapeutic immune response insufficient, because the host immune system is still actively responding to the priming at the onset of immunotherapy. This situation might be called "immune exhaustion" making the host unable to mount a sufficient local anti-tumor immune response while still responding to the specific priming. Further experiments are needed to clarify whether a modification of the time schedule of such a "prime-immunotherapy" protocol could prevent the negative effects observed in this study and might even enhance immunotherapy with PstS1.
Nonetheless, our combined in vitro and in vivo analyses clearly identified PstS1 from M. tuberculosis as a potent immunostimulant and a potential immunotherapeutic anti-cancer agent for topical treatment strategies. Our data do not show and do not imply that PstS1 is the major or only immunostimulatory component of whole BCG mycobacteria in BCG immunotherapy of bladder cancer.
Conclusions
We have identified the immunodominant mycobacterial PstS1 antigen as a potent biological response modifier for tumor immunotherapy. Using a human in vitro system of PBMC activation and a murine model of experimental bladder cancer immunotherapy we could show strong immunostimulatory capacity of PstS1 as well as significant anti-tumor activity. In a model of prime-boost immunotherapy we observed that antigen-specific sensitization might jeopardize the positive effects of topical immunotherapy and therefore has to be considered and evaluated with caution. GMP-production of PstS1 and a clinical trial in humans is currently being established and might open the door for an efficient and safe alternative in the field of bladder cancer immunotherapy.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
CS carried out most of the experiments, provided experimental protocols and prepared the initial version of the manuscript. ABu contributed to figure 2 and worked on the manuscript. GB performed the immunohistochemistry and cell culture. RS and FJ performed the α-PstS1 IgG ELISA and provided the recombinant PstS1. MS, ABö and SB jointly participated in fund raising and coordination. SB is the principal investigator, edited the manuscript and advised CS on experimental design and study concept.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This work was supported by the ProInno program of the BMWA of Germany.
Figures and Tables
Figure 1 Activation of PBMC by PstS1 is dose-dependent. PBMCs were stimulated for 7 days with different PstS1 concentrations in a range from 0.1 μg/ml to 100 μg/ml. A 4-hour chromium release assay against T-24 bladder tumor cells. Effector-target cell ratio of 40:1 B IFN-γ release was determined by ELISA from supernatants of the stimulated PBMCs C PBMC proliferation was measured by overnight 3H-Thymidine incorporation assay (1 μCi/2 × 105 cells). One representative experiment out of 3–6 is shown (mean ± SD). n.d. = not detectable
Figure 2 Time-course of PBMC stimulation. PBMC were stimulated with PstS1 (10μg/ml), BCG (4 × 104 CFU/ml), PHA (2,5 μg/ml) or left unstimulated. Cytotoxicity (A), IFN-γ-production (B) and proliferation (C) of PBMC were measured after 2, 5 and 7 days of stimulation. A Cytotoxicity assay. 4-hour chromium release assay against T-24 bladder tumor cells. Effector-target cell ratio of 40:1. B IFN-γ release measured by ELISA from culture media of PBMC. C PBMC proliferation measured by overnight 3H-Thymidine incorporation assay (1 μCi/2 × 105 cells). For A and B one representative experiment out of 3 is shown (mean ± SD), for C a composite of seven independent experiments is shown (mean ± SEM). For PHA-stimulation one out of two experiments is shown (mean ± SD) . n.d. = not detectable
Figure 3 Activation of human monocyte-derived dendritic cells by PstS1. A Monocytes were differentiated for seven days with GM-CSF and IL-4 (500 U / ml each), stimulated with BCG (MOI = 0.01) or PstS1 (10 μg / ml) for three days and afterwards analyzed by flow cytometry for the expression of CD1a, CD14, CD83 and CD86. Isotype control = filled histograms, specific antibody = open histograms. Cells were gated on intact dendritic cells according to forward scatter and sideward scatter. B Supernatants of the stimulated DCs were collected and ELISAs for TNF-α and IL-12p70 were performed. One representative experiment out of 5 is shown (mean ± SD). n.d. = not detectable.
Figure 4 Intravesical PstS1 immunotherapy in nonsensitized and PstS1-sensitized mice. Ten days before inoculation of MB-49 tumor cells mice were s.c. sensitized by injection of empty L-particles (A/B) or L-particle loaded with PstS1 (C). On days 1, 8, 15 and 22 after tumor challenge, mice were treated by intravesical instillation of PstS1 (A/C) or PBS (B). Mice which received s.c. PBS and intravesical PBS served as control. Survival of mice was analyzed by Kaplan-Meier curve and log rank test.
Figure 5 Intravesical PstS1 treatment after sensitization with PBS, PLG-particles or PLG-particles/BSA. Ten days before inoculation of MB-49 tumor cells mice were s.c. sensitized by injection of PBS (A), L-particles (B) or L-particles loaded with BSA (C). On days 1, 8, 15 and 22 after tumor challenge, mice were treated by intravesical instillation of PstS1 (A/B/C). Mice which received s.c. PBS and intravesical PBS served as control. Survival of mice was analyzed by Kaplan-Meier curve and log rank test.
Figure 6 Splenocyte Restimulation. Treatment of mice was performed as indicated (s.c. sensitization / intravesical instillation). One day after the fourth intravesical PstS1 treatment splenocytes were isolated and restimulated with PBS as control (open circles) or 10 μg/ml PstS1 (filled boxes) for five days and proliferation was analyzed by 3H-Thymidine incorporation assay.
Table 1 Serum antibody response after PstS1 immunotherapy
S.C. / Intravesical Treatment Responders in %
PBS/PBS 0
L-Particle/PBS 0
L-Particle-PstS1/PBS 100
PBS/PstS1 100
L-particle/PstS1 100
L-particle-PstS1/PstS1 100
Five mice per group were sensitized s.c. and treated intravesically on days 10, 17, 24 and 31 as indicated in the table. On day 32 serum of mice was obtained and analyzed for anti-PstS1 IgG by ELISA. The percentage of mice with positive anti-PstS1 antibody responses is indicated.
Table 2 Local immune response in the bladder wall after PstS1 immunotherapy
group S.C. / Intravesical Treatment CD11b GR-1 CD4 CD8 CD11c
1 PBS/PBS 11± 8 3 ± 2 28 ± 24 8 ± 9 2,2 ± 1,7
2 L-Particle/PBS 6 ± 7 3 ± 2 17 ± 6 4 ± 3 1,6 ± 1,4
3 L-Particle-PstS1/PBS 24 ± 20 4 ± 7 69 ± 60 16 ± 15 22,2 ± 24,4
4 PBS/PstS1 86 ± 46 44 ± 30 45 ± 18 10 ± 4 1,9 ± 2,1
5 L-Particle/PstS1 114 ± 25 56 ± 20 56 ± 16 13 ± 14 3 ± 2,4
6 L-Particle-PstS1/PstS1 143 ± 84 49 ± 15 75 ± 29 36 ± 29 4,5 ± 4,2
Five mice per group were sensitized s.c. and treated intravesically on days 10, 17, 24 and 31 as indicated in the table. On day 32 bladders were dissected and used for histochemistry. CD11b: Granulocytes and Macrophages, GR-1: Granulocytes, CD4: CD4-Lymphocytes, CD8: CD8-Lymphocytes, CD 11c: Dendritic cells. The number of positive cells in the entire bladder specimens was counted using visual fields and light microscopy. The data are depicted as the mean number of positive cells per visual field ± standard deviation (n = five mice per group).
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| 15566565 | PMC544192 | CC BY | 2021-01-04 16:03:02 | no | BMC Cancer. 2004 Nov 26; 4:86 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-86 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-541554649310.1186/1471-2458-4-54Research ArticleFemales do not have more injury road accidents on Friday the 13th Radun Igor [email protected] Heikki [email protected] Traffic Research Unit, Department of Psychology, University of Helsinki, PO Box 9, 00014 Helsinki, Finland2004 16 11 2004 4 54 54 14 7 2004 16 11 2004 Copyright © 2004 Radun and Summala; licensee BioMed Central Ltd.2004Radun and Summala; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
This study reinvestigated the recent finding that females – but not males – die in traffic accidents on Friday the 13th more often than on other Fridays (Näyhä S: Traffic deaths and superstition on Friday the 13th. Am J Psychiatry 2002, 159: 2110–2111). The current study used matched setting and injury accident data base that is more numerous than fatality data. If such an effect would be caused by impaired psychic and psychomotor functioning due to more frequent anxiety among women, it should also appear in injury crashes.
Methods
We used the national Finnish road accident database for 1989–2002. To control seasonal variation, 21 Fridays the 13th were compared in a matched design to previous and following Fridays, excluding all holidays, on number of accidents, male/female responsibility for accidents, and the number of dead, injured and overall number of active participants (drivers, pedestrians and bicyclists) as a consequence of the accident.
Results
There were no significant differences in any examined aspect of road injury accidents among the three Fridays, either in females or males. Women were not overrepresented in crashes that occurred on Fridays 13th.
Conclusion
There is no consistent evidence for females having more road traffic crashes on Fridays the 13th, based on deaths or road accident statistics. However, this does not imply a non-existent effect of superstition related anxiety on accident risk as no exposure-to-risk data are available. People who are anxious of "Black Friday" may stay home, or at least avoid driving a car.
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Background
One widely spread superstition is that Friday the 13th brings bad luck. However, the few studies published on human behaviour and its consequences on that day show inconsistent results, whether they be on economic behaviour [1-3] or health risks [4-6]. A recent nationwide study by Näyhä [7] on the 1971–97 death statistics in Finland found that men's deaths did not increase on Friday the 13th but females' did by a factor of 1.61, and by 1.63 when adjusted for age, time period, temperature, and extra Poisson variation. The author's conclusion was that Friday the 13th may be a dangerous day for some women, presumably because of anxiety from superstition and, possibly, anxiolytic medications.
This interpretation is not without problems. First, although the author repeatedly refers to driving, it should be noted that he also included water and air traffic accidents. Secondly, as the author pointed out himself, his data included passengers killed in accidents, who typically have no control on the task. Impaired psychic and psychomotor functioning due to anxiety, which could indeed be more frequent in females due to their higher neuroticism rate [8], superstition [9,10] and smaller amount of driving experience [11] should primarily affect safety in cases where females were active traffic participants. Third, weather conditions were controlled by the mean daily temperature obtained from one place close to the population centered midpoint of the country. However, Finland is more than 1000 km in length, located between the 60th and 70th deg of Northern latitude with much variation in weather. The vicinity of the sea increases variation in weather and road conditions even more in the southern coast where the population and traffic are heavily concentrated. Any adjustment based on one location cannot be effective. Fourth, by excluding only Good Fridays the author had a sample of Fridays the 13th without holidays because no major holiday in Finland falls on the 13th of month. However, there are plenty of holidays among all other Fridays with quite different travel patterns and life style. For example, Midsummer Eve always falls on Friday in the second part of June, which gives 27 such days in study period 1971–1997. The Midsummer Eve is a marked peak in alcohol consumption in Finland [12], as well as of crashes of male drivers. Friday can also fall on Christmas day, New Years day, First of May and some other holidays with much reduced traffic volumes and exposure to risk. Finally, in spite of the long study period, the data only included 41 female deaths on 43 Fridays the 13th, which means 16 deaths more than expected from all other Fridays during the study period.
In spite of Näyhä's fairly conservative conclusion, his results have been widely publicised as evidence that superstitious female drivers die on Fridays the 13th [13] in marked contrast to men. Due to the shortcomings listed above, and fairly small sample size, the results deserve reinvestigation to avoid premature conclusions and improper interpretations which tend to promote sexist attitudes about women drivers.
We reinvestigated the case using the national Finnish road accident data base of injury accidents [14] for 1989–2002, all years available in a comparable format. These data also include road-traffic fatalities, and for that part they overlap with Näyhä's study period and data. A matched design was selected which makes it possible to control seasonal effects and to avoid the problems due to holidays. Injury accidents are much more numerous than fatalities. If women's assumed more frequent superstitious (and traffic-related) anxiety indeed would result in attentional and psychomotor dysfunctioning on Fridays the 13th, claimed by Näyhä [7] on the basis of fatality statistics, the effect should also be found in injury crashes.
Methods
There were 24 Fridays the 13th during the study period. However, three of them were excluded because two were Good Fridays and one followed a Thursday holiday. To control seasonal variation in traffic and weather-type, the remaining 21 Fridays the 13th were compared with the previous Fridays the 6th and the following Fridays the 20th on the number of accidents, male/female responsibility for accidents (police officer judgment), the number of dead, injured and overall number of active participants as a consequence of accident, separately for women and men. Active participants included drivers, bicyclists and pedestrians who actively controlled their motion in traffic and may get involved in crashes. Motor vehicle passengers were excluded. Nine holidays or otherwise unusual control Fridays were replaced by the mean values of the accident variables (e.g. number of accidents, number of injured) from the previous and following years' closest Fridays. For example, Friday the 20th in June 1997 fell on Midsummer holiday eve, and was replaced by mean values gathered from Fridays June 14th, 1996 and June 12th, 1998. This was done to preserve size of the sample. To avoid violating parametric assumptions, the Friedman analysis of variance by ranks [15] was used to test differences across 21 matched triplets of Fridays.
Results
Tables 1, 2, 3 present accidents and active participants and victims by gender for the Fridays the 13th and the preceding and next Fridays. Figure 1 depicts daily means of active participants by gender on each of three Fridays.
Table 1 The number of injury accidents on Fridays the 13th, the previous (the 6th) and following (the 20th) Fridays. N for the matched triplets of Fridays = 21.
Friday Total Daily Mean
6th 542.5* 25.83
13th 608 28.95
20th 546.5 26.02
* Decimal numbers in Tables 1-3 are due to replacement of seven holidays or otherwise unusual control Fridays with the mean values of the variables from the previous and following years' closest Fridays.
Table 2 The number of active participants* by gender on Fridays the 13th, the previous (the 6th) and following (the 20th) Fridays.
Female Male
Friday Total Drivers Pedestrians and Bicyclists Total Drivers Pedestrians and Bicyclists
6th 299.5 193.5 106 713.5 618 95.5
13th 317 198 119 824 705 119
20th 299 183 116 748.5 661.5 87
* Active participants included drivers, bicyclists and pedestrians who actively control their motion in traffic and may get involved in crashes.
Table 3 The number of victims* by gender on Fridays the 13th, the previous (the 6th) and following (the 20th) Fridays.
Female Male
Friday Dead Injured Dead Injured
6th 6 (9)* 196 (290.5) 27.5 (33.5) 308.5 (372)
13th 15 (21) 214 (304) 30 (35) 340 (430)
20th 11 (13) 195.5 (296) 22.5 (25) 329.5 (418.5)
* Numbers for victims refer to active participants while those in parenthesis also include passengers.
Figure 1 The average daily number of active participants involved in injury road crashes by gender on the Fridays 13th and the preceding and following Fridays for 1989–2002.
Comparisons of 21 triplets showed no significant difference in injury accidents (Friedman χ2 = 3.534, df = 2, p = 0.171); in active participants, for females (χ2 = 0.025, df = 2, p = 0.987) or males (χ2 = 0.173, df = 2, p = 0.917); in injured active participants, for females (χ2 = 1.162, df = 2, p = 0.559) or males (χ2 = 0.532, df = 2, p = 0.767); and in dead active participants, for females (χ2 = 2.735, df = 2, p = 0.255) or males (χ2 = 0.448, df = 2, p = 0.799) among three Fridays. To test the Gender × Day interaction, we also computed female/male ratios of active participants for each Friday of each triplet, and applied the Friedman test to check whether this ratio is systematically higher on Fridays 13th, as expected from Näyhä's [7] results. The ratio was quite similar on each Friday (6th: 0.420, 13th: 0.385, 20th: 0.452; χ2 = 0.400, df = 2, p = 0.819) indicating that, with respect to men, women are not overrepresented in crashes that occur on Fridays 13th. The odds for women being involved in an injury tend to be even somewhat smaller on Friday 13th. There was no such overrepresentation in injured (χ2 = 1.615, df = 2, p = 0.446) or dead (χ2 = 2.1, df = 2, p = 0.350) women among active participants.
Finally, we similarly checked a possible Gender × Day effect in legally responsible crashes (responsibility drawn from police reports), computing female/male ratios of guilty participants for each day and triplet, but did not found any effect(Friedman test, χ2 = 0.514, df = 2, p = 0.774).
Discussion
This study could not find any indication of overrepresentation of women in injury crashes on Friday the 13th. This is inconsistent with Näyhä's [7] results and conclusions that were based on less numerous deaths statistics (41 women died in all traffic accidents on Fridays 13th 1971–97) compared to injury road traffic accidents (317 active female participants on Fridays 13th 1989–2002), and also inconsistent with earlier British results [4]. Given that women's more frequent superstition and related anxiety would cause unsafe traffic behaviour, injury accidents should increase on Friday the 13th as well as fatalities. This was definitely not the case in the Finnish road accident statistics. Although injury accidents are not reported as completely as fatalities, we do not see any reason for biased reporting on Fridays the 13th. Our analysis did not even show any significant gender effect in fatalities.
It is to be noted that both Näyhä's study [7] and this study are based on aggregated data (number of accidents per day in the country). In contrast to individual level analysis, such data mixes individual confounders and outcomes and, therefore, confounding factors cannot be fully controlled [16]. Our matched countrywide setting is a quasi-experimental design well suited to simple comparisons of crash rates in gender populations which keep constant in each triplet of successive Fridays (see also [4]). We also assume that this design is quite powerful in controlling seasonal variation (e.g. in traffic and weather).
However, our data only implies that, in comparison to men, women are not overrepresented in injury road accidents on Fridays 13th in Finland for 1989–2002. We do not and we cannot conclude anything about women's performance in traffic on Fridays 13th, or about their accident risk (given certain exposure to risk), or about the effect of superstition on those risks. For such conclusions, disaggregated individual level data is needed with detailed information of exposure to risk and respective accident outcome. People themselves adjust their exposure to risk at several levels, while making trip decisions, choosing transport mode, or selecting routes to the destination (see the "multiple sieve model" of accident output [17]). Therefore, those who are really anxious about Friday 13th may stay at home, use public transportation instead of car, avoid rush hours, choose safer routes, or avoid dangerous junctions. But for one left turn while driving, or for one crossing of street while walking, their risk may be higher.
Conclusion
We conclude that, in the Finnish traffic accident statistics for 1989–2002, females have not incurred more injury (or fatal) road traffic accidents on Fridays the 13th than expected, as a driver, bicyclist or pedestrian. We suggest that Näyhä's contradicting result on fatalities is due to different sampling, non-optimal setting and chance in a fairly small data. However, this does not imply a non-existent effect on accident risk as no exposure-to-risk data [18] are available. People who are anxious of "Black Friday" may stay home, or at least avoid driving a car. The only relevant data [4], suggesting a small decrease in highway traffic, is rather limited and should be confirmed with more extensive research.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
Both authors participated in each stage of research and manuscript preparation.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank Dr. Bryan Porter for his valuable comments to the manuscript. This study was presented in the 3rd International Congress of Traffic and Transport Psychology, ICTTP 2004, Nottingham, UK, September 5–9, 2004.
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| 15546493 | PMC544193 | CC BY | 2021-01-04 16:28:48 | no | BMC Public Health. 2004 Nov 16; 4:54 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-54 | oa_comm |
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BMC UrolBMC Urology1471-2490BioMed Central London 1471-2490-4-151559600610.1186/1471-2490-4-15Research ArticleNondestructive analysis of urinary calculi using micro computed tomography Zarse Chad A [email protected] James A [email protected] Andre J [email protected] Samuel C [email protected] Erin K [email protected] James E [email protected] Andrew P [email protected] James C [email protected] Department of Anatomy and Cell Biology, Indiana University School of Medicine, Indianapolis, Indiana (USA)2 Molecular Microspectroscopy Laboratory, Department of Chemistry and Biochemistry, Miami University, Oxford, Ohio (USA)3 Methodist Hospital Institute for Kidney Stone Disease, Methodist Hospital, Indianapolis, Indiana (USA)2004 13 12 2004 4 15 15 24 6 2004 13 12 2004 Copyright © 2004 Zarse et al; licensee BioMed Central Ltd.2004Zarse et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Micro computed tomography (micro CT) has been shown to provide exceptionally high quality imaging of the fine structural detail within urinary calculi. We tested the idea that micro CT might also be used to identify the mineral composition of urinary stones non-destructively.
Methods
Micro CT x-ray attenuation values were measured for mineral that was positively identified by infrared microspectroscopy (FT-IR). To do this, human urinary stones were sectioned with a diamond wire saw. The cut surface was explored by FT-IR and regions of pure mineral were evaluated by micro CT to correlate x-ray attenuation values with mineral content. Additionally, intact stones were imaged with micro CT to visualize internal morphology and map the distribution of specific mineral components in 3-D.
Results
Micro CT images taken just beneath the cut surface of urinary stones showed excellent resolution of structural detail that could be correlated with structure visible in the optical image mode of FT-IR. Regions of pure mineral were not difficult to find by FT-IR for most stones and such regions could be localized on micro CT images of the cut surface. This was not true, however, for two brushite stones tested; in these, brushite was closely intermixed with calcium oxalate. Micro CT x-ray attenuation values were collected for six minerals that could be found in regions that appeared to be pure, including uric acid (3515 – 4995 micro CT attenuation units, AU), struvite (7242 – 7969 AU), cystine (8619 – 9921 AU), calcium oxalate dihydrate (13815 – 15797 AU), calcium oxalate monohydrate (16297 – 18449 AU), and hydroxyapatite (21144 – 23121 AU). These AU values did not overlap. Analysis of intact stones showed excellent resolution of structural detail and could discriminate multiple mineral types within heterogeneous stones.
Conclusions
Micro CT gives excellent structural detail of urinary stones, and these results demonstrate the feasibility of identifying and localizing most of the common mineral types found in urinary calculi using laboratory CT.
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Background
Clinical laboratory assessment of urinary stones is typically conducted using destructive methods of analysis [1-5] and is usually geared to identify stones only by their primary mineral content. It is rare that stones get classified as having multiple components, and even less likely that notation is given to describe the pattern or distribution of different minerals in heterogeneous stones. Thus, stones are commonly classed as being calcium oxalate monohydrate (COM), uric acid, cystine, etc. When this information makes its way to the patient's chart, the individual may be classified simply, for example, as a COM stone former. This tends to underestimate the complexity of an individual's stone history as, indeed, it has been determined that the vast majority of stones actually contain more than one type of mineral [6].
Knowing the mineral composition of a patient's stones has obvious value in determining a treatment plan, but stone structure may be important as well. It has long been appreciated that there is variability in stone fragility to shock waves in lithotripsy, that stones of a given mineral type do not all break the same [7]. Stone fragility, and the factors that may influence variability in fragility, are not well known. There are some clues, but the story is incomplete and deserves attention. As an example, consider the case of cystine stones. Rough surface cystine stones tend to break readily, but smooth surfaced cystine can be very difficult to break [8]. Rough cystine typically contains internal radio-lucent regions (possible voids). Numerical modeling, and studies with artificial stones made to contain voids, suggests that such defects could be sites of structural weakness [9]. Thus, the internal structure of a stone may very well contribute to its fragility.
The purpose of this report is to present micro computed tomography (micro CT) as a potential method for the analysis of urinary stone composition and morphology in a nondestructive manner at very high resolution. Micro CT, which has seen considerable use as a research tool in bone biology [10], has the ability to reconstruct 2-D and 3-D images of urinary stones that allow the 3-D image of the stone to be cut and viewed in multiple planes with voxel sizes of 8–34 μm. In the present study, we observed that in addition to providing exceptional detail of the fine structure of human urinary stones, micro CT was able to differentiate six common mineral constituents using x-ray absorption (attenuation) values alone.
Methods
Human urinary stones were obtained following percutaneous nephrolithotomy procedures or from a stone analysis laboratory (Beck Analytical Services, Indianapolis, IN). A representative collection of urinary stones of pure and heterogeneous mineral content was selected for use in this study.
Calibrating micro CT attenuation to mineral content
Stones for this work had already been analyzed using conventional methods (microscopic, chemical and IR spectroscopic), with portion used here typically the half of the stone remaining intact after that analysis. For some stones, pre-CT analysis was completed on a cohort stone taken during the same surgical procedure. Compositions of the 11 stones used for combined micro CT and IR microspectroscopy included pure COM, pure calcium oxalate dihydrate (COD), 91% COD/9% COM, 99% COM/1% hydroxyapatite, 52% COM/48% uricite, pure uricite, pure struvite, two pure cystine stones, and two pure brushite stones.
Stones were embedded within methyl methacrylate and sliced into 1–2 mm slabs using a diamond wire saw (Well Saw, Delaware Diamond Knives, DE). A Perkin-Elmer AutoImage infrared microscope interfaced to a Perkin-Elmer Spectrum 2000 fourier transform infrared (FT-IR) spectrometer (Perkin Elmer, Shelton, CT) was used to collect infrared spectra on the flat surface of the stone slices. The spectrometer directs infrared light onto the specimen through a microscope lens, and collects reflected light onto a cooled mercury-cadmium-telluride detector. Spectra were collected in grids of 200 × 200 μm intervals, averaging 16 individual scans on each spot, yielding spectral resolution of 4 cm-1. Spectra for COM, COD, apatite, uric acid, struvite, cystine, and brushite were all easily identified and distinguishable from one another. Uricite and uric acid dihydrate could not be distinguished above the background noise of the spectra, and so regions are reported as uric acid.
The same stone slices analyzed by FT-IR microspectroscopy were also scanned by micro CT, using a Scanco MicroCT 20 instrument (Scanco Medical AG, Bassersdorf, Switzerland) (Figure 1). The system utilizes a 7 μm spot-size microfocus x-ray source (0.16 mA, 50 kVp) that is detected by a charge coupled device array. The scans on stone slices were completed using standard resolution (512 × 512 pixels) and a 17.4 mm specimen holder which produced image slice thicknesses and pixel widths of 34 μm. For each stone slice, a CT image slice was obtained within ~ 50 μm of the cut surface.
Figure 1 Micro CT system (Scanco MicroCT 20). The micro CT unit itself is on the right, a cube approximately 0.5 m across. The computer workstation to the left of the micro CT unit controls the collection of scan data, storage and archival of data, and is used for image reconstruction.
The FT-IR analysis yielded a 2-D map of mineral composition for a defined region of each stone slice. Regions of the 2-D map showing pure mineral were compared to the identical region on the micro CT image slice taken just below the cut surface (Figure 2). Micro CT attenuation values from these pure mineral regions were recorded.
Figure 2 Calibrating micro CT attenuation to pure mineral. On stone slice shown (top) FT-IR spectra were collected on each of 126 regions indicated by the grid area. Examples of spectra are shown for three regions, which represent the three classes of spectra collected from cut surface of this stone. Spectra indicated that the mineral was either purely calcium oxalate monohydrate (COM), purely apatite (HA), or a mixture of these two minerals. Corresponding regions-of-interest on micro CT image slice (bottom) taken just beneath the cut surface are shown with blue squares, and micro CT attenuation values are given below spectra. Using this method, regions of pure mineral were identified on stone slices and corresponding regions-of-interest measured in micro CT images to determine CT attenuation of different minerals.
Imaging of intact stones: nondestructive stone analysis
In scanning intact stones with micro CT, actual slice thickness and pixel widths were dependent on the diameter of the specimen holder and resolution parameters, and ranged from 25–34 μm for images shown in this study. Examples of scans of intact stones are presented to show the potential for performing analyses of stone composition and structure using micro CT alone.
Results
The imaging capability of micro CT is simply remarkable. Additional Data File 1 shows a stack of micro CT images of a single stone, archived as a movie. Refer to this file to view the reconstruction ability of micro CT, as well as to appreciate the power of micro CT to clearly display very high resolution images of stone structure.
Nine sectioned stones, analyzed as shown in Figure 2, were found to contain six different mineral types by IR spectrum. Micro CT attenuation values were taken from regions of compositional homogeneity, as shown by FT-IR microspectroscopy, and Figure 3 displays the results. Values for micro CT attenuation were non-overlapping for the six minerals. Means ± standard deviations for attenuation values were 22,207 ± 709 for hydroxyapatite, 17,771 ± 837 for COM, 14,767 ± 680 for COD, 9434 ± 439 for cystine, 7633 ± 248 for struvite, and 4201 ± 564 for uric acid. Two brushite stones were also analyzed by this method, but no regions on the cut stone surface larger than about 0.5 mm across were found that were pure; spectra frequently showed intermixing of brushite with COD. Micro CT attenuation for 11 ROI's on these two stones averaged 19,145 ± 486, with a tight range of 18,540 to 19,944.
Figure 3 Micro CT attenuation values taken from pure mineral regions identified by FT-IR microspectroscopy. Regions-of-interest representing pure mineral, confirmed by FT-IR mapping, were drawn on micro CT images collected just beneath the cut surface of stone slice, and average value for CT attenuation (in machine-specific units) was recorded. Horizontal lines indicate minimum and maximum values, and number of regions-of-interest indicated in parentheses. Note that each mineral composition is associated with non-overlapping attenuation values (uric acid 3515 – 4995, struvite 7242 – 7969, cystine 8619 – 9921, calcium oxalate dihydrate 13815 – 15797, calcium oxalate monohydrate 16297 – 18449, and hydroxyapatite 21144 – 23121).
Since micro CT attenuation values for six of these minerals did not overlap, mineral composition of pure mineral regions could be inferred using micro CT alone. For example, Figure 4 displays a simple, homogeneous stone and a complex, heterogeneous stone for which micro CT attenuation values were used to identify mineral composition. The homogeneous stone (left panel) showed uniform CT attenuation values in regions-of-interest of various sizes and shapes, placed at various positions across the image, all consistent with this stone being purely COM. The image of the heterogeneous stone (right panel) displays intricate structure in which the grayscale color difference in selected regions of interest yielded different (i.e., non-overlapping) x-ray attenuation values – values that when compared to FT-IR calibration data (Figure 3) could be identified as uric acid, COM, and hydroxyapatite.
Figure 4 Example of micro CT identification of mineral composition. Visually, there appears to be only one mineral contained in the stone in the left panel and three different minerals that comprise the stone in the right panel. Micro CT attenuation identified the left stone as pure COM, and the right stone as a mixture of hydroxyapatite (bright white, highest attenuation), COM (gray) and uric acid (close to black). Speckled nature of colors – particularly apparent in image at left – is due to image noise as a result of increased magnification.
Over the past few years, we have imaged hundreds of urinary stones using micro CT, and we have begun to learn some of the capabilities of this method that can enhance the value of the analytical information presented above. Figure 5 shows some of the imaging capabilities of micro CT software. Panel A is a photograph of a COM stone and panel B is a representative micro CT image slice in which the concentrically lamellar nature of the stone is easily seen. The dark lines separating lamellae indicate regions that absorb x-rays poorly, possibly representing regions of organic matrix. The 3-D surface rendering capability of micro CT software is shown in Figure 5C, where surface topography is shown. Further, the software permits the image to be cut and rotated in multiple planes, as shown by the wedge cut that exhibits the 3-D lamellar distribution of voids in two planes of view (Figure 5D).
Figure 5 Imaging and software power of micro CT. Pure COM stone viewed by conventional photography (A) and micro CT (B-D). B shows a typical micro CT image slice, C a 3-D reconstruction of the stone surface, and D a wedge cut that displays the internal 3-D morphology of the stone.
Figure 6 shows features of a stone revealed using micro CT data in a different way. Panel A is a photograph of a mulberry-type COM stone and panel B is a single image slice of this stone by micro CT. The micro CT image shows a COM outer shell deep to which is a thin ring of hydroxyapatite (white) surrounding a core of low attenuation. The core consists of void spaces, which are defined to be regions of extremely low attenuation, again perhaps representing organic matter. Panel C shows a 3-D surface rendering of the stone from the micro CT image stack. Panel D displays a surface rendering of the apatite regions, superimposed within a translucent version of panel C so that the position of the apatite within the stone is easily visible. Rendering for panels C and D were obtained using MacVol, a freeware program.
Figure 6 Visualization of components of a mixed COM/HA stone. A. Digital photograph of stone, on 1 mm grid. B. Micro CT slice through middle portion of stone; arrow indicates ring of high attenuation (hydroxyapatite). Gray material outside this ring had attenuation value in the range for COM. C. 3-D surface rendering from micro CT displaying surface topology. D. 3-D surface rendering with transparent shell to show 3-D localization of hydroxyapatite within stone.
Total stone volume and volume of internal voids can also be calculated using images obtained by micro CT. Figure 7 shows a transparent 3-D reconstruction of a cystine stone with void spaces appearing as internal granular objects. The stone volume is 257.7 mm3 and the volume of internal voids is 0.3 mm3. This method can also be used to display and quantitate the 3-D distribution of mineral components within a complex, heterogeneous stone.
Figure 7 3-D reconstruction of a cystine stone with regions of low attenuation (voids). Outer shell of stone was made transparent to display distribution of inner void regions depicted as small bodies. Total stone volume was 257.7 mm3 and total volume of internal voids was 0.3 mm3.
Discussion
Micro CT yields excellent high resolution analysis of stone structure. It is a relatively fast method, taking approximately 2 hours for a complete 30 μm slice scan of a 1 cm diameter urinary stone. Micro CT allows nondestructive mapping of the internal and surface structure of urinary stones and permits identification of mineral composition based on x-ray attenuation values. Micro CT cannot differentiate mineral types when the stone is highly complex and micro-heterogeneous with significant mixing of different mineral types at a scale below the spatial resolution of the instrument. With this limitation, complete analysis for most urinary stones seems feasible using micro CT alone.
Establishing micro CT standards for complete stone analysis will require a larger data set than was obtained in the present study; for example, the measurements obtained for apatite were from only one stone that was composed mostly of COM. The x-ray attenuation of apatite was the highest of all the minerals tested, which would be expected from other CT studies [12], but the attenuation value in pure apatite stones might well be different.
It will also be important to study more brushite stones to see how this clinically important mineral can be distinguished from other minerals. Brushite stones are rare, making up only about 1% of total stones [6], but when present they are a special clinical problem, as brushite stone formers tend to form stones rapidly, and the stones are difficult to break with shock wave lithotripsy [13]. A recent study showed that less than 2% of brushite stones are pure brushite [6], but the incidence of close intermixing of mineral within brushite stones, as seen in the present study, has not been studied.
The source of variation of micro CT attenuation in stone regions that are pure by IR analysis is not apparent. Note in Figure 3 that the range of attenuation values measured for COM is greater than that seen for other minerals. This range of values could be due to varying amounts of matrix included among the COM crystals [14], or it could be due to mixing of small amounts of COD or other mineral, amounts small enough not to be detected by IR. The vast majority of stones contain more than one component [6], but many of these mixed stones show obvious spatial separation of the different materials, as observed in the stones shown in Figures 4b and 6. If a significant number of stones show close intermixing of minerals, the use of micro CT for stone analysis may be more limited than is suggested in the present study.
One application of stone analysis using micro CT will be the study of stone fragility in shock wave lithotripsy. It has been known from the earliest days of shock wave lithotripsy that some kinds of urinary stones are broken by shock waves more easily than others [11]. However, only recently has it been better appreciated that stone behavior in lithotripsy is highly variable, even among stones composed of the same major mineral type [7]. Some of this variable behavior in lithotripsy may be accounted for by the structural arrangement of stone components, which can be viewed by CT [15]. Further testing of hypotheses concerning stone structure and stone fragility can be done in vitro, using micro CT to analyze stones before breakage in the lithotripter.
Another logical application of micro CT is for materials testing of stones. Several studies have reported materials properties of stones [16-18], but stone heterogeneity complicates this effort. One study by Zhong et al. demonstrated that depending on the site of hardness testing within the same stone, different measurements could be obtained [19]. Thus, micro CT could help identify regions of homogeneous mineral content within a stone and also show any structural internal weakness, such as a crack or void space, prior to testing. This process would enable researchers to identify regions suitable for materials testing and, therefore, provide more reliable data.
Our findings with micro CT suggest that similar stone analysis may one day be possible as a preoperative diagnostic tool. Clinical helical CT is evolving and enhancements continue to be made to increase overall imaging functionality. For instance, newer multi-detector helical CT (MDCT) affords much greater spatial resolution than conventional single-detector helical CT. Williams et al. used four-row MDCT to show that some degree of internal structure can already be seen in urinary stones [15]. Zarse and colleagues also used four-row MDCT and demonstrated that CT can identify mineral composition in vitro when suggested scanning parameters were used [20]. Eight-row MDCT has been found to improve z-axis resolution and scan time, while reducing artifact streaking for an overall improvement in diagnostic imaging [21]. Sixteen-row MDCT instruments are now available that use isotropic imaging, the same technology used in micro CT. This essentially translates to equal resolution and voxel size in any plane (sagittal, coronal, and axial) [22]. Moreover, like micro CT, MDCT has the option to reconstruct 3-D images of the entire viewing area. This is advantageous since mapping the 3-D spatial distribution of mineral content in stones could yield important information useful in determining proper treatment for the patient. Overall, the continued development of helical CT technology points to imaging capability in the future that could provide stone analysis equivalent to what we describe for micro CT.
Conclusions
Micro CT provides high resolution in vitro imaging of urinary calculi for nondestructive stone analysis. Fine resolution coupled with the 2-D and 3-D reconstruction capabilities of micro CT yields an imaging diagnostic that offers excellent images of surface and internal stone structure. Mineral deposition pattern and regions of potential structural weakness, such as voids, were easily visible. Six common stone minerals were found to occupy non-overlapping ranges of attenuation value, which allows the identification of mineral types using micro CT alone. This technology carries the potential for immediate application to non-destructive analysis of stone structure and composition in clinical stone analysis laboratories. The demonstration that stone composition can be determined by micro CT is proof of concept and an important step toward the use of helical CT to provide similar analysis in patients.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
CZ did the experimental work for calibrating the attenuation values, put together several of the figures, including the image stack movie, and drafted the manuscript. AJ performed the FT-IR analyses. EH did many of the micro CT scans and managed the library from which stones were chosen. SK and JL provided some of the stones from patients, and SK performed the 3-D imaging of cystine stone. JM, JL, and AE initiated much of the intellectual direction for the study. JM and AE also played important roles in the FT-IR analysis part of the study. JW performed the 3-D image construction in MacVol, drafted several of the figures, and performed the primary editing of the manuscript. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
Micro CT image stack through a urinary stone.MOV 6.61 MB
Click here for file
Acknowledgements
Authors would like to thank Sharon Bledsoe, Mary Hooser, and Keith Condon for preparation of stone slices. Special thanks to James Stanton III for his efforts in preliminary studies and to David Lounsbery for excellent technical assistance. Final thanks to Philip Blomgren for photograph in Figure 1. This work was supported by NIH grants P01 DK43881, R01 DK55674, and R01 DK59933.
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| 15596006 | PMC544194 | CC BY | 2021-01-04 16:37:48 | no | BMC Urol. 2004 Dec 13; 4:15 | utf-8 | BMC Urol | 2,004 | 10.1186/1471-2490-4-15 | oa_comm |
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BMC Health Serv ResBMC Health Services Research1472-6963BioMed Central London 1472-6963-4-361560692410.1186/1472-6963-4-36Research ArticleSARS and hospital priority setting: a qualitative case study and evaluation Bell Jennifer AH [email protected] Sylvia [email protected] Tania [email protected] Ross EG [email protected] Mark [email protected] Douglas K [email protected] University of Toronto Joint Centre for Bioethics, University of Toronto, Toronto, Canada2 Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada3 Department of Public Health Sciences, University of Toronto, Toronto, Canada4 Department of Surgery, University of Toronto, Toronto, Canada5 Department of Family and Community Medicine, University of Toronto, Toronto, Canada6 Division of Neurosurgery, Toronto Western Hospital, Toronto, Canada7 Pharmacy, Toronto Western Hospital, Toronto, Canada8 Primary Care Research Unit, Sunnybrook and Women's College Health Sciences Centre, Toronto, Canada2004 19 12 2004 4 36 36 28 5 2004 19 12 2004 Copyright © 2004 Bell et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Priority setting is one of the most difficult issues facing hospitals because of funding restrictions and changing patient need. A deadly communicable disease outbreak, such as the Severe Acute Respiratory Syndrome (SARS) in Toronto in 2003, amplifies the difficulties of hospital priority setting. The purpose of this study is to describe and evaluate priority setting in a hospital in response to SARS using the ethical framework 'accountability for reasonableness'.
Methods
This study was conducted at a large tertiary hospital in Toronto, Canada. There were two data sources: 1) over 200 key documents (e.g. emails, bulletins), and 2) 35 interviews with key informants. Analysis used a modified thematic technique in three phases: open coding, axial coding, and evaluation.
Results
Participants described the types of priority setting decisions, the decision making process and the reasoning used. Although the hospital leadership made an effort to meet the conditions of 'accountability for reasonableness', they acknowledged that the decision making was not ideal. We described good practices and opportunities for improvement.
Conclusions
'Accountability for reasonableness' is a framework that can be used to guide fair priority setting in health care organizations, such as hospitals. In the midst of a crisis such as SARS where guidance is incomplete, consequences uncertain, and information constantly changing, where hour-by-hour decisions involve life and death, fairness is more important rather than less.
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Background
As of August 12, 2003 there were 438 probable and suspected cases of Severe Acute Respiratory Syndrome (SARS) in Canada – the majority located in Toronto. In Toronto there were forty-four SARS related deaths and over 100 health care workers contracted SARS, placing intense pressure on Toronto's public health and hospital system [1].
Due to both the importance of hospitals in any health system and the difficulties they face, improving priority setting (also known as rationing or resource allocation) within hospitals is crucial. Priority setting is one of the most difficult issues facing hospitals because of funding restrictions and changing patient need. A deadly communicable disease outbreak, such as SARS in Toronto in 2003, amplifies the difficulties of hospital priority setting.
Key goals of priority setting in any context are legitimacy and fairness. 'Accountability for reasonableness' is an explicit ethical framework for legitimate and fair priority setting in health care [2]. It is internationally recognized as a framework that can guide priority setting in health systems and their institutions [3-5]. According to 'accountability for reasonableness', an institution's priority setting may be considered fair if four conditions are met: relevance, publicity, appeals, and enforcement (see Table 1).
In 2003, the outbreak of SARS further challenged priority setting decision makers in Toronto hospitals, creating decision making difficulties in relation to both SARS and non-SARS patients. To what extent should the need for containment over-ride other important needs? To what extent should the need for quick decisions over-ride the need for legitimate and fair decision making?
Only a few studies have directly examined priority setting in hospitals – two focused on technology acquisition [6,7], one on strategic planning [8], one on a hospital drug formulary [9], and two on hospital ICUs [10,11]. The latter four studies used 'accountability for reasonableness' as the study framework. To our knowledge there have been no studies of hospital priority setting during an emergency response to a communicable disease outbreak.
The purpose of this study was to describe priority setting in a hospital in response to SARS and evaluate it using 'accountability for reasonableness'.
Methods
Design
To describe priority setting we used qualitative case study methods. A case study is "an empirical inquiry that investigates a contemporary phenomenon within its real-life context" [12]. This is an appropriate method because priority setting in hospitals is complex, context-dependent, and involves social processes. To evaluate the description we used the four conditions of 'accountability for reasonableness' (described in Table 1).
Setting
This case study was conducted at a large tertiary hospital in Toronto, Canada.
Sample
We sampled key documents and people. We used theoretical sampling to determine which people and documents were 'key'. Included among the individuals sampled were senior administrators, managers, physicians, nurses, patients and family members.
Data collection
There were two sources of data: (1) over two hundred key documents (e.g. emails, minutes of meetings), and (2) 35 interviews with key informants (senior administrators (6), physicians (10), managers (5), nurses (5), a patient (1), family members (2), and other staff (6)). Interviews were audiotaped and transcribed. Interview participants were asked to describe priority setting decisions in relation to SARS and their thoughts about it. We developed an interview guide based on previous research and improved it through pilot interviews with personnel from other hospitals. As is traditional with qualitative studies, the interview guide was modified during the study to explore emerging themes.
Data analysis
The data were analyzed concurrent with collection using a modified thematic approach in three phases: open coding, axial coding, and evaluation. In open coding, the data were fractured by identifying chunks of data that relate to a concept or idea. In axial coding, the concepts were organized under overarching themes (i.e. the four conditions of 'accountability for reasonableness'). In evaluation, the descriptive data were compared with the conditions of 'accountability for reasonableness' – correspondence with the framework was considered 'good practice'; instances where the conditions are not met were considered 'opportunities for improvement'. Concepts were formalized and made explicit through the writing of the findings [13].
The validity of the interpretations was enhanced in four ways [14]. First, the coding was conducted in collaboration between two researchers, thus limiting the influence of any one person's biases. Second, the coding was reviewed and modified by an interdisciplinary team who provided challenges that were resolved through consensus. Third, the findings were presented to participants who verified the findings – traditionally called a 'member check'. Finally, all research activities were rigorously documented to permit a critical appraisal of the methods [15].
Research ethics
Approval for this project was obtained from the participating hospital's Research Ethics Board. Written informed consent was obtained from each individual before being interviewed. All data were kept confidential and viewed only by the research team. No individuals have been identified without their explicit agreement.
Results
In this section we describe one hospital's priority setting in response to SARS by focusing on the types of decisions, the decision making process, and the supportive reasoning. We then evaluate our findings using the four conditions of 'accountability for reasonableness'. We have also included verbatim quotes from participants to illustrate key points.
I. Description of priority setting
Types of priority setting decisions
There were two distinct phases of priority setting at the hospital during the SARS outbreak. First, during the initial days of the outbreak, decisions were made in order to contain the spread of the virus. The Ontario Ministry of Health and Long-Term Care (MOHLTC) directed Toronto acute care hospitals to establish or maintain as necessary a SARS isolation area, restrict patient visits, limit visitors and suspend selected patient transfers. Second, after the initial weeks of the outbreak, was a 'ramp up' phase during which the hospital gradually increased its level of activity. Throughout both phases, priority setting decisions can be organized according to four specific types: decisions relating to staff and patients, beds/rooms, clinical activity, and visitors.
1) Staff and patients
Staff were allocated for SARS patients in the SARS unit and ICU, screening at the doors, manning the site command centre, or helping out in occupational health; pregnant and immunosuppressed staff were either redeployed to low-risk activities or sent home; staff deemed non-essential were sent home with pay; students were sent home and educational rounds were cancelled. General medicine patients were transferred to other medical units to maintain the SARS isolation unit; out-patients who had been waiting for non-emergency surgery or clinic appointments were told to wait indefinitely.
2) Beds/rooms
The SARS isolation unit required negative pressure beds. The hospital created these spaces in a specific isolation unit on a general medicine floor, and in the ICU and Emergency. Admissions to negative pressure beds were decided case-by-case and were based on the assessment of the referring physician, the hospital's infectious disease representative, and the hospital intensivist.
3) Clinical activity
All non-emergency surgery and ambulatory care were cancelled during the initial 7–10 days of SARS. During the ramp up phase, clinical activity volumes increased in percentages allowing for urgent cases to be seen as determined by physicians. Operating room time was allotted by division – each individual surgeon reported to their division head the cases they considered urgent. This activity was coordinated by the hospital's command center.
4) Visitors
A 'no visitors' policy was enforced during the initial stages of SARS except for compassionate grounds as determined by the nurse manager or nurse in charge of the particular unit, in consultation with the attending physician and the hospital command centre. During the ramp up phase, the hospital relaxed the no visitor policy according to changing directives from the MOHLTC and the human resources available for screening at the doors.
Decision making process
Priority setting decisions were made across all levels of the institution. We identified four groups of key decision makers: Corporate Command, Hospital Command, Department Management/Chiefs, and Individual Clinicians. MOHLTC directives were interpreted by a team of senior administrators (corporate command) and then communicated to the hospital's command centre. The hospital's command centre implemented the recommendations from the corporate command in accordance with patient population requirements and physical logistics. Department managers and clinical staff also participated in allocating human resources and determining patient care priorities. The corporate command was in constant communication with the hospital's command centre who maintained communication with the managers and other leaders through teleconferencing and email.
Priority setting reasoning
Throughout each stage of the SARS outbreak, safety was the primary rationale underpinning priority setting. During the early stages of SARS, decisions were made for infection control focused on protecting staff. During the ramp up phase, decisions were based more on a duty to care for patients, emphasizing the hospital's role in the community. However, in addition, there were several other reasons used in support of each decision (See Figure 1).
During the ramp up phase the reasoning shifted to address patient need. Leaders recognized that the hospital could not operate under the shut down conditions for very long; urgent cases were quickly becoming emergent. Thus, though staff and patient safety remained a primary concern, very few of the decisions can be linked solely to safety; rather, decisions involved clusters of reasons. Table 2 describes decisions made, the reasons used, and the level at which they were made.
II. Evaluation of priority setting using 'accountability for reasonableness'
1) Relevance
Many participants confirmed that staff and patient safety was, appropriately, the primary criterion used in the decision making process.
"I think due to the fact that this was so communicable, and I think, certainly felt, most people felt this was all being done in our best interest."
Some expressed concern about the relevance of the reasoning used in the allocation of OR time and the visitor policy. For example, one surgeon commented that the OR schedule was allocated by division as opposed to being allocated by patient need. Similarly, the visitor policy was appreciated by family members of patients on an abstract level but some still could not understand why exceptions to the policy could not be made in certain instances.
Many participants found it difficult to evaluate the relevance of the reasons underlying many of the MOHLTC directives because the directives did not explicitly describe the reasoning involved.
2) Publicity
Priority setting decisions and the reasons behind them were readily accessible to those directly involved in making the decisions. However, even though decisions and reasons were regularly distributed via email, or posted on the hospital intranet and world-wide web, many felt that communication beyond the core group of decision makers was incomplete. At the height of the crisis, MOHLTC directives were changing almost hourly, and this made real-time communications to the front lines difficult. It was generally felt that communication was excellent, with room for improvement.
"Even though we have a very good communications team you know some people are still left out of the loop – they don't read the paper they don't listen to the radio they don't read their emails or they don't have email. So, there are still small pockets of lack of or miscommunication, so communication is always something that we need to improve."
3) Revision/appeals
There was no formal revisions/appeals mechanism. Instead the hospital CEO felt it was important to address all disagreements personally. Many participants thought there were ample opportunities for informal discussion and debate in meetings and email communication. However, one participant commented that without formal appeals mechanisms, some stakeholders participated unfairly by using a 'squeaky wheel' approach.
" [B]y appealing the process, the sickly squeaky wheel method of appeal, we just begged, pleaded, ranted, raved, called back, called back."
4) Enforcement
Overwhelmingly, participants thought the process was as fair as it could have been given the time constraints and the knowledge base at the particular time. The hospital leadership made an effort to meeting the conditions of 'accountability for reasonableness'. However, some felt that the decision making was not ideal.
"At a moment of crisis which I think that SARS was, there's not always opportunity for full and open and even decision making."
Some stated that more support and accountability implementing decisions could have occurred – that there was a gap between the decisions that were made in high level administration and the implementation of those decisions at the front lines.
"Most of us felt, you know the decisions were made, up there, and we could understand them, we could agree with them, but we were the ones who had to live with them. And there was nobody who really came and asked us what that was like. There was some, it wasn't that there was nothing – but there wasn't a sense of being listened to the way that we needed to be listened to, the way that we needed to be supported."
Some participants expressed concern that many staff started relying on senior management to make many of the decisions for them.
"When you go into another mode that commands and controls, doesn't take too long until you understand how comfortable and actually how easy that is. It is way easier to do what you're told."
Conclusions
This study examined priority setting at one Toronto hospital as it responded to the 2003 SARS crisis. Even though the crisis created safety concerns and time constraints that impinged upon decision making, this hospital endeavoured to meet the four conditions of 'accountability for reasonableness'.
By describing and evaluating priority setting using the four conditions of 'accountability for reasonableness', we are able to identify examples of 'good practices' that other hospital should emulate, and 'opportunities for improvement' that this and other hospitals should consider.
We identified the following good practices: 1) staff and patient safety was the primary criterion in decision making, but each decision was based on a cluster of relevant reasons – decision makers' use of clusters of relevant reasons has been identified and discussed in a previous study [16], 2) decisions were regularly accessible on hospital email, intranet and the world-wide web; 3) challenges were addressed directly by the CEO; 4) hospital leadership made a concerted effort to meet the conditions of 'accountability for reasonableness'. We also identified the following opportunities for improvement: 1) patients and families did not have access to the reasons for many decisions, including the visitation policy and ramp-up of clinical activities; 2) a formal revision/appeals mechanism could help improve the quality of decision making and alleviate the unfair reliance on the 'squeaky wheel' phenomenon; 3) OR time was allocated by division, rather than by patient need, and these decisions should be discussed more fully; 4) institutional leaders should maintain two-way contact with front-line staff who are implementing priority setting decisions – this will provide support and enhance accountability for decision making by staff.
'Accountability for reasonableness' is a framework that can be used to guide legitimate and fair priority setting in health care organizations, such as hospitals. It assumes that the time and effort required for meeting the conditions of fairness is justified for two reasons: First, it is important to act ethically and be perceived to be acting ethically – in this case, fairly. Second, acting ethically can help an organization achieve 'goodwill' benefits including, but not limited to, increased trust and satisfaction and decreased complaints. However, it is clear from this scope of decisions examined in this study that time constraints imposed on a health care organization by a highly communicable and potentially fatal infectious disease creates significant priority setting difficulties. It may appear that the conditions of 'accountability for reasonableness' are too demanding to implement in the time constraints – that perhaps containment should take precedence over procedural requirements. We disagree. During the SARS outbreak the hospital's leadership developed and implemented several sophisticated processes to help with their crisis management. Tailoring those processes to meet the four conditions of 'accountability for reasonableness' is not any more difficult or demanding. Moreover, we argue, and some of the participants also argued, that in the midst of a crisis where guidance is incomplete, consequences uncertain, and information constantly changing, where hour-by-hour decisions involve life and death, fairness is more important rather than less.
Our findings are limited in that they may not be generalizable to other hospitals. However, generalizability is not the goal of qualitative studies like this. We expect that other hospitals may benefit from the insights provided in this study. For example, this was the first time that 'accountability for reasonableness' has been used to evaluate priority setting in response to an infectious disease outbreak. Other hospitals in similar crises can use 'accountability for reasonableness' to help evaluate and enhance the fairness of their priority setting [17].
The best assurance of fair priority setting in a crisis is fair priority setting everyday. A health care organization that incorporates legitimate and fair decision making everyday, where the decision making culture of the organization is permeated with the conditions of 'accountability for reasonableness', will be primed to meet the challenges of fair priority setting in a crisis. This may be the most important lesson to take from this study.
Competing interests
SH and MB are employees of the organization studied here.
Authors' contributions
JB was primarily responsible for acquisition and analysis of data. All authors made substantial contributions to conception and design of the study, data analysis and interpretation, and have been involved in drafting the article or revising it critically for important intellectual content. All authors have given final approval of the version to be published.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors wish to thank all the administrators, physicians, nurses, staff, patients, and family members who participated in this study.
This research project was funded by an Interdisciplinary Capacity Enhancement grant to the Canadian Priority Setting Research Network from the Canadian Institute for Health Research. Martin is supported by an Ontario Ministry of Health and Long-Term Care Career Scientist Award. Upshur is supported by a Canadian Institutes of Health Research New Investigator Award.
Figures and Tables
Figure 1 Reasons justifying priority setting decisions
Table 1 The four conditions of 'accountability for reasonableness'
Relevance Rationales for priority setting decisions must rest on reasons (evidence and principles) that 'fair-minded' people can agree are relevant in the context. 'Fair-minded people seek to cooperate according to terms they can justify to each other – this narrows, though does not eliminate, the scope of controversy, which is further narrowed by specifying that reasons must be relevant to the specific priority setting context.
Publicity Priority setting decisions and their rationales must be publicly accessible.
Revision/Appeals There must be a mechanism for challenge, including the opportunity for revising decisions in light of considerations that stakeholders may raise.
Enforcement There is either voluntary or public regulation of the process to ensure that the first three conditions are met.
Table 2 List of decisions, reasons, and decision level
Decisions: Staff and Patients Reasons Decision Level
Determine which staff to deploy to help with screening at the doors Operational need; Screening capability; Infection control; Medical need Hospital Command
Determine urgent patients and care for those first Medical need Individual Clinicians
The hospital as a whole determined few hospital workers unessential Operational need; Screening capability; Infection Control Hospital Command
Redeploy staff from screening back to clinical areas Medical need; Duty to care; Operational need Hospital Command
Hire screeners Medical need; Operational need; Infection control Hospital Command
Remove pregnant staff from the clinical environment Staff safety Corporate Command; Hospital Command
Decant staff and inpatients (25) from 8th floor general medicine to make room for SARS unit and potential SARS patients Operational need; Medical need Hospital Command; Department Managers/Chiefs
Separate staff entrance from visitor and patient entrance Operational need; Infection control Corporate Command; Hospital Command
Send staff home Infection control Department Managers/Chiefs
Decisions: Beds Reasons Level Made At
Accept SARS patient transfers from other hospitals Duty to care Corporate Command; Individual Clinicians
Each GTA and Simcoe County hospital to establish a SARS specific isolation unit. Infection control MOHLTC
Hospitals greater than 500 beds will be expected to provide a 30 bed unit each. (Mar 27)
Create SARS unit physical space on 8B with negative pressure capabilities Directive; Infection control; Medical need; Operational need; Duty to care Hospital Command; Department Managers/Chiefs; Individual Clinicians
Decisions: Clinical Activity Reasons Decision Level
Maintain emergency based activity during initial days of outbreak Duty to care; Medical need Corporate Command; Hospital Command
Ramp up clinical activity Duty to care; Medical need Corporate Command
Allocate OR time by division Medical need; Surgeon activity Department Managers/Chiefs
Determine which patient needed urgent OR care this could be listed second Medical need Individual Clinicians
SARS II – the decision not to cancel surgery again Medical need; Duty to care Corporate Command
Treat some 'elective cases' in the OR as being urgent Medical need; Surgeon activity; Duty to care; Squeaky wheel Individual Clinicians; Department Managers/Chiefs
Determine what/who is emergent and urgent in terms of clinical volumes in family medicine Screening capability; Medical need; Squeaky wheel Department Managers/Chiefs; Individual Clinicians
Family Medicine did not go out into the community to provide care in the initial stages of SARS (care to detox centres, shelters) Infection control; Screening capability Corporate Command; Department Managers/Chiefs
Decisions: Visitors Reasons Decision Level
No Visitor Policy except for compassionate grounds (such as palliative care, critically ill children or visiting a patient whose death may be imminent) Infection control MOHLTC
Restrict visitors for certain hours (5–9 pm) Screening capability Hospital Command
Lift visiting restrictions on case-by-case basis Compassion; Squeaky wheel; Medical need Department Managers/Chiefs
Hospitals must restrict access to each hospital site. Ideally, access should be restricted to one staff and one public entrance for each building Infection control MOHLTC
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| 15606924 | PMC544195 | CC BY | 2021-01-04 16:03:28 | no | BMC Health Serv Res. 2004 Dec 19; 4:36 | utf-8 | BMC Health Serv Res | 2,004 | 10.1186/1472-6963-4-36 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1831556373410.1186/1471-2105-5-183Methodology ArticleProtein family comparison using statistical models and predicted structural information Chung Richard [email protected] Golan [email protected] Department of Computer Science, Cornell University, Ithaca, NY 14850, USA2004 25 11 2004 5 183 183 31 5 2004 25 11 2004 Copyright © 2004 Chung and Yona; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
This paper presents a simple method to increase the sensitivity of protein family comparisons by incorporating secondary structure (SS) information. We build upon the effective information theory approach towards profile-profile comparison described in [Yona & Levitt 2002]. Our method augments profile columns using PSIPRED secondary structure predictions and assesses statistical similarity using information theoretical principles.
Results
Our tests show that this tool detects more similarities between protein families of distant homology than the previous primary sequence-based method. A very significant improvement in performance is observed when the real secondary structure is used.
Conclusions
Integration of primary and secondary structure information can substantially improve detection of relationships between remotely related protein families.
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Background
Detecting an evolutionary relationship between proteins is the basis for functional inference. Existing methods most often rely on sequence information in an attempt to quantify the evolutionary divergence or similarity between the sequences compared. A significant similarity would suggest that the proteins are related. However, in many cases sequences have diverged to the extent that their similarity is undetectable by standard sequence comparison algorithms. Nevertheless, they may still have similar structures and functions [1,2].
It has long been postulated that evolutionary pressure acts upon the three-dimensional structure of proteins and intra-protein interactions rather than at the level of the primary sequence [3,4]. Indeed, there is plenty of evidence to suggest that 3D structure is more conserved than sequence [5,6]. Since the protein structure usually prescribes the function of a protein, relying on structural information (for example, through structure comparison) for functional inference is more effective and reliable than using only the primary sequence. However, although methods of sequencing proteins have become faster and more cost-efficient due to recent technological advancements, methods to determine structure are still in their infancy. In fact, less than 5% of newly sequenced proteins have a known structure. Current empirical processes used to determine structure of proteins are neither efficient nor scalable to use upon the entire known protein space.
There have been many attempts to build algorithms that predict protein structure from amino acid sequence. Unfortunately, this is a hard problem, and existing methods are only partially successful [7]. On the other hand, predicting the secondary structure of a protein has been more successful. There are various algorithms that predict the secondary structure from primary amino acid sequence information alone [8-13]. The accuracies of these algorithms have been steadily increasing, and one of the most successful algorithms to date is PSIPRED [13], which has an average accuracy of about 80%. Since the architecture of the secondary structure elements of a protein affects its global structure, it has been suggested that secondary structure information can be used to detect subtle similarities between proteins that have diverged substantially in the course of evolution. This principle was tested in [14] where a dynamic programming algorithm with a secondary-structure based scoring matrix was used to compare protein sequences over the alphabet of secondary structures. However, relying solely on secondary structure information might lead to poor performance overall, as much of the original information about the individual amino acids is lost.
Alternatively, one can use both representations to assess protein similarity. Incorporating secondary structure information into protein comparison is not a new idea. Several researchers have attempted to boost performance and sensitivity of various methods by adding this extra degree of information with some success. Yu et al. encoded functionally conserved sequence patterns into probabilistic structural models (that comprise a family of hidden Markov models) [15]. The models were benchmarked against the trypsin-like serine protease family and the globin family, and in both cases proved to have high specificity and sensitivity compared to the models in use at the time (primarily, BLAST) in remote homology detection. One of the limitations of this model, however, was the reliance on threading methods requiring at least one determined structure to build a model. Hedman et al. [16] included information about predicted transmembrane segments into the standard Smith-Waterman and profile-search algorithms for membrane proteins by adding an extra delta (score) when two residues that are both predicted to belong to transmembrane segments are aligned. This method was found to improve the detection rate, mainly by increasing specificity (ie. decreasing the number of false positives). Ginalski et al. [17] generalized a method of creating "meta profiles" by combining sequence information with predicted secondary structure information. Total scores were calculated by summing the raw score obtained from the shifted dot product of the sequence profile vectors and the shifted dot product of the secondary structure probability vectors (weighted by some factor). This technique was derived from hybrid threading approaches and was found to be more sensitive than the sequence-only approach or sequence-to-structure threading approach. Teodorescu et al. [18] proposed a linear combination of threading and sequence-alignment to produce a single (mixed) scoring table. This method was found to be particularly sensitive in detecting sequences with less than 25% of sequence identity, yet with similar structures. The final model outperforms the individual scoring methods.
These and similar studies have indicated that the incorporation of secondary structure information, even if predicted, can increase sensitivity and specificity of a protein comparison model. Here we describe a method that integrates secondary structure information with primary sequence information in a single scoring scheme, using a single statistical representation. The model can be applied to any protein family and does not require the application of expensive threading algorithms. Our method extends our previous work on profile-profile comparison [19]. Specifically, we use the profile representation (generated by PSI-BLAST) as a statistical model of a protein family and augment the profile with structural information. We then compare profiles of different protein families, in search of possible remote kinship, using an information theory-based scoring function. By comparing models of protein families we can detect similarities that are not detected when comparing individual sequences. We show that the new algorithm improves performance and can detect more similarities between remote protein families. These similarities can be used to classify protein families into super-families and detect higher order structure within the protein space.
Methods and Results
Data sets
We use a data set of domain families derived from the SCOP classification of protein structures [20], release 1.50. This set contains 23,780 protein domains classified into 1,287 protein families. Each of the 1,287 families is represented by a profile that was generated using PSI-BLAST [21]. The seed of the profile was selected to be the sequence whose average distance from all other members of the family is the smallest. Families for which there is only one member, or for which PSI-BLAST failed to generate a profile, were represented by a profile generated directly from the seed sequence by using probabilities derived from the original BLOSUM62 frequency matrix [22]. A subset of 456 families was used in our study, all of which belong to superfamilies that contain at least 3 families. A smaller subset of 120 families was used for parameter optimization.
Sequence profiles
The PSI-BLAST profiles are the basis for our representation of a protein family. Each profile is a n-tuple of probability distributions of amino acids, derived from a group of related proteins, where n is the length of the multiple alignment of these proteins. It is represented in software as a two dimensional matrix of 20 rows and n columns, where each column (known as a profile column), is a probability distribution p over the 20 amino acids in one position in the multiple alignment. These profile columns form the basis of profile-profile comparisons.
Secondary structure information
We use two types of secondary structure information in our experiments: true information and predicted information. The true secondary structure information is gleaned from the PDB files of the seed proteins using STRIDE [23]. Stride defines eight types of secondary structures b, B, C, E, H, I, G, T where b and B stand for Bridge, C = Coil, E = Strand, H = AlphaHelix, I = PiHelix, G = 310Helix and T = Turn. We reduce this set to the three main secondary structures (helix, strand and coil) by mapping H, I, G to H, and b, B, C, T to C.
The predicted secondary structure information is predicted using PSIPRED [13]. PSIPRED uses the intermediate sequence profiles generated by PSI-BLAST as input for the prediction algorithm. This profile matrix is fed into a standard feed-forward back-propagation neural network with a single hidden layer using a window of 15 residues. This net has three output units corresponding to each of the three states of secondary structures. Another window of 15 positions of these three outputs (per amino acid) are then used as input into a second neural network to filter and smooth outputs from the main network. The final output is the probability that a certain amino acid position in the seed sequence of a profile is in a coil, helix, or strand. PSIPRED reports an average Q3 score of approximately 80% accuracy.
Integrating secondary structure with primary structure
Apriori, it is unclear how one should integrate secondary structure with primary structure in a single model. For example, one might think of a representation over a generalized alphabet, that considers all possible pair combinations of amino acids and secondary structure elements. Assuming independence between positions (which is the underlying assumption of position specific scoring matrices, as well as of HMMs that are used in computational biology), then this representation implies that for each position i we have a statistical source that emits amino acid a and secondary structure s with probability Pi(a, s) such that
and every position can be represented by a vector of 60 probabilities over this pair alphabet. This representation implicitly implies that the amino acid emitted and the secondary structure are two different features of the objects generated by the source, while in reality the secondary structure is not a "character" or an independent property of the emitted objects, but rather a characteristic of the source itself that is usually unknown. This property introduces some special constraints on the distribution of amino acids that are emitted by the source. In other words, the secondary structure and the amino acid distribution in a position are strongly dependent on each other, but one is hidden while the other is visible. Noting that Pi(a, s) can be written as Pi(a/s)Pi(s), we can decompose the parameter space into the parameters of the secondary structure distribution, and the parameters of the conditional probability distributions over amino acids. However, the typical amino acid distributions that are available from multiple alignments of protein families differ from these conditional probability distributions by definition. Furthermore, there are other subtleties that one should bear in mind when designing an integrated statistical model for a protein family.
More precisely, assume we have a protein family, where all proteins adopt a certain structural conformation of length n. This conformation can be described in terms of the set of 3D coordinates of the n positions, or in terms of the set of distances between coordinates S = () where is the set of distances from the i-th residue to all other residues – the latter being more amenable for a representation as a statistical source, as it is invariant to translations and rotations. Although there is structural variation across the different instances of the protein family, it is significantly smaller than the sequence variation, and we will assume that a single consensus conformation S reliably describes the protein family. The structural conformation determines the statistical properties of the source distributions. Namely, it induces certain constraints on the sequence space that can be mapped to that conformation, based on the physical properties of its topology. In other words, it induces a probability distribution over the sequence space of O(20n) sequences that can be mapped to that conformation
P(a1, a2, ..., an/S).
Note that due to convergent evolution it is possible that two disconnected regions in the sequence space (two families of homologous proteins) will be mapped to the same conformation (although experimental evidence and simulation results [24] suggest that this is not very likely, and for most protein families it is reasonable to assume that the sequence space that is mapped to a structural conformation is connected). This 20n-dimensional distribution clearly introduces dependencies between remote positions, and the exact probability distribution in a position depends on the amino acids observed in all other positions P(ai/a1, a2, ..., ai-1, ai + 1, ..., an, S). Accurate knowledge of the all-position probability distribution P(a1, a2, ..., an/S) would allow one to compare two sources of protein families theoretically by comparing these high-dimensional distributions. However, because of (limited) data availability and for mathematical simplicity, the marginal probabilities
are usually used in practice to describe the source. Given a multiple alignment of a specific protein family, and the corresponding profile, the empirical distribution of amino acids at position i, denoted by , is essentially the marginal probability of amino acids at that position, as constrained by the global conformation, i.e.
The complete model is represented as a set of marginal probability distributions, one per position. So far we have not considered the secondary structures explicitly. The secondary structure sequence s is a reduced representation of S that, while incomplete, describes quite accurately the topology of the protein. Given S, the knowledge of s however does not affect the distribution of amino acids at a position, i.e.
Pi(a/s, S) = Pi(a/S)
Nevertheless, the secondary structure information can still be useful when comparing protein families. This is because some information is lost if one is to use just the marginal amino acid distributions. For example, the same marginal amino acid distribution can be observed in different secondary structure conformations and on the other hand, even highly similar fragments of secondary structures can be associated with different amino acid distributions.
The most effective way of comparing two protein families is by comparing their (consensus) structural conformations S1 and S2. Indeed, it has been shown that structure comparison is much more effective in detecting remotely related families [19,20,25]. In statistical terms, one can formulate the problem of comparing consensus structures S1 and S2 as comparing two sources that induce different probability distributions over the conformation space P1(S) and P2(S). However, characterizing these distributions is very difficult. Moreover, convergent evolution might result in two different sequence sources with structurally similar conformations. These relations are usually perceived weaker than families that are similar both in sequence and structure [20]. Therefore, a proper comparison should account for both the primary and tertiary structure. In statistical terms, we are interested in comparing the joint distributions and , where the distributions are again marginalized over all positions other than i, and is a vector of inter-residue distances. The joint distributions can be rewritten as
where the last step uses the more accurate marginal probabilities Pi(a/S) that are based on all vectors of inter-residue distances (and match the empirical distributions ).
As was mentioned earlier, obtaining S is difficult (and therefore also characterizing the distributions of inter-residue distances). On the other hand, secondary structure (which can be viewed as an approximation of S) is more readily available, and can be predicted quite reliably from sequence information. Therefore we suggest to approximate
where Pi(s) is the probability to observe a secondary structure s at the i-th position. (When the secondary structure is known the distribution over secondary structures assigns probability 1 to one of the structures and zero otherwise. However, with predicted information, each state is usually assigned a non-zero probability based on the amino acids in that position and neighboring positions.) Plugging in the empirical distributions for Pi(a/S) we get
i.e., the empirical distribution of amino acids at a position, , is conditionally independent of the distribution Pi(s). Therefore, to completely describe the source one needs to provide the parameters of the marginal distribution of amino acids, and the parameters of the secondary structure distribution. Since the two distributions are assumed independent, they are amenable to a representation in which their parameters are appended together. I.e. the secondary structure probabilities are appended to the probabilities of the 20 amino acids.
Our method is based on an extension of the original profile representation in [19]. Using the three PSIPRED probabilities, we augment the profile columns of primary information to make a probability distribution over 23 values (the 20 amino acids plus 3 secondary structures). Note that by doing so, each profile column is now dependent upon and contains information about its neighbors, since PSIPRED uses the profile columns surrounding each profile column to deduce the probability that the position in question is in a specific secondary structural conformation. This is the key element that enhances the accuracy of this tool in protein family comparisons. Moreover, the method is "self-contained" in the sense that for the secondary structure prediction, PSIPRED uses the same profiles that are generated by PSI-BLAST. To use the profile-profile metrics described next, the 23-dimensional profile columns have to be normalized to conform with probability distributions. However, apriori it is not clear if the primary information and the secondary structure information should be weighted equally. To control the impact of the secondary structure information on the representation we introduce a mixing parameter γ that ranges from 0 to 1. The secondary structure probabilities are normalized such that they sum to γ while the amino acid probabilities are normalized such that they sum to 1 - γ. The higher γ is, the more dependent the profile column is upon secondary structure information. This parameter is optimized as described in section 'Parameter optimization'.
Note that our normalization maintains the conditional independence of the two types (primary and secondary), as described above. Each component of the extended profile can be viewed as a sub-profile. Since each one of the two components is summed independently to a non-zero probability then two symbols must be "emitted": an amino acid and a secondary structure.
Profile-Profile comparison
In this section we review the main elements of our profile-profile comparison algorithm that was introduced in [19]. We compare profiles using the dynamic programming algorithm with an information theoretic-based scoring function to score pairs of profile columns. Given two profiles P = p1p2p3...pn and Q = q1q2q3...qm, where n and m are the lengths of the profiles (the number of positions or columns) and pi, qj are probability distributions over the 23 letter alphabet of amino acids and secondary structures, we define the similarity score between two columns pi and qj based on their statistical similarity. The similarity score is composed of two elements: the divergence score and the significance score.
The divergence score
To estimate the divergence of two probability distributions we use the Jensen-Shannon (JS) divergence measure [26]. Given two (empirical) probability distributions p and q, for every 0 ≤ λ ≤ 1, the λ-JS divergence is defined as
where DKL[p||q] is the Kullback-Leibler (KL) divergence [27], defined as
and
r = λp + (1 - λ)q
can be considered as the most likely common source distribution of both distributions p and q, with λ as a prior weight (here set to 0.5). We call the corresponding measure the divergence score and denote it by DJS. This measure is symmetric and ranges between 0 and 1, where the divergence for identical distributions is 0. Besides being symmetric and bounded, an attractive feature of the DJS divergence measure is that it is proportional to the minus logarithm of the probability that the two empirical distributions represent samples drawn from the same ("common") source distribution [28]. It has also been shown that is a metric [29].
The significance score
The divergence score measures one aspect of the statistical similarity of p and q: their relative distance. However, it does not consider the uniqueness of the two distributions. A match between two distributions that resemble the background distribution is not as significant as a match of two distributions that resemble each other, but are very different from the background distribution. In other words, the more unique the distributions are (and hence, also their common source), the more significant is a match between them.
To assess the significance score S of a match we measure the JS divergence of the (common) source distribution, r, from the base (background) distribution P0.
S = DJS[r||P0]
In this study the background distribution is composed of two components: the background distribution of amino acids (estimated from a large sequence database) and the background distribution of secondary structure elements (estimated from all PDB structures). The components are mixed using the same mixing parameter γ described in section 'Integrating secondary structure with primary structure'. The significance measure reflects the probability that the source distribution, r, could have been obtained by chance. The higher r is, the more distinctive the common source distribution, and the lower the probability that it could have been obtained by chance.
The similarity score
We define the similarity score of two probability distributions p and q as a combination of the divergence score and the significance score:
With this expression, the similarity score of two similar distributions (D → 0) whose common source is far from the background distribution (S → 1), tends to one. On the other hand, the similarity score of two dissimilar distributions (D → 1) whose most likely common source distribution resembles the background distribution (S → 0) tends to zero. This scoring scheme also distinguishes two distributions that each are similar to the background distribution (D → 0 and S → 0 giving Score - 1/2) from two dissimilar distributions, but whose common source is similar to the background distribution (D → 1 and S → 0 giving Score = 0). In a recent study [30] it has been shown that this scoring function is one of the best, when compared to other methods for profile-profile comparison.
Note that our measures are functionals of the probability distributions, based on variations of the entropy function, and specifically the KL divergence function. One of the nice properties of this function is that it is additive in the following sense. Assume we have a probability distribution p over a set X that is obtained by "mixing" two probability distributions over two disjoint subsets: p1 over the subset X1 and p2 over the subset X2 (where X = X1 ∪ X2 and X1 ∩ X2 = θ). Let γ be the mixing parameter, i.e. the total weight of the first distribution p1 in the combined distribution p. Assume q is obtained in a similar manner from q1 and q2. Then,
In other words, this measure preserves independence between the two subsets. Therefore, with our extended profile representation, the new functionals are simply a weighted sum of the individual functionals over the subsets X1 (the secondary structure) and X2 (the primary structure).
Note however that this property holds for the divergence and the significance measures but not for the final similarity score that is a combination of the divergence and the significance scores. An alternative would be to compute the divergence, significance and similarity scores independently for the secondary and primary structures, and then combine the two similarity scores into one, with weights γ and (1 - γ) respectively.
The effect of secondary structure on the pairwise scores
It is interesting to compare the similarity scores before and after the addition of secondary structure information. To assess the impact of this information, we computed the distribution of similarity scores for five types of profile columns, depending on the type of their seed amino acid. We refer to the amino acid at position i of the seed sequence (see section 'Data sets') as the seed amino acid of the i-th profile column.
Two seed amino acids are defined as similar, neutral, or dissimilar based on their BLOSUM62 scoring matrix [22], with positive, zero and negative substitution scores respectively.
The five types of column pairs are: (1) a column with itself (identical columns), (2) different columns that are associated with the same seed amino acid (strongly similar columns) (3) different columns that are associated with similar seed amino acids (similar columns), (4) different columns with mutually neutral seed amino acids (neutral columns), and (5) different columns with dissimilar seed amino acids (dissimilar columns). We repeated this calculation before and after the integration of true secondary structure information (using the optimal mixing parameter γ, see section 'Parameter optimization') and the results are plotted in Figure 1. As the figure indicates, there is a slight shift between the distributions, and the addition of secondary structure information pushes the distributions further apart, decreasing the distribution overlap, as desired. Although the differences are small (due to the very small value of the optimal mixing parameter), the effect on the performance is significant as is demonstrated in section 'Discussion'.
Comparison of scoring functions
We compared our information-theoretic scores to other popular scoring schemes. We tested the correlation scores based on the scalar product of the vectors (as was suggested in [31]).
We also tested the ALLR (Average Log Likelihood Ratio) scoring function that was suggested in [32]. This scoring function is also based on information-theoretic principles, and resembles ours. Given two empirical probability distributions p and q, their ALLR score is defined as
where np (nq) is the number of total counts from which p (q) is derived, and P0 is the background distribution.
We computed the correlation scores and ALLR scores for the same sets of columns defined in the previous section and compared it to the information-theoretic scores (Figure 2). Note that the correlation scores are less successful in distinguishing related columns from columns which are likely to be unrelated (compare Figure 2a and Figure 2b). The overlap is larger and the tail of the fifth distribution (dissimilar columns) falls well within the first distribution (identical columns). Specifically, 24% of the pairs of dissimilar columns have correlation scores that overlap with scores of identical columns, compared to only 2.1% when using our similarity scores. We believe that this may affect the performance significantly. On the other hand, the ALLR scores have very similar properties to ours, although the overlap between dissimilar columns and identical columns is greater (4.4%).
Parameter optimization
Our algorithm (prof_ss) depends on several parameters: (1) a shift parameter is introduced to convert the similarity scores to scores that are suitable for local protein comparison (other transformations were tested in [19] and proven less effective); (2) gap penalties for the dynamic programming algorithm; (3) the mixing parameter γ
Shift parameter and gap penalties
as Figure 2a shows, the distributions of identical columns (red line) and distributions with dissimilar seed amino acids (black line) are quite well separated around 0.5. In addition, distributions with mutually neutral seed amino acids peak at a similarity score around 0.45. Note that the new similarity scores (after the addition of the secondary structure information) preserve the overall behavior (quantitatively and qualitatively) as the old similarity scores (see Figure 1). The mean of the scores is unchanged and only the variance has increased. Therefore, we decided to maintain the same set of parameters that were optimized in [19]. Specifically, we used the same shift value of 0.45 and the same gap penalties of 2 (gap opening) and 0.2 (gap extension). We have also tested position-specific gap penalties based on the SS information, but without any apparent improvement in performance.
Mixing parameter
To estimate the best value for γ we used a subset of 120 families and assessed the performance for different values of γ. Our performance evaluation procedure works as follows: true relationships are defined to be between those families that share a superfamily and all others are defined as false relationships. For each family within the test set, we calculate the profile-profile similarity against all 1287 families for a single value of the mixing parameter γ. These results are sorted by raw score and the number of true family-family relationships are counted before the first false relationship is detected (this is basically a sum of ROC1 scores). The tradeoff between the primary sequence information and secondary structure probabilities was varied from zero to one. With zero dependence on secondary structure the method is equivalent to prof_sim (profile-profile comparison based on just primary structure). The results are shown in Figure 3. As the graph indicates, setting γ = 0.055 (i.e. 0.055 weight on the secondary structure information and 0.945 on the sequence information) gave the best performance. (Note that if each secondary structure was given as much weight as a single amino acid γ would be or ~0.13).
When only secondary structure information was used (γ = 1), the performance was much worse than when only sequence information was used (γ = 0). These corner-case results and the fact that the best results were obtained with γ << 0.5 suggest that for protein family comparison, the coarse-grained secondary structure information is noisier and less reliable than sequence information. However, as the graphs indicate, using both sources of information clearly improves performance. Our tests were done using actual secondary structure information in the profile; however, similar results were obtained when the predicted information was used for one or both of the profiles (see Figure 3b).
Statistical significance
To differentiate true similarity values from those that may be observed by chance, it is essential to establish a baseline empirical distribution for the scores. Here we used the statistical framework of the extreme value distribution (EVD). Although rigorous mathematical proof has not been found for local gapped similarity scores, empirical studies have shown that the distribution of these scores can be approximated by this distribution. An empirically fit EVD also has the benefits of being a true fit to the quirks of a particular protein family. Three such distributions were established to assess the significance of the profile-profile matches. All distributions were fit with the 'fit' function in gnuplot using the nonlinear least-squares (NLLS) Marquardt-Levenberg algorithm.
The first distribution is based upon comparisons between unrelated families (defined as families that belong to different SCOP classes and do not share significant structural similarity). This distribution is useful in that it can be used to assess the significance of a score in comparing any pair of protein families, without further need for computations. Practically, this aggregates all comparisons between non-related families into a single list. This is essentially the distribution of similarity scores of random profiles, as shown in Figure 4a. By fitting an EVD to this distribution we can estimate the statistical significance (e-value) of any raw similarity score. We refer to this method as the uniform approach (uniform parameters).
The second distribution is similar to the first, except a correction was made for the length of a profile, similar to the approach employed by FASTA [33]. By chance, the raw score of a profile-profile comparison is greater for those profiles with many more residues than the score of two smaller profiles. To correct for this occurrence, all raw scores were fit to a logarithmic curve of the product of the two profile lengths. The mean and variance of this fit was used to calculate a zscore. Accounting for undersampling at the ends of the spectrum, the means were fit to a linear curve and the variance was constant throughout. The distribution of zscores was then fit to an EVD, as is shown in Figure 4b. This distribution estimates better the statistical significance of raw similarity scores since it accounts for the biases introduced due to the lengths of the profiles.
The third distribution proves to be the best approach in assessing significance of matches with a particular profile. This distribution is created on a per-family basis. The scores of each family against all (unrelated) SCOP families were fit to an EVD. Since many of the family profiles are unrelated to the query family, the corresponding scores provide a relatively reliable baseline distribution. This approach is a robust method to assessing the significance of matches for a particular profile since it allows for any unusual properties of the query profile (like unusual amino acid composition) and the parameters are adjusted accordingly (see Figure 5). Once again, from the fitted EVD, the e-value of the raw similarity scores is estimated from this fit.
The third method of measuring statistical significance is self-calibrating and provably more accurate than the previous two methods, and our performance evaluation tests indicate that this is the best method overall (see Figure 6). However, it is an intractable method when given a single pair of profiles to compare, since there is no prior knowledge about the baseline distributions of either profile. As a result, we must rely on the second method to measure statistical significance in these cases.
Discussion
We evaluate the performance of our algorithm using the SCOP database as a benchmark and two measures of performance. The first counts the number of weak relationships between protein families (as implied by the SCOP classification) that can be detected with our method. Specifically, each family in our test set is compared with all other protein families and the results are sorted based on the p-value. Given the sorted list we count the number of true family-family relationships that are detected before the first false positive occurs. This measure is applied to each family individually, and the results, summed over all families in the test set are given in Table 1. We compare our results to Gapped-BLAST, PSI-BLAST and prof_sim (as reported in [19]).
Usually a false positive is defined as a relation between families that do not belong to the same superfamily. This popular criterion, however, is somewhat strict as relations between families that belong to the same fold can also be considered as positives. We use the following terminology to distinguish between the different types of "false positives". We define a relationship between two protein families to be a true relationship if both families belong to the same superfamily, a possible relationship if both families belong to the same SCOP fold, a weak relationship if they belong to the same class, suspicious if they belong to different classes (excluding the case of an all-alpha ↔ all-beta pair) and an error if one family is all-alpha and the second is all-beta. We repeat the procedure described above, each time using a different definition of a false positive. The results are summarized in Table 1.
The second measure we use is the receiver operator characteristic (ROC) measure, a common measure in assessing sensitivity and selectivity. Given a sorted list of results, the ROC index measures the area under the curve that plots the positives versus the negatives. Maximal performance translates to a perfect separation and a maximal normalized ROC score of 1. The ROC-N measure is a variation over the ROC measure, where the plot is truncated at N negatives. In other words, the ROC-N measure is the number of true positives detected up to N false positives. Here we used the popular ROC-50 measure. To obtain the ROC-50 scores for each method we pool together all pairwise comparisons for all protein families, and sort them by their normalized e-value. The number of true positives is aggregated until 50 false positives occur. As before, we repeated this procedure with different definitions of false positives, and the results are summarized in Table 2. A detailed break-up of the pairwise similarities detected with each method is given in Table 3 (using the most strict definition of a false positive). Note that prof_ss improves over prof_sim (for all types of false positives) although the improvement is smaller compared to the one reported in Table 1. The difference in performance is striking when the true secondary structure information is used. Despite the moderate contribution to the profile (the optimal γ was set to 0.055), the new algorithm almost doubles the number of pairwise relationships that are detected.
Examples
In this section we give several interesting examples of alignments between remote protein families that exemplify the differences between sequence-based profile-profile alignments and the new generalized profile alignments.
The "winged helix" DNA-binding domain superfamily
This superfamily is part of the DNA/RNA-binding 3-helical bundle fold. We compared two families from that superfamily: the restriction endonuclease FokI, N-terminal recognition domain (family a.4.5.12, seed scop domain d2foka3), and the replication terminator protein (family a.4.5.7, seed scop domain dlbm9a_). Although designated as all-alpha, proteins in this superfamily contain a small beta-sheet at the core. The similar substructures have three alpha helices and a couple beta strands, prof_sim is able to roughly match up the helices but not the beta strands with a rms of 11.96. The predicted secondary structure does not improve the alignment in this case, however, when the true secondary structure is used, prof_ss is able to completely align the helices as well as most of the strands with a much better rms of 4.45 (Figure 7 and Figure 8).
The concanavalin A-like lectins/glucanases superfamily
This superfamily belongs to the concanavalin A-like lectins/glucanases fold, characterized by a sandwich structure with 12–14 strands in 2 sheets. We compared two families in this superfamily: the beta-Glucanase-like family (b.29.1.2, seed domain dlcpm__) and the vibrio cholerae sialidase, N-terminal and insertion domains (b.29.1.8, seed domain dlkit_2).
These class beta proteins have complex topology and are hard to align even with structure alignment algorithms. In this example, the two sets of beta sheets are nicely aligned by prof_ss both when using the predicted information and the true secondary structure information. On the other hand, prof_sim is unable to align the sheets at all (see Figure 9 and Figure 10).
The alpha/beta-Hydrolases superfamily
The alpha/beta-Hydrolases belong to the fold by the same name. Proteins with that fold are composed of 3 layers at the core, of alpha/beta/alpha. We compared two families in this superfamily: the carbon-carbon bond hydrolase family (c.69.1.10, seed domain dlc4xa_) and the bromoperoxidase A2 family (c.69.1.12, seed domain dlbrt__). These are large and complex proteins with many helices and strands. prof_sim reports an alignment that aligns perfectly one small alpha helix and two beta strands. With predicted secondary structures, prof_ss is able to generate a much longer alignment, with γ alpha helices and 4 beta strands. The alignment is not perfectly in sync, but all secondary structures are roughly in position. When using the true secondary structure information in prof_ss the alignment improves and a better overlap is observed (see Figure 11 and Figure 12).
Conclusion
This paper presents a simple method to improve remote homology detection between protein families. We use statistical models of protein families in the form of profiles, and by incorporating secondary structure information within that model, we can reuse existing comparison methods for comparing profiles. It is shown that this method improves over the previous method that is based only on primary sequence information.
As opposed to other methods that compare single proteins, our method compares models of protein families. Instead of summing over different models, our model combines structural and primary sequence information within the profile itself. Our method allows us to explore a wide range of scenarios, between purely sequence-based representation and a purely secondary-structure based representation. The optimization of the single mixing parameter shows that the slight incorporation of predicted secondary structural information is invaluable. Since predicted structure information in PSIPRED comes from neighboring profile columns, this proves that each profile column confers extra information that is relevant to its neighbors and is useful to inferring protein relationships.
Furthermore, it is shown that if true secondary structure information is used, performance improvements are very significant and the number of relationships that can be detected is almost doubled. We conclude that despite the high overall accuracy of the secondary structure prediction method, its imperfect nature can greatly affect the performance. However, our method can be generalized to any secondary structure prediction method that produces estimated probabilities for secondary structure, so should a new prediction method be found that performs better than the current methods, the model presented here is expected to reflect the improved performance and consequently improve homology detection.
Authors' contributions
RC extended the prof_sim program and integrated secondary structure information, optimized the model, ran experiments, and analyzed the result sets. GY conceived of the study, designed the model and analyzed the results.
Acknowledgments
This work is supported by the National Science Foundation under Grant No. 0133311 to GY.
Figures and Tables
Figure 1 Distribution of similarity scores before and after adding secondary structure information for identical columns (a column with itself) and dissimilar columns.
Figure 2 (a) Distribution of similarity scores for different column types. (b) Distribution of correlation scores. (c) Distribution of ALLR scores. The distributions are based on the largest 100 families in SCOP 1.50 database. The pairs of profile columns are divided into five categories depending on the nature of the seed amino acids, as described in the text. Note that in general the distributions of correlation scores overlap more than the other scoring functions, and specifically, the overlap between the scores of identical columns and the scores of dissimilar columns is greater (24%) than the overlap between the same types of columns, using our similarity scores (2.1%) or the ALLR scores (4.4%).
Figure 3 The effect of the mixture parameter γ on the performance. Performance is measured by the number of true relations that are detected before the first false positive (see section for more details), (a) Exploring γ in the range [0,1] using prof_ss with true secondary structure information, (b) Magnification of the range 0–0.15. The peak is obtained at γ = 0.055. Approximately the same value is obtained when using prof_ss with PSIPRED predicted secondary structure information and also when using predicted information after the probabilities were rounded such that the most probable state is assigned the probability of 1 and the other two states are assigned probability of 0.
Figure 4 Distribution of profile similarity scores. (a) The uniform approach: the scores follow the EVD distribution (with λ = 0.707 and u = 4.77. (b) The normalized length-corrected scores (zscores) also follow the EVD (zscores are shifted and multiplied by 10 to emulate typical values for raw similarity scores).
Figure 5 Distribution of profile similarity scores. The per-family distribution can differ quite markedly from the overall distribution of scores due to unique composition effects, etc, as is demonstrated for the g.3.7.1 family.
Figure 6 Comparison of different methods for assessing pvalue. For each method we plot the ROC50 curve (see section for details). Note that the zscore-based approach is better than the uniform approach, and is outperformed by the per-family approach.
Figure 7 Alignment of SCOP families a.4.5.12 and a.4.5.7
Figure 8 Structural superposition of SCOP families a.4.5.12 and a.4.5.7 based on Profile-Profile alignment. (a) prof_sim alignment (rms 11.96) (b) prof_ss alignment (rms of 4.45).
Figure 9 Alignment of SCOP families b.29.1.2 and b.29.1.8
Figure 10 Structural superposition of SCOP families b.29.1.2 and b.29.1.8 based on Profile-Profile alignment. (a) prof_sim alignment (b) prof_ss alignment (predicted ss). (c) prof_ss alignment (true ss).
Figure 11 Alignment of SCOP families c.69.1.10 and c.69.1.12
Figure 12 Structural superposition of SCOP families c.69.1.10 and c.69.1.12 based on Profile-Profile alignment. (a) prof_sim alignment (b) prof_ss alignment (predicted ss). (c) prof_ss alignment (true ss).
Table 1 Performance evaluation results (ROC1). Methods compared: Gapped BLAST, PSI-BLAST, prof_sim and prof_ss. For each method, the number of true family-family relationships that are detected before the first false connection occurs, is given (applied individually to each family, and summed over all families in the test set). Results are given for the following types of false connections: possible, suspicious and error (see text for details).
Number of true family-family relationships detected by
Type of first false-relation Gapped-BLAST PSI-BLAST prof_sim prof_ss (predicted ss) prof_ss (true ss)
Different superfamily, same fold ("possible" relationship) 163 189 231 245 391
Different fold, same class ("weak" relationship) 163 191 231 245 398
Different class ("suspicious" relationship) 174 205 253 299 483
Alpha ↔ Beta ("error" relationship) 709 690 1586 1617 1771
Table 2 Performance evaluation results (ROC50). Our test set is composed of 456 families that have at least 2 related families within the same superfamily (see section 'Data sets'). For each method, we report the number of true relationships that are detected until 50 false connections occur. We repeat this analysis four times, each time with a different definition of a false positive. A break up of the relationships by relationship-type is given in Table 3 for the first type of false positive.
Number of true family-family relationships detected by
Type of false-positive Gapped-BLAST PSI-BLAST prof_sim prof_ss (predicted ss) prof_ss (true ss)
Different superfamily, same fold ("possible" relationship) 116 146 173 178 272
Different fold, same class ("weak" relationship) 116 148 173 178 274
Different class ("suspicious" relationship) 124 153 189 209 328
Alpha ↔ Beta ("error" relationship) 214 268 308 >395 >584
Table 3 Performance evaluation results (break up of ROC50 results). For each method, we report the number of true, possible, suspicious and error relationships that are detected until 50 false positives occur (here, a false positive is any relationship between families that do not belong to the same superfamily). Also given are the e-value at which the 50th error occurs. The last column lists the number of relationships that are detected with prof_ss when using the true secondary structure assignments.
Number of relationships detected by
Relationship-type Gapped-BLAST PSI-BLAST prof_sim prof_ss (predicted ss) prof_ss (true ss)
Same superfamily (true relationship) 116 146 173 178 272
Same fold ("possible" relationship) 0 2 1 1 6
Same class ("weak" relationship) 18 17 17 26 31
Different class ("suspicious" relationship) 31 30 31 23 13
Alpha ↔ Beta ("error" relationship) 1 1 1 0 0
Total 166 196 223 228 322
e-value 0.1 0.1 0.39 0.34 0.33
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| 15563734 | PMC544344 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Nov 25; 5:183 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-183 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1971559835110.1186/1471-2105-5-197Research ArticleSCOPmap: Automated assignment of protein structures to evolutionary superfamilies Cheek Sara [email protected] Yuan [email protected] S Sri [email protected] Lisa N [email protected] Nick V [email protected] Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, Texas 75390, USA2 Department of Biochemistry, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, Texas 75390, USA2004 14 12 2004 5 197 197 23 8 2004 14 12 2004 Copyright © 2004 Cheek et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Inference of remote homology between proteins is very challenging and remains a prerogative of an expert. Thus a significant drawback to the use of evolutionary-based protein structure classifications is the difficulty in assigning new proteins to unique positions in the classification scheme with automatic methods. To address this issue, we have developed an algorithm to map protein domains to an existing structural classification scheme and have applied it to the SCOP database.
Results
The general strategy employed by this algorithm is to combine the results of several existing sequence and structure comparison tools applied to a query protein of known structure in order to find the homologs already classified in SCOP database and thus determine classification assignments. The algorithm is able to map domains within newly solved structures to the appropriate SCOP superfamily level with ~95% accuracy. Examples of correctly mapped remote homologs are discussed. The algorithm is also capable of identifying potential evolutionary relationships not specified in the SCOP database, thus helping to make it better. The strategy of the mapping algorithm is not limited to SCOP and can be applied to any other evolutionary-based classification scheme as well. SCOPmap is available for download.
Conclusion
The SCOPmap program is useful for assigning domains in newly solved structures to appropriate superfamilies and for identifying evolutionary links between different superfamilies.
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Background
Protein structure classifications are commonly used for studying structural and evolutionary relationships between proteins (namely remote homology inference), protein structure and function prediction, identification of potential functional residues and binding sites, understanding sequence/structure/function relationships in proteins, and as an aid in describing protein folds and families.
Several structural classification schemes such as SCOP [1], CATH [2], and Dali Domain Dictionary [3] have been developed for the purpose of cataloguing all available protein structures. These databases are commonly used for studying structural and evolutionary relationships between proteins. Detecting remote homology between protein structures is a difficult task because of the challenge in differentiating between distant homologs and structural analogs. Several researchers have reported the inadequacy of various structural similarity measures for distinguishing homologous and analogous relationships [4-7]. Therefore, although the databases mentioned above are associated with automatic methods for identifying potential structural neighbors of a new protein query, they are often incapable of assigning domains to a unique position in the classification according to evolutionary relationships. Determining appropriate evolutionary relationships within a database is usually accomplished by expert manual analysis. Although manual classification of protein structures remains the gold standard, the necessity for reliable automatic tools that can reproduce the results of such a classification scheme becomes increasingly apparent as available databases continue to grow in size. Such tools must be capable of detecting homology between distantly related proteins while keeping false positives at a minimum.
Available tools for assigning proteins to existing classification schemes use either structure-based or sequence-based comparison methods. Classification predictions from structure comparison tools like SSM [8], GRATH[9], and F2CS [10] are generally accurate to the fold or topology level but do not necessarily have evolutionary implications. Consequently, establishing homology between the query and the predicted neighbors often requires a more thorough examination. Classification assignments from sequence comparison tools such as SUPERFAMILY [11] can detect homology but often miss the more remote homologous relationships suggested by structural similarities. These tools are generally reliable for homology detection in easy to moderate cases but frequently produce many false positive results for more distant relationships. A strategy combining information from both sequence and structure comparisons would be expected to perform better than either method alone by exploiting the advantages of each approach.
In this paper, we describe an algorithm developed to map domains within protein structures with their homologs in an existing classification scheme. The general strategy employed by this algorithm is to combine the results of several existing sequence and structure comparison tools in order to determine classification assignments. The comparison tools incorporated in the algorithm each utilize a different methodology for identifying homologous domains, and consequently, these tools have different advantages and limitations. An approach combining different methods of homology detection is expected to capitalize on the proficiencies of each comparison tool while the limitations of those tools are neutralized by the inclusion of other methods.
Our algorithm, named SCOPmap, has been developed to map domains in protein structures to the SCOP database, which is a manually curated hierarchical classification scheme based on the structural and evolutionary relationships between proteins. SCOPmap assigns protein domains at the superfamily level, which is the broadest level of homology in the SCOP database. SCOPmap also performs assignments at the SCOP fold level when confident superfamily level assignments cannot be made. SCOPmap has two general applications. First, domains within newly solved protein structures can be identified and assigned to the appropriate SCOP superfamily. Second, SCOPmap can be used to find new links in SCOP by identifying potential evolutionary relationships between existing SCOP superfamilies. The strategy employed by this algorithm is not limited to SCOP and could be applied to any other similar database or classification scheme as well.
We have evaluated the performance of SCOPmap on two test sets, each of which includes over 4500 protein domains. The first set is comprised of the proteins that are included in SCOP v1.63 but not in SCOP v1.61, while the second set contains the proteins that are included in SCOP v1.65 but not in SCOP v1.63. SCOPmap was able to correctly map greater than 94% of both test sets at the SCOP superfamily level. Comparison of SCOPmap results and SUPERFAMILY [11] results for the same test set indicates that SCOPmap performs better than SUPERFAMILY both in terms of overall correct assignments and in accurate definition of the domain boundaries of those assignments. We have analyzed SCOPmap's performance at both the SCOP superfamily and SCOP fold levels. We have also evaluated the performance of the individual comparison tools incorporated in the algorithm. Furthermore, we describe examples of difficult cases that are successfully mapped and investigate the reasons why some domains are not mapped automatically by our algorithm.
Results
Evaluation of SCOPmap performance
Mapping of the tweaking set domains
Results of SCOPmap performance on the tweaking set are shown in Table 1 (see Methods for description of tweaking and testing sets). Correct SCOP superfamily assignments were made for 87.8% of the tweaking set domains. For an additional 0.3% of the tweaking set domains, the superfamily assigned by SCOPmap is not the same as the SCOP-assigned superfamily. However, in each of these cases, the superfamily assigned by SCOPmap and the superfamily specified by SCOP are homologous. For example, SCOPmap assigns the 7-bladed β-propeller domain of an archael surface layer protein to a homologous SCOP superfamily of 6-bladed β-propellers [12]. Because the purpose of the SCOPmap is to assign domains at the broadest level of homology in the classification (i.e. the SCOP superfamily level), such cases are not considered false positives but instead reflect special cases in the SCOP database. 6.2% of the tweaking set domains were given no superfamily assignment by SCOPmap, but are domains that belong to SCOP superfamilies that are new in v1.63. Because such domains cannot be appropriately assigned to a superfamily that is represented in the library used by SCOPmap (v1.61 in this case), these are also considered correctly mapped (i.e. true negative assignments). Thus, a total of 94.3% of the tweaking set domains are correctly mapped by SCOPmap. The remaining 5.7% of the tweaking set are false negative assignments. These domains belong to superfamilies that exist in SCOP v1.61, but no superfamily assignment is made by SCOPmap.
Mapping of the testing set domains
Results of SCOPmap performance on the testing set (see Methods) are shown in Table 1. Correct SCOP superfamily assignments were made for 91.2% of the testing set domains. In an additional 0.2% of the test set, the domain assignments given by SCOPmap are homologous to the superfamilies specified by SCOP. 3.1% of the tweaking set domains are given true negative assignments. These are cases in which the appropriate superfamily assignment is not a part of the library used by SCOPmap (based on SCOP v1.63 in this case), and no superfamily assignment is made by SCOPmap. Thus, a total of 94.5% of the testing set domains are given correct assignments by SCOPmap. 5.3% of the testing set domains are false negative assignments in which the domain belongs to a superfamily that is present in SCOP v1.63, but no superfamily assignment is made by SCOPmap. The remaining 0.2% of the testing set domains are given false positive assignments.
False positive assignments in the testing set
Because the score cutoffs used by SCOPmap's individual comparison tools were determined while considering domains from the tweaking set, those cutoffs were therefore influenced by the specific collection of domains in that set. Had a different test set been considered when establishing these cutoffs, it is likely that the score cutoffs would be slightly different. Thus, the few false positive assignments observed in the second test set are not unexpected. Furthermore, the number of false positive domain assignments made is higher than the number of incorrect hits between query and library domains that are accepted. Due to redundancy in the test set (e.g. often one structure contains several identical chains and therefore several identical domains), the 7 domains mapped incorrectly essentially reflect only 3 different examples of false positive assignments.
Each incorrectly assigned domain has less than 10% sequence identity to the nearest library representative from the same SCOP superfamily. Furthermore, all of the false positive assignments are due to scores from the individual comparison tools which barely meet the cutoffs required for acceptance. Such cases reflect the influence that a few specific domains can have in determining the exact values of the minimum score threshold requirements. All incorrect assignments were made due to a hit accepted by one of the comparison tools that includes both sequence and structure components.
For example, addiction antidote protein MazE from Escherichia coli (PDB code: 1mvf, chains D and E[13]; SCOP domains: d1mvfd_ and d1mvfe_) belongs to the Kis/PemI addiction antidote superfamily in SCOP and forms a pseudobarrel as a homodimer. SCOPmap incorrectly maps this protein to the "Transcription-state regulator AbrB, the N-terminal DNA recognition domain" superfamily in SCOP, which is a 2-layer α/β protein. This assignment is due to a hit found to the N-terminal DNA recognition domain of AbrB from Bacillus subtilis (PDB code: 1ekt [14]; SCOP domain: d1ekta_). Although the aligned regions of these two domains have the same secondary structure (an α-helix, a β-strand, and followed by a β-hairpin) and similar spatial arrangement, the overall topologies of these folds are highly dissimilar. This hit is accepted due to the 18 pairs of residues from the query and library representative which are equivalently aligned in pairwise alignments produced by PSI-BLAST (E-value = 55) and DaliLite (Z-score = 0.2). As the score cutoffs required by this comparison tool are E-value ≤ 100, Z-score > 0, and number of equivalent residue pairs ≥15, this particular query-library hit clearly falls just within the boundaries of the accepted score ranges.
The nuclease domain of putative ATP-dependent RNA helicase Hef from Pyrococcus furiosus (PDB codes: 1j22, 1j23, 1j24, and 1j25 [15]; SCOP domains: d1j22a_, d1j23a_, d1j24a_, and d1j25a_), a member of the restriction endonuclease-like superfamily in SCOP, is incorrectly mapped to the FAD/NAD(P)-binding domain superfamily. This assignment is made because of a conservation pattern analysis hit to NADH-dependent ferredoxin reductase BphA4 from Pseudomonas strain KKS102 (PDB: 1d7y [16]; SCOP domain: d1d7ya2). Although the core of both the query and the library representative is an α/β domain containing a 5-stranded β-sheet, the overall topology is not similar. This query-library pair hit is accepted because of the matrix-based conservation score of 0.32, which is based on the structural alignment of these two domains by DaliLite (Z-score = 3.7), while the score cutoffs required by this comparison tool are matrix-based score ≥ 0.25 and DaliLite Z-score ≥ 2. Again, the scores for this hit fall near the boundaries of the accepted score ranges.
The proteolytically-cleaved peptide C from bovine lysosomal α-mannosidase (PDB code: 1o7d [17]; SCOP domain: d1o7d.2) belongs to the galactose mutarotase-like superfamily in SCOP, but is incorrectly mapped to the "alpha-Amylases, C-terminal domain β-sheet domain" superfamily. This assignment is due to a hit identified by conservation pattern analysis to the C-terminal domain of neopullulanase from Bacillus stearothermophilus (PDB code: 1j0h [18]; SCOP domain: d1j0ha2). Although the core of lysosomal α-mannosidase peptide C and the C-terminal domain of neopullulanase each form a β-sandwich-like fold, the topologies of these folds are different. The COMPASS-based conservation score for this query-library pair (0.52) is based on the structural alignment of the two domains by DaliLite (Z-score = 4.6). These scores fall just within the required ranges for acceptance by the conservation pattern comparison method (COMPASS-based conservation score ≥ 0.5 and DaliLite Z-score ≥ 2).
Comparison of tweaking and testing set results
Table 1 shows that the SCOPmap results are comparable for the tweaking set and the testing set. SCOPmap performance on the two test sets are nearly equivalent: 94.3% (tweaking set) vs 94.5% (testing set) correct assignments; 5.7% (tweaking set) vs 5.3% (testing set) false negative assignments; and 0.0% (tweaking set) vs 0.2% (testing set) false positive assignments. The most significant apparent differences are in the results for the specific types of correct assignments: true positives with ranges accurate within 10 residues, true positives with ranges that are not accurate within 10 residues, and true negatives. These seemingly disparate results are predominantly reflections of inconsistencies in test set composition rather than in SCOPmap performance. More specifically, these variations are primarily due to the number of query domains that belong to new SCOP superfamilies. The most obvious consequence is the fraction of each test set given true negative assignments (6.2% in tweaking set, 3.1% in testing set), which is directly dependent on the fraction of each test set that belongs to new SCOP superfamilies. If domains from new SCOP superfamilies are ignored, the apparent disparity in SCOPmap boundary definition accuracy is reduced. For example, if the entire test sets are considered, there is a 2.3% difference in the number of domains correctly assigned whose ranges are accurate within 10 residues of the SCOP-defined boundaries. However, when considering only domains that can potentially be mapped correctly (i.e. domains that do not belong to new SCOP superfamilies), 86.8% of the tweaking set domains are correct assignments that are accurate within 10 residues, compared to 86.4% of the testing set domains. Similarly, 92.4% of all correctly assigned domains in the tweaking set are accurate within 10 residues, compared to 91.6% for the corresponding domains in the testing set.
The comparable results are a reliable indication of the consistency of SCOPmap performance because the two test sets are of nearly equivalent difficulty. First, the two test sets include approximately the same fraction of trivial assignments: 73.7% of mappable domains in the tweaking set are assigned by gapped BLAST while 73.6% of mappable domains in the testing set are assigned by gapped BLAST, where "mappable" means the domain is both evolutionarily relevant and is a member of a SCOP superfamily that exists in the version of SCOP used as the library. Of the non-trivial mappable domains (i.e. mappable domains that are not assigned by gapped BLAST), the average sequence identity between the query domain and the closest library representative from the same SCOP superfamily is 29.2% in the tweaking set and 28.6% in the testing set.
Fold level assignments
Fold level assignments are attempted for regions of query chains at least 20 residues in length for which no superfamily assignment was made. Results are shown in Figure 1. In the tweaking set (v1.61-v1.63 test set), fold level assignments are made for ~30% of the 545 SCOP-defined domains with no superfamily level assignment. 92% of these fold level assignments are correct. In the testing set (v1.63-v1.65 test set), fold level assignments are made for ~44% of the 414 SCOP-defined domains with no superfamily level assignment. Of these assignments, ~94% are correct.
Similar to the superfamily level assignments, the apparent disparity in fold level assignments are due primarily to the relative composition of the two test sets rather than inconsistency in performance. There are two principal attributes of test set composition that result in improved fold level results. First, domains from new folds are typically given no fold level assignment by SCOPmap, so a smaller fraction of unmapped domains from new folds will result in a decreased number of domains for which no assignment is made. Second, because the structural similarity between two domains from the same superfamily is likely to be greater than that between two domains from different superfamilies within the same fold, a larger fraction of unmapped domains from existing superfamilies will result in an increased number of correct fold level assignments. Both of these attributes favor the testing set over the tweaking set (results not shown). This indicates that the testing set is less challenging in terms of fold level assignments, which is consistent with the improved results relative to the tweaking set (Figure 1).
Although no fold level assignment is made in a large number of cases (~70% of tweaking set unmapped domains and ~56% of testing set unmapped domains), this result is not altogether unexpected for several reasons. First, as discussed above, a significant fraction of the unmapped domains in each set belong to new SCOP folds, so no appropriate fold level assignment exists among the set of library representatives. Next, the minimum Z-score cutoff required for making fold level assignments is strict in order to minimize false positive assignments. While Ortiz et al. report that MAMMOTH Z-scores greater than 5.25 are generally reliable for fold predictions [19], we find that a MAMMOTH Z-score of 10 is required for making reliable fold assignments. Although 45% of domains in the tweaking set from existing folds but without a fold assignment (171 of 380 domains) have at least one MAMMOTH hit to a representative of the appropriate fold with a Z-score between 5.25 and 10, results in this range are not used due to many occurrences of false positive assignments (data not shown). Conversely, because MAMMOTH Z-scores greater than 22 are sufficient for assignments at the superfamily level (see Methods), fold assignments are neither necessary nor made for query-library domain pairs with such overwhelming structural similarity. Furthermore, because query-library domain pairs with sufficient sequence similarity to be recognized by automatic methods are mapped at superfamily level, unmapped domains have very little sequence similarity to the corresponding library representatives. Consequently, fold assignments are made only for a rather limited set of queries: domains with extremely low sequence similarity as well as significant but not overwhelming structural similarity to library representatives.
The false positive rates are nearly identical in the two test sets (~2.6%). In both sets, the false positive rate of fold level assignments is significantly higher for domains that belong to new SCOP folds compared to those from existing SCOP folds. For example, in the second testing set, 6 of the 86 domains that belong to new folds have incorrect fold level assignments (7.0%) while only 5 of the 328 domains from existing folds are given an incorrect assignment (1.5%). Because false positive hits are likely to fall just above the Z-score cutoff for fold level assignment, many false positives are ignored due to other hits found with better Z-scores, which are true positives in most cases. Thus, because domains that belong to existing SCOP folds should have significant structural similarity to at least one library domain (i.e. the library representative(s) of that particular SCOP fold), the negative effect of false positive hits to these domains is minimized in the false positive rate relative to that for domains from new SCOP folds.
False positive fold level assignments are typically due to a query and library representative sharing similar but not identical topology. For example, the structure of riboflavin kinase (PDB code: 1n06 [20]; SCOP domain: d1n06b_) is a query in v1.61-v1.63 test set and belongs to a SCOP superfamily that is new to SCOP v1.63. Appropriately, no superfamily level assignment is made. The fold of riboflavin kinase is a n = 6, S = 10 β-barrel with strand order 163452, but SCOPmap assigns this domain to the double psi β-barrel fold in SCOP, which is an n = 6, S = 10 β-barrel with strand order 163425. In this case, the incorrect fold assignment is based on similarity of overall topology, but other false positive fold assignments occur when a region within a query domain and a region within a SCOP representative have similar topology despite overall dissimilarity of the folds. For example, the structure of the ε-subunit of the plasmid maintenance system (PDB code: 1gvn [21]; SCOP domain: d1gvna_) is another query in v1.61-v1.63 test set which also belongs to a new superfamily in SCOP v1.63. Again, no superfamily level assignment is made, as appropriate. The fold of the ε-subunit is a 3-helix up-and-down bundle with left-handed twist, but SCOPmap assigns this domain to a 4-helix up-and-down bundle fold. The three α-helices in the query domain and the last three α-helices of the SCOP representative have identical topology, similar lengths, and equivalent spatial orientation to each other. This false positive is a result of the query topology matching a region of a SCOP representative. The opposite case, when a region of the query domain is the same as the topology of an entire SCOP representative, occurs as well. For example, the structure of viral chemokine binding protein m3 (PDB code: 1mkf [22]; SCOP domain: d1mkfa_), a query in v1.61-v1.63 test set, belongs to a new fold in SCOP v1.63. Appropriately, no superfamily level assignment is made for this query. The fold of this domain is a 10-stranded β-sandwich with 6 β-strands in one sheet and 4 in the other. This domain is mapped at the fold level to an 8-stranded β-sandwich with 4 β-strands in each sheet. Although the overall folds of these two domains are different, 7 β-strands from each of these two β-sandwich folds have identical topology and mutual spatial arrangement.
Unsurprisingly, correct fold assignments are made predominantly for typical globular proteins while no fold assignments are made for small protein or coiled coil folds. Outside of this observation, there are no recognizable trends suggesting types of folds for which assignments are more easily made.
Furthermore, it should be noted that fold assignments are not our main goal. Rather, these assignments are a by-product of the comparison tools that are used for mapping at the superfamily level by SCOPmap. The purpose of making fold level assignments is merely to assist the user in further study of those domains which SCOPmap does not assign at the superfamily level. The fold level mapping strategy and score cutoffs have not been optimized to perform fold mapping with high sensitivity or low false positives.
Performance of SCOPmap compared to SUPERFAMILY
Overall performance
SUPERFAMILY is another tool that attempts to assign domains within a query protein to the superfamily level of SCOP. It is the only package that we are aware of that meets our two requirements for direct comparison: the program performs a similar task and is available for download. The results of the performance of SUPERFAMILY relative to SCOPmap are shown in Table 1. Overall, SCOPmap performs better than SUPERFAMILY. SUPERFAMILY correctly maps 91.4% of domains compared to the 94.3% assigned to the correct SCOP superfamily by SCOPmap. Furthermore, SCOPmap is much more proficient at defining accurate domain boundaries. SCOPmap delineates domain boundaries within 10 residues of the SCOP-defined boundaries for 81.4% of domains, while SUPERFAMILY performs as well in only 70.1% of cases. This difference is due partly to the use of MAMMOTH and DaliLite in our algorithm. However, the results of our algorithm when using only sequence comparison tools show that there is still a 6.5% advantage over SUPERFAMILY in terms of accurately defined ranges (Table 1). Thus, the inclusion of structure comparison methods is not solely responsible for the dramatic improvement in boundary definition. Presumably, a second predominant factor in the increased domain boundary accuracy is the strict coverage criteria for sequence comparison methods incorporated in SCOPmap.
Table 1 shows the results of using only the BLAST, RPS-BLAST, PSI-BLAST, and COMPASS portions of our algorithm. This modified version of SCOPmap (henceforth referred to as the "sequence-only algorithm") was expected to perform similarly, if not better than, SUPERFAMILY. It was therefore surprising to observe significantly more false negative assignments by the sequence-only algorithm compared to the SUPERFAMILY algorithm (12.5% and 8.6%, respectively). Investigation of the 573 false negatives from the sequence-only algorithm indicates three general explanations for these missed assignments (data not shown). In ~47% of these cases (270 of 573 domains), there are no sequence comparison hits below the required E-value thresholds. Next, in ~17% of cases (97 of 573 domains), sequence hits that pass both the E-value and coverage criteria are found, but the domain is not assigned due to an unresolved choice between conflicting superfamilies. In the remaining 36% of cases (206 of 573 domains), sequence comparison hits to at least one superfamily representative are found that pass the required E-value cutoffs but fail the coverage criteria. These 206 domains correspond to ~4.5% of this test set and account for the difference in false negative rates between the sequence-only algorithm and SUPERFAMILY, which does not have a coverage requirement.
Performance on non-trivial domain assignments
Because nearly 70% of the domains can be mapped using only gapped BLAST (Table 3), the results of both SCOPmap and SUPERFAMILY are skewed in favor of trivial domain assignments. In order to evaluate the performance of these two programs on more challenging assignments, the results were re-tabulated excluding all domains assigned via gapped BLAST (Table 2). Here, SCOPmap assigns 81.6% of domains to the appropriate SCOP superfamily while SUPERFAMILY correctly maps 77.1% of domains, so SCOPmap's advantage in correctly assigned domains increases from 2.9% for all domains to 4.5% for only non-trivial assignments. SCOPmap's proficiency in domain boundary definition is also accentuated, as the difference in percent of domains with accurately defined domain boundaries increases from 11.3% for all domains (SCOPmap: 81.4%, SUPERFAMILY: 70.1%) to 12.8% for non-trivial assignments (SCOPmap: 42.8%, SUPERFAMILY: 30.0%). Thus, evaluating only the non-trivial assignments emphasizes the advantages of SCOPmap over SUPERFAMILY.
Comparison of false negative assignments
The false negative assignments made by SCOPmap (261 domains) and by SUPERFAMILY (395 domains) were compared in order to determine the degree of overlap between the two sets of unassigned domains. One might expect that a significant number of the false negative assignments would be shared by the two algorithms and would represent those cases that are too difficult to be confidently mapped by existing automatic comparison tools. Indeed, 205 domains are given false negative assignments by both SCOPmap and SUPERFAMILY.
Therefore, of the 261 false negative assignments made by SCOPmap, only 56 domains (21%) are correctly mapped by SUPERFAMILY. 38 of these domains were correctly identified by at least one of the comparison methods used but were not assigned (due, for example, to an unresolved choice of superfamily assignment). Most of the remaining domains that were assigned by SUPERFAMILY but not identified by SCOPmap represent cases that are typically difficult for automatic methods: 8 are small disulfide-rich domains, 3 are relatively short domains (74, 75, and 126 residues) that are interrupted by very large insertions (290, 289, and 282 residues respectively), and 1 domain contains many short breaks in the sequence and structure. The few remaining examples are domains that could have been reasonably expected to be mapped by SCOPmap: E. coli succinate dehydrogenase subunit SdhC (PDB codes: 1nek [23], chain D and 1nen [23], chain D; SCOP domains: d1nekd_ and d1nend_) is a helical bundle protein that belongs to the succinate dehydrogenase/fumarate reductase transmembrane segment superfamily in SCOP, and the PKD-like domain of Methanosarcina mazei surface layer protein (PDB codes: 1l0q [12], chains A, B, C, and D; SCOP domains: d1l0qa1, d1l0qb1, d1l0qc1, d1l0qd1) is an immunoglobulin-like domain that belongs to the PKD domain superfamily in SCOP. Other than the low sequence identity between these queries and the library representatives of the corresponding SCOP superfamilies, there are no convincing arguments for why these assignments might not be made. In each of these cases, significant hits are found by the structure comparison tools used in SCOPmap: SdhC has a DaliLite Z-score of 8.7 to a library representative of its SCOP superfamily, and surface layer protein PKD-like domain has a MAMMOTH Z-score of 10.6 to the library representative of its SCOP superfamily. However, the limited sequence similarity between the query and representative domains results in insufficient BLOSUM scores to meet the required score cutoffs of these methods. Although these are consequently false negative assignments at the superfamily level, the correct fold level assignment was made in each of these last 6 cases.
Conversely, approximately half of the false negative assignments made by SUPERFAMILY (190 of 395 domains) are correctly mapped by SCOPmap. Of these domains, ~54% are first identified by a sequence comparison tool in SCOPmap (gapped BLAST, RPS-BLAST, PSI-BLAST, or COMPASS), ~29% are first identified by a structure comparison tool (MAMMOTH or DaliLite), and the remaining ~17% are first identified by a method that combines both sequence and structure information (correlation of conservation patterns or the agreement of DaliLite alignments with gapped BLAST, RPS-BLAST, or PSI-BLAST alignments).
Discussion
Performance of individual comparison methods
In order to assess the relative performance of the individual comparison tools used by SCOPmap, the number of assignments in the tweaking set gained by each additional comparison method was evaluated. The results are summarized in Table 3. For each comparison tool, the number of domains first identified by that method was determined, and the percent of previously unassigned domains gained by that method was calculated. The comparison tools are listed in order of increasing sensitivity to distant homologs: sequence comparison methods (BLAST, RPS-BLAST, PSI-BLAST, and COMPASS), structure comparison methods (MAMMOTH and DaliLite), and finally comparison methods that incorporate both sequence and structure information (correlation of conservation patterns and agreement of DaliLite alignments with BLAST, RPS-BLAST, or PSI-BLAST alignments). Domains are included in the total count for only the least sensitive comparison tool that identified the hit.
The most number of assignments are made by gapped BLAST and RPS-BLAST, which give 69.1% gain and 36.3% gain of previously unmapped domains, respectively. However, these assignments are among the easiest in the set. The average sequence identity between the query domain and the closest library representative of that superfamily is 80.1% for gapped BLAST assignments and 41.1% for RPS-BLAST assignments. Furthermore, these numbers are considerably inflated as a consequence of the surfeit of trivial assignments in the tweaking set (Figure 2).
PSI-BLAST, MAMMOTH, and DaliLite each give between 10% and 20% gain of previously unmapped domains. The average sequence identities between the identified query domains and the library domains indicate that these assignments are neither trivial nor unusually difficult. The two structure comparison methods show similar overall performance by this assessment, although DaliLite does have the advantage over MAMMOTH both in number of assignments and percent gain as well as in difficulty of assignments made. This seemingly implies that comparison via MAMMOTH is an unnecessary step, and indeed, nearly all domain assignments made by MAMMOTH are also made by DaliLite (data not shown). However, MAMMOTH is both necessary for and proficient at determining potential hits by DaliLite. The pre-identification of potential hits drastically reduces the running time compared to comprehensive comparison of the query domains to all library domains by DaliLite. Furthermore, MAMMOTH is essential for making fold level assignments.
The conservation pattern analysis and the calculation of agreement between DaliLite alignments and BLAST, RPS-BLAST, or PSI-BLAST alignments have 4.2% and 5.9% gain of previously unmapped domains, respectively. Although the numbers of additional assignments are among the lowest of any of the comparison tools, these two methods also make the most challenging assignments of any of the comparison tools included in SCOPmap. The average sequence identity between query domains and library representatives for assignments made first by these methods is less than 15%. Specific examples are discussed below.
Thus, the general observation is that, as expected, those comparison tools more sensitive to distant homology typically make more challenging assignments, but with lower percent gains. The only clear exception to this trend is COMPASS. COMPASS has the lowest percent gain of any step at 3.3%, and the domains first identified by this method are only moderately difficult assignments (average sequence identity 27.2%). This is presumably due in part to the extremely strict E-value cutoff necessary for avoiding false positives (1 × 10-10). Furthermore, of the four sequence comparison tools used in SCOPmap, COMPASS is most sensitive to remote homologs. Therefore, if the query-library domain pair has sufficient sequence similarity to be recognized by automatic methods, it is likely that the hit would also be identified by one of the less sensitive sequence comparison tools and consequently be accounted for earlier in Table 3.
SCOPmap performance on remote homologs
Correctly mapped remote homologs
The similarity of the tweaking set to the representative library domains is shown in Figure 2 (white bars). Nearly 50% of tweaking set domains are more than 70% identical to one of the library representatives from the same SCOP superfamily. Furthermore, 69.1% of the tweaking set domains can be correctly mapped by gapped BLAST (Table 3). Other domains, however, are more difficult to assign due to limited similarity of the query domain to the representative library domains. SCOPmap is able to make several such assignments, including nearly 300 domains with less than 20% sequence identity to the closest library domain from the same SCOP superfamily (black bars, Figure 2).
One prevalent difficulty in making classification assignments by automatic methods is correctly assigning domains that have very limited sequence similarity to the library representatives. One such example of a difficult but correctly assigned domain is the N-terminal domain of mannitol 2-dehydrogenase from Pseudomonas fluorescens (PDB code: 1lj8 [19], N-terminal domain; SCOP domain: d1lj8a2). In SCOP, this domain belongs to the NAD(P)-binding Rossmann-fold domains superfamily. There are 90 representatives of this superfamily in the library, all of which have less than 10% sequence identity to the query domain. There are no BLAST, RPS-BLAST, PSI-BLAST, COMPASS, MAMMOTH, or DaliLite hits to these library representatives that pass both the required coverage and E-value or Z-score thresholds. Hits to three of the 90 superfamily representatives are identified by DaliLite: the N-terminal domain of glycerol-3-phosphate dehydrogenase from Leishmania mexicana (PDB code: 1evy [24], N-terminal domain; SCOP domain: d1evya2) with Z-score 6.9, the N-terminal domain of conserved hypothetical protein MTH1747 from Methanobacterium thermoautotrophicum (PDB code: 1i36 [25], N-terminal domain) with Z-score 6.3, and the N-terminal domain of lactate/malate dehydrogenase from Methanococcus jannaschii (PDB code: 1hye [26], N-terminal domain; SCOP domain: d1hyea1) with Z-score 6.4. Because of the poor BLOSUM scores calculated for the pairwise alignments given by DaliLite, none of these hits are accepted by the DaliLite comparison method. However, these relatively high Z-scores indicate that the DaliLite alignments are reliable enough for use in the comparison of conservation patterns method, and hits to two of these superfamily representatives are accepted based on correlation of conservation patterns: the N-terminal domain of glycerol-3-phosphate dehydrogenase (SCOP domain: d1evya2) has matrix-based conservation score = 0.26, and the N-terminal domain of conserved hypothetical protein MTH1747 (SCOP domain: d1i36a2) has matrix-based conservation score = 0.11. In both of these cases, approximately 75% of the most conserved positions in the query domain and in the library domain are equivalent (Figure 3c). Furthermore, these most conserved positions are clustered around the nucleotide-binding sites, which are equivalent in these domains (Figure 3a,b). The N-terminal domain of this query structure is therefore mapped to the NAD(P)-binding Rossmann-fold domain superfamily in SCOP based on the high degree of correlation between the conservation patterns of the query domain and these two superfamily representatives.
Conformational differences between similar protein domains also result in challenging classification assignments for automatic structure comparison tools. One such example is the antimicrobial cathelicidin motif of protegrin-3 from Sus scofa (PDB code: 1lxe [27]; SCOP domain: d1lxea_). The crystal structure of this protein shows the domain in a swapped dimer conformation (Figure 4a). The closest library representative to this query domain is cystatin from Gallus gallus (PDB code: 1cew [28]; SCOP domain: d1cewi_), which belongs to the cystatin/monellin superfamily in SCOP. This domain is a monomer in the crystal structure (Figure 4b). The sequence identity between the query (cathelicidin motif of protegrin-3) and this library representative (cystatin) is approximately 19%. The hit between the query and this library representative is found by both the RPS-BLAST and DaliLite methods. However, the scores for these hits are relatively poor as a result of the low sequence identity and the conformational variation between the two domains. The scores for these comparisons (RPS-BLAST E-value = 16 and DaliLite Z-score = 2.4) fail the score cutoff criteria for these methods individually. Comparison of the alignments produced by these two methods, however, indicates that a significant portion of the domain is aligned equivalently by RPS-BLAST and DaliLite (Figure 4c). Thus, based on the agreement of these two methods, the cathelicidin motif of protegrin-3 is correctly mapped to the cystatin/monellin superfamily of SCOP.
Another common problem for many automatic comparison methods is the presence of large insertions or deletions in the query domain. This third example demonstrates the ability of the mapping program to correctly assign such cases. Monomeric isocitrate dehydrogenase from Azotobacter vinelandii (PDB code: 1itw [29]; SCOP domain: d1itwa_) belongs to the isocitrate/isopropylmalate dehydrogenase superfamily in SCOP. There are two representatives of this superfamily in the library, both of which have less than 15% sequence identity to the query domain. Furthermore, the query domain has an approximately 250-residue insertion relative to the superfamily representatives (Figure 5). There are no BLAST, RPS-BLAST, PSI-BLAST, or COMPASS hits to either library representative. Although the MAMMOTH hit to 3-isopropylmalate dehydrogenase from Salmonella typhimurium (PDB code: 1cnz [30]; SCOP domain: d1cnza_) is accepted with Z-score 22.2, the presence of the large insertion in the query results in an erroneous range definition by MAMMOTH (Figure 5c). Comparison of the query to this same library representative by DaliLite identifies residues 164–397 as an insertion in this domain (Figure 5c). Although SCOP assigns the entire chain of monomeric isocitrate dehydrogenase as one domain (residues 1–741), residues 150–404 are defined as an insert region. Thus, the DaliLite-based assignment made by SCOPmap (residues 2–163, 398–671) is a reasonably accurate domain definition.
Domains without SCOPmap assignments at the superfamily level
In 5.7% of the tweaking set, no superfamily assignment is made for domains that should belong to superfamilies that are included in SCOP v1.61. General explanations for these false negative assignments are summarized in Table 4. Of the 261 unmapped domains, 19.2% percent (50 domains) are found by meeting the required score cutoffs of one or more of the comparison tools used, but these domains are not assigned due to a conflict with another domain identified in the same query chain. There are two ways in which this may happen: there may be an unresolved choice of superfamily assignment over a certain region of the query chain, or the boundary of one domain may erroneously extend over a second domain resulting in one domain being assigned while the another domain is missed.
In the remaining 80.8% of unmapped domains, comparison of the query to the library domains do not pass the score cutoffs of any of the methods used. These domains typically have only limited structural similarity as well as less than 20% sequence identity to the library representatives. All domains that have greater than ~20% sequence identity to a library representative from the same SCOP superfamily but are not identified by any of the comparison tools used in SCOPmap are small protein domains less than 50 residues in length. Because automatic methods often perform poorly on small proteins, such cases are not unexpected. These unmapped small protein examples comprise only 0.2% of the tweaking set. Furthermore, the unmapped domains often have inserted or deleted structural elements relative to the library domains. The unmapped and unidentified domains fall into three general categories in terms of structural similarity to the library representatives. First, 33.3% of unmapped domains have very little structural similarity to the corresponding library domains. When the MAMMOTH scores for a query domain are insufficient for making superfamily assignments, these scores are used as an initial indicator of whether specific query-library domain pairs are likely to be assigned by DaliLite (see Methods). For these unmapped domains, the MAMMOTH scores to library domains are too poor to be identified even as potential hits. Next, there are a small number of cases (6.1% of unmapped domains) that have potential but unconfirmed structural similarity to library representatives. In these cases, one or more potential hits are identified by MAMMOTH, but DaliLite does not produce output for those pairs. This could mean that the DaliLite Z-score is less than zero for the given pair of domains, or that either the query domain, the library representative, or both could not be handled by DaliLite because, for example, the structure lacks recognizable secondary structure, contains only Cα coordinates, or is less than 30 residues in length, etc. Finally, the remaining 41.4% of unmapped domains have recognizable but insufficient structural similarity to the library representatives. For these domains, hits are found via DaliLite but the scores of the hits do not meet the required cutoffs. Because such scores cannot be confidently distinguished from false positives, no superfamily assignment is made.
Since the inception of the SCOP database, the rapid growth in the number of available protein structures has resulted in a classification scheme that is not equally uniform in all parts. This is primarily apparent in overpopulated folds and superfamilies, such as TIM β/α-barrels, where intermediate relationships exist but are difficult to describe within the original SCOP classification scheme. These special cases in the SCOP database also contribute to the rate of false negative assignments by SCOPmap. In a later section, the conservative nature of SCOP is demonstrated by cases in which homologous proteins are assigned to different superfamilies. As a consequence of this attribute of the SCOP database, good hits via automatic comparison methods are sometimes found to multiple SCOP superfamilies. In some cases, SCOPmap is not capable of selecting one final assignment out of several correct choices. These 28 examples, which make up the unresolved choice of superfamilies category in Table 4, account for less than 1% of the tweaking set but 10.7% of all false negative assignments. Conversely, there are also numerous instances in which the SCOP classification is quite liberal. Examples are rampant in the sections of the database that the authors describe as not a part of the proper SCOP classification, such as the low resolution structures and peptides classes. These classes are not included in the SCOPmap library and are therefore not considered by our algorithm. However, cases were also observed in the evolutionarily relevant multi-domain proteins class of SCOP. The multi-domain proteins class is problematic in the sense that it deviates from the format followed by the remainder of the SCOP database. Members of this class have not been classified at the domain level, and there is often wide variation in the size and domain composition of the entries. One such example was detected during the manual investigation of false negative assignments from the tweaking set. Reovirus polymerase λ3 (PDB code: 1n1 h [31]; SCOP domain: d1n1ha_) belongs to the DNA/RNA polymerases superfamily in the multi-domain proteins class of SCOP. The structural fold of domains in the DNA/RNA polymerases superfamily has been described as a "right-hand" configuration containing "palm", "fingers", and "thumb" subdomains. Domains in this superfamily, of which there are >200, typically include 2 or 3 subdomains of the "right-hand" fold. For example, Moloney murine leukemia virus (MMLV) reverse transcriptase (PDB code: 1mml [32]; SCOP domain: d1mml__), which is one of the representatives of this superfamily included in the v1.61 library, is a 265-residue fragment containing only the "palm" and "fingers" subdomains. Reovirus polymerase λ3, however, also includes a 380-residue N-terminal domain as well as a 377-residue C-terminal "bracelet" domain, in addition to the "palm", "fingers", and "thumb" subdomains. Thus, a 1267-residue, 3-domain protein (reovirus polymerase λ3) and a 265-residue, single domain fragment (MMLV reverse transcriptase) are classified equivalently at the superfamily level in SCOP. Naturally, such variations within the database are problematic for making appropriate classifications via automatic methods.
Examples of false negative SCOPmap assignments
Some superfamily assignments are missed due to extremely limited similarity between the query domain and the corresponding library representatives. One such example is Saccharomyces cerevisiae DNA-binding domain from transcription factor Ndt80 (PDB code: 1mnn [33]; SCOP domain: d1mnna_), which belongs to the p53-like transcription factors superfamily in SCOP. Members of this superfamily bind DNA through an s-type Ig fold. There are seven library representatives of this superfamily, all of which have less than 10% sequence identity with the query domain. There are no hits to these representatives found by BLAST, RPS-BLAST, or PSI-BLAST with E-value less than 100 or by COMPASS with E-value less than 1 × 10-3. Because the MAMMOTH hits to these representatives are very poor (Z-scores below 2.5), MAMMOTH finds neither accepted hits nor potential hits for comparison via DaliLite. Although the conserved core of this superfamily is observable by eye (Figure 6a), the many inserted structural elements relative to the library representatives contribute to the poor performance of the automatic structural comparison methods. The DNA-binding function of this domain may have contributed to its inclusion in this superfamily by the SCOP authors.
Superfamily assignments are also missed in cases where the similarity to library representatives is moderately significant but still insufficient for distinction from false positives. One such example is adaptor protein ClpS from E. coli (PDB code: 1lzw [34], chain A; SCOP domain: d1lzwa_) (Figure 6b), which belongs to the ClpS-like superfamily in SCOP. The one representative of this superfamily in the library shares ~11% sequence identity with the query domain. BLAST, RPS-BLAST, and PSI-BLAST hits to this library representative are not found with E-values less than 100, and a COMPASS hit to the library domain is not found with E-value less than 1 × 10-3. Comparison of the query and library domain by MAMMOTH and DaliLite give more substantial results: a MAMMOTH Z-score of 10.4 with BLOSUM score -1.0 × 10-2 for the pairwise alignment produced by MAMMOTH, and a DaliLite Z-score of 8.8 with BLOSUM score 4.5 × 10-4 for the pairwise alignment produced by DaliLite. Unfortunately, these scores fall just below the required cutoffs for superfamily assignment via these methods. Thus, no superfamily assignment is made. However, the MAMMOTH Z-score does meet the fold level cutoff, so a correct fold assignment is made for this query domain.
Additionally, technical shortcomings of automatic methods contribute to missed superfamily assignments. For example, δ-conotoxin TxVIA from Conus textile (PDB code: 1fu3 [35]; SCOP domain: d1fu3a_) is a 27-residue small protein that belongs to the omega toxin-like superfamily in SCOP. There are 21 library representatives of this superfamily, some of which share up to 40% sequence identity with the query domain. However, there are no hits to these representatives found by BLAST, RPS-BLAST, or PSI-BLAST with E-value less than 100 or by COMPASS with E-value less than 1 × 10-5. The MAMMOTH hits to these 21 representatives all have Z-scores well below 4. Furthermore, DaliLite cannot handle this protein due to the short length, thus precluding DaliLite comparisons with library representatives. Thus, despite significant sequence and structural similarity of δ-conotoxin TxVIA to several library representatives (Figure 6c), no superfamily assignment is made due to the poor performance of automatic methods on small proteins.
Finding new links between SCOP superfamilies: examples of homologs in different SCOP superfamilies identified by SCOPmap
The thiamin phosphate synthase superfamily and the ribulose-phosphate binding barrel superfamily are one example of homologous SCOP superfamilies identified by SCOPmap. Both superfamilies have a TIM β/α-barrel fold. When thiamin phosphate synthase is used as the query, hits to 8 different members of the ribulose-phosphate binding barrel superfamily are identified. These hits are found by PSI-BLAST, COMPASS, DaliLite, and the agreement between pairwise alignments produced by DaliLite and by RPS-BLAST or PSI-BLAST. Because confident hits are identified by both sequence and structure comparison methods, the homology between the two superfamilies is considered reliable, despite the limited sequence identity (<20%). The structure of thiamin phosphate synthase and indole-3-glycerophosphate synthase, which is a representative of the ribulose-phosphate binding barrel superfamily, are shown in Figure 7a,b. The RPS-BLAST alignment (E-value 1 × 10-10) (Figure 7c) and the DaliLite alignment (Z-score 15.4) of these two proteins are similar: 101 pairs of residues (~40% of the proteins) are equivalently aligned by the two comparison tools. Furthermore, three phosphate-binding residues are in equivalent positions both spatially and in the sequences of these proteins (Figure 7). The homology between these two superfamilies has been previously reported [36].
The C-terminal domain of RNA polymerase alpha subunit and the DNA repair protein Rad51, N-terminal domain superfamilies are another pair of homologous superfamilies identified by SCOPmap. The domains in these two superfamilies have a 5-helix bundle structure (SAM domain-like fold), with one classic and one pseudo HhH motif as noted in SCOP. Members of both superfamilies have DNA-binding functions, and the observed or predicted DNA-binding surfaces are similar between the two superfamilies (Figure 7d,e). The closest representatives from each of these two superfamilies share ~32% sequence identity with each other. When the C-terminal domain of RNA polymerase alpha subunit superfamily is used as the query, all three members of this superfamily find hits to the single member of the DNA repair protein Rad51, N-terminal domain superfamily. RPS-BLAST (E-value 0.002), COMPASS (E-values ~10-16), and MAMMOTH (Z-scores ~9) identify these hits. The detection of both confident sequence and structure comparison hits further supports the link between these two superfamilies.
The examples discussed here are two cases among many. The examination of the complete list of potential homologs from different SCOP superfamilies is in progress.
Conclusions
We have developed an algorithm for mapping domains within protein structures to an existing classification scheme. When applied to the SCOP database, this algorithm performs with ~95% accuracy (i.e. the correct superfamily assignment is made or no superfamily level assignment is made, as appropriate). SCOPmap produces better results than SUPERFAMILY, both in terms of overall correct assignments and in the definition of the domain boundaries of those assignments. Examination of difficult cases has demonstrated the ability of SCOPmap to make non-trivial assignments, including some domains that represent common problems associated with automatic comparison tools. SCOPmap is also capable of identifying potential evolutionary links between proteins from different SCOP superfamilies. SCOPmap should be useful to researchers interested in determining the SCOP classification of domains within newly solved protein structures. Furthermore, SCOPmap can be modified to perform similar mapping tasks within other protein classification databases. An additional potential use of the algorithm would be as an internal check in the preparation of new classifications or the maintenance and updating of existing classifications. Reliable methods for automatic updates to existing classification schemes become increasingly important with the rapid growth in sequence and structure database size.
Methods
Mapping strategy of the SCOPmap algorithm
General strategy
The purpose of SCOPmap is to assign domains within protein structures to the SCOP classification at the broadest level of homology, i.e. the SCOP superfamily level. The general strategy is to combine the results of several existing sequence and structure comparison tools to determine superfamily assignments as well as domain boundaries. Because the basis for identifying relationships between proteins varies between the different comparison tools, this combinatorial approach is expected to perform better than a single comparison tool alone. Furthermore, an approach utilizing multiple comparison tools is consistent with the conclusions reached by Novotny et al. from an analysis of several fold comparison servers [37].
There are three main steps in this mapping strategy. First, hits are identified between the query protein and proteins with known SCOP assignments using several existing comparison tools. Next, the results of those comparison tools are used to determine the appropriate SCOP superfamily level assignment for domains within the query. Assignments are made by a consensus-like method in which more reliable comparison tools are given preference. Finally, the algorithm uses the results of the comparison tools to define the boundaries of the domain assignments by identifying the longest non-overlapping segments.
Library of representative SCOP domains
A subset of SCOP domains with less than 40% identity to each other was downloaded from the ASTRAL [38,39] database. This set contains domains from the all alpha proteins, all beta proteins, alpha and beta proteins (a+b and a/b), multi-domain proteins, membrane and cell surface proteins and peptides, and small proteins classes of SCOP. Domains from the coiled coil proteins class were manually added to the library. In this paper, results using two different SCOP libraries are discussed. The library based on SCOP v1.61 contains 4813 domains from 1110 SCOP superfamilies, while the library based on SCOP v1.63 contains 5265 domains from 1232 superfamilies. Each library includes at least one representative of each SCOP superfamily.
Set of representative query chains
Input for SCOPmap is a list of PDB [40] identifiers. Each chain in these structures is considered as a separate query. The BLASTCLUST program (I. Dondoshansky and Y. Wolf, unpublished; ) is used for preliminary clustering of all chains at 95% sequence identity and 95% length coverage. A representative set of query chains is constructed from the first member of each BLASTCLUST cluster, excluding chains fewer than 20 residues in length. Chains less than 20 residues in length are designated as fragments and are ignored by SCOPmap.
Mapping step 1: identifying hits between query and library domains using existing comparison methods
The gapped BLAST [41], RPS-BLAST[42], PSI-BLAST [41], COMPASS [43], MAMMOTH [19], and DaliLite [44] tools are used in SCOPmap. The first four of these are sequence comparison tools and are listed in order of increasing sensitivity to remote homologs: a query sequence against a database of sequences (gapped BLAST), a query sequence against a database of profiles (RPS-BLAST), a query profile against a database of sequences (PSI-BLAST), and a query profile against a database of profiles (COMPASS). The two structure comparison tools used are the MAMMOTH and DaliLite algorithms. Additionally, SCOPmap includes two tools which incorporate elements of both sequence and structure comparisons: correlation of conservation patterns and the agreement of pairwise alignments produced by structure comparison tools (DaliLite or MAMMOTH) with those produced by sequence comparison tools (gapped BLAST, RPS-BLAST, or PSI-BLAST). Thus, similarities between proteins are identified using eight different comparison methods, which are described in detail below.
Method 1) gapped BLAST [41]: query sequence against database of sequences
Gapped BLAST is run for each representative query sequence against sequences of all chains from PDB structures in SCOP (37,007 sequences in SCOP v1.61; 41,066 sequences in SCOP v1.63). The criteria for an accepted BLAST hit are an E-value ≤ 0.005 and coverage of all but 10 residues at each end of both the query and database sequences. Hits are also accepted if the query and library sequences are at least 80% identical and all but 10 residues at each end of the query sequence are covered by the alignment, irrespective of E-value. Because the database sequences used for gapped BLAST are complete chains, the accepted hits are then converted from library chains to library domains according to the SCOP-defined domain boundaries of those library sequences. This conversion is not necessary for accepted hits from the other seven comparison methods since the library representatives in those methods are domains rather than complete chains. For all query chains with accepted BLAST hits, superfamily assignment is based solely on the BLAST results and no other comparison tools are used. All query chains with no BLAST hits passing the described criteria are submitted to each of the remaining methods.
Method 2) RPS-BLAST [42]: query sequence against database of profiles
RPS-BLAST is run for the query sequence against a database of profiles for the library of representative SCOP domains. Profiles were constructed for each library domain by running PSI-BLAST against the non-redundant database for 5 iterations or until convergence with an E-value cutoff of 0.005. The criteria for an accepted RPS-BLAST hit are an E-value ≤ 0.005 and coverage of all but 10 residues at each end of the library domain.
Method 3) PSI-BLAST [41]: query profile against database of sequences
A profile for the query sequence is constructed by running PSI-BLAST against the non-redundant protein database for 5 iterations or until convergence with an E-value cutoff of 0.001. This profile is subsequently used as an input for a PSI-BLAST search against a database of all SCOP domain sequences (42465 domain sequences in SCOP v1.61; 47013 domain sequences in SCOP v1.63). The criteria for an accepted PSI-BLAST hit are an E-value ≤ 10-4 and coverage of all but 10 residues at each end of the SCOP domain database sequence.
Method 4) COMPASS [43]: query profile against database of profiles
The profiles for the query (constructed in the PSI-BLAST step) and the SCOP library domains (constructed in the RPS-BLAST step) are prepared for COMPASS by: 1) deleting all columns with gaps in the query sequence, 2) removing all sequences identical to the query, and 3) retaining only 1 copy of any sequences in the profile that have greater than 97% identity. COMPASS is then run for the query profile against each of the SCOP library domain profiles. Accepted COMPASS hits have an E-value ≤ 10-10 and coverage of all but 10 residues at each end of the library domain.
Method 5) MAMMOTH [19]: query structure against database of structures
The query structure is compared to each library domain structure via MAMMOTH. For each query-library domain pair, the MAMMOTH Z-score (ZM) and the normalized BLOSUM [45] score for the pairwise alignment made by MAMMOTH (BSM) are calculated. MAMMOTH hits are accepted if they meet all of the following criteria:
1) ZM ≥ 4.0;
2) coverage of ≥50% of the library domain;
3) (BSM ≥ 0.3) or (BSM ≥ ZM-1/2 - 0.24) or (ZM ≥ 22.0).
For hits meeting only the first two criteria, the COMPASS E-value (CEM) is calculated for the two domains, with the alignment of the two profiles guided by the pairwise alignment made by MAMMOTH. Thus, additional accepted hits are identified that pass the following criteria: ZM ≥ 4.0, coverage of ≥ 50% of the library domain, and CEM ≤ 1.0. The cutoffs for accepted hits were determined based on the MAMMOTH Z-score (ZM), BLOSUM score (BSM), and COMPASS E-value (CEM) of 106,310 randomly chosen pairs of SCOP domains from SCOP v1.61. Approximately 1/3 of these pairs of domains belong to the same SCOP superfamily while the remaining 2/3 of the pairs belong to different SCOP superfamilies.
Method 6) DaliLite [44]: query structure against library structure comparisons
Additional structure comparisons are performed for queries with a segment of 20 residues or longer that did not correspond to an accepted MAMMOTH hit. Query-library domain pairs for which BSM ≥ -0.01*ZM - 0.03, ZM > 0, and the pairwise alignment made by MAMMOTH covered at least 40% of the library domain are identified. If more than 200 query-library domain pairs met these criteria, only the 200 query-library domain pairs with the highest ZM scores are selected. If no pairs meet these criteria, the 50 query-library domain pairs with the highest ZM scores are identified. The score cutoffs for selecting pairs for comparison via DaliLite were determined by evaluating the MAMMOTH Z-scores (ZM) and BLOSUM scores (BSM) for randomly chosen pairs of SCOP domains that pass the DaliLite score cutoffs (see below) but fail the MAMMOTH score cutoffs (see above). The threshold was chosen by determining the score cutoffs that would identify the most number of pairs passing the DaliLite cutoffs and the fewest pairs failing the DaliLite cutoffs, thereby maximizing the number of potential accepted hits while minimizing the overall computation time required. DaliLite structure comparison is performed for each of the selected query-library domain pairs, and the DaliLite Z-score (ZD) and the normalized BLOSUM score for the pairwise alignment made by DaliLite (BSD) are calculated. Hits are accepted if they meet one of the following sets of criteria:
1) ZD ≥ 4.0, BSD ≥ -0.01*ZD + 0.15, and coverage of ≥50% of the library domain;
2) BSD ≥ 0.3 and coverage of ≥50% of the library domain;
3) ZD ≥ 14.0 and coverage of ≥50% of the library domain.
The cutoffs for accepted hits were determined based on the DaliLite Z-score (ZD) and BLOSUM score (BSD) of 4000 randomly chosen pairs of SCOP domains from SCOP v1.61, where half of these pairs belong to the same superfamily and half of the pairs belong to different superfamilies.
Method 7) CSV: correlation of conservation patterns
Because homologous domains often have similar conservation patterns, the degree of correlation between the conservation patterns of two domains can be used for remote homolog detection. Distant homologs typically display drastically diminished overall sequence similarity. Thus, such cases of remote homology are more likely to be identified by conservation pattern analysis, which considers only the most conserved residues, rather than by typical sequence comparison methods, which are highly dependent on overall sequence similarity. Conservation scores for query-library domain pairs are calculated by two methods: using a conservation substitution matrix and using the COMPASS algorithm.
The query-library domain pairs selected for conservation pattern comparison are determined based on the results of the DaliLite pairwise comparisons in the previous method. The correlation of conservation patterns are calculated for all query-library domain pairs with ZD ≥ 4.0, or for the 20 pairs with highest DaliLite Z-score (ZD ≥ 2.0 required) if no pairs have DaliLite Z-score ≥ 4. Only pairs for which the library domain profile (constructed for the RPS-BLAST step and modified for the COMPASS step) contains 5 or more sequences are considered. The AL2CO algorithm [46] (window size 3) is used to calculate the entropy-based conservation index for each position in the query profile and in the library domain profile. DaliLite-aligned positions scoring in the top 25% of either profile are selected, henceforth referred to as the chosen positions.
Any two given positions from the profiles of the query and library domains can be compared to determine their similarity in terms of conservation patterns. The degree of correlation between those conservation patterns is referred to as the position-pair conservation score. For example, if both positions are highly conserved, the position-pair conservation score for that specific pair will be high. Conversely, if one position is highly conserved while the amino acid distribution in the other position is random, the position-pair conservation score will be low. In the first scoring system, position-pair conservation scores are determined based on the entropy-based conservation indices for the chosen positions with a conservation substitution matrix used as a scoring matrix. Then, the scoring matrix-based conservation score is calculated for the query-library domain pair by:
CSVcons,D = [Sn - Srand]/ [(S1+S2)/2 - Srand],
where Sn is the sum of position-pair conservation scores of the aligned query positions vs. library domain positions ("chosen positions" only, see above), S1 is the sum of position-pair conservation scores of the chosen query positions against themselves (query positions vs. query positions), S2 is the sum of position-pair conservation scores of the chosen library domain positions against themselves (library domain positions vs. library domain positions), and Srand is the sum of position-pair conservation scores of the chosen positions for all-against-all query positions vs. library domain positions normalized over length.
A COMPASS-based conservation score is also calculated for each query-library domain pair. In this scoring system, a COMPASS-based position-pair score, which describes the similarity between any two given positions, is determined based on the methodology introduced in the COMPASS method [43]. Then, the COMPASS-based conservation score for the query-library domain pair is calculated by:
CSVcompass,D = [CSn - CSrand]/ [(CS1+CS2)/2 - CSrand],
where CSn is the sum of COMPASS-based position-pair scores of the aligned query positions vs. library domain positions ("chosen positions" only, see above), CS1 is the sum of COMPASS-based position-pair scores of the chosen query positions against themselves (query positions vs. query positions), CS2 is the sum of COMPASS-based position-pair scores of the chosen library domain positions against themselves (library domain positions vs. library domain positions), and CSrand is the sum of COMPASS-based position-pair scores of the chosen positions for all-against-all query positions vs. library domain positions normalized over length.
Conservation score hits are accepted if they meet one of the following sets of criteria:
1) CSVcons,D ≥ 0.1 and ZD ≥ 5;
2) CSVcons,D ≥ 0.25 and ZD ≥ 2;
3) CSVcompass,D ≥ 0.4 and ZD ≥ 5;
4) CSVcompass,D ≥ 0.5 and ZD ≥ 2.
These cutoffs for accepting hits were determined based on the CSVcons,D scores, CSVcompass,D scores, and DaliLite Z-scores of 4000 randomly chosen pairs of SCOP domains from SCOP v1.61.
In cases for which the DaliLite program produces no output, conservation pattern analysis is performed using pairwise alignment produced by MAMMOTH instead of FSSP alignments. The conservation analysis is done for the query-library domain pairs that would have otherwise been submitted to the DaliLite algorithm for structural comparison (see above). Only those residue pairs in which the Cα atoms are located within 4Å, which are indicated by an asterisk (*) by the MAMMOTH algorithm, are considered. Again, a window size of 3 is used in the AL2CO program and only the top scoring 25% of positions are used for calculating the conservation scores. Matrix-based and COMPASS-based conservation scores are calculated as described above. Conservation score hits based on MAMMOTH alignments are accepted if they meet one of the following sets of criteria:
1) CSVcons,M ≥ 0.3 and ZM ≥ 4;
2) CSVcompass,M ≥ 0.4 and ZM ≥ 4
These cutoffs for accepting hits were determined based on the CSVcons,M scores, CSVcompass,M scores, and MAMMOTH Z-scores of 2000 randomly chosen pairs of SCOP domains from SCOP v1.61.
Method 8) agreement of DaliLite or MAMMOTH alignments with gapped BLAST, RPS-BLAST, or PSI-BLAST alignments
Remote evolutionary links between protein domains can be gleaned using a combination of sequence and structural information, even when neither of these methods alone is capable of providing convincing evidence for common descent. In this method, the degree of correlation between a pairwise alignment made by DaliLite and alignments made by the sequence comparison methods is determined so that DaliLite can be used to evaluate potential hits from BLAST, RPS-BLAST, or PSI-BLAST. For any query-library domain pair with ZD > 0 and BLAST, PSI-BLAST, or RPS-BLAST E-value ≤ 100, the number of correctly aligned residues (Nali) in the sequence alignment is calculated using the DaliLite alignment as a reference. Hits are accepted for which ZD > 0, E-value ≤ 100, and Nali ≥ 15. These cutoffs were determined based on the DaliLite Z-scores, E-values, and number of equivalently aligned residues from 1000 randomly chosen pairs of SCOP domains from SCOP v1.61. If an error occurs while running DaliLite for the query domain, agreement of the MAMMOTH alignment and BLAST, RPS-BLAST, or PSI-BLAST alignments is instead calculated for the same potential hits. In these cases, hits are accepted for which ZM > 2.0, E-value ≤ 100, and Nali ≥ 15. These cutoffs were determined based on the MAMMOTH Z-scores, E-values, and number of equivalently aligned residues from 1000 randomly chosen pairs of SCOP domains from SCOP v1.61.
Mapping step 2: assigning domains from query chains to SCOP superfamilies
Accepted hits from the sequence and structure comparison methods are mapped onto the query chain and domains within the chain are then assigned to SCOP superfamilies. In cases where accepted hits from multiple SCOP superfamilies mapped to the same region of the query chain, SCOPmap attempts to choose only one correct SCOP superfamily assignment. If the overlap between two different SCOP superfamily representatives covers <50% of both domains, the conflict is resolved by the domain boundary definition (see "Mapping Step 3" below). Otherwise, SCOPmap attempts to determine which SCOP superfamily among the accepted hits is most likely to be the correct assignment.
First, for each of two conflicting assignments, all accepted hits that overlap by at least 75% and are from the same SCOP superfamily are identified. For each set of accepted hits (one set corresponding to each of the conflicting SCOP superfamilies), the number of methods that identified accepted hits to that SCOP superfamily is determined. If one SCOP superfamily is found by more methods than the other SCOP superfamily, the assignment with hits from the greater number of methods is accepted as correct. If both SCOP superfamilies are identified by an equal number of methods, the priority of those methods is used to choose the correct SCOP superfamily. The methods are ranked by reliability, which was subjectively determined based primarily on the observed number of false positives accepted by a given method during SCOPmap development. Priority rankings are as follows: BLAST > RPS-BLAST or PSI-BLAST > MAMMOTH or DaliLite > COMPASS > conservation pattern correlation or agreement of DaliLite and sequence method alignments. If both SCOP superfamilies are found by methods with equivalent priorities, the Z-scores and E-values of the hits are evaluated. If only one of the two conflicting SCOP superfamilies has E-values from any sequence comparison method below 10-10 or Z-scores (ZM or ZD) above 14.0, that SCOP superfamily assignment is accepted as correct. If a SCOP superfamily assignment has still not been made, the domain assignments to that query chain are flagged as unresolved. Of the 4580 tweaking set domains (see Results), only 25 domains (0.5%) were unassigned due to unresolved choice between conflicting SCOP superfamilies. The results obtained by inverting the order of these two steps (e.g. first comparing E-values and Z-scores, and then considering priority rankings of the eight methods) were also evaluated. There were no cases where the inverted order gave additional correct assignments, and there was a small number of cases that could be resolved by the original strategy but not by the inverted strategy. Thus, the methodology described above is used for choosing between conflicting superfamily assignments.
Mapping step 3: defining boundaries of domain assignments
Domain boundary definitions are assigned by identifying the longest non-overlapping domain assignments, with priority given to assignments made by structure comparison methods. First, DaliLite is run for all query-library domain pairs found by MAMMOTH, and the DaliLite range is used in place of the MAMMOTH range unless there is an error in the DaliLite output. Then, ranges of accepted hits are given priority rankings based on which method determined the range of that hit. DaliLite ranges have highest priority, followed by MAMMOTH ranges, and then all sequence comparison method ranges. The longest non-overlapping segments with the highest priority rankings are then identified. A 3-residue cushion for overlap is allowed. Overlapping domains for which boundaries cannot be reconciled within 3 residues are flagged as unresolved. Of 4580 tweaking set domains, only 3 domains (0.1%) were unassigned due to unresolved domain boundary definition.
Assignments at the SCOP fold level
For query chains with a segment at least 20 residues in length which is not assigned to a SCOP superfamily, mapping at the SCOP fold level is attempted. In the SCOPmap algorithm, MAMMOTH is run comprehensively against the library of representative structures. Therefore, no additional comparisons must be made in order for fold level assignments to be determined. For this reason, MAMMOTH is used for fold level assignments rather than DaliLite, which is typically run against less than 5% of the library domains. The single criterion for potential SCOP fold assignment is a MAMMOTH Z-score > 10. Fold level assignments are made by selecting the hit to an unmapped region with the highest MAMMOTH Z-score (>10) that also covers at least 50% of the library domain. The fold level Z-score cutoff was determined based on the MAMMOTH Z-scores of 106,310 randomly chosen pairs of SCOP domains from SCOP v1.61. These same pairs of domains were used for determining the superfamily assignment cutoffs (see above). Approximately 2/3 of these pairs of domains belong to the same SCOP fold while the remaining 1/3 of the pairs belong to different SCOP folds.
Description of test sets
SCOPmap performance was evaluated on two separate test sets. The first set is comprised of the proteins that are included in SCOP v1.63 but not in SCOP v1.61. SCOPmap was run using a library based on the previous SCOP release (v1.61), and the SCOPmap domain assignments were compared to the SCOP-defined classification in subsequent SCOP release (v1.63). This set contains 5133 SCOP-defined protein domains, but analysis of SCOPmap performance is based only on the 4580 SCOP-defined domains with evolutionary relevance: 464 low resolution structure domains, 63 peptides, 21 designed proteins, and 5 domains that were later removed from the database are intentionally excluded. The first test set was used to establish whether the score cutoffs for the individual comparison tools used by SCOPmap were strict enough to avoid false positive assignments. After first running SCOPmap for this set of domains, a false positive rate of ~1.5% was observed. The score thresholds for some of the individual comparison tools were subsequently made more strict in order to avoid all false positive assignments in this set. For example, the E-value cutoff for PSI-BLAST was changed from 5 × 10-3 to 1 × 10-4, and the E-value cutoff for COMPASS was adjusted from 1 × 10-4 to 1 × 10-10. Because some of the domains in this set were considered while establishing the score thresholds, the first test set is more correctly described as a "tweaking" set rather than a testing set. This set was also used for comparison to SUPERFAMILY, for which the score threshold was also chosen specifically for the purpose of precluding false positive assignments. The recommended 0.02 E-value cutoff for SUPERFAMILY, which would allow for the correct assignment of only an additional ~1% of the tweaking set domains, was not chosen due to the 4.3% false positive rate it incurs. Instead, the E-value cutoff was set at 1 × 10-5, the maximum value for which no false positive assignments were observed. For this comparison, the SUPERFAMILY algorithm was used with the library of SAM [47] hidden Markov models based on SCOP v1.61.
The second set of domains used to evaluate SCOPmap performance contains proteins included in SCOP v1.65 but not in SCOP v1.63. The second test set can be considered a true testing set. The testing set contains 5335 SCOP-defined protein domains, but only the 4941 SCOP-defined domains with evolutionary relevance were used for analysis of SCOPmap performance. Low resolution structures, peptides, and designed proteins were ignored. The library of SCOP representative domains used for mapping the queries in this set is based on SCOP v1.63.
Using SCOPmap to identify homologs between SCOP superfamilies
SCOPmap can also be used to identify potentially homologous proteins that belong to different SCOP superfamilies. Detection of such homologs is accomplished with a slightly altered strategy from the mapping algorithm described above. The modified algorithm evaluates one SCOP superfamily at a time by attempting to detect potential hits to SCOP domains belonging to other superfamilies via the comparison methods described above. A set of query domains is constructed from the domains that are currently included in that SCOP superfamily (based on SCOP v1.63). As in the original mapping algorithm, the query sequences are first clustered at high sequence identity to reduce the computational time. Next, each of the 8 comparison methods described above is employed for each representative query. In the original mapping strategy, queries for which accepted hits are detected via gapped BLAST are not submitted to any of the other comparison methods. However, in this modified strategy, all comparison tools are run for all representative queries, regardless of the results of the gapped BLAST step. The output is a list of all accepted hits from each of the comparison methods to SCOP domains that do not belong to the query superfamily. All hits to SCOP domains within the query superfamily are simply ignored and excluded from the output. Finally, manual analysis of potential hits was performed for selected examples in order to evaluate the significance of those hits and to determine whether an evolutionary link is likely to exist between the two SCOP superfamilies in question.
Program availability
The SCOPmap script and instructions for library construction are available for download at . SCOPmap results for representative PDB structures that are not included in the SCOP database are available here as well.
Authors' contributions
SC developed the code, tested program performance, analyzed the results, and drafted the manuscript. YQ contributed to code development. SSK determined score thresholds for the individual comparison tools used. LNK proposed many additional suggestions for improving algorithm performance. NVG conceived of the study and participated in its design and coordination. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by NIH grant GM67165 to NVG. SC was supported by NIH training grant T32 GM08297 to the University of Texas Southwestern Graduate Program in Molecular Biophysics.
Figures and Tables
Figure 1 Fold level assignments.
Figure 2 Sequence identity between tweaking set domains and the closest library representative from the same SCOP superfamily.
Figure 3 Correctly mapped remote homolog: N-terminal domain of mannitol 2-dehydrogenase. a) Ribbon diagram of mannitol 2-dehydrogenase from Pseudomonas fluorescens (PDB: 1lj8 [19]). The N-terminal domain is shown in color. Regions in red are positions among the top 25% of most conserved positions in both the query (1lj8_A, N-terminal domain) and library representative (1i36_A, N-terminal domain). Regions in orange are positions among the top 25% of most conserved positions in either the query or the library representative domain, but not both. Positions in this domain that are not among the most highly conserved are blue (α-helices), yellow (β-strands), and green (coils). The C-terminal domain is shown in grey, and the bound nucleotide in shown in ball-and-stick format and is colored magenta. This and all other structure figures were prepared using MOLSCRIPT[48]. b) Ribbon diagram of conserved hypothetical protein MTH1747 from Methanobacterium thermoautotrophicum (PDB: 1i36). The N-terminal domain, shown in color, is a representative of the NAD(P)-binding Rossmann-fold superfamily. The colors have equivalent meaning to those in figure 3a. The C-terminal domain is shown in grey, and nucleotide in shown in ball-and-stick format and is colored magenta. Dashed lines indicate breaks in the chain. c) Pairwise alignment of the query (1lj8_A) and library representative (1i36_A) from DaliLite results. Residues in red bold text are among the top 25% of most conserved positions in at least one of the domains. Residues indicated with an asterisk are among the top 25% of most conserved positions in both the query and library domains. Secondary structure is indicated above the alignment, with E signifying β-strand residues and H signifying α-helix residues. In this and other alignments, the numbers flanking the alignment indicate the residue number in the sequence of the first (or last) aligned residue on that line. Numbers in brackets specify the number of residues in an insert that are not shown. In all alignments produced based on DaliLite results, capital letters are aligned residues and lower-case letters are unaligned residues.
Figure 4 Correctly mapped domain with conformational variation: cathelicidin motif of protegrin-3. a) Ribbon diagram of cathelicidin motif of protegrin-3 from Sus scofa (PDB: 1lxe[27]) in a swapped dimer conformation. One monomer in the complex is colored, and the second monomer is grey. Chain breaks are indicated by dashed lines. b) Ribbon diagram of cystatin from Gallus gallus (PDB: 1cew[28]), a library representative of the cystatin/monellin superfamily. c) Pairwise alignments of this query (1lxe_A) and library (1cew_I) domain produced by RPS-BLAST and DaliLite. Residues aligned equivalently by these two comparison tools are in red bold. The equivalently aligned regions are shown in red in the structure figures.
Figure 5 Correctly mapped domain with large insertion: monomeric isocitrate dehydrogenase. a) Ribbon diagram of monomeric isocitrate dehydrogenase from Azotobacter vinelandii (PDB: 1itw[29]). The insert region as defined by SCOP is shown in grey. b) Ribbon diagram of isopropylmalate dehydrogenase from Salmonella typhimurium (PDB: 1cnz[30]), a library representative of the isocitrate/isopropylmalate dehydrogenase superfamily. c) Range assignments as made by MAMMOTH, DaliLite, and SCOP. The regions assigned to the isocitrate/isopropylmalate dehydrogenase superfamily are red and insert regions are grey.
Figure 6 Examples of false negative domain assignments. a) Ribbon diagrams of unmapped domain (left) transcription factor Ndt80 (PDB: 1mnn[33]) and library representative (right) p52 subunit of NF-kappa B, N-terminal domain (PDB: 1a3q[49], residues A37-A226). β-strands that belong to the Ig fold core are yellow, and additional structural elements are grey. Dashed lines indicate breaks in the chain. b) Ribbon diagrams of unmapped domain (left) E. coli adaptor protein ClpS (PDB: 1lzw[34], chain A) and library representative (right) ribosomal protein L7/12 from E. coli, C-terminal domain (PDB: 1ctf[50]). Dashed lines indicate breaks in the chain. c) Cα traces of unmapped domain (left) δ-conotoxin TxVIA from Conus textile (PDB: 1fu3[35]) and library representative (right) ω-conotoxin TXVII from Conus textile (PDB: 1f3k[51]). These two conotoxin domains share ~40% sequence identity. Disulfide bonds are shown in ball-and-stick format.
Figure 7 Homologous SCOP superfamilies. a) Ribbon diagram of thiamin phosphate synthase from Bacillus subtilis (PDB: 1g69[52], chain B) from the thiamin phosphate synthase superfamily. b) Ribbon diagram of indole-3-glycerophosphate synthase from Thermotoga maritima (PDB: 1i4n[53], chain A) from the ribulose-phosphate binding barrel superfamily. c) Pairwise alignment of representatives of the thiamin phosphate synthase and the ribulose-phosphate binding barrel superfamily produced by PSI-BLAST. Residue pairs that are equivalently aligned by DaliLite are showed in red bold letters. The three phosphate-binding residues in conserved positions in these two proteins are highlighted in green in the alignment and shown in ball-and-stick format in the structure figures. d) Ribbon diagram of α subunit C-terminal domain from E. coli RNA polymerase (PDB: 1lb2[54], chain B) from the "C-terminal domain of RNA polymerase alpha subunit" superfamily. Regions of the domain involved in DNA binding are in red. e) Ribbon diagram of the N-terminal domain of Rad51 from Homo sapiens (PDB: 1b22[55], chain A) from the "DNA repair protein Rad51, N-terminal domain superfamily". Putative DNA-binding surface is in red.
Table 1 Results of the automatic mapping of PDB structures to SCOP superfamilies.
v1.61-v1.63 test set v1.63-v1.65 test set
Result SCOPmap SCOPmap, sequence comparison tools only SUPERFAMILY SCOPmap
# of domains % of test set (bold: correct assignments) # of domains % of test set (bold: correct assignments) # of domains % of test set (bold: correct assignments) # of domains % of test set (bold: correct assignments)
Assignment to correct SCOP superfamily, boundaries accurate within 10 residues 3730 81.4% 3507 76.6% 3211 70.1% 4136 83.7%
Assignment to correct SCOP superfamily, boundaries not accurate within 10 residues 292 6.4% 211 4.6% 662 14.4% 372 7.5%
Domain belongs to a new SCOP superfamily, no assignment made 284 6.2% 289 6.3% 241 5.3% 154 3.1%
Acceptable assignment, but not the same assignment as given in SCOP 13 0.3% 0 0% 71 1.5% 12 0.2%
Incorrect assignment 0 0% 0 0% 0 0% 7 0.2%
Domain belongs to an existing SCOP superfamily, no assignment made 261 5.7% 573 12.5% 395 8.6% 260 5.3%
Table 2 Results for the 1417 non-trivial assignments.
SCOPmap SUPERFAMILY
Result # of domains % of test set (bold: correct assignments) # of domains % of test set (bold: correct assignments)
Assignment to correct SCOP superfamily, boundaries within 10 residues 607 42.8% 425 30.0%
Assignment to correct SCOP superfamily, boundaries not within 10 residues 252 17.8% 379 26.7%
Domain belongs to a new SCOP superfamily, no assignment made 284 20.0% 241 17.0%
Acceptable assignment, but not the same assignment as given in SCOP 13 0.9% 48 3.4%
Domain belongs to an existing SCOP superfamily, no assignment made 261 18.4% 324 22.9%
Table 3 Tweaking set domain assignments by increasingly sensitive comparison tools.
Comparison Method Number of Domains First Identified By This Method [4035 mapped domains plus 50 domains that are identified but not assigned (see Table 4)] Average Sequence Identity Between Query and Closest Superfamily Representative % of Domains Unmapped by Less Sensitive Methods that are Identified by This Method
BLAST 3163 80.1% 69.1%
RPS-BLAST 514 41.1% 36.3%
PSI-BLAST 104 26.1% 11.5%
COMPASS 26 27.2% 3.3%
MAMMOTH 100 29.7% 12.9%
DaliLite 124 17.4% 18.4%
correlation of conservation patterns 23 11.1% 4.2%
agreement of alignments produced by DaliLite and by gapped BLAST, RPS-BLAST, or PSI-BLAST 31 12.1% 5.9%
Table 4 SCOPmap: Automated assignment of protein structures to evolutionary superfamlies.
Whether Domain is Identified by at Least One Comparison Method Reason Domain is Unmapped Number of Domains % of Unassigned Domains
The domain is identified by one or more methods, but is not assigned. The boundary assigned to one domain in the query chain is extended too far and, as a result, a second domain assignment is missed. 22 8.5%
19.2%
Unresolved choice between conflicting superfamilies. 28 10.7%
Domain is not identified by any comparison tool used in SCOPmap. DaliLite hits to superfamily representatives fail "accepted hit" cutoffs. 108 41.4%
80.8%
At least one superfamily representative identified as potential hit via MAMMOTH, but DaliLite produces no output for the comparison. 16 6.1%
No superfamily representatives have MAMMOTH scores high enough to be identified as potential hits via DaliLite. 87 33.3%
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| 15598351 | PMC544345 | CC BY | 2021-01-04 16:02:41 | no | BMC Bioinformatics. 2004 Dec 14; 5:197 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-197 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-2081561932810.1186/1471-2105-5-208DatabaseA database for G proteins and their interaction with GPCRs Elefsinioti Antigoni L [email protected] Pantelis G [email protected] Ioannis C [email protected] Stavros J [email protected] Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece2004 24 12 2004 5 208 208 30 7 2004 24 12 2004 Copyright © 2004 Elefsinioti et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
G protein-coupled receptors (GPCRs) transduce signals from extracellular space into the cell, through their interaction with G proteins, which act as switches forming hetero-trimers composed of different subunits (α,β,γ). The α subunit of the G protein is responsible for the recognition of a given GPCR. Whereas specialised resources for GPCRs, and other groups of receptors, are already available, currently, there is no publicly available database focusing on G Proteins and containing information about their coupling specificity with their respective receptors.
Description
gpDB is a publicly accessible G proteins/GPCRs relational database. Including species homologs, the database contains detailed information for 418 G protein monomers (272 Gα, 87 Gβ and 59 Gγ) and 2782 GPCRs sequences belonging to families with known coupling to G proteins. The GPCRs and the G proteins are classified according to a hierarchy of different classes, families and sub-families, based on extensive literature searchs. The main innovation besides the classification of both G proteins and GPCRs is the relational model of the database, describing the known coupling specificity of the GPCRs to their respective α subunit of G proteins, a unique feature not available in any other database. There is full sequence information with cross-references to publicly available databases, references to the literature concerning the coupling specificity and the dimerization of GPCRs and the user may submit advanced queries for text search. Furthermore, we provide a pattern search tool, an interface for running BLAST against the database and interconnectivity with PRED-TMR, PRED-GPCR and TMRPres2D.
Conclusions
The database will be very useful, for both experimentalists and bioinformaticians, for the study of G protein/GPCR interactions and for future development of predictive algorithms. It is available for academics, via a web browser at the URL:
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Background
G protein-coupled receptors (GPCRs), form one of the major groups of receptors in eukaryotes; they possess seven transmembrane α-helical domains, as confirmed by analysis of the crystal structure of Rhodopsin [1]. The study of GPCRs, and the way that they are activated by their ligands, is of great importance in current research aiming at the design of new drugs [2,3]. The importance of GPCRs in pharmaceutical industry, is reflected in the fact, that an estimated 50% of current prescription drugs target GPCRs [4-6]. Characteristically, the human genome, possesses approximately 700–800 GPCRs [7]. Understanding and studying the molecular mechanisms, through which the GPCRs transduce their signal into the cell, could also be an issue of great importance. There is a strong and accumulated body of evidence indicating that many GPCRs, form hetero-, or homo-dimers in order to transduce their signal [8].
Agonist binding to GPCRs leads to association of the hetero-trimeric G protein with the receptor, GDP-GTP exchange in the G protein α subunit followed by dissociation of the G protein into α-GTP and βγ complexes. The dissociated subunits can activate or inhibit several effector proteins such as adenylyl cyclase 1–9, PLCβ 1–4, tyrosine kinases, phosphodiesterases, phosphoinositide 3-kinase, GPCR kinases, ion channels, and molecules of the mitogen-activated protein kinase pathway, resulting in a variety of cellular functions [9]. However, there is evidence that some GPCRs transduce their signal through in a way that is not G protein-dependent [10], and also that hetero-trimeric G proteins are involved in mediating the action of some single-spanning membrane receptors [11]. Furthermore, some GPCRs have been shown to transduce signals into cells by coupling to small G proteins such as ADP ribosylation factor (Arf) and the dimeric Gh protein [10]. However, in the rest of this paper we will use the term G proteins to refer to hetero-trimeric G proteins, in order to avoid confusion, concerning the subunit composition of the trimers.
As mentioned above, G proteins, form hetero-trimers composed of Gα, Gβ and Gγ subunits. G protein α subunits, possess an intrinsic GTPase activity, which enables them to act as time switches: Hydrolysis of the bound GTP to GDP promotes the re-association of the α subunit with the βγ dimer and renders the G protein in an inactive form [12-14]. G protein trimers, are named after their α-subunits, which on the basis of their amino acid similarity and function are grouped mainly into four families [15]. These include, Gαs and Gαi/o, which stimulate and inhibit respectively an adenylate cyclase [16,17], Gαq/11 which stimulates a phospholipase C [18], and the less characterized Gα12/13 family that activates the Na+/H+ exchanger pathway [19]. At least 16 discrete subtypes of α subunits have been identified and classified into the above-mentioned families [20]. GPCRs, interact specifically with the α subunits of the G proteins, through their intracellular domains, however the same G protein may be activated by several receptors and the same receptor may couple to different G proteins, under different circumstances [15]. It is interesting to note, that not the whole intracellular loops of GPCRs, but rather the cytoplasmic extensions of the transmembrane helices, are directly involved in the interaction between G protein and GPCRs, as reported in studies involving site-directed mutagenesis and chimeric receptors [10,15]. This is confirmed in part, by a computational study, aiming at finding specific regular expression patterns that discriminate GPCRs with different coupling specificity [21].
Today, there exist general-purpose databases gathering information for receptors [22], and others, more specialised, focusing on GPCRs [23], and receptors of other types i.e. tyrosine kinase receptors [24], or ligand gated anion channels [25], but not a database focusing on the coupling specificity of the G proteins to their respective receptors. We have constructed a database, gpDB, built on a sophisticated relational scheme focusing on the coupling specificity of the α subunits of G proteins to their respective receptors. Such a database will be a complement to the already existing databases, and will be a useful tool for the study of the coupling specificity and the interaction of G proteins with GPCRs. Furthermore, the data collected in the database will be useful in the design of algorithms predicting the coupling specificity, and may provide useful insight towards understanding several aspects of protein-protein interactions.
Construction and content
Datasets
In order to construct the database, initial sequence information was retrieved from the publicly available databases: PIR [26], SWISS-PROT and TrEMBL [27]. In particular, a total of 418 entries for G proteins were retrieved, while, at the same time, we also retrieved 2782 GPCRs sequences with known coupling preference from SWISS-PROT/TrEMBL. The entries were obtained using suitable scripts written in Perl, in order to parse the DE (description) or the TITLE field in a SWISS-PROT or a PIR entry respectively. The datasets were then checked in order to eliminate duplicates. GPCRs sequences were obtained by using the keyword "G protein coupled receptor" and excluding those that were present in viruses. After the completion of the Uniprot database [28], all entries were checked again, and now we provide links solely to Uniprot (see below). Additional sequences that were not identified with the above-mentioned procedure were obtained manually after literature search. We used user-written Perl scripts to manipulate the data, whereas the annotations regarding: G Protein coupling specificity and effectors, GPCR dimerization and accessory proteins, and the corresponding references were appended manually in a spreadsheet. Regarding the GPCR/G protein interaction, the data was collected after an exhaustive and detailed literature search, mainly, following the classification of TiPS [29], and also [15] and references therein. At this point, we may emphasise, that the database does not report the potential coupling preference of a G protein to a GPCR, but only the naturally occurring coupling specificity. For instance, opsins that normally couple to Gαt (transducin) are expected to be able to functionally couple also to other members of the Gαi/o family. Since these Gα proteins, are not expressed in the same tissues as photoreceptors do, such a coupling is not reported. However, since there are also a lot of GPCRs, showing promiscuous coupling preference in heterologous expression systems [30,31], we could not fully discriminate cases of falsely reported coupling. This could be done, perhaps in a later version, when accumulated evidence of tissue expression patterns of GPCRs could be appended to the database. For G Protein/GPCRs coupling specificity, we provide links to PUBMED corresponding to original articles reporting the coupling preference observed in heterologous expression systems. We also provide links to published original articles, providing information about the dimerization status of a GPCR, and similar links for G Protein effectors and GPCRs accessory proteins.
Implementation
The data has been organized on the basis of a relational model and is stored in a PostgreSQL database system. The user has supervisory access through our Apache web-server. The database is managed by interferential software, written in Java, which tends to settle any web-server's query. The main innovation of the database, resides on its relational scheme (Figure 1). It is well known, that the coupling specificity of G proteins to GPCRs, is not a one-to-one function. Thus, a particular GPCR, may couple to more than one G protein (promiscuous coupling), and vice-versa, one single G protein may couple to several GPCRs of the same organism, which is usually the case, considering the large number of different GPCRs and the much fewer types of G proteins. We have to mention here, that biologically functional complexes, involve trimers of G Proteins [10,20] and in many cases dimers of GPCRs [8,32], whereas there is also a variety of other molecules that could potentially interact with them, such as accessory proteins, scaffolds, and effectors [10]. Also, there is evidence, that there also exist single-spanning membrane receptors, whose actions are mediated by G Proteins [11]. However, even though we provide information on these interactions (where available), we did not attempt to organize the database in such a more complicated scheme, for several reasons. Firstly, there is not reported information in the literature for the majority of biological active G Protein heterotrimers, and the role that might play the trimer's different composition regarding subunits Gβ and Gγ. Secondly, even though there is a lot of evidence supporting the idea that GPCRs act as homo-, or hetero-dimers (evidence that the database is pointing to) [8,32,33], we could not provide a general scheme involving dimeric GPCRs activation, until more evidence will emerge, without the risk to fall in inaccuracies for the majority of receptors. Such features could be available in later versions of the database.
Entry description – detailed view of an entry
Each database entry contains the following fields: gpDB name, gpDB id, UniProt accession number, Protein description and classification, sequence, species, organism common name, taxonomy, links to other databases (such as PDB, InterPro, Prints, Prosite, Pfam, GPCRDB, MIM or Smart) and coupling preference (if existent). Information on coupling preference is accompanied by links to PUBMED, corresponding to original articles reporting the interaction. There is also a field showing the reported effector molecules on which G Proteins act, and GPCRs accessory proteins, also accompanied by links to original articles. As we already noted, G proteins are classified into three classes (Gα, Gβ, Gγ). Gα class is further subdivided into four families (Gi/o, Gq/11, Gs, G12/13) and each family is subdivided into different subfamilies and types. This classification is mainly based on proteins present in vertebrates and in the vast majority of invertebrates, while some invertebrates (C. elegans) and all plants and fungi do not have such a detailed classification. Gβ and Gγ are subdivided into 6 and 13 different types, respectively. GPCRs are usually classified into several classes, according to the sequence similarity shared by the members of each class. Here, we have to mention that in this classification scheme, the classes are usually termed families, but we chose as before [34,35] to reserve the term family for a lower level of classification. Class A of GPCRs (rhodopsin-like GPCRs) contains the majority of GPCRs, including receptors for structurally diverse ligands (biogenic amines, nucleotides, peptides, glycoprotein hormones etc). Class B (secretin-like GPCRs) contains purely peptide receptors, whereas class C (metabotropic glutamate family receptors) contains metabotropic glutamate and GABA-B receptors and some taste receptors. Class D contains the fungal pheromone receptors, class E contains the cAMP receptors of Dictyostelium and last is the Frizzled/Smoothened class. There are also a number of putative classes of newly discovered GPCRs, whose nomenclature has not been accepted yet from the scientific community. Further details for this higher level of classification can be found in [10,23,36] and in the references therein.
We further classified GPCRs into 64 different families and each family is further subdivided into different subfamilies, based mainly on TIPS classification scheme that takes into account the native ligand(s) that binds to a particular GPCR. Currently, information on coupling specificity is available only for GPCRs, belonging to the classes A, B, C, D, E and Frizzled/Smoothened, thus only GPCRs belonging to these classes are deposited in the database. A sample entry of the database is shown in Figure 2.
Utility
The application possesses a user-friendly environment, through which, the user may retrieve the necessary information, find available resources and cross-references and perform additional tasks such as running predictive algorithms, performing alignments, etc. In the main page of gpDB the user may find links for the following tools: Navigation, Text Search, BLAST Search, Pattern Search. There is also an extensive user's manual page, describing in detail the available tools (Figure 3). In summary, the available tools are summarised and described below.
Navigation tool
Through the navigation tool, the user has the ability to browse the database following the hierarchy (Figure 1). The navigation can be performed on either the GPCR or the G PROTEIN hierarchy. Following the link of GPCRs, the user may be navigated through: GPCR CLASSES, GPCR FAMILIES, GPCR SUB-FAMILIES and individual RECEPTORS.
Alternatively, following the link of G PROTEINS, the user may browse through: G PROTEIN CLASSES, G PROTEIN FAMILIES, G PROTEIN SUB-FAMILIES, G PROTEIN TYPES and finally to individual G proteins. At each point, the user may navigate up or down the hierarchy tree. Finally, the user may obtain a detailed view of a particular GPCR or G protein (See Entry description).
Text search tool
In the Text Search area, the user can search for any text in the fields of his/her preference. The user can enter any word in one or more of the available boxes under the name: 'Protein Name', 'Species', 'Description', 'Gene Name' and 'Cross-References'. Advanced queries can be performed using parentheses, and logical operators such as AND, OR, NOT, AND NOT as described in the documentation. Expressions in separate search fields are combined with the AND operator, so every entry of the result set will satisfy the expressions of all the search fields the user has chosen. The user has the option to choose whether the query will be performed against the GPCRs or the G proteins included in the database.
BLAST tool
With the BLAST search tool [37], the user may submit a sequence and search the database for finding homologues. The user has the option to choose whether to perform the BLAST search against GPCRs sequences or G proteins sequences or both. The output of the BLAST query consists of a list of sequences in the database having significant E-values in a local pairwise alignment, ranked by statistical significance. Selecting a particular hit, the user may visualize the local alignment, and from there, may retrieve the detailed view of the entry corresponding to the particular target sequence.
Pattern search tool
Using the Pattern Search tool (a home made tool), the user may perform searches for finding specific patterns in protein sequences of the database. The user, once again, has the option to choose whether to perform the Pattern search against the GPCR sequences or the G proteins sequences. The input of the Pattern Search tool could be either a standard regular expression pattern, or a pattern following the PROSITE [38] syntax. For example, the regular expression pattern:
DRY. [AGS].{3, 6}A
taken from the work of Moller and co-workers [21], that was shown to occur more frequently to the 2nd intracellular loop of the Gi/o coupled GPCRs, has the simple interpretation, that we must have the consecutive residues Aspartate, Arginine Tyrosine (DRY), followed by any single residue(.), followed by one only of the following residues Alanine (A), Glycine (G) or Serine (S), followed by 3 to 6 residues of any type, ending up to an Alanine (A). We have collected, the 40 most discriminative patterns for each one of the three classes of coupling specificity, reported in [21] (found at ), and the user has the option, to use them in order to perform searches against the database.
The output of the Pattern search application consists of a list of the sequences matching the particular pattern. Following the appropriate links the user may retrieve the detailed view of the target sequence(s).
Other tools
Furthermore, from the detailed view of an entry the user has the option to perform some additional tasks. These include, running PRED-TMR [39], PRED-GPCR [34,35] and TMRPres2D [40]. The aforementioned tools, will be extremely useful when it comes to GPCR sequences, for which the user may obtain predictions regarding the transmembrane segments, the family classification and the visual representation, respectively.
Discussion
The database that we present here has some innovative and unique features not available in any other publicly accessible resource. The relational scheme, on which the database is organised, is especially designed to capture the coupling preferences of G proteins to GPCRs according to the reported data in the scientific literature. General sequence databases, such as Uniprot, do not include fields showing the coupling preference of GPCRs, but rather contain such information (if they do) in the free-text field of FUNCTION. Other specialised databases already exist focusing only in specific groups of receptors. For example GPCRDB [23], is the main publicly available resource for the classification of GPCRs. Other such approaches are the RTKdb [24], focusing on information of tyrosine protein kinase receptors and the Ligand-Gated Ion Channel Database, focusing on the Ligand-Gated Ion Channel receptors [25]. The database presented here however, not only combines information for both G proteins and GPCRs, but also includes information regarding their coupling specificity, the known effector molecules on which G proteins act, the accessory proteins interacting with GPCRs and information about the dimerization of GPCRs, all accompanied by links to original research articles from which the information was derived, features that are not available in any other publicly accessible resource. We have to note here, that gpDB does not aim at being a universal resource for GPCRs. A simple comparison with GPCRDB will show that the number of sequences included there, is at least two-three times larger than the sequences deposited in gpDB. This discrepancy arises from the fact that we do not report GPCRs, belonging to families, of which not a single member possesses a known coupling preference to G proteins. Thus, this database will be acting complementary to the existing databases regarding GPCRs, and interaction such as cross-referencing, will be useful.
The database provides a starting point for the development of algorithms predicting the coupling specificity of GPCRs to G proteins, an issue addressed already in the past by some teams [21,41], but with moderate success. This database consists of a larger, and well-organised dataset, on which we may build and test more effectively, such predictive algorithms. The database will be updated on a regular (yearly) basis, as new information emerges from genome sequencing projects, and verified experimentally. Also we plan to enrich the database in various ways, for instance developing methods for predicting the coupling specificity, and visualising, if possible, the potential interaction. Other possible additions, would be the the update of the relational scheme of the database in order to allow for dimeric receptors, for which information is already available and described in the database, or for hetero-trimeric G proteins, in case where information on specific subunit composition emerges. Furthermore, sequence information on the G proteins' effectors and the GPCRs' accessory proteins, could be combined in order to develop a fully automated computational resource for the study of protein-protein interactions in the cell membrane, that could describe the signal transduction to the interior of the cell.
The information, which the database comprises of, is essentially information regarding protein-protein interactions, which in turn, may be utilised in various ways. Currently, the most informative publicly available general purpose resource, concerning protein-protein interaction data, is the Database of Interacting Proteins [42]. Protein-protein interaction data arising either from databases, or from predictive algorithms may provide useful insight on the study of protein-protein interactions [43], but also may enable better functional annotation of proteins in the genomic context [44]. In particular, this kind of information may help in the construction of protein interaction networks, applied in genomic context [45].
Conclusions
We present here a relational database, gpDB, summarizing the existing publicly available information regarding G proteins and their interactions with GPCRs. This database fills a gap in the already available resources, regarding GPCRs, and maintains an excellent functionality and interconnectivity with the publicly available databases and web-tools. The database is unique, since no other such database already exists, and will be useful for both molecular biologists conducting experiments, but also for bioinformaticians that manage large amount of data, building algorithms and performing functional classification of proteins in the genomic context.
Availability and requirements
The gpDB is freely available for academic users at . Non-academics should contact Prof S. J. Hamodrakas at [email protected] to obtain a license. All comments, suggestions, corrections and additions, should be sent to [email protected].
Authors' contributions
ALE carried out the data collection and annotation, classified the G proteins and drafted the manuscript, PGB designed the relational scheme of the database, classified GPCRs and drafted the manuscript, ICS implemented the SQL database and the web-server pages and SJH coordinated and supervised the whole project. All authors read and approved the final manuscript.
Acknowledgements
PGB was supported by a grant from the IRAKLEITOS fellowships program of the Greek Ministry of National Education, supporting basic research in the National and Kapodistrian University of Athens. The authors would like to thank the anonymous referees for valuable comments and suggestions and constructive criticism, and Theodore D. Liakopoulos for his valuable help and suggestions during the implementation of the database.
Figures and Tables
Figure 1 The Relational Scheme of the database Single-edged arrows indicate a "one-to-one" relation, whereas double-edged arrows indicate "one-to-many" relation. When there are double-edged arrows in both ends of a connection (for instance the Receptor subfamily – G protein type, interaction), this is an indication of relation of the type "many-to-many", i.e. a particular G protein may couple to receptors of different subfamilies, whereas a single receptor may couple to G proteins of different types.
Figure 2 A sample entry Detailed view of an entry of the database. The user may observe, the classification of the protein, the available cross-references, the sequence, etc. At the bottom of the page, there is information concerning the coupling specificity of the particular protein; following any of the links, the user will be re-directed to the respective detailed view of the corresponding entry.
Figure 3 The Home page of the database A snapshot of the database's homepage. The main menu is shown with the available links for the tools, and the user's manual.
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| 15619328 | PMC544346 | CC BY | 2021-01-04 16:02:46 | no | BMC Bioinformatics. 2004 Dec 24; 5:208 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-208 | oa_comm |
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-5-21562906310.1186/1471-2148-5-2Methodology ArticleRooting a phylogenetic tree with nonreversible substitution models Yap Von Bing [email protected] Terry [email protected] Mathematics Department, University of California, 970 Evans Hall, Berkeley, CA 94720, USA2 Statistics Department, University of California, 367 Evans Hall, Berkeley, CA 94720, USA3 Division of Genetics and Bioinformatics, The Walter & Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Vic 3052, Australia2005 4 1 2005 5 2 2 5 8 2004 4 1 2005 Copyright © 2005 Yap and Speed; licensee BioMed Central Ltd.2005Yap and Speed; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
We compared two methods of rooting a phylogenetic tree: the stationary and the nonstationary substitution processes. These methods do not require an outgroup.
Methods
Given a multiple alignment and an unrooted tree, the maximum likelihood estimates of branch lengths and substitution parameters for each associated rooted tree are found; rooted trees are compared using their likelihood values. Site variation in substitution rates is handled by assigning sites into several classes before the analysis.
Results
In three test datasets where the trees are small and the roots are assumed known, the nonstationary process gets the correct estimate significantly more often, and fits data much better, than the stationary process. Both processes give biologically plausible root placements in a set of nine primate mitochondrial DNA sequences.
Conclusions
The nonstationary process is simple to use and is much better than the stationary process at inferring the root. It could be useful for situations where an outgroup is unavailable.
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Background
Several approaches for inferring a phylogenetic tree from the substitution patterns in multiply aligned sequences are available; they include maximum parsimony, distance-based, maximum likelihood and Bayesian methods [1]. Typically, the inferred tree is unrooted, because the explicit or implicit substitution process used is usually time-reversible. An effective way to put the root on the unrooted tree is to perform a phylogenetic analysis on the sequences of interest together with an outgroup, which is a set of distantly related sequences [2,3]. If the ingroup is monophyletic in the combined phylogenetic tree, then the point where the outgroup touches the ingroup tree is the estimated root. The practical challenge is to find suitable outgroups, and if no such outgroup is available, then one is forced to root the tree using just the ingroup. Several such methods include the molecular clock and nonreversible substitution processes. It seems clear that compared to the outgroup method, the success of these methods is more dependent on the extent to which the accompanying assumptions about the substitution process are satisfied in the data. For example, the molecular clock method should work well if the lineages indeed evolved more or less at the same rate. Likewise, as shown by Huelsenbeck et al. [4], a nonreversible process is more likely to succeed the less reversible the real substitution process is.
The nonreversible substitution process, introduced by Yang [5], is stationary, i.e., the sequence composition is unchanged in time, and is equal to the equilibrium distribution of the rate matrix Q. The consensus is that it does not have enough power to discriminate among the candidate rooted trees. In this paper, we investigate a slightly more general, nonstationary process: in which the initial distribution π may not be the equilibrium distribution of the rate matrix Q. A priori, giving up stationarity is expected to produce a much better fit to data, since sequence composition is known to evolve, and should be accounted for. Indeed, substitution models where each branch has its own rate matrices had been used to resolve deep splittings in certain phylogenetic trees; see Yang and Roberts, and Galtier and Gouy [6,7]. Our process, which to our knowledge has not been investigated in this context, may be viewed as the simplest case of such nonstationary processes, with many fewer parameters. Thus, it can be used to decide whether the substitution processes on certain branches should be modeled differently. The input to our procedure is a multiple alignment and the topology of an unrooted binary tree. For each rooted tree associated with the given unrooted tree, we seek the maximum likelihood (ML) estimates of the branch lengths, π and Q. The rooted trees are then ranked in descending order of likelihoods. We model systematic variation in substitution rates among sites by assigning sites into several classes, and the relative rate for each class is estimated by ML; this is equivalent to the combined analysis framework of Yang [8].
We compared the ability of the stationary and nonstationary processes to place the root in three groups of species where the answer is considered well-known: (1) human, chimpanzee and gorilla, (2) human, chimpanzee, gorilla and orangutan, (3) human, mouse, chicken and frog (xenopus laevis). The analyses were based on all available mitochondrial protein-coding genes, as well as two nuclear protein-coding genes. Next, we applied the methods to a set of primate mitochondrial DNA sequences.
Results
Verification studies
We fitted the nonstationary (NONSTA), stationary (STA) and reversible (REV) substitution models to all available mitochondrial protein-coding genes, as well as the nuclear genes albumin and c-myc, for three groups of organisms: (1) human, chimpanzee and gorilla, (2) human, chimpanzee, gorilla and orangutan, and (3) human, mouse, chicken and frog (xenopus laevis). The sequences were downloaded from Genbank and aligned using the CLUSTALW alignment of the amino acid sequences. Most alignments looked quite solid [see Additional files]. The beginning of the alignments for the genes COX1, CYTB, ND1 and ND6 were slightly adjusted. The root positions are assumed to be on the (1) gorilla, (2) orangutan, and (3) frog branch, respectively. The branches on a tree are referred to by the organism names, except for the case of four taxa, where there is an internal branch (Figure 1). For groups (2) and (3), it was assumed that human was most closely related to chimpanzee and mouse respectively; thus the unrooted tree is determined.
In group 1, the NONSTA and STA processes correctly placed the root in 8 and 6 genes respectively, out of 13 genes (Table 1). In group 2, NONSTA correctly placed the root in 9 genes out of 13 genes, compared to 2 genes for STA (Table 2). In group 3, NONSTA correctly placed the root in 11 genes out of 15 genes, compared to 7 genes for STA (Table 3). Furthermore, NONSTA gives stronger signal, or has better discriminative power: the highest-scoring rooted tree often has noticeably higher log likelihoods than competing rooted trees; this is not so with STA. Thus, NONSTA is much better than STA in placing the root at the individual gene level. Combining the log likelihoods across genes yields overall evidence for the root placements. Table 4 shows that NONSTA is unambiguously correct in all three analyses, while STA only gets the root correctly in group 3, and the signal is weak.
Figure 1 Unrooted tree with four taxa The four branches adjacent to leaf nodes will be referred to by the corresponding taxon names.
Table 1 Human, chimpanzee and gorilla Log-likelihoods (rounded to closest integer) of the MLEs for three rooted trees under the nonstationary (NONSTA), stationary (STA) and reversible (REV) models. If NONSTA or STA places the root correctly, the corresponding log likelihood appears in bold.
gene root placement NONSTA STA REV
human -1320 -1324 -1324
ATP6 chimp -1318 -1323 -1324
gorilla -1318 -1322 -1324
human -384 -389 -392
ATP8 chimp -384 -389 -392
gorilla -384 -389 -392
human -2842 -2876 -2877
COX1 chimp -2846 -2874 -2876
gorilla -2834 -2875 -2876
human -1285 -1293 -1295
COX2 chimp -1286 -1294 -1295
gorilla -1281 -1292 -1295
human -1477 -1493 -1496
COX3 chimp -1476 -1493 -1496
gorilla -1472 -1493 -1496
human -2205 -2236 -2236
CYTB chimp -2208 -2235 -2236
gorilla -2203 -2235 -2236
human -1787 -1804 -1805
ND1 chimp -1783 -1804 -1805
gorilla -1776 -1802 -1805
human -1949 -1974 -1975
ND2 chimp -1950 -1974 -1975
gorilla -1941 -1974 -1975
human -663 -679 -680
ND3 chimp -666 -679 -680
gorilla -665 -679 -680
human -2593 -2612 -2613
ND4 chimp -2589 -2612 -2613
gorilla -2579 -2612 -2613
human -519 -525 -525.8
ND4L chimp -523 -525 -525.8
gorilla -520 -526 -525.8
human -3600 -3624 -3629
ND5 chimp -3611 -3628 -3629
gorilla -3583 -3628 -3629
human -913 -917 -918
ND6 chimp -912 -917 -918
gorilla -913 -917 -918
Table 2 Human, chimpanzee, gorilla and orangutan Log-likelihoods (rounded to closest integer) of the MLEs for five rooted trees under the nonstationary (NONSTA), stationary (STA) and reversible (REV) models. If NONSTA or STA places the root correctly, the corresponding log likelihood appears in bold.
gene root placement NONSTA STA REV
human -1649 -1654 -1655
chimp -1647 -1654 -1655
ATP6 gorilla -1647 -1654 -1655
orangutan -1642 -1654 -1655
interior -1647 -1654 -1655
human -510 -514 -517
chimp -510 -515 -517
ATP8 gorilla -509 -515 -517
orangutan -509 -515 -517
interior -509 -515 -517
human -3456 -3465 -3467
chimp -3450 -3464 -3467
COX1 gorilla -3448 -3465 -3467
orangutan -3437 -3465 -3467
interior -3453 -3465 -3467
human -1485 -1496 -1497
chimp -1485 -1496 -1497
COX2 gorilla -1481 -1492 -1497
orangutan -1479 -1492 -1497
interior -1480 -1492 -1497
human -1769 -1791 -1796
chimp -1780 -1791 -1796
COX3 gorilla -1781 -1794 -1796
orangutan -1772 -1794 -1796
interior -1780 -1791 -1796
human -2593 -2673 -2674
chimp -2594 -2673 -2674
CYTB gorilla -2590 -2672 -2674
orangutan -2581 -2672 -2674
interior -2588 -2672 -2674
human -2214 -2234 -2236
chimp -2210 -2235 -2236
ND1 gorilla -2205 -2234 -2236
orangutan -2191 -2233 -2236
interior -2209 -2235 -2236
human -2441 -2469 -2470
chimp -2443 -2469 -2470
ND2 gorilla -2437 -2469 -2470
orangutan -2423 -2469 -2470
interior -2437 -2469 -2470
human -837 -855 -856
chimp -840 -855 -856
ND3 gorilla -838 -856 -856
orangutan -834 -855 -856
interior -838 -855 -856
human -3151 -3206 -3209
chimp -3149 -3205 -3209
ND4 gorilla -3141 -3205 -3209
orangutan -3169 -3207 -3209
interior -3145 -3206 -3209
human -623 -631 -631
chimp -622 -631 -631
ND4L gorilla -620 -631 -631
orangutan -619 -631 -631
interior -621 -631 -631
human -4469 -4501 -4503
chimp -4474 -4502 -4503
ND5 gorilla -4453 -4502 -4503
orangutan -4448 -4503 -4503
interior -4466 -4502 -4503
human -1069 -1076 -1078
chimp -1067 -1076 -1078
ND6 gorilla -1070 -1077 -1078
orangutan -1068 -1077 -1078
interior -1069 -1076 -1078
Table 3 Human, mouse, chicken and frog Log-likelihoods (rounded to closest integer) of the MLEs for five rooted trees under the nonstationary (NONSTA), stationary (STA) and reversible (REV) models. If NONSTA or STA places the root correctly, the corresponding log likelihood appears in bold.
gene root placement NONSTA STA REV
human -7722 -7728 -7731
mouse -7708 -7728 -7731
Albumin chicken -7723 -7731 -7731
frog -7705 -7728 -7731
interior -7723 -7728 -7731
human -2608 -2619 -2620
mouse -2607 -2619 -2620
ATP6 chicken -2590 -2619 -2620
frog -2585 -2618 -2620
interior -2585 -2618 -2620
human -679 -680 -682
mouse -677 -681 -682
ATP8 chicken -675 -679 -682
frog -678 -680 -682
interior -675 -680 -682
human -3872 -3885 -3887
mouse -3869 -3885 -3887
Cmyc chicken -3854 -3883 -3887
frog -3814 -3882 -3887
interior -3853 -3883 -3887
human -4704 -4792 -4794
mouse -4709 -4791 -4794
COX1 chicken -4700 -4794 -4794
frog -4679 -4791 -4794
interior -4698 -4792 -4794
human -2382 -2399 -2400
mouse -2382 -2399 -2400
COX2 chicken -2377 -2398 -2400
frog -2375 -2398 -2400
interior -2376 -2399 -2400
human -2502 -2537 -2542
mouse -2503 -2540 -2542
COX3 chicken -2483 -2538 -2542
frog -2485 -2539 -2542
interior -2486 -2540 -2542
human -3782 -3833 -3836
mouse -3783 -3832 -3836
CYTB chicken -3760 -3832 -3836
frog -3747 -3832 -3836
interior -3760 -3833 -3836
human -3457 -3483 -3486
mouse -3443 -3483 -3486
ND1 chicken -3435 -3484 -3486
frog -3434 -3482 -3486
interior -3442 -3482 -3486
human -4275 -4298 -4300
mouse -4275 -4298 -4300
ND2 chicken -4258 -4298 -4300
frog -4253 -4296 -4300
interior -4255 -4299 -4300
human -1348 -1353 -1355
mouse -1347 -1351 -1355
ND3 chicken -1337 -1353 -1355
frog -1335 -1352 -1355
interior -1335 -1353 -1355
human -5382 -5406 -5406
mouse -5380 -5406 -5406
ND4 chicken -5366 -5404 -5406
frog -5345 -5405 -5406
interior -5365 -5405 -5406
human -1259 -1261 -1265
mouse -1259 -1264 -1265
ND4L chicken -1254 -1262 -1265
frog -1245 -1263 -1265
interior -1254 -1263 -1265
human -7053 -7089 -7094
mouse -7053 -7091 -7094
ND5 chicken -7034 -7093 -7094
frog -7006 -7090 -7094
interior -7029 -7091 -7094
human -2022 -2025 -2028
mouse -2020 -2025 -2028
ND6 chicken -1995 -2023 -2028
frog -1998 -2025 -2028
interior -1998 -2025 -2028
Table 4 Combined analysis Combined log likelihoods over all genes under the nonstationary (NONSTA), stationary (STA), and reversible (REV) models. If NONSTA or STA places the root correctly, the corresponding log likelihood appears in bold.
group root placement NONSTA STA REV
human -21536 -21743 -21765
1 chimp -21551 -21746 -21765
gorilla -21470 -21744 -21765
human -26266 -26566 -26589
2 chimp -26270 -26567 -26589
gorilla -26223 -26566 -26589
orangutan -26172 -26566 -26589
interior -26241 -26563 -26589
human -53049 -53387 -53427
mouse -53029 -53393 -53427
3 chicken -52848 -53388 -53427
frog -52682 -53382 -53427
interior -52833 -53391 -53427
The nuclear genes albumin and c-myc and three mitochondrial genes, COX1, COX2 and ATP6 from group 3 (with some mouse genes replaced with rat genes) were studied by Huelsenbeck et al. [4]. For these five genes, NONSTA and STA performed equally, getting all the correct root placements, except for ATP6, with NONSTA again noticeably more discriminative.
Primate mitochondrial DNA
Brown et al. and Yang [5,9] studied a set of mitochondrial DNA (mtDNA) sequences from human, chimpanzee, gorilla, orangutan, gibbon, crab-eating monkey, squirrel monkey, tarsier and lemur. The topology of Yang's unrooted tree and the branch labels are shown in Figure 2. The mtDNA sequences consist of two protein-coding fragments, separated by three RNA genes. Thus, four site classes are required. Analysis with NONSTA shows that the root is most likely on the tarsier branch, followed closely by the lemur and "f" branches, and the corresponding log likelihoods are quite different from the others (see Table 5). Under STA, the most likely root placements are on the squirrel monkey and lemur branches. Thus, both processes give predictions that are consistent (NONSTA more than STA) with the idea that the root should be somewhere near tarsier and lemur. However, as observed before, NONSTA has much greater discriminative power, and fits the data much better, than STA.
Figure 2 Unrooted tree for nine primate mtDNA sequences The assumed unrooted tree is that presented in Yang [5]. The branches adjacent to leaf nodes are referred to by the corresponding organisms, while the interior branches are labelled a through f as indicated.
Table 5 Nine primates Log-likelihoods (rounded to closest integer) of the MLEs for 15 rooted trees under the nonstationary (NONSTA), stationary (STA) and reversible (REV) models.
root placement NONSTA STA REV
human -4960 -4965 -4965
chimp -4959 -4965 -4965
gorilla -4961 -4965 -4965
orangutan -4961 -4965 -4965
gibbon -4962 -4964 -4965
crab-eating macaque -4955 -4963 -4965
squirrel monkey -4941 -4961 -4965
tarsier -4932 -4963 -4965
lemur -4935 -4961 -4965
a -4962 -4965 -4965
b -4961 -4965 -4965
c -4961 -4964 -4965
d -4957 -4964 -4965
e -4948 -4963 -4965
f -4936 -4963 -4965
Discussion
Our results confirmed earlier findings that the stationary process (STA) is not very good at discriminating among rooted trees corresponding to the same unrooted tree. In contrast, the nonstationary (NONSTA) process seems much more effective, with individual genes, and with combined genes. It is quite clear that the difference in log likelihoods between fitting STA and the reversible process (REV) is often small, and statistically insignificant, based on the likelihood ratio test, while those between NONSTA and STA, and between NONSTA and REV, are often large, and statistically very significant. Though the chi-square distribution may be inappropriate [10], it seems to be satisfatory in practice [11]. This indicates that NONSTA fits the data much better than STA and REV. Thus it appears that allowing an initial distribution that is uncoupled with the rate matrix gives a better description of the data, and that the greater capacity of NONSTA over STA at estimating the root placement may stem from the ability of NONSTA to allow for some amount of evolution in base composition.
Although Huelsenbeck et al.'s analysis using STA failed to place the root correctly in any of the genes albumin, c-myc, COX1, COX2 and ATP6, there are some differences between the analyses. The raw data were different: the rat albumin and c-myc genes were used by Huelsenbeck et al.; since mouse and rat are very similar, this is not likely to matter much. Secondly, the alignments were probably different, though since the sequences are quite similar, this should not be too important. It is plausible that most of the discrepancies between the results is due to the difference in the estimation procedure (maximum likelihood vs. Bayesian) and to the fact that in Huelsenbeck et al., site variation was modeled by the gamma distribution [12], whereas here we only accounted for the codon position effect.
Estimates of the relative rates are quite independent of the model used, and their relative magnitudes are largely within expectations. In particular, for group 3, the relative rates for codon positions 1, 2, and 3 fall between .2 and 1.1, .1 and .6, and 1.5 and 2.7 respectively. For all genes, the third codon position evolved the fastest, followed by the first and second positions. To gauge the contribution from the third codon position, we left out the corresponding bases in group 3 and reran the analysis with NONSTA. This gave the correct root placement in only three genes: albumin, c-myc and ND2, showing the usefulness of the third codon position in this dataset, despite its markedly higher substitution rates. We also found that the pairwise identity at the third codon positions for all genes in groups 3 ranges from 34% to 61%. Base composition being generally nonuniform, the expected pairwise identity at saturation (i.e., infinite evolutionary distance) is lower than 25%. This seems to indicate that the third codon position is not saturated, and hence the phylogenetic information from this position is not just the base composition at each taxon. In addition, the base composition at the third codon position for some genes is quite different from the other positions. Our model does not fit these genes as well as a model where separate processes are associated with the codon positions. Such a model will be investigated in future.
The NONSTA process is only slightly more complicated to apply, compared to the STA and REV processes. The fact that it works quite well in the verification studies and predicts biologically plausible roots for the nine-primate data demonstrates its utility and perhaps argues for its use in routine phylogenetic analysis. In any case, if no suitable outgroup is available, it could be worthwhile to try it. Though the NONSTA process is the most general time-homogeneous Markov process, it is still simplistic and imposes a severe constraint on the evolution of base composition: if two leaf nodes are at the same distance from the root, then the process stipulates that the corresponding sequences must have the same composition. This is patently unrealistic: once lineages split, they should evolve quite independently, and may explain the failure of the process at estimating the root placement for some genes. However, it is still valuable even if it does not always work, in that it can serve as a base from which exploration of richer models can be launched. For instance, one could identify lineages where the evolution significantly deviates from expectations, and then allow these lineages to have different rate matrices, which brings us closer to the very rich models of [6,7,13,14].
Conclusions
The nonstationary substitution process is simple to use, has much greater power at estimating the root compared to the stationary process, and also fits data much better than the stationary and reversible processes. It seems feasible to use this process in analyses where a suitable outgroup is not easily available. It is also a good starting point for conducting more sophisticated phylogenetic analysis with richer models.
Methods
Substitutions in DNA sequences are assumed to occur independently at each site according to a Markov process, i.e., given the present base, future substitutions are independent of past substitutions.
Furthermore, it is assumed that the process is time-homogeneous, i.e., substitution rates stay constant in time. As usual, the substitution rate from base a to b is the (a, b)-entry in a 4 × 4 rate matrix Q; the diagonal entries are such that each row sums to 0. For any t > 0, the transition probability P(t) is given by P(t) = exp(Qt). Let π be a probability distribution on the DNA bases. The pair (π, Q) defines a substitution process on a rooted tree, as follows: pick a base at the root according to π, then run the substitution process according to Q down the tree, splitting into independent copies whenever a branching is encountered. The joint probability of the observed bases at the leaf nodes can be computed using almost exactly the same algorithm by [15].
There are two important special cases of the time-homogeneous process (π, Q). Associated with the rate matrix Q is a unique distribution πQ, called the equilibrium distribution of Q, such that the matrix product πQ × Q is the zero vector. The process (πQ, Q) is stationary, i.e., the sequence composition remains unchanged through time, and is described by πQ. Q is said to be reversible if it satisfies the detailed balance condition:
ΠQQ = Q'ΠQ
where ΠQ is the diagonal form of πQ and Q' is the transpose of Q. The process (πQ, Q) is then reversible, i.e., statistically the process looks the same in forward and backward time. In particular, as shown in [15], the joint distribution of the leaf bases is the same regardless of where the root is placed on the tree. The reversible process is known as the REV or time-reversible process in the molecular evolution literature [5,16,17]. Special cases of the REV process include those by Jukes and Cantor, Kimura, Felsenstein (two processes), Hasegawa, Kishino and Yano, and Tamura and Nei [15,18-22]. The nonreversible stationary process was first explored by Yang [5], and subsequently by Huelsenbeck et al. [4]. Yang referred to this process as "unrestricted", but we use the abbreviation STA here. We shall refer to the nonstationary process as NONSTA. The numbers of free parameters in the NONSTA, STA and REV processes are respectively 15 (3 in π and 12 off-diagonal entries in Q), 12 (off-diagonal entries in Q) and 9 (3 in πQ and 12 off-diagonal entries in Q, minus 6 detailed balance constraints). Since the models are nested, the likelihood ratio test can be used to assess the relative goodness-of-fit of the MLEs. It is standard practice to allow only calibrated rate matrices, i.e., Q satisfies
so that a branch length is the average number of substitution events per site. We adopt this practice, and remark that for the nonstationary process (π, Q), with calibrated Q, since in general π ≠ πQ, it is not true that the expected number of substitutions in 1 time unit is 1, but the difference gets arbitrarily small as time goes to infinity.
The sites in a DNA sequences can have very different substitution rates, the most well-known example being coding sequences, where the third codon positions evolved much faster than the others because of the degeneracy of the genetic code. In cases where the assignment of sites into several classes is known in advance, such as a coding sequence, the easiest way to deal with it is to associate to class i an unknown positive number ri, with the constraint that
where ni is the number of sites in class i. The relative rate ri either expands or shrinks the tree depending on whether it is more or less than 1. The constraint gives a new interpretation of a branch length: it is now the average over all sites of their expected number of substitutions. Thus, this approach is similar to [8]: effectively, the classes are treated as separate datasets. In this study, coding sequences are divided into three classes by codon position. In the last dataset consisting of nine primate mitochondrial sequences, an additional class is created to account for the RNA-coding bases. Another source of site variation is related to the three-dimensional structure of the protein. For example, hydrophilic residues are usually exposed, hence tend to evolve faster than hydrophobic residues which are deeply buried. Our present approach does not model this and other less obvious sources of site variation. Possible remedies include using the gamma distribution [12] or the hidden Markov model [23].
Given a rooted tree relating aligned coding sequences, we seek the ML estimates of the branch lengths, the substitution parameters, and the relative rates. For other sequences, the relative rates are not estimated. Gradient-based methods are perhaps the most efficient at finding the maximum. The EM algorithm [24] is another possibility. We implemented the simplex method [25], which is slower but is less likely to be misled to local maxima than gradient-based methods. To further reduce the chance of being fooled by local maxima, different initial estimates were used, and the final estimates with the highest likelihood was picked. The initial estimates were obtained by first deriving a reversible rate matrix from a pairwise comparison of two sequences, then using the associated REV process to find the most likely branch lengths and relative rates; all pairwise comparisons were used in this study, so that, for example, four taxa give six initial estimates.
The estimation procedure was implemented in C, and the source code can be requested from the first author.
Authors' contributions
The idea was conceived by the first author and was inspired and refined by the second author. The first author composed the code and performed the data analysis.
Supplementary Material
Additional File 1
A text file containing the amino acid sequence alignments for group 1.
Click here for file
Additional File 2
A text file containing the amino acid sequence alignments for group 2.
Click here for file
Additional File 3
A text file containing the amino acid sequence alignments for group 3.
Click here for file
Acknowledgements
Ziheng Yang kindly provided the alignment of the primate mtDNA sequences and entertained numerous enquiries. Several programs in the PAML package were used in the data analysis. We thank the referees for many comments and suggestions, and agree that more work is needed to investigate the notion of evolutionary time in nonstationary substitution processes, as well as the utility of our method in rooting larger trees.
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| 15629063 | PMC544347 | CC BY | 2021-01-04 16:29:15 | no | BMC Evol Biol. 2005 Jan 4; 5:2 | utf-8 | BMC Evol Biol | 2,005 | 10.1186/1471-2148-5-2 | oa_comm |
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BMC GastroenterolBMC Gastroenterology1471-230XBioMed Central London 1471-230X-4-321561933210.1186/1471-230X-4-32Research ArticleGastric emptying is slow in chronic fatigue syndrome Burnet Richard B [email protected] Barry E [email protected] Department of Endocrinology and Metabolism, Royal Adelaide Hospital, North Terrace, Adelaide, South Australia 5000, Australia2 Department of Nuclear Medicine, Royal Adelaide Hospital, North Terrace, Adelaide, South Australia 5000, Australia2004 26 12 2004 4 32 32 15 10 2004 26 12 2004 Copyright © 2004 Burnet and Chatterton; licensee BioMed Central Ltd.2004Burnet and Chatterton; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Gastrointestinal symptoms are common in patients with Chronic Fatigue Syndrome (CFS). The objective of this study was to determine the frequency of these symptoms and explore their relationship with objective (radionuclide) studies of upper GI function.
Methods
Thirty-two (32) patients with CFS and 45 control subjects completed a questionnaire on upper GI symptoms, and the 32 patients underwent oesophageal clearance, and simultaneous liquid and solid gastric emptying studies using radionuclide techniques compared with historical controls.
Results
The questionnaires showed a significant difference in gastric (p > 0.01) symptoms and swallowing difficulty. Nocturnal diarrhoea was a significant symptom not previously reported.
5/32 CFS subjects showed slightly delayed oesophageal clearance, but overall there was no significant difference from the control subjects, nor correlation of oesophageal clearance with symptoms. 23/32 patients showed a delay in liquid gastric emptying, and 12/32 a delay in solid gastric emptying with the delay significantly correlated with the mean symptom score (for each p ≪ 0.001).
Conclusions
GI symptoms in patients with chronic fatigue syndrome are associated with objective changes of upper GI motility.
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Background
Chronic Fatigue Syndrome (CFS) is a descriptive term used to define a classifiable pattern of symptoms that cannot be attributed to any alternative condition [1]. It can be associated with immunological alterations, neuro-endocrine changes [2], sleep disturbance and disturbed neurocognitive performance with abnormal cerebral perfusion [3], but the pathophysiological significance of these is uncertain. Skeletal neuromuscular function is usually normal in CFS sufferers [4].
Many with CFS have gastro-intestinal (GI) symptoms, which are often unrecognised as being part of CFS. The commonest of the upper GI symptoms include fullness and bloating after a small meal, abdominal distension, nausea, and loss of appetite. Lower GI tract symptoms have considerable overlap with irritable bowel syndrome [5].
The hypothesis explored in this paper is that symptoms of possible upper gastrointestinal origin are more common in patients with CFS and are related to upper gastrointestinal motility as assessed by radionuclide methods.
Methods
Subjects
Consecutive patients with CFS who met the Fukuda criteria [6] for CFS were all seen by a single physician (RB). Patients with any medical condition which could account for chronic fatigue, a BMI > 30, previous GI surgery or medication affecting the rate of gastric emptying were excluded. Overt psychiatric disease was excluded at the interview. The patients were asked to self assess their percentage reduction in activity from prior to the onset of CFS as a marker of severity. Gastro-Intestinal symptoms were evaluated in patients and controls by a standard questionnaire prior to the gastric emptying studies [7].
Symptoms were divided into "oesophageal" (dysphagia, heart burn, acid regurgitation), "gastric": (anorexia, nausea, early satiety, bloating, abdominal distension, intermittent abdominal pain), "other" frequency of bowel actions, consistency of stools, presence or absence of diarrhoea, urgency and timing.
Symptoms were scored. 0, none, 1, mild (symptom could be ignored), 2, moderate (symptom could not be ignored, but did not influence daily activities), 3, severe, (symptom influenced daily activities). A mean symptom score (maximum score 3) for the 6 gastric symptoms, and 3 oesophageal symptoms was obtained.
The volunteer control subjects who completed the questionnaire were in regular full time employment, with no history of excessive fatigue, on no GI medication, and had no previous GI surgery.
Radionuclide measurement of upper GI motility
Details and normal ranges of this double isotope test have been previously published [8]. The solid meal consisted of 100 g of cooked ground beef containing 40MBq in-vivo labelled 99mTc-sulfur colloid-chicken liver, and the liquid meal consisted of 150 ml of 10% dextrose in water containing with 20 MBq of 67Ga-ethylenediaminetetraacetic acid (EDTA). All medication (except oral contraceptives) was discontinued for 24 hours prior to each study. The test was performed at 10 am (after an overnight fast) and monitored for at least two hours with the subject in the sitting position with the scintillation camera behind. The study commenced with a standardised oesophageal clearance study (solid bolus) followed by eating the solid meal and then immediately drinking the glucose solution. Each study was continued for at least 2 hours. Oesophageal clearance was expressed as time to 95% clearance (ref range < 93 sec) [9], Liquid gastric emptying as half-clearance time (ref 4–31 minutes) and solid emptying as amount remaining at 100 min (ref 4–61%).
The GI questionnaires were compared between CFS and control by Chi2, and Gastric emptying indices compared with historical normal range (t test comparison of means), and correlated with the mean symptom score (± SD).
The Study was approved by The Human Research Ethics Committee of the Royal Adelaide Hospital and informed consent given by the subjects.
Results
Thirty-two (32) CFS patients (22F), with a mean age of 38.5 years had gastric emptying studies. Forty-five (45) control subjects undertook the questionnaire. The demographic details of the controls vs. patients are shown in table 1 Gastro-intestinal symptoms were more common in the CFS group (mean symptom score {MSS] 1.01 ± 0.87) than controls (MSS 0.24 ± 0.34) (table 2).
Table 1 Characteristics of CFS subjects vs controls. (SD)
CFS CONTROLS
Number 32 45
Sex F 22, M10 F 37, M 8
Age (yr) 38.5 ± 13.9 34.4 ± 8.5
Weight Kg 68.1 ± 12.1 71.8 ± 11.6
Duration CFS (yr) 9.5 ± 6.8
Severity, % reduction activity 65
Smoke % 18 17.8
Table 2 Percentage frequency of any gastrointestinal symptoms.
CFS % (n = 32) CONTROLS % (n = 45)
GASTRIC
Abdominal discomfort 39 22.
Fullness after small meal 70 31*
Nausea 67 15*
Abdominal pain 76 27*
Loss of appetite 42 12*
Vomiting 22 2*
OESOPHAGEAL
Acid regurgitation 30 28
Heart burn 48 27
Swallowing difficulty 45 9*
OTHER
Bowel movements/ day (mean) 1.6 1.2
Constipation % 26 30
Consistency Formed % 67 80
Loose/Watery % 33 20
Nocturnal diarrhoea % 21 0*
Faecal Urgency % 51 16*
* indicates symptoms more frequent in CFS group p <.05, Chi2
The overall, grouped gastric emptying studies of CFS subjects showed no significant slowing of oesophageal clearance p = 0.45 from the control population, and no significant correlation between emptying and oesophageal symptom score (r = 0.15) although 5 of the symptomatic and 2 of the asymptomatic subjects 7/32 (22%), were slower than the 95% confidence limits, (fig 1), this did not reach statistical significance. The major abnormality shown is a delay in the emptying of the liquid phase in 23/32 72% of the patients, whereas 12/32 (38%) of solid emptying was delayed compared with the historic controls (t comparison of means, figs 2 and 3, group p ≪ 0.005). When the gastric emptying results were compared to the mean symptom score there was a highly significant correlation of solid (r = 0.81) and liquid (r = 0.65) delay which increased with the symptom score (p ≪ 0.001).
Figure 1 Oesophageal clearance time compared with oesophageal symptoms in Chronic Fatigue Syndrome. (Shaded area represents 95% confidence limit of normal reference range)
Figure 2 Per-cent gastric retention of solid food compared with mean symptom score in chronic fatigue syndrome. (Shaded area represents 95% confidence limit of normal reference range)
Figure 3 Time to 50% gastric emptying of liquid compared with mean symptom score in chronic fatigue syndrome. (Shaded area represents 95% confidence limit of normal reference range)
Discussion
G-I symptoms are common in patients with CFS. Abdominal pain is distressing [10], often requiring analgesia for relief. A previously unrecorded symptom in CFS patients is nocturnal diarrhoea, which disrupts an already disturbed sleep pattern. The most common upper GI symptom is fullness and bloating after a small meal. The usual medical explanation for the gut symptoms is 'irritable bowel'. Unless specific G-I questions are put to the CFS patient they will not spontaneously discuss these symptoms.
An abnormality in solid or liquid emptying or combinations of these study parameters was more common in the more symptomatic patients, and liquid was more frequently affected. This is the opposite of the abnormality seen in diabetic subjects [8], where the major abnormality, delay in the solid phase of gastric emptying has been ascribed to autonomic dysfunction or hyperglycaemia. A group of elderly subjects with a number of neurological defects showed a delay in the liquid rather than the solid emptying [11].
Symptoms and delayed gastric emptying in diabetic gastroparesis studies have not correlated well. In this study there is a good correlation with symptoms. The commonest of these was early satiety, fullness and bloating after eating. There was though a poor correlation with oesophageal symptoms and a disorder of oesophageal emptying.
GI motility is complex, with central, local neuromuscular and humoral influences. Non-specific endocrine disturbances have been demonstrated in CFS, but the relevance of these is unknown with regard to GI disturbances. Skeletal muscle fatigue appears to be of central rather than peripheral origin, but again it is not known whether this may be extrapolated to visceral muscle.
Inconclusive central changes have been documented. The impact of disturbed sensory function is unknown, and this could also involve peripheral nerves or the central processing of sensory information.
Diagnostically, there is overlap between CFS, functional dyspepsia and fibromyalgia and all may be related to abnormal sensory processing [10,12,13]. Altered gastric emptying has been shown in association with irritable bowel syndrome [14].
Conclusions
These observations indicate that there is measurable disturbance in upper gut motility corresponding with symptoms in CFS. Although the cause for these findings is not apparent in this study, the more prominent delay in liquid rather than solid emptying may point to a central rather than a peripheral aetiology. The gastro-intestinal tract and function should be properly investigated and the symptoms not necessarily be ascribed to irritable bowel syndrome.
Competing interests
The author(s) declare that they have no competing interests.
Authors contribution
RB examined the patients and analysed the clinical data, BC performed the Nuclear Medicine studies, drafted the manuscript and performed the statistics. Both read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15619332 | PMC544348 | CC BY | 2021-01-04 16:29:54 | no | BMC Gastroenterol. 2004 Dec 26; 4:32 | utf-8 | BMC Gastroenterol | 2,004 | 10.1186/1471-230X-4-32 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-601560147610.1186/1471-2334-4-60Research ArticleStreptococcal necrotising fasciitis from diverse strains of Streptococcus pyogenes in tropical northern Australia: case series and comparison with the literature Hassell Marilyn [email protected] Peter [email protected] Phillip [email protected] Bart J [email protected] Infectious Diseases Unit, Northern Territory Clinical School, Flinders University, Royal Darwin Hospital, Darwin Northern Territory Australia2 Infectious Diseases Division, Menzies School of Health Research, Charles Darwin University, Darwin Northern Territory Australia3 Department of Surgery, Northern Territory Clinical School, Flinders University, Royal Darwin Hospital, Darwin Northern Territory Australia2004 16 12 2004 4 60 60 30 7 2004 16 12 2004 Copyright © 2004 Hassell et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Since the mid-1980's there has been a worldwide resurgence of severe disease from group A streptococcus (GAS), with clonal clusters implicated in Europe and the United States. However GAS associated sepsis and rheumatic fever have always remained at high levels in many less developed countries. In this context we aimed to study GAS necrotising fasciitis (NF) in a region where there are high background rates of GAS carriage and disease.
Methods
We describe the epidemiology, clinical and laboratory features of 14 consecutive cases of GAS NF treated over a seven year period from tropical northern Australia.
Results
Incidence rates of GAS NF in the Aboriginal population were up to five times those previously published from other countries. Clinical features were similar to those described elsewhere, with 7/14 (50%) bacteremic and 9/14 (64%) having associated streptococcal toxic shock syndrome. 11/14 (79%) had underlying chronic illnesses, including all four fatalities (29% mortality overall). Important laboratory differences from other series were that leukocytosis was absent in 9/14 (64%) but all had substantial lymphopenia. Sequence typing of the 14 NF-associated GAS isolates showed no clonality, with only one emm type 1 and two emm type 3 strains.
Conclusions
While NF clusters can occur from a single emergent GAS clone, this was not evident in our tropical region, where high rates of NF parallel high overall rates of GAS infection from a wide diversity of strains. The specific virulence factors of GAS strains which do cause NF and the basis of the inadequate host response in those patients who develop NF on infection with these GAS require further elucidation.
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Background
Necrotising fasciitis (NF) is an aggressive and rapidly destructive soft tissue infection resulting in high mortality and significant long term morbidity. Two groups of infectious agents have been described – Type I which are polymicrobial, involving anaerobic bacteria and streptococci other than serogroup A, and Type II from Streptococcus pyogenes (Group A streptococci, GAS) alone, or in association with Staphylococcus aureus or Staphylococcus epidermidis [1]. The latter was originally described by Meleney in 1924 as 'haemolytic streptococcal gangrene' [2] and has since been considered a rare entity.
Since the mid-1980's a worldwide increase in infections due to GAS has been noted. In affluent populations where GAS disease is uncommon aside from pharyngitis in childhood, increasing numbers of necrotising fasciitis and streptococcal toxic shock syndrome (STSS) have been seen, as well as an upsurge of acute rheumatic fever apparently restricted to parts of the United States [3-6]. This has been attributed in some locations to dissemination of a virulent M1 serotype GAS clone[7]. In developing nations the pattern of GAS disease is different, with continuing high rates of streptococcal pyoderma and post-streptococcal disease[8].
Recent case series of GAS NF have been published from Norway[4] and Ontario, Canada[5,6]. The Northern Territory (NT) of Australia consists of a combination of affluent, urban residents, as well as residents in remote communities, who are predominantly indigenous Aboriginals with high levels of poverty and overcrowding. As in developing nations, these remote communities have very high rates of GAS disease in the form of streptococcal pyoderma, rheumatic fever and post-streptococcal glomerulonephritis[9]. We examine the epidemiology, clinical features and streptococcal sequence typing of a series of cases of GAS NF from the tropical top end of the NT and compare the results with the literature.
Methods
The Royal Darwin Hospital services 135,000 inhabitants of the NT, with around 100,000 located in the city of Darwin. Indigenous Aboriginal Australians account for 24% of the population[10]. Cases of possible GAS NF treated at Royal Darwin Hospital were prospectively identified. Case definition for inclusion in the analysis required GAS to be cultured from sterile sites, notably operative tissue specimens +/- blood cultures. NF was defined by necrosis of subcutaneous tissue, specifically fascial oedema and necrosis detected either macroscopically at surgery, or microscopically on histopathology.
Fourteen cases fulfilling the case definition were identified between May 1994 and April 2001. A chart review was conducted to identify epidemiological characteristics, clinical features, laboratory results, treatment and outcomes. Age, gender, ethnicity, urban or rural residence and any concurrent medical conditions were documented. The dates of presentation, presumptive diagnosis of NF and of surgery were documented. Symptoms, signs, hemodynamic status and temperature were documented for these dates, as was the presence of STSS as defined by the Working Group on Severe Streptococcal Infections[11]. Antibiotic treatment, surgery, and any adjuvant therapies were documented. Baseline hematological and biochemical parameters were recorded for the date of presentation, and blood and tissue culture results were documented. Duration of Intensive Care Unit (ICU) admission, and type of ICU support required were recorded.
Incidence rates of GAS NF for this region of the NT were calculated using regional population numbers from census data. There was possibly an increase in case ascertainment after 1998 and incidence rates were also calculated by year.
Procedures for emm sequence typing and analysis of emm-specific PCR products were carried out as previously described[12]. DNA sequences were subject to homology searches against all emm sequences deposited in GenBank and in the Centers for Disease Control (CDC) Streptococcus pyogenes emm sequence database[13].
Approval for the study was obtained from the Health Research Ethics Committee of Royal Darwin Hospital and the Menzies School of Health Research.
Results
Table 1 summarizes the details for each patient.
From 1994 to 2001, 14 cases of confirmed GAS NF were treated at Royal Darwin Hospital. The number of cases per year ranged from zero in 1995 and 1998 to five in 2001. This gives a maximum yearly incidence of 3.8 cases per 100,000. Half of the cases occurred in Aboriginal patients, with an incidence of 5.8 per 100,000 for Aboriginals in the 2001 cluster of cases. Eight cases occurred in women (57%), five of whom were Aboriginal. Ages ranged from 27–61 years in males (mean 41.5 yrs) and 27–59 years in females (mean 41.5 yrs). Eleven cases were community acquired and three nosocomial. All seven Aboriginal patients had multiple concurrent medical conditions, whereas three of the seven non-Aboriginals had no additional pathology.
Patients presented most consistently with pain in the affected area. The other common complaints were swelling, and systemic symptoms of fever and rigors. Signs on presentation were typically induration and tense swelling of the area. Only four of the fourteen cases (29%) were noted to have extensive skin erythema, but nine (64%) had local pre-existing wounds or ulceration. Ten patients had only limb involvement, two had chest wall involvement, one had extension from right leg to the abdominal wall and one had extension from the left buttock to perineum/labia. STSS occurred in nine patients. Ten patients required inotropic support, six had a documented coagulopathy and eleven had renal impairment. Thirteen patients (93%) required ICU stay of 1–24 days. Eleven patients were ventilated, and six received renal replacement therapy.
Total white cell count (WCC) was found to be raised (>11 × 109/L) in only 5/14 (36%) patients, although lymphocyte count was globally reduced, being <1.0 × 109/L in all patients, and <0.5#215;109/l in nine cases (64%). Renal function was frequently impaired, with 11/14 (79%) patients having raised urea (mean 17.7 mmol/L, NR 3.0–8.0 mmol/L) and 12/14 (86%) having raised creatinine (mean 301 μmol/L, NR 50–100 μmol/L). All patients were hypoalbuminemic with 11 having albumin <30 g/L (mean 24.5, range 11–34, NR 35–45 g/L) Plasma creatine kinase (CK) ranged from 25 U/L to 2526 U/L (NR <220 U/L), being elevated in 5/9 (56%) patients measured. C-Reactive Protein (CRP) was substantially raised in all 11 where measured, being between 108–522 mg/L (NR<10). Blood cultures were positive for GAS in seven cases (50%). GAS was isolated from all operative tissue specimens, with four also positive for S. aureus.
One GAS isolate could not be sequenced. GAS from the other 13 patients showed a large diversity of emm sequence types, with no clonality. Where blood and tissue GAS were both recovered from an individual, the emm sequence types were the same. One patient's GAS isolate was a previously uncharacterized emm sequence type and there was only one case with emm sequence type 1 GAS isolated. The two emm 3 isolates were from the 2001 cluster of five cases. However they were very divergent on random amplified polymorphic DNA analysis (data not shown), one being emm sequence type 3.2, and the other three isolates in this cluster were all different emm types.
All patients underwent surgery 0–7 days after presentation. Debridement alone was conducted in nine patients, and combined with fasciotomy in one case. Fasciotomy alone was done in one case. Three patients underwent limb amputation. At surgery it was established that three patients (21%) had concurrent myonecrosis. Antibiotic therapy was instituted with a beta lactam in all patients (benzylpenicillin in nine, meropenem in three, cephazolin in one and flucloxacillin in one). Clindamycin was also given in eleven patients. Adjuvant hyperbaric oxygen therapy was given in four cases, and intravenous immunoglobulin (IVIG) in one.
Four patients (29%) died, all being Aboriginal females with co-morbidities. Two of the four patients with truncal infection died in comparison to only two of the ten with only peripheral involvement. The mean time to surgery from presentation was 4.25 days in patients who died and 3.7 days in survivors (not significant). Blood cultures were positive in 3/4 fatalities with STSS in 3/4. All four were in ICU for 1–5 days until death, with all requiring inotropes and mechanical ventilation, two given renal replacement therapy and one IVIG. Two received clindamycin in addition to beta-lactam antibiotic therapy. Surgery in the fatal cases consisted of debridement and/or fasciotomy, with no amputation.
Discussion
Demographics
We describe a series of 14 cases of GAS necrotising fasciitis in a region with a high background incidence of GAS disease[9]. Our incidence of NF in 2001 of 3.8/100,000 and 5.8/100,000 in the Aboriginal population is higher than previously published rates which range from 0.4/100,000 in Canada[5] to 2/100,000/yr in Norway [4]. However the population denominators in the Canadian and Norwegian studies are far greater than in our study. Nevertheless, previous data from our region showed that rates of GAS bacteremia in the Aboriginal population were five times those in the Caucasian population[9]. Therefore NF in our population parallels an overall greater rate of GAS infection, most notably in the Indigenous population where conditions reflect those of developing countries[8].
The mean age of our patients (41.5 years) is lower than other reports showing an average age of 56–58 years, with notable increases in incidence with increasing age[4-6]. Unlike all other series which had a male predominance, 57% of our cases were female, with 71% females amongst the Aboriginal cases. The age and sex pattern seen in our series reflects the high rates of chronic diseases in the Indigenous population, with mortality from all causes being higher in all age cohorts, but especially so in females[14,15]. 79% of our patients had concurrent chronic disease, compared with 46–71% in previous series[4-6,16]. In this series 64% had pre-existing wounds, consistent with reported rates of 47–66%[4-6,16]. None of our patients had preceding varicella infection.
Laboratory results
Two studies have looked at the value of early blood test results in predicting NF as opposed to non-NF soft tissue infection. One found that CK >600 U/L achieved PPV of 58% for NF[17]. However only 1/9 cases in this series had CK >600 U/L. CRP of >16 mg/L was also thought to be indicative of NF[16]. CRP was over 100 in all those measured in our series. Raised WCC has also been suggested as useful in a predictive model of early NF[18], with rates of 66–73% in other series[4-6]. However, only 36% of our patients had a raised WCC. We found lymphopenia to be a more consistent factor, with 100% of patients having a lymphocyte count of <1.0 × 109/L, 64% of those being <0.5 × 109/L. Low albumin was also universal.
Clinical features
STSS occurred in 64% of our cases in comparison to 40–46% in other series[4-6]. 71% of our patients were hypotensive at presentation, with 86% having renal impairment in comparison to 35–61% in other series. Similarly to the other series, 93% of our patients were admitted to the ICU but more of our patients required mechanical ventilation (79%) and inotropic support (71%). Positive blood cultures are not a prerequisite for GAS NF, as stated by Meleney in the initial descriptions[2], and reinforced in subsequent studies showing bacteremic rates of 38–60%[4-6,16], consistent with 50% in this study.
Case-fatality rates for NF previously reached 50%, with mortality of 80–100% in GAS myositis[3]. Recent series report lower case-fatality rates of 20–34%[4-6], with our mortality being 28%. As in previous studies, deaths were more common in those with underlying illness, truncal infection, bacteremia, STSS, myonecrosis and delay in diagnosis and appropriate therapy.
Therapy
Immediate, extensive surgical debridement of all necrotic tissue is considered essential for optimal treatment of NF, with one early observational study showing a mortality of 0% with aggressive early surgery in comparison to 50% in historical controls[19,20]. Surgery was conducted on the day of presumptive diagnosis of NF in all our patients, but none of the four fatal cases underwent amputation and one with upper limb involvement had only a fasciotomy performed.
Combination therapy with benzylpenicillin and clindamycin is now recommended treatment for proven GAS NF and STSS[21-23]. Experimental data suggest that penicillin is not as bactericidal when there are large numbers of GAS organisms present[24], with decreased expression of penicillin binding proteins when large inocula reach a stationary growth phase[25]. The potential benefits of clindamycin have been supported by a murine model of streptococcal myositis[26]. Clindamycin is a protein synthesis inhibitor, potentially also suppressing bacterial toxin and M protein production[25]. Furthermore, clindamycin may modulate the host immune response, by reducing lipopolysaccharide-induced monocyte production of TNFα[27]. A combination of β-lactam antibiotic and clindamycin has been shown to be superior to β-lactams alone in a retrospective review of invasive GAS infection in 56 children[28]. A recent retrospective review of notified invasive GAS infections in Florida, USA showed the use of clindamycin to be associated with lower mortality in NF cases, but not in other invasive GAS infections[29]. However there have been no randomised clinical trials.
Hyperbaric oxygen (HBO) therapy has been advocated as adjuvant therapy for both microbiological types of NF. Although there are no randomised trials, NF is an approved Undersea and Hyperbaric Medical Society indication for HBO therapy[30]. It is suggested that increased tissue oxygen partial pressures increases bacterial killing by increased respiratory burst and increased formation of oxygen free radicals. HBO is postulated to facilitate wound healing by supporting fibroblast proliferation and angiogenesis[30,31]. In a murine model of GAS myositis, the combination of penicillin and HBO therapy exerted at least additive effects in decreasing bacterial counts in vivo and increasing survival[32]. An observational study of 29 patients with NF showed significantly lower mortality, and need for fewer debridements in those receiving adjunctive HBO, despite the HBO group being more seriously ill[33]. Patients with non-clostridial fasciitis appeared to have greater benefit. However more recently a retrospective evaluation of 37 patients treated for NF showed higher mortality with greater need for debridement in those receiving HBO[34].
The clinical picture of STSS has been attributed to superantigens produced by GAS, including pyrogenic exotoxins, streptococcal superantigen SSA and a mitogenic exotoxin[35]. This is supported by selective depletion of Vβ-bearing T cells in patients with STSS[36]. However GAS isolates from both severe and uncomplicated disease can produce large amounts of toxins. Furthermore, in comparison to patients with uncomplicated infection the sera of patients with severe GAS disease had low antibody levels against erythrogenic toxins[37], suggesting an important role for host humoral immunity. IVIG has therefore been proposed as therapy to increase neutralizing antibody levels in patients with severe GAS disease. A study of 12 patients, 11 with STSS and one with GAS NF, showed increased capacity of plasma to neutralize superantigenic activity following IVIG[38]. Post-IVIG plasma from each patient completely blocked cytokine production elicited by their respective GAS culture supernatants or by purified streptococcal pyrogenic exotoxins. GAS superantigens and cytokines suggestive of superantigen response have been isolated from tissue samples in patients with GAS NF[39]. More severe clinical disease correlated with significantly higher bacterial load in biopsy samples, and bacterial load in turn correlated with expression of superantigenic toxins. This supports a role for IVIG in GAS NF, with or without STSS. Adjunctive IVIG therapy in STSS was evaluated in a multicenter, randomized, double-blind, placebo-controlled trial[40]. Although prematurely terminated due to slow patient recruitment, there was a non-significant 3.6 fold higher mortality rate in the placebo group and a significant decrease in sepsis-related organ failure assessments on days two and three in the IVIG group. There are case reports and two case series of four and seven patients describing clinical improvements of patients with STSS following IVIG[41-43]. In a series of 20 cases of GAS NF and myonecrosis, 16 were treated with IVIG[6]. Although the case fatality rate was not significantly lower in those receiving IVIG, the overall survival rates of 80% in patients with NF and 63% in patients with myonecrosis were much higher than previously reported[6].
In summary, there is in vitro and in vivo experimental data to suggest a benefit for clindamycin, HBO therapy and IVIG in GAS NF and myositis. This is supported by limited case series but not by randomized controlled trials. The primary role of early, aggressive surgery in GAS NF remains probably the most critical factor in minimizing mortality. Clindamycin is recommended for GAS NF but whether clindamycin alone is as effective as penicillin plus clindamycin remains unclear. Benefit from HBO remains unproven, while there is emerging evidence for the use of IVIG, especially if STSS is present.
Molecular epidemiology
Following a reported resurgence in invasive GAS infections in the 1980's, epidemiological studies proposed that dissemination of a new, more virulent strain of M1 serotype GAS was the cause for a large proportion of these infections[7]. In reports from the United States, Europe and New Zealand the proportion of this strain appeared to increase, both in those with uncomplicated pharyngitis and in invasive GAS infections[37,44-47]. The M1 serotype has also been reported to have a stronger association with STSS[48]. In contrast to these findings, other groups have noted a significant diversity amongst the organisms causing severe streptococcal infections, as well as significant clinical variation amongst patients infected with identical organism clones, suggesting that the severe infections are not related to one specific virulent clone[49-51]. Our findings are consistent with the latter, with GAS sequence typing showing all NF isolates to be different and only 1/13 to be emm type 1. This reflects previous findings of enormous diversity amongst organisms causing GAS bacteremia in our region, with no evidence of a dominant clone[9]. As well as finding no correlation between NF and specific M types, a recent Dutch study also found that no specific toxin genes or genes encoding matrix binding proteins were associated with NF[48].
Conclusions
In conclusion we have described a case series of necrotising fasciitis due to GAS for a unique mixed population of patients living in both affluent and disadvantaged society. Our overall incidence rates of GAS NF are higher than previously reported, with rates in the Aboriginal population up to five times those previously published. The majority of those with NF had underlying chronic illnesses and mortality was also higher in this group. Important laboratory findings were that leukocytosis was absent in more than half of cases but all had substantial lymphopenia. Emm sequencing typing of GAS isolates showed none to be the same and only 1/13 to be emm1. GAS NF in our region is not clonal but occurs from a wide diversity of GAS strains that reflect the very high background rate of both uncomplicated GAS infection and invasive disease. While NF case clusters can occur from a single emergent GAS clone, this is not evident in our tropical region. The specific virulence factors of the GAS which do cause NF and the basis of the inadequate host response in those patients who develop NF on infection with these GAS require further elucidation.
Competing interests
The authors declare they have no competing interests.
Authors' contributions
MH collated and analysed the case data and was principal writer of the manuscript, PF carried out the molecular studies, PC participated in the study design and patient management, BC conceived the study and participated in the study design and coordination and patient management. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We are grateful to Gary Lum and our microbiology laboratory colleagues at Royal Darwin Hospital for providing the streptococcal isolates for sequence typing, Megan Duffy for technical help with the typing and Anne Arthur and the nursing staff from the Infection Control Department of Royal Darwin Hospital for information from their database.
Figures and Tables
Table 1 Demographic, clinical and laboratory details of 14 patients with GAS necrotising fasciitis
No Age/Gender/Aboriginal/Year Co-morbidities Prior lesions Systolic BP at presenttion (mmHg) Creatinine (μmol/L) (NR 50–100) Blood culture
emmST
a
STSSb
1 45/F/Y/2001 Alcohol excess Right knee abrasion 85 810 Neg new emmST Y
2 50/F/Y/1994 Rheumatoid arthritis Nil 100 352 Pos emm56 N
3 47/F/Y/2000 Hemodialysis, Hypertension Nil 85 739 Pos emmST3757 Y
4 38/F/Y/2001 Type 2 diabetes, Hemodialysis, Cryptococcal pneumonitis Buttock ulcer 60 408 Pos no sequence Y
5 61/M/N/2001 Rheumatoid arthritis, Corticosteroid therapy, Splenectomy Vasculitic leg ulcer 70 249 Pos emm3.2 Y
6 60/M/N/2001 Rheumatoid arthritis, Methotrexate therapy, Pulmonary fibrosis Leg ulcer 90 237 Neg emm70 Y
7 27/M/N/1996 Alcohol excess Nil 70 172 Neg emm58 Y
8 34/F/Y/1996 SLE, Anti-Phospholipid syndrome, Corticosteroids, Renal impairment Left thigh skin sore 100 329 Pos emm69 N
9 44/M/Y/2000 Type 2 diabetes, Alcohol excess, Renal impairment Wound from catfish barb 90 271 Neg emm49.3 Y
10 27/F/N/1999 Nil Nil 90 77 Neg emm104 N
11 32/F/N/1999 Nil Buttock excoriation post-cycling 100 177 Neg emm1 N
12 59/F/N/1999 Nil Nil 90 118 Neg emm22 Y
13 50/M/Y/1997 Alcohol excess, Cirrhosis Leg ulceration 100 112 Pos emm4 N
14 61/M/N/2001 Alcohol excess, Hypertension, Gout Ulcerated gouty tophus 90 659 Pos emm3 Y
No WCC/Lymphc (×109/L) Albumin (g/L) (NR 35–45) Infection site Time to surgery after presentation Operation performed Antibiotic therapy Myo-necrosis Died
1 5.7/0.2 21 Right leg 4 days Debridement, Fasciotomy Meropenem, Clindamycin Yes Yes
2 5.2/0.5 22 Right arm 6 days Fasciotomy Penicillin Yes Yes
3 9.6/0.3 33 Right breast/Chest wall 5 days Debridement Penicillin No Yes
4 9.3/0.4 34 Left leg/Abdominal wall 2 days Debridement Penicillin, Clindamycin No Yes
5 36.7/0.7 23 Left leg 1 day Above knee amputation Meropenem, Tobramycin, Clindamycin No No
6 23.7/0.5 29 Right leg 0 days Below knee amputation Cefazolin, Clindamycin No No
7 5.2/0.1 25 Right leg 5 days Debridement Penicillin, Clindamycin No No
8 7.0/0.3 33 Left leg/Abdominal wall 7 days Debridement Penicillin, Clindamycin Yes No
9 22.8/0.8 11 Right foot 2 days Above knee amputation Flucloxacillin, Metronidazole, Gentamicin Yes No
10 23.3/1.0 29 Left shoulder/Chest wall 6 days Debridement Meropenem, Clindamycin No No
11 21.8/0.5 17 Right leg/Buttock/Perineum 2 days Debridement Penicillin, Clindamycin No No
12 4.8/0.4 18 Left leg/Abdominal wall 2 days Debridement Penicillin, Clindamycin No No
13 10.5/0.7 21 Left leg 5 days Debridement Penicillin, Clindamycin No No
14 4.0/0.2 28 Right leg 4 days Above knee amputation Penicillin, Clindamycin No No
a Emm sequence type (see methods for details)
b Streptococcal toxic shock syndrome (see methods for details)
c WCC-total blood white cell count (normal range <11.0 × 109/L) Lymph-blood lymphocyte count
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| 15601476 | PMC544349 | CC BY | 2021-01-04 16:03:30 | no | BMC Infect Dis. 2004 Dec 16; 4:60 | utf-8 | BMC Infect Dis | 2,004 | 10.1186/1471-2334-4-60 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-611561323210.1186/1471-2334-4-61Research ArticleMicronutrient malnutrition and wasting in adults with pulmonary tuberculosis with and without HIV co-infection in Malawi van Lettow Monique [email protected] Anthony D [email protected] Johnny J [email protected] Ed E [email protected] Tamara D [email protected] Taha E [email protected] Richard D [email protected] Johns Hopkins University School of Medicine, Baltimore, USA2 National Tuberculosis Control Programme, Lilongwe, Malawi3 College of Medicine, Blantyre, Malawi4 Johns Hopkins University School of Public Health, Baltimore, USA2004 21 12 2004 4 61 61 4 5 2004 21 12 2004 Copyright © 2004 van Lettow et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Wasting and micronutrient malnutrition have not been well characterized in adults with pulmonary tuberculosis. We hypothesized that micronutrient malnutrition is associated with wasting and higher plasma human immunodeficiency virus (HIV) load in adults with pulmonary tuberculosis.
Methods
In a cross-sectional study involving 579 HIV-positive and 222 HIV-negative adults with pulmonary tuberculosis in Zomba, Malawi, anthropometry, plasma HIV load and plasma micronutrient concentrations (retinol, α-tocopherol, carotenoids, zinc, and selenium) were measured. The risk of micronutrient deficiencies was examined at different severity levels of wasting.
Results
Body mass index (BMI), plasma retinol, carotenoid and selenium concentrations significantly decreased by increasing tertile of plasma HIV load. There were no significant differences in plasma micronutrient concentrations between HIV-negative individuals and HIV-positive individuals who were in the lowest tertile of plasma HIV load. Plasma vitamin A concentrations <0.70 μmol/L occurred in 61%, and zinc and selenium deficiency occurred in 85% and 87% respectively. Wasting, defined as BMI<18.5 was present in 59% of study participants and was independently associated with a higher risk of low carotenoids, and vitamin A and selenium deficiency. Severe wasting, defined as BMI<16.0 showed the strongest associations with deficiencies in vitamin A, selenium and plasma carotenoids.
Conclusions
These data demonstrate that wasting and higher HIV load in pulmonary tuberculosis are associated with micronutrient malnutrition.
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Background
Approximately one-third of the world's population is infected with Mycobacterium tuberculosis, and the majority live in less developed countries where human immunodeficiency virus (HIV) infection is spreading rapidly. The World Health Organization (WHO) estimates that the number of new cases of tuberculosis and the proportion with coexisting HIV infection will continue to increase [1]. Immunosuppression increases the risk of developing clinical tuberculosis, which contributes to the increased prevalence of tuberculosis in association with HIV infection. Malnutrition and wasting are associated with tuberculosis, and co-infection with HIV and tuberculosis may potentially exacerbate the wasting that occurs in tuberculosis or HIV infection alone [2-5]. Micronutrient deficiencies have been described in individuals with tuberculosis [6-17] and in those with HIV infection [17-23]. Several cross-sectional studies suggest that patients with tuberculosis are at high risk of deficiencies of vitamin A [7,10-12], thiamin [13], vitamin B6 [14], folate [6,15], vitamin E [16], and zinc [10]. Poor selenium status has recently been shown to increase the risk of developing mycobacterial disease among HIV-infected injection drug users [24], but selenium status among HIV-infected adults with pulmonary tuberculosis has not been well characterized. Selenium plays an important role in the selenoenzyme glutathione peroxidase that protects cells against free radical damage and oxidative stress.
The relationship between severity of HIV disease and micronutrient malnutrition needs further elucidation. Such information would help identify subgroups that might benefit the most from nutritional interventions. Plasma HIV load was used as an indicator of severity of HIV disease, as HIV load tends to be higher in more active HIV disease. We hypothesized that wasting in pulmonary tuberculosis is associated with micronutrient malnutrition and that HIV-infected adults with pulmonary tuberculosis who have more active HIV disease, as reflected by higher HIV load, also have more severe micronutrient malnutrition. To test these hypotheses, we conducted a cross-sectional study to examine the relationship between wasting and micronutrient malnutrition in HIV-positive and HIV-negative adults with pulmonary tuberculosis in Zomba, Malawi.
Methods
The study population consisted of adults who presented with new sputum-positive pulmonary tuberculosis in Zomba Central Hospital between July 1999 and April 2003. Subjects were offered HIV testing and were screened for HIV antibodies after signing a written informed consent form. All subjects were given appropriate pre- and post-test HIV counseling. Subjects commenced treatment after enrollment and received standard short course chemotherapy for tuberculosis as per guidelines of the Malawi National Tuberculosis Program [25]. Adults with a previous history of treated pulmonary tuberculosis were excluded. Three sputum samples from each subject were examined with Auramine-O dark-fluorescent staining method. Sputum positive pulmonary tuberculosis was considered proven when at least one out of three sputum stains showed acid-fast bacilli. HIV infection was diagnosed on the basis of a positive rapid test (Determine 1/2 Rapid test by Abbott, Abbott Laboratories, Johannesburg, SA) and confirmed by a positive enzyme-linked immunosorbent assay for HIV-1 antibodies (Wellcozyme; Wellcome Diagnostics, Dartford, Kent, UK). Plasma HIV load was measured using quantitative HIV-1 RNA PCR (Roche Amplicor Monitor, version 1.5, Branchburg, NJ, USA) with a sensitivity limit of 400 HIV RNA copies mL. CD4+ lymphocyte counts were not conducted due to limited resources. None of the participants were taking antiretroviral treatment.
The protocol was approved by the institutional review boards at the Johns Hopkins School of Medicine (Baltimore, Maryland, USA) and the College of Medicine, University of Malawi (Blantyre, Malawi), with final approval by the Office for Protection from Research Risk of the National Institutes of Health.
Nutritional assessment
Body weight was determined to the nearest 0.1 kg using an adult balance (Seca 700 balance, Seca Corporation, Hanover, MD, USA), and standing height was determined to the nearest cm. Body mass index (BMI) was calculated as body weight/height2.
Plasma micronutrient concentrations
A venous blood sample was collected by venipuncture (Sarstedt Monovette, Newton, NC). Blood samples were shielded from bright light and immediately aliquoted and stored in cryotubes at -70°C. α-carotene, β-carotene, β-cryptoxanthin, lycopene, lutein, zeaxanthin, retinol, and α-tocopherol concentrations were measured in 100 uL of plasma by high performance liquid chromatography using a modified method from the Nutrition Laboratory, Inorganic Toxicology and Nutrition Branch Division of Laboratory Sciences, National Center of Environmental Health, Centers of Disease Control and Prevention (Rosemary Schleicher, personal communication) [27]. Total plasma carotenoids were defined as the sum of α-carotene, β-carotene, β-cryptoxanthin, lycopene, lutein and zeaxanthin in μmol/L. Plasma trace element concentrations were measured using a Perkin Elmer model AAnalyst 600 atomic absorption spectrometer equipped with Zeeman background correction, a THGA graphite furnace, and an AS800 auto sampler (Perkin Elmer Corp., Norwalk, CT). Quality control was assessed by repeated analysis of pooled human plasma controls run at the beginning and the end of each analysis. Standard curves were run periodically using standard reference material 986C (National Institute of Standards and Technology, Gaithersburg, MD). Throughout all analyses, the plasma samples were run in a masked fashion.
Data and statistical analysis
Data and statistical analysis were conducted using SAS 8.01 (SAS Institute Cary, NC, USA) and SPSS 9.0 (SPSS, Inc., Chicago, IL, USA). Comparisons between groups were made using t-tests and nonparametric Mann-Whitney U-tests. Univariate analysis of variance was used to test for linear trends across categories of plasma HIV load and BMI.
HIV load was categorized into tertiles. HIV negative subjects were assigned a fourth category of HIV load (category 0) when groups were merged for analysis. Nutritional status was assessed in adults with pulmonary tuberculosis with and without HIV co-infection. Subjects were separated into groups according to their extent of wasting. Mild wasting was defined as BMI 17.0–18.49, moderate wasting as BMI 16.0–16.99, and severe wasting as BMI <16.0, conform the WHO strata for BMI grading of severity of malnutrition [27].
Plasma retinol <0.70 μmol/L was considered consistent with vitamin A deficiency [28]. Vitamin E deficiency was defined as plasma α-tocopherol <11.6 μmol/L [28]. Zinc deficiency was defined as plasma zinc <11.5 μmol/L and selenium deficiency as plasma selenium <0.89 μmol/L [28]. Because there is no standard cut-off for deficiency of carotenoids, we divided total plasma carotenoids into quartiles, with the lowest quartile considered to be the most consistent with deficiency.
To examine the risk of micronutrient deficiencies at different severity level of wasting, logistic regression models were fitted with retinol <0.70, α-tocopherol <11.6, zinc <11.5, selenium <0.89, and the lowest quartile of total carotenoids as the outcome variable. Multivariate logistic regression models were conducted to adjust for sex, age and HIV load. A significance level of P < 0.01 was used in this study.
Results
The study population consisted of 579 HIV-positive and 222 HIV-negative adults with sputum-positive pulmonary tuberculosis. Among the total study population, 66% (232/352) of male and 77% (347/449) of female participants were HIV-positive. The mean age among all subjects was 33 years (range 18–59 years). The majority of subjects were wasted, as 59% of subjects had a BMI <18.5, 32% of subjects had a BMI <17.0, and 17% of all subjects were severely wasted as defined by BMI<16.0. Plasma retinol concentrations <0.70 μmol/L occurred in 61% of all subjects. Vitamin E, zinc, and selenium deficiency occurred in 13%, 85% and 87% respectively. Table 1 shows characteristics of study participants, such as sex, age, BMI, and plasma carotenoids, retinol, α-tocopherol, zinc and selenium by categories of plasma HIV load. BMI, plasma retinol, total carotenoids and selenium concentrations decreased by increasing plasma HIV load. Age, the proportion of individuals with BMI <18.5, BMI <16.0 and selenium deficiency were increased with increasing plasma HIV load. Plasma α-tocopherol, zinc or the proportion of individuals with vitamin A, vitamin E, or zinc deficiencies were not significantly different across the categories of plasma HIV load. When exploring across the categories separately, there were no significant differences between HIV-negative individuals compared with HIV-positive individuals in the lowest tertile of viral load.
Table 2 shows adjusted odds ratios (O.R.) and 95% confidence intervals (C.I.) for independent associations between wasting and micronutrient deficiencies. Wasting defined as BMI<18.5 was associated with vitamin A deficiency, low plasma carotenoids and selenium deficiency. The odds ratio for an independent association with vitamin A deficiency was 2.86 (95% C.I. 2.11–3.89) when adjusted for sex, age, and plasma HIV load. The adjusted odds ratio for an independent association with the lowest quartile of total carotenoids was 2.96 (95% C.I. 1.99–4.44). The adjusted odds ratio for an independent association with selenium deficiency was 1.59 (95% C.I. 1.04–2.43). When separating severity levels of wasting; mild wasting did not show association with deficiencies, moderate wasting was associated with vitamin A deficiency and severe wasting was significantly associated with vitamin A deficiency, low plasma carotenoids and selenium deficiency. (Table 2) Wasting was not associated with vitamin E or zinc deficiency.
Figures 1, 2 and 3 show plasma retinol, total plasma carotenoids, and plasma selenium concentrations with 95% C.I by severity of wasting and categories of plasma HIV load.
Plasma retinol concentrations significantly decreased with the increase of plasma HIV load among non-wasted adults with pulmonary tuberculosis (P = 0.004). Total carotenoid concentrations significantly decreased with the increase of plasma HIV load among non-wasted, mildly wasted, moderately wasted and severely wasted adults (P = 0.0001, P = 0.002, P = 0.001 and P = 0.001, respectively). Selenium concentrations decreased significantly with the increase of plasma HIV load among non-wasted and severely wasted adults with pulmonary tuberculosis (P = 0.0001 and P = 0.03, respectively). Among the HIV negative adults and those in the 1st and 2nd tertile of HIV load, plasma retinol, total carotenoids and selenium concentrations significantly decrease with the increasing severity of wasting. Among those in the 3rd tertile of HIV load, only plasma retinol concentrations significantly decreased with the increasing severity of wasting. This trend did not reach significance for plasma carotenoid and selenium concentrations.
Discussion
The present study shows that micronutrient malnutrition and wasting are more severe in adults with pulmonary tuberculosis who have higher plasma HIV load. The association between high plasma HIV load and nutrient deficiencies was strongest for the major plasma carotenoids and selenium. Overall in this study population, both HIV-positive and HIV-negative adults with pulmonary tuberculosis were extremely malnourished as indicated by BMI and plasma micronutrient concentrations. About one-third of the adults in this study had a BMI <17.0, a cut-off that is predictive of mortality in adults co-infected with tuberculosis and HIV [29].
To our knowledge, this is the first study to demonstrate that selenium status is extremely poor among HIV-infected adults with pulmonary tuberculosis, and that the extent of selenium deficiency is associated with higher plasma HIV load. This observation may be of potential importance because selenium deficiency has been associated with increased mortality during HIV infection [30], and selenium supplementation for HIV-infected adults has been shown to reduce morbidity [31]. In the present study, selenium deficiency occurred in 87% of the participants, which, to our knowledge, may be the highest prevalence of selenium deficiency reported in an HIV-infected group of patients. It is unknown whether selenium supplementation will reduce morbidity and mortality among HIV-infected adults with pulmonary tuberculosis.
Carotenoids are among the most important dietary antioxidants found in human plasma, and this study shows that poor carotenoid status was associated with higher HIV load and with wasting. Plasma carotenoid concentrations are widely considered to be the most accurate indicator of dietary carotenoid intake [32]. It is not known whether adults with pulmonary tuberculosis and higher HIV load have lower plasma carotenoid concentrations because of increased oxidative stress, or whether these individuals are sicker and unable to consume enough carotenoid-rich foods. Further studies are needed in the future to address dietary intake of carotenoids in HIV-infected adults with pulmonary tuberculosis.
Low BMI is a known risk factor for mortality [5,29], and the present study showed that the risk of micronutrient deficiencies is highest in those with low BMI.
HIV-infected adults with wasting and high viral load were at the highest risk of more severe micronutrient malnutrition, suggesting that this subgroup might potentially benefit the greatest from nutritional interventions.
The cross sectional design of this study restricts our conclusions and does not provide information on whether poor nutritional status is a predictor of more severe pulmonary tuberculosis. It is unknown whether nutritional interventions will slow progression of disease or reduce wasting associated with morbidity and mortality if added to tuberculosis chemotherapy. Controlled clinical trials currently in progress in developing countries should help provide insight into the role of micronutrient supplementation for HIV-positive and HIV-negative adults with pulmonary tuberculosis.
Conclusions
The present study shows that micronutrient malnutrition and wasting are more severe in adults with pulmonary tuberculosis who have higher HIV load. The association between high plasma HIV load and nutrient deficiencies was strongest for the major plasma carotenoids and selenium. Further longitudinal investigations are needed to determine whether deficiencies in micronutrients are independent risk factors for increased morbidity and mortality.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
Overall guidance and initial study design was provided by RS. MvL has been in charge of the collection and analysis of data and writing of the manuscript. Provision of advice was given by AH, JK, EZ, TC and TT. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Supported in part by the National Institutes of Health (AI41956), the Fogarty International Center, and the United States Agency for International Development (Cooperative Agreement HRN A-0097-00015-00).
We thank Dana Totin Moncrief, Barbara Dancheck, Amanda Ray, and Michelle Ricks for their contributions and guidance in laboratory and data analyses. We thank the research team for their diligence
Figures and Tables
Figure 1 Log-transformed mean plasma retinol concentrations with 95% C.I. are depicted by severity of wasting and plasma HIV load. Among the not-wasted adults with pulmonary tuberculosis log mean plasma retinol concentration significantly decrease with the increase of plasma HIV load (P = 0.004). Among those with mild, moderate and severe wasting this linear trend did not reach significance. Among the HIV negative adults with pulmonary tuberculosis, log mean plasma retinol concentration significantly decrease with the increasing severity of wasting (P = 0.0001). The same trend appears among those in the 1st, 2nd and 3rd tertile of HIV load; P = 0.0001, P = 0.0001 and P = 0.01 respectively.
Figure 2 Log-transformed mean plasma total carotenoid concentrations with 95% C.I. are depicted by severity of wasting and plasma HIV load. Among not-wasted, mildly wasted, moderately wasted and severely wasted log mean plasma total carotenoid concentrations significantly decrease with the increase of plasma HIV load (P = 0.0001, P = 0.002, P = 0.001 and P = 0.001, respectively). Among the HIV negative adults, and those in the 1st and 2nd tertile of plasma HIV load, log mean plasma total carotenoid concentrations significantly decreased with the increasing severity of wasting (P = 0.007, P = 0.002 and P = 0.0001, respectively). This trend did not reach significance among those in the 3rd tertile of plasma HIV load.
Figure 3 Mean plasma selenium concentrations with 95% C.I. are depicted by severity of wasting and plasma HIV load. Among not-wasted and severely wasted adults, mean plasma selenium concentrations significantly decrease with the increase of plasma HIV load (P = 0.0001 and P = 0.03, respectively). This trend did not reach significance among those with mild and moderate wasting. Among the HIV negative adults, and those in the 1st and 2nd tertile of plasma HIV load, mean plasma selenium concentrations significantly decreased with the increasing severity of wasting (P = 0.02, P = 0.008 and P = 0.0001, respectively). This trend did not reach significance among those in the 3rd tertile of plasma HIV load.
Table 1 Characteristics of adults presenting with pulmonary tuberculosis in Zomba, Malawi – by plasma HIV load
Characteristic1 HIV negative HIV positive* Plasma HIV Load (copies/mL) P-value**
0 ≤ 133 200 133 200 – 406 000 > 406 000
n = 222 n = 185 n = 186 n = 186
Sex (% Female) 45.9 63.2 58.1 55.9 0.07
Age (years) 33 ± 12 32 ± 12 33 ± 12 33 ± 12 0.01
Body mass index (BMI) (wt/ht2) 18.6 ± 2.9 19.0 ± 2.6 18.3 ± 3.0 17.3 ± 2.7 0.0001
Wasting:2 No, BMI ≥ 18.5 42.3 51.4 41.9 30.1 0.005
Mild, BMI 17.0–18.49 (%) 32.0 27.0 22.6 24.2 0.04
Moderate, BMI 16.0–16.99 (%) 13.1 10.3 17.2 18.8 0.04
Severe, BMI <16.0 (%) 12.6 11.4 18.3 26.9 0.001
Retinol (μmol/L) 0.636 (0.367, 1.104) 0.603 (0.336, 1.085) 0.585 (0.321, 1.066) 0.522 (0.157, 1.738) 0.001
Vitamin A deficiency, retinol <0.70 μmol/L (%)3 58.6 57.3 64.0 66.7 0.05
Total Carotenoids (μmol/L)4 0.846 (0.490, 1.459) 0.795 (0.476, 1.329) 0.700 (0.385, 1.279) 0.509 (0.279, 0.929) 0.0001
α-tocopherol (μmol/L) 15.18 (11.71, 19.65) 14.90 (11.60, 19.16) 15.66 (11.85, 20.71) 16.07 (11.53, 22.40) 0.02
Vitamin E def., α-tocopherol <11.6 μmol/L (%)3 13.1 14.6 11.8 12.4 0.67
Zinc (μmol/L) 8.95 (7.01, 11.43) 8.83 (6.94, 11.25) 8.49 (6.44, 11.19) 9.15 (6.47, 12.94) 0.82
Zinc deficiency, zinc <11.5 μmol/L (%)3 84.2 88.1 87.6 77.4 0.09
Selenium (μmol/L) 0.687 ± 0.23 0.664 ± 0.22 0.624 ± 0.22 0.559 ± 0.21 0.0001
Selenium deficiency, selenium<0.89 μmol/L(%)3 79.7 84.9 90.3 92.5 0.001
1 Mean ± SD for continues variables with normal distribution, geometric mean (lower, upper SD) when distribution was not normal
2 Grading based on WHO Expert report, reference 27.
3 Cut-offs based on reference 28.
4α-carotene + β-carotene + β-cryptoxanthin + lycopene + lutein + zeaxanthin
* HIV load could not be determined for 21 individuals.
* *ANOVA, linear trend across the 4 categories of plasma HIV load.
Table 2 Risk of micronutrient deficiencies at different severity levels of wasting in adults with pulmonary tuberculosis with and without HIV co-infection.
Mild wasting Moderate wasting Severe wasting
BMI 17.0–18.49 BMI 16.0–16.99 BMI <16.0
Deficiency O.R. (95% C.I.)* P-value O.R. (95% C.I.)* P-value O.R. (95% C.I.)* P-value
Vitamin A deficiency 0.81 (0.58–1.13) 0.20 1.59 (1.03–2.47) 0.03 3.51 (2.19–5.72) 0.0001
Lowest quartile of Total Carotenoids 0.92 (0.62–1.36) 0.67 2.46 (1.57–3.85) 0.0001 1.82 (1.18–2.83) 0.007
Vitamin E deficiency 0.86 (0.54–1.37) 0.54 1.24 (0.70–2.18) 0.46 1.13 (0.64–1.97) 0.68
Zinc deficiency 0.71 (0.45–1.15) 0.17 0.61 (0.37–1.00) 0.05 0.76 (0.46–1.24) 0.27
Selenium deficiency 1.59 (1.04–2.43) 0.03 1.16 (0.62–2.17) 0.65 3.25 (1.38–7.62) 0.007
* Adjusted for sex (male), age (per year) and HIV load (quartiles, where category 0 is HIV negative and category 3 is the highest.
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| 15613232 | PMC544350 | CC BY | 2021-01-04 16:03:30 | no | BMC Infect Dis. 2004 Dec 21; 4:61 | utf-8 | BMC Infect Dis | 2,004 | 10.1186/1471-2334-4-61 | oa_comm |
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BMC Med GenetBMC Medical Genetics1471-2350BioMed Central London 1471-2350-5-291561055810.1186/1471-2350-5-29Research ArticleAllele frequencies of hemojuvelin gene (HJV) I222N and G320V missense mutations in white and African American subjects from the general Alabama population Barton James C [email protected] Charles A [email protected] Sandrine [email protected] Sean B [email protected] Ronald T [email protected] Southern Iron Disorders Center, Birmingham, Alabama2 Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama3 Immunogenetics Program and Departments of Microbiology and Epidemiology and International Health, University of Alabama at Birmingham, Birmingham, Alabama2004 20 12 2004 5 29 29 18 8 2004 20 12 2004 Copyright © 2004 Barton et al; licensee BioMed Central Ltd.2004Barton et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Homozygosity or compound heterozygosity for coding region mutations of the hemojuvelin gene (HJV) in whites is a cause of early age-of-onset iron overload (juvenile hemochromatosis), and of hemochromatosis phenotypes in some young or middle-aged adults. HJV coding region mutations have also been identified recently in African American primary iron overload and control subjects. Primary iron overload unexplained by typical hemochromatosis-associated HFE genotypes is common in white and black adults in Alabama, and HJV I222N and G320V were detected in a white Alabama juvenile hemochromatosis index patient. Thus, we estimated the frequency of the HJV missense mutations I222N and G320V in adult whites and African Americans from Alabama general population convenience samples.
Methods
We evaluated the genomic DNA of 241 Alabama white and 124 African American adults who reported no history of hemochromatosis or iron overload to detect HJV missense mutations I222N and G320V using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique. Analysis for HJV I222N was performed in 240 whites and 124 African Americans. Analysis for HJV G320V was performed in 241 whites and 118 African Americans.
Results
One of 240 white control subjects was heterozygous for HJV I222N; she was also heterozygous for HFE C282Y, but had normal serum iron measures and bone marrow iron stores. HJV I222N was not detected in 124 African American subjects. HJV G320V was not detected in 241 white or 118 African American subjects.
Conclusions
HJV I222N and G320V are probably uncommon causes or modifiers of primary iron overload in adult whites and African Americans in Alabama. Double heterozygosity for HJV I222N and HFE C282Y may not promote increased iron absorption.
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Background
Homozygosity or compound heterozygosity for coding region mutations of the hemojuvelin gene (HJV) in whites is a cause of early age-of-onset iron overload (juvenile hemochromatosis), and of hemochromatosis phenotypes in some young or middle-aged adults [1-4]. HJV coding region mutations have also been identified recently in African American primary iron overload and control subjects [5]. We estimated the frequency of the HJV missense mutations I222N and G320V in Alabama white and African American adults who reported no history of hemochromatosis or iron overload, because primary iron overload unexplained by typical hemochromatosis-associated HFE genotypes is common in whites and blacks in Alabama [6-8], and HJV I222N and G320V were detected in an Alabama juvenile hemochromatosis index patient [3]. In addition, HJV I222N and G320V have been described in white adults with hemochromatosis phenotypes in other geographic areas [1,2,9].
Methods
General criteria for selection of study subjects
Performance of this study was approved by the Institutional Review Boards of the University of Alabama at Birmingham and Brookwood Medical Center. Unrelated white subjects were spouses of patients who attended a hematology and medical oncology outpatient clinic; hospital employees; and controls who participated in a sleep study. Unrelated control black subjects were spouses of patients who attended a hematology and medical oncology outpatient clinic; hospital employees; controls who participated in a sleep study; and controls who participated in a study of gestational diabetes mellitus. All subjects were adults (≥ 18 years old). No subject reported a previous diagnosis of hemochromatosis or iron overload. Serum transferrin saturation, serum ferritin concentration, or other indicators of iron metabolism were not measured in most study subjects.
Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analyses
Genomic DNA was isolated from blood buffy coat as described previously [6]. HJV I222N and G320V were genotyped by standard PCR-RFLP and agarose electrophoresis techniques. Exon 4 of the HJV locus was amplified using the primers HJEx4AF and HJEx4BR previously described [3]; this allowed for the amplification of the region containing both the I222N and G320V mutations of the HJV locus. Reaction conditions were as follows: 10 mM KCl, 10 mM (NH4)2SO4, 20 mM Tris-HCl (pH 8.8), 0.1% Triton X-100, 3.2 mM MgSO4, 200 μM dATP, 200 μM dCTP, 200 μM dGTP, 200 μM dTTP, 1 μM of each primer, 0.2 U/μL of Taq Polymerase. Amplification conditions on a Biometra UNO II thermocycler (LabRepCo/LabRepNet, Horsham, PA) consisted of an initial denaturing at 94°C for five minutes followed by thirty cycles of 94°C for 30 seconds, 60°C for 30 seconds and 72°C for 30 seconds.
Delineation of c.665T→A of the HJV exon 4 nucleotide sequence defining I222N was made utilizing the restriction endonuclease Bcc I (New England Biolabs, Beverly, MA), which recognizes the nucleotide c.665T of I222 as a restriction site, and not the nucleotide c.665A of N222. Similarly, the nucleotide polymorphism site c.959G→T defining the G320V polymorphism of the HJV exon 4 locus was determined by using the restriction endonuclease Ban I (New England Biolabs, Beverly, MA). Ban I recognizes the nucleotide c.959G of G320 as part of the restriction site sequence, but not the nucleotide c.959T of V320. DNA specimens from the parents of a white juvenile hemochromatosis index case were used as positive controls for HJV I222N and G320V [3].
Statistical considerations
The data set consisted of observations on 241 Alabama white and 124 African American adults. Analysis for HJV I222N was performed in 240 whites and 124 African Americans. Analysis for HJV G320V was performed in 241 whites and 118 African Americans. Results are expressed as allele frequencies and 95% confidence intervals (CI). To estimate the CI for frequencies of HJV mutations that were not detected in the present subjects, we computed the allele frequency as the quotient of single minimal hypothetical value (= 0.01) and the number of chromosomes corresponding to the respective groups of subjects. Computations were performed with GB-STAT (v10.0; Dynamic Microsystems, Inc., Silver Spring, MD).
Results
Allele frequencies of HJV I222N and G320V
These results are displayed in Table 1. One of 240 white subjects was heterozygous for HJV I222N, but this mutation was not detected in African American subjects. HJV G320V was not detected in either white or African American subjects.
Table 1 HJV missense mutations in Alabama adult subjects from the general population.1
HJV mutation I222N G320V
Whites tested, n 240 241
Whites with mutation, n 1 (heterozygote) 0
Allele frequency (95% CI) 0.0021 (0, 0.0062) 0 (0, ~0.00006)
African Americans tested, n 124 118
African Americans with mutation, n 0 0
Allele frequency (95% CI) 0 (0, ~0.00012) 0 (0, ~0.00013)
1HJV mutations were detected using PCR-RFLP technique. DNA specimens from the parents of a white juvenile hemochromatosis index case were used as positive controls for HJV I222N and G320V [3]. CI = confidence interval.
Characteristics of a HJV I222N heterozygote
Medical records of the white subject heterozygous for HJV I222N were available for review. This 29 year-old woman was also heterozygous for HFE C282Y. She reported that she had had two normal pregnancies and one spontaneous abortion. She has always experienced heavy menstrual flow, but she reported no other significant blood loss. She reported no blood donation. She reported that she eats a variety of meats, vegetables, and other foods; there was no history of supplemental iron ingestion. Her hemoglobin level was 12.8 g/dL, platelets 113,000/mm3, transferrin saturation 21%, and serum ferritin 23 ng/mL; marrow examination performed to evaluate thrombocytopenia revealed normal cellular morphology and normal iron stores.
Discussion
A variety of HJV coding region mutations occur in homozygous or compound heterozygous configuration in persons with juvenile or adult-onset hemochromatosis phenotypes [1-5,9-11]. However, these types of HJV-associated hemochromatosis are collectively uncommon [1-3,11,12]. HJV mutations were also uncommon in control whites studied with dHPLC (denaturing high-performance liquid chromatography) [9]. In 200 Greek volunteer blood donors, none carried the HJV G320V mutation detectable by PCR-RFLP analysis, suggesting that the frequency of the G320V allele in the Greek population is lower than 0.004 [11]. In French control subjects, for example, only the HJV missense mutations L101P (two heterozygotes) and E302K (one heterozygote) were identified in 333 control subjects, representing an aggregate HJV mutation frequency of 0.0045 (95% CI: 0, 0.0096) [9]. The present results in Alabama white control subjects are consistent with these previous reports. Taken together, these observations confirm and extend previous observations that the individual or aggregate frequency of HJV coding region mutations is low in general populations of whites [9].
Heterozygosity for a HJV missense mutation was associated with increased severity of iron overload in nine of 310 French patients with hemochromatosis and HFE C282Y homozygosity who were studied with dHPLC [9]. In 48 white U.S. hemochromatosis patients with HFE C282Y homozygosity studied with dHPLC, no HJV coding region mutation was detected [5]. Thus, aggregate HJV coding region mutation frequencies in C282Y homozygotes with a hemochromatosis phenotype in France and the U.S. are similar (0.0148 vs. 0, respectively; p = 0.2716, Fisher exact test). The aggregate frequency of HJV coding region mutations was similar in French hemochromatosis patients with HFE C282Y homozygosity and in French control subjects (0.0145 vs. 0.0045, respectively; p = 0.0564, Fisher exact test), although the hemochromatosis patients with HJV coding region mutations had more severe iron overload, on the average, than did hemochromatosis patients without HJV coding region mutations [9].
Double heterozygosity for HJV I222N and HFE C282Y was not associated with evidence of increased iron absorption in the present case. Some persons with JH have common HFE genotypes, including C282Y heterozygosity, H63D heterozygosity or homozygosity, and S65C heterozygosity [13-18]. However, these HFE genotypes are infrequently associated with a severe hemochromatosis phenotype [6,7,19-25]. Further, sequencing HFE introns and exons in English and French Canadian JH cases did not reveal novel HFE mutations that could likely explain the development of iron overload [14,15]. In 310 C282Y homozygotes in France, HJV mutations were relatively common and were associated with greater severity of iron overload [9]. In 48 C282Y homozygotes in the U.S. who had severe iron overload phenotypes, no HJV coding region mutation was detected [5]. The frequency of H63D is significantly greater in persons with cardiomyopathy than in normal control subjects [26]. The brother of a Spanish JH proband had evidence of iron overload associated with H63D homozygosity and heterozygosity for a putative JH-associated Ch1q haplotype [17]. Although these reports are consistent with observations in another JH patient with cardiomyopathy who had HFE H63D [3,12], other persons with JH and cardiomyopathy did not have H63D [15]. Altogether, these reports indicate that further exploration of the potential role of variant HJV alleles in the clinical expression of iron overload with particular reference to HFE hemochromatosis is warranted [2], and that the frequency and biological significance of HJV alleles may vary among racial/ethnic groups.
Primary iron overload in African Americans is phenotypically and genetically heterogeneous [8]. Some patients have common HFE mutations, a common allele of the ferroportin gene (FPN1 Q248H), or heritable types of anemia [8,27,28], but the genetic basis of most cases remains unknown. HJV I222N and G320V were not detected in the present African American subjects. However, HJV coding region mutations were recently identified in two of 51 African American subjects with iron overload: exon 3, nt 205–207 ins GGA (ins G69) (frequency 0.0098); and exon 4, nt 929 C→G (A310G) (allele frequency 0.0196) [5]. The HJV alleles ins G69 and A310G were also identified in African American control subjects (allele frequencies 0.0038 and 0.0720, respectively) [5]. Taken together, these observations suggest that HJV coding region mutations are uncommon in African Americans, but may account for some cases of primary iron overload [5].
Conclusions
HJV I222N and G320V are uncommon causes or modifiers of primary iron overload in adult whites and African Americans in Alabama. Double heterozygosity for HJV I222N and HFE C282Y may not promote increased iron absorption.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
JCB participated in conceiving the study, provided DNA specimens, performed statistical evaluation, and wrote part of the manuscript. CAR devised the PCR-RFLP assay, provided DNA specimens, participated in data collection, and wrote part of the manuscript. SN and SB performed testing on DNA specimens and participated in data collection. RTA participated in conceiving the study, provided DNA specimens, and wrote part of the manuscript. All authors approved the final version of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
This work was supported in part by the Immunogenetics Program, U.S. Department of Education McNair Scholars Program grant P217A030260, National Institutes of Health/ National Heart, Lung, and Blood Institute Short-Term Training Grant for Students in Health Professional Schools T35 HL07473, and Southern Iron Disorders Center.
==== Refs
Papanikolaou G Samuels ME Ludwig EH MacDonald ML Franchini PL Dube MP Andres L MacFarlane J Sakellaropoulos N Politou M Nemeth E Thompson J Risler JK Zaborowska C Babakaiff R Radomski CC Pape TD Davidas O Christakis J Brissot P Lockitch G Ganz T Hayden MR Goldberg YP Mutations in HFE2 cause iron overload in chromosome 1q-linked juvenile hemochromatosis Nat Genet 2004 36 77 82 14647275 10.1038/ng1274
Lanzara C Roetto A Daraio F Rivard S Ficarella R Simard H Cox TM Cazzola M Piperno A Gimenez-Roqueplo AP Grammatico P Volinia S Gasparini P Camaschella C Spectrum of hemojuvelin gene mutations in 1q-linked juvenile hemochromatosis Blood 2004 103 4317 4321 14982873 10.1182/blood-2004-01-0192
Lee PL Beutler E Rao SV Barton JC Genetic abnormalities and juvenile hemochromatosis: mutations of the HJV gene encoding hemojuvelin Blood 2004 103 4669 4671 14982867 10.1182/blood-2004-01-0072
Robson KJ Merryweather-Clarke AT Cadet E Viprakasit V Zaahl MG Pointon JJ Weatherall DJ Rochette J Recent advances in understanding haemochromatosis: a transition state J Med Genet 2004 41 721 730 15466004 10.1136/jmg.2004.020644
Lee PL Barton JC Brandhagen D Beutler E Hemojuvelin (HJV) mutations in persons of European, African-American and Asian ancestry with adult onset haemochromatosis Br J Haematol 2004 127 224 229 15461631 10.1111/j.1365-2141.2004.05165.x
Barton JC Shih WW Sawada-Hirai R Acton RT Harmon L Rivers C Rothenberg BE Genetic and clinical description of hemochromatosis probands and heterozygotes: evidence that multiple genes linked to the major histocompatibility complex are responsible for hemochromatosis Blood Cells Mol Dis 1997 23 135 145 9215758 10.1006/bcmd.1997.0129
Barton JC Sawada-Hirai R Rothenberg BE Acton RT Two novel missense mutations of the HFE gene (I105T and G93R) and identification of the S65C mutation in Alabama hemochromatosis probands Blood Cells Mol Dis 1999 25 147 155 10575540 10.1006/bcmd.1999.0240
Barton JC Acton RT Rivers CA Bertoli LF Gelbart T West C Beutler E Genotypic and phenotypic heterogeneity of African Americans with primary iron overload Blood Cells Mol Dis 2003 31 310 319 14636644 10.1016/S1079-9796(03)00166-9
Le Gac G Scotet V Ka C Gourlaouen I Bryckaert L Jacolot S Mura C Ferec C The recently identified type 2A juvenile haemochromatosis gene (HJV), a second candidate modifier of the C282Y homozygous phenotype Hum Mol Genet 2004 13 1913 1918 15254010 10.1093/hmg/ddh206
Huang FW Rubio-Aliaga I Kushner JP Andrews NC Fleming MD Identification of a novel mutation (C321X) in HJV Blood 2004 104 2176 2177 15138164 10.1182/blood-2004-01-0400
Pissia M Polonifi K Politou M Lilakos K Sakellaropoulos N Papanikolaou G Prevalence of the G320V mutation of the HJV gene, associated with juvenile hemochromatosis, in Greece Haematologica 2004 89 742 743 15194541
Barton JC Rao SV Pereira NM Gelbart T Beutler E Rivers CA Acton RT Juvenile hemochromatosis in the southeastern United States: a report of seven cases in two kinships Blood Cells Mol Dis 2002 29 104 115 12482411 10.1006/bcmd.2002.0545
Kaltwasser JP Barton JC and Edwards CQ Juvenile hemochromatosis Hemochromatosis Genetics, pathophysiology, diagnosis, and treatment 2000 Cambridge, Cambridge University Press 318 328
Rivard SR Mura C Simard H Simard R Grimard D Le Gac G Raguenes O Ferec C De Braekeleer M Clinical and molecular aspects of juvenile hemochromatosis in Saguenay-Lac-Saint-Jean (Quebec, canada) Blood Cells Mol Dis 2000 26 10 14 10772871 10.1006/bcmd.2000.0271
Kelly AL Rhodes DA Roland JM Schofield P Cox TM Hereditary juvenile haemochromatosis: a genetically heterogeneous life-threatening iron-storage disease QJM 1998 91 607 618 10024915 10.1093/qjmed/91.9.607
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| 15610558 | PMC544351 | CC BY | 2021-01-04 16:31:07 | no | BMC Med Genet. 2004 Dec 20; 5:29 | utf-8 | BMC Med Genet | 2,004 | 10.1186/1471-2350-5-29 | oa_comm |
==== Front
BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-911560146710.1186/1471-2407-4-91Research ArticleWnt1 is epistatic to Id2 in inducing mammary hyperplasia, ductal side-branching, and tumors in the mouse Marino Susan [email protected] Claire [email protected] Yoshifumi [email protected] Roel [email protected] Department of Developmental Biology, Howard Hughes Medical Institute, Beckman Center, Stanford University Medical School Stanford, CA 94305, USA2 Department of Biochemistry, Fukui Medical University, Shimoaizuki 23-3, Matsuoka, Fukui 910-1193, Japan2004 15 12 2004 4 91 91 19 8 2004 15 12 2004 Copyright © 2004 Marino et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
During pregnancy, the mammary glands from Id2 mutant animals are deficient in lobulo-alveolar development. This failure of development is believed to be due to a proliferation defect.
Methods
We have asked whether functional Id2 expression is necessary for Wnt induced mammary hyperplasia, side branching, and cancer, by generating mice expressing a Wnt1 transgene in an Id2 mutant background.
Results
We show in this work that forced expression of Wnt1 in the mammary gland is capable of overcoming the block to proliferation caused by the absence of Id2. We also show that Wnt1 expression is able to cause mammary tumors in an Id2 mutant background.
Conclusions
We conclude that functional Id2 expression is not required for Wnt1 to induce mammary hyperplasia and mammary tumors.
==== Body
Background
Basic helix-loop-helix (bHLH) transcription factors such as MyoD, E12, and E47 are key regulators of gene expression and control many differentiation events during development [1-3]. These transcription factors bind E-box or E-box-like sequences as homo-or heterodimers, and control the transcription of target genes containing these sequences in their promoters. The HLH domains dimerize with each other, whereas the basic domains bind to DNA. The Id (Inhibitor of DNA binding) proteins are HLH proteins that lack a basic domain. Id proteins act as dominant inhibitors of bHLH transcription factors by blocking their ability to bind to DNA and activate gene transcription [2,3]. Since the bHLH proteins regulate cell-type specific gene expression during cell commitment and differentiation, the formation of inactive heterodimers of bHLH proteins with Id proteins inhibits the commitment and differentiation the bHLH proteins promote.
There are 4 mammalian Id genes, which show differences in their patterns of expression and function [2,3]. One of them, Id2, is expressed in glandular and ductal epithelium of the mouse mammary gland and has also been implicated in its development. Mammary glands of female mice that are homozygous mutant for Id2 have impaired lobulo-alveolar development [4]. In several tissues, including colon tumors induced by activation of the Wnt pathway, the expression of Id2 is regulated by Wnt-β catenin signaling [5,6]. It has been proposed that Wnt signaling may inhibit differentiation and promote the maintenance of a proliferative state by increasing Id2 expression, thereby leading to cancer. We have asked therefore whether functional Id2 expression is necessary for Wnt induced mammary hyperplasia, side branching and cancer, by generating mice expressing a Wnt1 transgene in an Id2 mutant background.
Methods
We used heterozygous Id2 males and females on a 129/Sv background. Id2 genotyping was done by PCR (95C, 5 min; 62C, 1 min, 72C, 1 min, 95C,1 min, 30 cycles; 62C, 1 min, 72C, 5 min) using primers Id2-S (5'-tctgagcttatgtcgaatgatagc-3'), Id-2AS (5'-cgtgttctcctggtgaaatggctg-3'), and neo 1 (5'-tcgtgctttacggtatcgccgctc-3").
Hemizygous transgenic MMTV-Wnt1 males on a mixed FVB/BL6/SJL background were obtained from Yi Li in the H.Varmus laboratory. Genotyping was done by PCR (94C, 4 min; 94C, 45 sec, 55C, 30 sec, 72C, 60 sec, 30 cycles; 72C, 10 min) using Wnt1 (5'-gaacttgcttctcttctcatagcc-3') and SV40 (5'-ccacacaggcatagagtgtctgc-3') primers that produce a 350 bp product in transgenic mice.
Carmine staining
Five mammary glands per mouse were removed and fat and muscle were dissected away. The glands were flattened between two slides and flooded with Carnoy's fixative (3:1 95% ethanol to glacial acetic acid) and fixed overnight. They were then de-fatted in 3 changes of acetone, rehydrated, stained overnight in 0.2% carmine and 0.5%KSO4, dehydrated, cleared in xylene, and mounted in Permount.
Results
We used mice carrying a transgene in which Wnt1 is under the control of the promoter of the Mouse Mammary Tumor Virus (MMTV-Wnt1 Tg) [7] and we crossed these to Id2 loss of function mutant mice [4,8]. Crosses were set up to avoid reliance on Id2 -/- or Wnt1 transgenic mothers, as these animals cannot feed their young [4,7]. Id2 +/- females were crossed with MMTV-Wnt1 hemizygous transgenic males, producing 7 MMTV-Wnt1 Tg; Id2+/- males (Figure 1). These males were then crossed with Id2 +/- females to produce the experimental and control classes of virgin female mice: MMTV-Wnt1 Tg; Id2 -/-, MMTV-Wnt1 Tg ; Id2 +/-, and MMTV-Wnt1 Tg; Id2, as well as smaller numbers of animals in Id2 -/-; Id2 +/-; and WT classes. (Figure 1)
The subject animals were kept in mixed groups in autoclaved cages because Id2 -/- mice have an immunologic defect. Even with this care, 50% die before maturity [8]. Id2 -/- mice were born in sub-Mendelian ratios, they were smaller than litter-mates, and several died of unknown causes.
We examined the morphology of the mammary gland. At 3, 4.5, and 6 months the ductal branching patterns in normal mammary glands of 24 virgin mice from all six classes were examined in carmine stained whole mounts (Figure 2). As has been previously observed by others, the MMTV-Wnt1 Tg female glands had excessive ductal side branching compared to those of WT females [7] while Id2 +/- and Id2 -/- females glands were similar to those of WT females [4]. Glands from MMTV-Wnt1 Tg ; Id2 +/- and MMTV-Wnt1 Tg ; Id2 -/- mice had branching patterns resembling those of MMTV-Wnt1 Tg females at 3 months (Figure 2). At six months the three classes of transgenic mammary glands had very extensive, dense hyperplasia and side branching resembling that of pregnant wild type mice, while the six month virgin Id2+/- and Id2-/- glands had no additional branching and the six month virgin WT glands had moderate additional branching. Therefore, it appears that forced expression of Wnt1 in virgin mammary glands can overcome the absence of Id2 and lead to a highly branched ductal tree resembling the tree achieved normally during pregnancy.
In parallel, we examined tumor incidence in the animals. We excluded MMTV-Wnt1 Tg ; Id2 -/- females that died without developing tumors before 34 weeks, our endpoint, leaving only 8 animals in this group. Mice were examined and palpated weekly for mammary tumors. The smallest tumors detected using this method were 0.5 cm in diameter, but could be as large as 2 cm. The MMTV-Wnt1 Tg ; Id2 +/+ and MMTV-Wnt1 Tg ; Id2 +/- cohorts consisted of 21 and 23 females respectively. All three cohorts showed a similar rate of tumorigenesis (Figure 3). We concluded that Wnt1 is epistatic to Id2 in tumorigenesis, just as it is in promoting hyperplasia and side branching of the mammary gland (Figure 2).
Discussion
The lack of Id2, a Wnt target, has severe consequences for mouse mammary gland development. During pregnancy, the Id2 mammary gland is deficient in lobulo-alveolar development. This failure of development is believed to be due to a proliferation failure rather than precocious differentiation of the mammary epithelia [4]. We show in this work that forced expression of Wnt1 in the mammary gland is capable of overcoming this block to proliferation. Although many targets of the Wnt pathway have been identified (see ), the mechanism through which hyperplasia and side branching is promoted by Wnt1 expression in the virgin mammary gland is unknown. Our results demonstrate that Wnt1 is not operating solely through Id2 or that it is not operating through Id2 at all.
Another known Wnt target with a similar loss of function phenotype is Cyclin D1 [9], a protein whose expression promotes advancement through the cell cycle and whose over expression results in hyperplasia and tumors in the mammary gland [10]. However, when Wnt1 is expressed in Cyclin D1 -/- mice, only a slight reduction in tumorigenesis is observed [11], suggesting that the Wnt1 pathway also does not operate primarily through Cyclin D1. Furthermore, over expression of Cyclin D1 did not promote lobulo-alveolar development in Id2-/- mice [12], a result that is in contrast to the dense side branching of the Wnt1 Id2-/- phenotype, suggesting that Wnt1 signaling is independent of both Cyclin D1 and Id2 in the mammary gland.
Conclusions
By showing that forced expression of Wnt1 in the mammary gland is capable of overcoming the block to proliferation caused by the absence of Id2, we conclude that functional Id2 expression is not required for Wnt1 to induce mammary hyperplasia and mammary tumors.
Competing interests
The author(s) declare that they have no competing interests.
Author's contributions
SM and CR carried out the experiments. YY participated in the design of the study. SM and RN conceived of the study, participated in its design and coordination and wrote the manuscript. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The work was supported by the Howard Hughes Medical Institute and a grant from the US ARMY Medical Research and Material Command (DAMD17-99-1-9386).
Figures and Tables
Figure 1 The Wnt1 transgene was crossed into an Id2 -/- background in order to produce cohorts of WT, Id2 +/-, and Id2 -/- females with and without the Wnt1 transgene. The scheme was designed so that mothers could feed their own young and so that all cohorts being compared would share a common background.
Figure 2 Carmine stained mammary gland whole mounts were made from virgin females. Top row, left to right: normal mammary gland development was observed in wild type (13 weeks), Id2 +/- (26 weeks), and Id2-/- (26 weeks) animals. Bottom row, left to right: Wnt1 induced hyperplasia and side branching were forced when the MMTV-Wnt1 transgene was present in wild type, Id2+/-, and Id2 -/- animals (all 19 weeks). Branching patterns are representative of a consistent phenotype observed in all mice and in both tumor-free glands and in glandular tissue associated with tumors in the MMTV-Wnt1 mice.
Figure 3 Three cohorts of mice, MMTV-Wnt1 Tg (n = 21), MMTV-Wnt1 Tg; Id2 +/- (n = 23), and MMTV-Wnt1 Tg; Id2 -/- (n = 8) were examined weekly by visual examination and palpation. No differences in tumor incidence were observed. MMTV-Wnt1 Tg : square; MMTV-Wnt1 Tg; Id2 +/- : diamond; MMTV-Wnt1 Tg; Id2 -/-: triangle.
==== Refs
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Norton JD Deed RW Craggs G Sablitzky F Id helix-loop-helix proteins in cell growth and differentiation Trends Cell Biol 1998 8 58 65 9695810 10.1016/S0962-8924(97)01183-5
Yokota Y Mori S Nishikawa SI Mansouri A Gruss P Kusunoki T Katakai T Shimizu A The helix-loop-helix inhibitor Id2 and cell differentiation control Curr Top Microbiol Immunol 2000 251 35 41 11036756
Mori S Nishikawa SI Yokota Y Lactation defect in mice lacking the helix-loop-helix inhibitor Id2. EMBO J 2000 19 5772 5781 11060028 10.1093/emboj/19.21.5772
Willert J Epping M Pollack JR Brown PO Nusse R A transcriptional response to Wnt protein in human embryonic carcinoma cells BMC Dev Biol 2002 2 8 12095419 10.1186/1471-213X-2-8
Rockman SP Currie SA Ciavarella M Vincan E Dow C Thomas RJ Phillips WA Id2 is a target of the beta-catenin/T cell factor pathway in colon carcinoma J Biol Chem 2001 276 45113 45119 11572874 10.1074/jbc.M107742200
Tsukamoto AS Grosschedl R Guzman RC Parslow T Varmus HE Expression of the int-1 gene in transgenic mice is associated with mammary gland hyperplasia and adenocarcinomas in male and female mice Cell 1988 55 619 625 3180222 10.1016/0092-8674(88)90220-6
Yokota Y Mansouri A Mori S Sugawara S Adachi S Nishikawa S Gruss P Development of peripheral lymphoid organs and natural killer cells depends on the helix-loop-helix inhibitor Id2 Nature 1999 397 702 706 10067894 10.1038/17812
Tetsu O McCormick F Beta-catenin regulates expression of cyclin D1 in colon carcinoma cells Nature 1999 398 422 426 10201372 10.1038/18884
Wang TC Cardiff RD Zukerberg L Lees E Arnold A Schmidt EV Mammary hyperplasia and carcinoma in MMTV-cyclin D1 transgenic mice Nature 1994 369 669 671 8208295 10.1038/369669a0
Yu Q Geng Y Sicinski P Specific protection against breast cancers by cyclin D1 ablation Nature 2001 411 1017 1021 11429595 10.1038/35082500
Mori S Inoshima K Shima Y Schmidt EV Yokota Y Forced expression of cyclin D1 does not compensate for Id2 deficiency in the mammary gland FEBS Lett 2003 551 123 127 12965216 10.1016/S0014-5793(03)00906-2
| 15601467 | PMC544352 | CC BY | 2021-01-04 16:02:59 | no | BMC Cancer. 2004 Dec 15; 4:91 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-91 | oa_comm |
==== Front
BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-911560146710.1186/1471-2407-4-91Research ArticleWnt1 is epistatic to Id2 in inducing mammary hyperplasia, ductal side-branching, and tumors in the mouse Marino Susan [email protected] Claire [email protected] Yoshifumi [email protected] Roel [email protected] Department of Developmental Biology, Howard Hughes Medical Institute, Beckman Center, Stanford University Medical School Stanford, CA 94305, USA2 Department of Biochemistry, Fukui Medical University, Shimoaizuki 23-3, Matsuoka, Fukui 910-1193, Japan2004 15 12 2004 4 91 91 19 8 2004 15 12 2004 Copyright © 2004 Marino et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
During pregnancy, the mammary glands from Id2 mutant animals are deficient in lobulo-alveolar development. This failure of development is believed to be due to a proliferation defect.
Methods
We have asked whether functional Id2 expression is necessary for Wnt induced mammary hyperplasia, side branching, and cancer, by generating mice expressing a Wnt1 transgene in an Id2 mutant background.
Results
We show in this work that forced expression of Wnt1 in the mammary gland is capable of overcoming the block to proliferation caused by the absence of Id2. We also show that Wnt1 expression is able to cause mammary tumors in an Id2 mutant background.
Conclusions
We conclude that functional Id2 expression is not required for Wnt1 to induce mammary hyperplasia and mammary tumors.
==== Body
Background
Basic helix-loop-helix (bHLH) transcription factors such as MyoD, E12, and E47 are key regulators of gene expression and control many differentiation events during development [1-3]. These transcription factors bind E-box or E-box-like sequences as homo-or heterodimers, and control the transcription of target genes containing these sequences in their promoters. The HLH domains dimerize with each other, whereas the basic domains bind to DNA. The Id (Inhibitor of DNA binding) proteins are HLH proteins that lack a basic domain. Id proteins act as dominant inhibitors of bHLH transcription factors by blocking their ability to bind to DNA and activate gene transcription [2,3]. Since the bHLH proteins regulate cell-type specific gene expression during cell commitment and differentiation, the formation of inactive heterodimers of bHLH proteins with Id proteins inhibits the commitment and differentiation the bHLH proteins promote.
There are 4 mammalian Id genes, which show differences in their patterns of expression and function [2,3]. One of them, Id2, is expressed in glandular and ductal epithelium of the mouse mammary gland and has also been implicated in its development. Mammary glands of female mice that are homozygous mutant for Id2 have impaired lobulo-alveolar development [4]. In several tissues, including colon tumors induced by activation of the Wnt pathway, the expression of Id2 is regulated by Wnt-β catenin signaling [5,6]. It has been proposed that Wnt signaling may inhibit differentiation and promote the maintenance of a proliferative state by increasing Id2 expression, thereby leading to cancer. We have asked therefore whether functional Id2 expression is necessary for Wnt induced mammary hyperplasia, side branching and cancer, by generating mice expressing a Wnt1 transgene in an Id2 mutant background.
Methods
We used heterozygous Id2 males and females on a 129/Sv background. Id2 genotyping was done by PCR (95C, 5 min; 62C, 1 min, 72C, 1 min, 95C,1 min, 30 cycles; 62C, 1 min, 72C, 5 min) using primers Id2-S (5'-tctgagcttatgtcgaatgatagc-3'), Id-2AS (5'-cgtgttctcctggtgaaatggctg-3'), and neo 1 (5'-tcgtgctttacggtatcgccgctc-3").
Hemizygous transgenic MMTV-Wnt1 males on a mixed FVB/BL6/SJL background were obtained from Yi Li in the H.Varmus laboratory. Genotyping was done by PCR (94C, 4 min; 94C, 45 sec, 55C, 30 sec, 72C, 60 sec, 30 cycles; 72C, 10 min) using Wnt1 (5'-gaacttgcttctcttctcatagcc-3') and SV40 (5'-ccacacaggcatagagtgtctgc-3') primers that produce a 350 bp product in transgenic mice.
Carmine staining
Five mammary glands per mouse were removed and fat and muscle were dissected away. The glands were flattened between two slides and flooded with Carnoy's fixative (3:1 95% ethanol to glacial acetic acid) and fixed overnight. They were then de-fatted in 3 changes of acetone, rehydrated, stained overnight in 0.2% carmine and 0.5%KSO4, dehydrated, cleared in xylene, and mounted in Permount.
Results
We used mice carrying a transgene in which Wnt1 is under the control of the promoter of the Mouse Mammary Tumor Virus (MMTV-Wnt1 Tg) [7] and we crossed these to Id2 loss of function mutant mice [4,8]. Crosses were set up to avoid reliance on Id2 -/- or Wnt1 transgenic mothers, as these animals cannot feed their young [4,7]. Id2 +/- females were crossed with MMTV-Wnt1 hemizygous transgenic males, producing 7 MMTV-Wnt1 Tg; Id2+/- males (Figure 1). These males were then crossed with Id2 +/- females to produce the experimental and control classes of virgin female mice: MMTV-Wnt1 Tg; Id2 -/-, MMTV-Wnt1 Tg ; Id2 +/-, and MMTV-Wnt1 Tg; Id2, as well as smaller numbers of animals in Id2 -/-; Id2 +/-; and WT classes. (Figure 1)
The subject animals were kept in mixed groups in autoclaved cages because Id2 -/- mice have an immunologic defect. Even with this care, 50% die before maturity [8]. Id2 -/- mice were born in sub-Mendelian ratios, they were smaller than litter-mates, and several died of unknown causes.
We examined the morphology of the mammary gland. At 3, 4.5, and 6 months the ductal branching patterns in normal mammary glands of 24 virgin mice from all six classes were examined in carmine stained whole mounts (Figure 2). As has been previously observed by others, the MMTV-Wnt1 Tg female glands had excessive ductal side branching compared to those of WT females [7] while Id2 +/- and Id2 -/- females glands were similar to those of WT females [4]. Glands from MMTV-Wnt1 Tg ; Id2 +/- and MMTV-Wnt1 Tg ; Id2 -/- mice had branching patterns resembling those of MMTV-Wnt1 Tg females at 3 months (Figure 2). At six months the three classes of transgenic mammary glands had very extensive, dense hyperplasia and side branching resembling that of pregnant wild type mice, while the six month virgin Id2+/- and Id2-/- glands had no additional branching and the six month virgin WT glands had moderate additional branching. Therefore, it appears that forced expression of Wnt1 in virgin mammary glands can overcome the absence of Id2 and lead to a highly branched ductal tree resembling the tree achieved normally during pregnancy.
In parallel, we examined tumor incidence in the animals. We excluded MMTV-Wnt1 Tg ; Id2 -/- females that died without developing tumors before 34 weeks, our endpoint, leaving only 8 animals in this group. Mice were examined and palpated weekly for mammary tumors. The smallest tumors detected using this method were 0.5 cm in diameter, but could be as large as 2 cm. The MMTV-Wnt1 Tg ; Id2 +/+ and MMTV-Wnt1 Tg ; Id2 +/- cohorts consisted of 21 and 23 females respectively. All three cohorts showed a similar rate of tumorigenesis (Figure 3). We concluded that Wnt1 is epistatic to Id2 in tumorigenesis, just as it is in promoting hyperplasia and side branching of the mammary gland (Figure 2).
Discussion
The lack of Id2, a Wnt target, has severe consequences for mouse mammary gland development. During pregnancy, the Id2 mammary gland is deficient in lobulo-alveolar development. This failure of development is believed to be due to a proliferation failure rather than precocious differentiation of the mammary epithelia [4]. We show in this work that forced expression of Wnt1 in the mammary gland is capable of overcoming this block to proliferation. Although many targets of the Wnt pathway have been identified (see ), the mechanism through which hyperplasia and side branching is promoted by Wnt1 expression in the virgin mammary gland is unknown. Our results demonstrate that Wnt1 is not operating solely through Id2 or that it is not operating through Id2 at all.
Another known Wnt target with a similar loss of function phenotype is Cyclin D1 [9], a protein whose expression promotes advancement through the cell cycle and whose over expression results in hyperplasia and tumors in the mammary gland [10]. However, when Wnt1 is expressed in Cyclin D1 -/- mice, only a slight reduction in tumorigenesis is observed [11], suggesting that the Wnt1 pathway also does not operate primarily through Cyclin D1. Furthermore, over expression of Cyclin D1 did not promote lobulo-alveolar development in Id2-/- mice [12], a result that is in contrast to the dense side branching of the Wnt1 Id2-/- phenotype, suggesting that Wnt1 signaling is independent of both Cyclin D1 and Id2 in the mammary gland.
Conclusions
By showing that forced expression of Wnt1 in the mammary gland is capable of overcoming the block to proliferation caused by the absence of Id2, we conclude that functional Id2 expression is not required for Wnt1 to induce mammary hyperplasia and mammary tumors.
Competing interests
The author(s) declare that they have no competing interests.
Author's contributions
SM and CR carried out the experiments. YY participated in the design of the study. SM and RN conceived of the study, participated in its design and coordination and wrote the manuscript. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The work was supported by the Howard Hughes Medical Institute and a grant from the US ARMY Medical Research and Material Command (DAMD17-99-1-9386).
Figures and Tables
Figure 1 The Wnt1 transgene was crossed into an Id2 -/- background in order to produce cohorts of WT, Id2 +/-, and Id2 -/- females with and without the Wnt1 transgene. The scheme was designed so that mothers could feed their own young and so that all cohorts being compared would share a common background.
Figure 2 Carmine stained mammary gland whole mounts were made from virgin females. Top row, left to right: normal mammary gland development was observed in wild type (13 weeks), Id2 +/- (26 weeks), and Id2-/- (26 weeks) animals. Bottom row, left to right: Wnt1 induced hyperplasia and side branching were forced when the MMTV-Wnt1 transgene was present in wild type, Id2+/-, and Id2 -/- animals (all 19 weeks). Branching patterns are representative of a consistent phenotype observed in all mice and in both tumor-free glands and in glandular tissue associated with tumors in the MMTV-Wnt1 mice.
Figure 3 Three cohorts of mice, MMTV-Wnt1 Tg (n = 21), MMTV-Wnt1 Tg; Id2 +/- (n = 23), and MMTV-Wnt1 Tg; Id2 -/- (n = 8) were examined weekly by visual examination and palpation. No differences in tumor incidence were observed. MMTV-Wnt1 Tg : square; MMTV-Wnt1 Tg; Id2 +/- : diamond; MMTV-Wnt1 Tg; Id2 -/-: triangle.
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| 15615590 | PMC544353 | CC BY | 2021-01-04 16:03:02 | no | BMC Cancer. 2004 Dec 22; 4:95 | latin-1 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-95 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-581557595310.1186/1471-2458-4-58Research ArticleEducational and economic determinants of food intake in Portuguese adults: a cross-sectional survey Moreira Pedro A [email protected]ão Patricia D [email protected] Faculty of Nutrition and Food Sciences, University of Porto, R. Roberto Frias, 4200-465 Porto, Portugal2004 2 12 2004 4 58 58 4 5 2004 2 12 2004 Copyright © 2004 Moreira and Padrão; licensee BioMed Central Ltd.2004Moreira and Padrão; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Understanding the influences of educational and economic variables on food consumption may be useful to explain food behaviour and nutrition policymaking. The aim of this study was to evaluate the importance of educational and economic factors in determining food pattern in Portuguese adults.
Methods
A cross-sectional study in a representative sample of Portuguese adults (20977 women and 18663 men). Participants were distributed in four categories according to years of education (≤4, 5–9, 10–12, and >12) and income (≤314 euros, 315–547 euros, 548–815 euros, and >815 euros). Logistic regression models were fitted to estimate the magnitude of the association between food groups and education/income, adjusting for confounders.
Results
In both genders, the odds favouring milk, vegetable soup, vegetables, fruit, and fish consumption, increased significantly with education, for those having >12 years of education compared to those with ≤4 years; the odds favouring wine, and spirits consumption decreased significantly with education, for those having >12 years of education compared to those with ≤4 years. In males, the odds favouring starchy foods and meat consumption decreased significantly with income, while for milk, the odds increased with higher income (those having >815 euros compared to those with ≤314 euros).
Conclusions
The low and high income groups are or tend to be similar in regard to several food groups consumption, and access to education/information appears to be the key element to a better food pattern as indicated by higher frequency of milk, vegetable soup, vegetables, fruit, and fish consumption.
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Background
There is a large published literature on associations between socio-economic position and chronic disease, with socioeconomically disadvantaged groups experiencing higher mortality and morbidity rates for coronary heart disease, noninsulin dependent diabetes mellitus and some cancers [1-4]. Chronic diseases are largely preventable diseases, and diet has been known fore many years to play a key role as a risk factor for chronic diseases. While age, sex and genetic susceptibility are non-modifiable, many of the risks associated with age and sex are modifiable. Such risks include a complex mixture of interacting socio-economic, cultural and other environmental factors [5-8].
The relationship between socio-economic factors and diet has been examined on the basis of food and nutrient intake and the results are mixed, sometimes contradictory, and often the observed differences are small [9,10]. When differences are found, it is usually the case that persons from socioeconomically disadvantage backgrounds have food intakes consistent with their higher rates of chronic diseases [11-17].
In the context of European countries, economic development and increased purchasing power have recently changed the food availability situation [18]. On the other hand, socio-cultural influences may contribute, along with economic constraints, to particular food choices, which may explain the still substantial differences in food consumption across European countries [19]. Understanding the influences of socio-economic variables on food consumption may be useful to predict the outcome of interventions, to change food behaviour, and generate hypotheses concerning food consumption in diverse circumstances, as well as to explain observations in epidemiological studies.
The aim of this study was to evaluate the importance of educational and economic factors in determining food choice in a representative sample of the Portuguese general adult population.
Methods
Subjects and general characteristics
Data for this study were drawn from the Portuguese third National Health Survey (National Health Systems Observatory, National Institute of Health – Dr. Ricardo Jorge, Ministry of Health) carried out in 1998–1999. The study sample included all subjects (20977 women and 18663 men) older than 18 years, who reported their education level, income, physical activity, smoking habits, weight, height, and food intake when participating in the National Health Survey. Subjects were selected from 21808 households distributed according to the five regions of Portugal (there are five regions in mainland Portugal, namely Norte, Centro, Lisboa/Vale do Tejo, Alentejo, and Algarve; these regions are the portuguese NUTS II subdivisions), using a multi-stage random probability design. This probabilistic sample is representative of the Portuguese population from the Continental area (Azores and Madeira islands were not included). The survey response rate was 82%.
Trained interviewers conducted face-to-face interviews with the person in each household and inquired participants on social and demographic characteristics, smoking status (non-smokers, ex-smokers, smoking less than one cigarette per day, and smoking one or more cigarettes per day), weight, height (those anthropometric measures were self-reported and body mass index – BMI – was then calculated), food and beverages intake, and daily physical activity (occupational and leisure-time physical activity).
Physical activity
Occupational physical activity was measured using the respondent's own occupation at the time of the survey. Respondents were asked about what best characterized their daily occupational activity, namely: usually seated and walking during short periods of time; standing activities or walking during long periods of time without carrying loads to often; carrying light objects or walking upstairs/downstairs several times; heavy physical work or carrying heavy objects; or don't know.
Respondents were asked to describe their leisure-activity using the following classification: heavy training and competitive sports more than once a week; running or practicing recreational sports or gardening activities ≥4 hours per week; walking for pleasure, bicycling (light effort) or doing other light activities ≥4 hours per week; reading, watching television or other sedentary activities; and don't know. Respondents were also asked to provide information about whether they had regular activities (once or more per week) such as running or bicycling (enough to make them feel tired).
Food and beverages intake
Respondents were asked twelve questions related to their intake of central food groups and beverages, namely milk, vegetable soup, meat, fish, vegetables, fruit, bread, starchy foods (pasta/rice/potatoes), beer, spirits, Port Wine, and wine, and the consumption was recorded as a yes (when the respondent indicated the consumption of the food) or no answer. Because the data were collected by interviewers within the framework of an epidemiological study that was not specifically designed to assess quantitative aspects of nutritional and food intake, the dietary assessment method employed generic classifications of food groups, rather than specific varieties or species (fish rather than fatty fish or salmon, etc.), or quantitative measures. Consumption of these food items was determined by asking "For each of the listed food items please indicate those consumed": "during the day before the interview" (vegetable soup, meat, fish, vegetables, fruit, bread, and starchy foods – pasta, rice and potatoes); "during the week before interview" (beer, spirits, and Port Wine); and "daily consumed" (milk and wine).
Education
Respondents were asked to provide information about whether they had attained further education since leaving school and if so, the highest qualification completed. Respondent's education was subsequently classified in four levels of education: less than 4 years, 5–9 years, 10–12 years, and more than 12 years.
Income
Respondent's were asked to estimate the total income (including pensions, allowances and investments) received by all household members in the last month and to indicate this using a single measure comprising ten narrow-ranged income categories. This measure was subsequently re-coded into four categories according the number of salaries: less than 315 euros, 315–547 euros, 548–815 euros, and >815 euros.
Statistical analysis
Separate logistic regression models were fitted for male and female to estimate the magnitude of the association between food groups consumption and education or income categories, adjusting for age, BMI, smoking habits, physical activity and income/education. An exploratory approach was chosen in the selection of explanatory variables in order to control for as many potentially significant variables as possible in the regression model. The choice of variables (age, BMI, smoking habits and physical activity) was based on findings reported in the literature, our own experience with specifically Portuguese factors associated with food consumption and their associations with the variables of interest; education was also adjusted for income and vice versa.
Student's t-tests, ANOVA, Spearman rank correlation analyses and chi-squared tests were used to compare BMI, age, frequency of smoking, physical activity categories between genders to determine the degree to which those variables correlated with education and income. A p-value of less than 0.05 was considered statistically significant. Statistics were performed using SPSS 12.0.
Results
The study sample comprised 20977 women (52,9%) and 18663 men, with mean ages of 50.3 (±18.88) and 47.7 (±18.51) years, respectively; BMI was significantly lower in women than in men (25.1 ± 4.53 Kg/m2 versus 25.6 ± 3.83, p < 0.001). There was a lower proportion of smokers among women compared to men (8.2% versus 30.5%, p < 0.001). General characteristics (gender, age, BMI, smoking status, and physical activity) by education and income categories are presented in Tables 1 and 2.
Table 1 Characteristics of Portuguese adults by education categories
Education
≤4 years 5–9 years 10–12 years >12 years
Gender
Female 54.0% 22.8% 12.6% 10.6%
Male 49.7% 28.7% 13.2% 8.4%
Age (years)
Female 55.6 ± 14.47 37.8 ± 14.44 31.7 ± 13.63 34.7 ± 13.50
Male 55.3 ± 14.72 37.0 ± 14.99 33.0 ± 14.44 38.7 ± 16.19
BMI (Kg/m2)
Female 26.4 ± 4.45 24.3 ± 4.03 22.5 ± 3.51 22.3 ± 3.45
Male 26.3 ± 3.91 25.1 ± 3.63 24.5 ± 3.52 24.6 ± 3.19
Smokers
Female 3.1% 16.1% 19.1% 18.7%
Male 26.0% 42.9% 32.6% 29.2%
Physical activity in females
Daily occupational activity best characterized by
Usually seated, walking short periods of time 29.2% 28.8% 30.2% 30.4%
Standing, walking long periods of time 44.5% 46.1% 41.7% 42.0%
Carrying light objects, walking up/downstairs 11.3% 11.0% 12.2% 11.1%
Heavy physical work or carrying heavy objects 14.9% 14.1% 15.8% 16.5%
Don't know 0.1% 0% 0.1% 0.1%
Leisure-activity best characterized by
Heavy training/competitive sports (>1x/wk) 3.4% 3.3% 3.6% 3.1%
Running/recreational sports/gardening (≥4 h/wk) 9.3% 7.8% 9.0% 9.7%
Walking for pleasure, bicycling light (≥4 h/wk) 20.4% 21.2% 19.3% 21.2%
Reading, watching TV, sedentary activities 66.8% 67.7% 68.0% 66.0%
Don't know 0.1% 0% 0.1% 0%
Regular activity such as running or bicycling, enough to feel tired
Yes 13.1% 11.9% 13.2% 14.2%
No 86.8% 88.1% 86.7% 85.8%
Don't know 0.1% 0% 0.1% 0%
Physical activity in males
Daily occupational activity best characterized by
Usually seated, walking short periods of time 36.4% 37.3% 37.5% 36.2%
Standing, walking long periods of time 51.6% 51.5% 50.9% 50.0%
Carrying light objects, walking up/downstairs 8.3% 8.2% 7.5% 10.5%
Heavy physical work or carrying heavy objects 3.6% 3.0% 3.9% 3.3%
Don't know 0.1% 0% 0.2% 0%
Leisure-activity best characterized by
Heavy training/competitive sports (>1x/wk) 0.9% 0.5% 1.1% 1.2%
Running/recreational sports/gardening (≥4 h/wk) 5.1% 4.6% 4.1% 4.9%
Walking for pleasure, bicycling light (≥4 h/wk) 15.9% 15.7% 16.8% 16.7%
Reading, watching TV, sedentary activities 78.1% 79.1% 77.8% 77.2%
Don't know 0.1% 0.1% 0.2% 0%
Regular activity such as running or bicycling, enough to feel tired
Yes 7.0% 6.0% 6.7% 7.2%
No 92.9% 94.0% 93.2% 92.8%
Don't know 0.1% 0.0% 0.2% 0%
Table 2 Characteristics of Portuguese adults by income categories
Income
≤314 euros 315–547 euros 548–815 euros >815 euros
Gender
Female 16.3% 25.4% 24.5% 33.8%
Male 20.8% 24.3% 22.8% 32.2%
Age (years)
Female 50.7 ± 18.97 50.3 ± 18.90 50.1 ± 18.68 50.2 ± 18.94
Male 47.9 ± 18.54 47.2 ± 18.45 47.6 ± 18.44 48.1 ± 18.55
BMI (Kg/m2)
Female 25.3 ± 4,55 25.0 ± 4.51 25.1 ± 4.52 25.1 ± 4.55
Male 25.6 ± 3.70 25.4 ± 3.95 25.6 ± 3.74 25.7 ± 3.88
Smokers
Female 7.4% 8.0% 8.6% 8.2%
Male 28.5% 31.9% 30.3% 30.7%
Physical activity in females
Daily occupational activity best characterized by
Usually seated, walking short periods of time 30.3% 26.0% 24.6% 35.7%
Standing, walking long periods of time 46.4% 43.8% 42.8% 43.6%
Carrying light objects, walking up/downstairs 10.3% 11.4% 13.5% 10.0%
Heavy physical work or carrying heavy objects 12.7% 18.7% 19.0% 10.6%
Don't know 0.2% 0% 0.1% 0.1%%
Leisure-activity best characterized by
Heavy training/competitive sports (>1x/wk) 1.2% 2.3% 3.1% 5.4%
Running/recreational sports/gardening (≥4 h/wk) 4.0% 7.3% 11.1% 11.1%
Walking for pleasure, bicycling light (≥4 h/wk) 16.7% 19.2% 19.0% 24.0%
Reading, watching TV, sedentary activities 77.9% 71.2% 66.7% 59.5%
Don't know 0.1% 0% 0.1% 0.1%
Regular activity such as running or bicycling, enough to feel tired
Yes 5.8% 9.1% 12.0% 19.1%
No 94.1% 90.9% 87.9% 80.8%
Don't know 0.1% 0% 0.1% 0.1%
Physical activity in males
Daily occupational activity best characterized by
Usually seated, walking short periods of time 41.8% 33.5% 32.2% 39.3%
Standing, walking long periods of time 48.1% 53.2% 53.1% 50.8%
Carrying light objects, walking up/downstairs 7.6% 9.6% 10.0% 7.0%
Heavy physical work or carrying heavy objects 2.4% 3.6% 4.7% 2.8%
Don't know 0.1% 0.1% 0% 0%
Leisure-activity best characterized by
Heavy training/competitive sports (>1x/wk) 0.3% 0.5% 0.8% 1.4%
Running/recreational sports/gardening (≥4 h/wk) 2.9% 4.4% 5.5% 5.4%
Walking for pleasure, bicycling light (≥4 h/wk) 10.3% 13.1% 16.3% 21.5%
Reading, watching TV, sedentary activities 86.4% 81.9% 77.4% 71.7%
Don't know 0.1% 0.2% 0% 0%
Regular activity such as running or bicycling, enough to feel tired
Yes 2.2% 4.7% 5.4% 11.7%
No 97.6% 95.2% 94.6% 88.3%
Don't know 0.1% 0.1% 0% 0%
In women, the odds favouring milk, vegetable soup, vegetables, fruit, and fish consumption, increased with increasing education (p-values for trends were always ≤0.046), being the odds ratios, respectively, 2.60 (2.24–3.01), 1.20 (1.05–1.38), 1.75 (1.44–2.13), 1.92 (1.49–2.49), and 1.40 (1.23–1.60) for those having >12 years of education compared to those with ≤4 years, after adjusting for age, BMI, smoking habits, physical activity and income (Tables 3 and 4). The odds favouring bread, starchy foods (other than bread), wine, and spirits consumption in women decreased with increasing education (p trend ≤ 0.002), being the odds ratios, respectively, 0.44 (0.34–0.56), 0.68 (0.53–0.87), 0.51 (0.41–0.62), and 0.13 (0.03–0.53) for those having >12 years of education compared to those with ≤4 years (Tables 3 and 4).
Table 3 Odds ratios for food consumption according level of education, adjusted for age, BMI, smoking habits, physical activity and income
Women Men
OR IC(95%) P trend OR IC(95%) P trend
Vegetable soup Vegetable soup
Education Education
≤4 years (reference) ≤4 years (reference)
5–9 years 0.90 0.82–1.00 5–9 years 0.99 0.90–1.09
10–12 years 0.94 0.83–1.07 10–12 years 1.07 0.94–1.21
>12 years 1.20 1.05–1.38 0.046 >12 years 1.15 1.00–1.32 0.045
Vegetables Vegetables
Education Education
≤4 years (reference) ≤4 years (reference)
5–9 years 1.05 0.92–1.20 5–9 years 1.09 0.97–1.23
10–12 years 1.17 0.99–1.39 10–12 years 1.38 1.17–1.62
>12 years 1.75 1.44–2.13 <0.001 >12 years 1.44 1.19–1.74 <0.001
Fruit Fruit
Education Education
≤4 years (reference) ≤4 years (reference)
5–9 years 1.30 1.09–1.55 5–9 years 1.30 1.13–1.49
10–12 years 1.63 1.29–2.06 10–12 years 1.75 1.45–2.13
>12 years 1.92 1.49–2.49 <0.001 >12 years 1.68 1.35–2.10 <0.001
Bread Bread
Education Education
≤4 years (reference) ≤4 years (reference)
5–9 years 0.78 0.63–0.96 5–9 years 0.97 0.75–1.25
10–12 years 0.50 0.39–0.64 10–12 years 0.73 0.54–1.00
>12 years 0.44 0.34–0.56 <0.001 >12 years 0.44 0.33–0.59 <0.001
Other starchy Other starchy
Education Education
≤4 years (reference) ≤4 years (reference)
5–9 years 0.99 0.81–1.20 5–9 years 1.09 0.88–1.35
10–12 years 0.72 0.57-0.92 10-12 years 1.07 0.80-1.43
>12 years 0.68 0.53-0.87 <0.001 >12 years 1.15 0.83-1.60 0.355
Fish Fish
Education Education
≤4 years (reference) ≤4 years (reference)
5-9 years 1.06 0.96-1.17 5-9 years 1.14 1.04-1.25
10-12 years 1.24 1.09-1.40 10-12 years 1.36 1.20-1.54
>12 years 1.40 1.23-1.60 <0.001 >12 years 1.50 1.31-1.72 <0.001
Meat Meat
Education Education
≤4 years (reference) ≤4 years (reference)
5-9 years 1.01 0.89-1.14 5-9 years 1.09 0.96-1.23
10-12 years 1.02 0.86-1.21 10-12 years 1.27 1.06-1.52
>12 years 0.95 0.80-1.13 0.693 >12 years 1.16 0.96-1.41 0.014
Table 4 Odds ratios for beverage consumption according level of education, adjusted for age, BMI, smoking habits, physical activity and income
Women Men
OR IC(95%) P trend OR IC(95%) P trend
Milk Milk
Education Education
≤4 years (reference) ≤4 years (reference)
5-9 years 1.54 1.38-1.70 5-9 years 1.53 1.39-1.68
10-12 years 2.24 1.95-2.57 10-12 years 3.00 2.62-3.44
>12 years 2.60 2.24-3.01 <0.001 >12 years 3.07 2.62-3.59 <0.001
Wine Wine
Education Education
≤4 years (reference) ≤4 years (reference)
5-9 years 0.79 0.68-0.93 5-9 years 0.73 0.63-0.84
10-12 years 0.45 0.36-0.55 10-12 years 0.41 0.35-0.49
>12 years 0.51 0.41-0.62 <0.001 >12 years 0.46 0.38-0.56 <0.001
Beer Beer
Education Education
≤4 years (reference) ≤4 years (reference)
5-9 years 0.92 0.72-1.18 5-9 years 0.86 0.77-0.97
10-12 years 0.76 0.56-1.03 10-12 years 0.61 0.52-0.71
>12 years 0.82 0.61-1.10 0.097 >12 years 0.57 0.48-0.68 <0.001
Spirits Spirits
Education Education
≤4 years (reference) ≤4 years (reference)
5-9 years 0.66 0.27-1.58 5-9 years 0.72 0.60-0.87
10-12 years 0.15 0.03-0.74 10-12 years 0.46 0.34-0.62
>12 years 0.13 0.03-0.53 0.002 >12 years 0.27 0.19-0.40 <0.001
Port Wine Port Wine
Education Education
≤4 years (reference) ≤4 years (reference)
5-9 years 1.44 1.03-2.01 5-9 years 1.00 0.81-1.24
10-12 years 1.22 0.80-1.88 10-12 years 0.97 0.73-1.30
>12 years 1.34 0.90-2.00 0.265 >12 years 1.42 1.07-1.89 0.093
In men, similar odds ratios were observed for milk, vegetable soup, vegetables, fruit, fish, bread, wine, and spirits (Tables 3 and 4). However, in men but not in women, odds favouring meat consumption increased with increasing education (OR = 1.16 (0.96–1.41) for those having >12 years of education compared to those with ≤4 years), while for beer consumption, odds decreased with increasing education (OR = 0.57 (0.48–0.68) for those having >12 years of education compared to those with ≤4 years).
No such significant trends were observed for these food groups and income with the exceptions of meat and starchy foods (other than bread) consumption, in men, which decreased with increasing income (p trend ≤ 0.022), and milk consumption which increased with increasing income (Tables 3 and 4).
Discussion
The main finding of the present study is that educational attainment was more frequently associated with food choices than income. There is general agreement among researchers [20-23] that education and income are conceptually distinct, and that they are likely to make separate and unique contributions to health-related outcomes [24]. In our study, the most educated consumed more frequently fruit, vegetables, milk and fish, and less wine and spirits, than their counterparts from less educated groups.
Over the last years, several studies have attempted to identify the influence of socioeconomic factors on individual's dietary intake [25-28]. Our interest in educational and economic determinants of food choice in Portuguese adults relate to these particular characteristics in the population. Portugal, according European standards, is a small and relatively poor country, exhibiting the highest level of social inequalities in the European Union [29]. Nevertheless, Portugal had significantly and positively changed in the last four decades, in several domains such as the economy and culture, although the census of 1991 revealed that 15.3% of the Portuguese were illiterate. That of 2000 showed that, despite the improvements and changes in the education of adults, 7% can still not read or write [29]. This is a reality that classifies Portugal as the country with the higher percentage of individuals with low level of education in all the European Union [29]. From the employment perspective, Portugal's unemployment rates in the last 25 years never surpassed 10% of the active population, which is a better indicator than the observed levels in the majority of the European countries. However, the percentage of individuals with low-remuneration in Portugal is much higher than the EU average [29].
Several studies have concluded that a strong relationship exists between countries' per capita national incomes and nutrition [30-32]. The economic issue is of considerable significance, and it is sometimes suggested that this is probably the key variable of all in influencing food choice [30]. Household income is expected to influence food choices, especially for relatively high-priced food items such as fish, fresh fruit and vegetables [33]. Nevertheless, this not seems to be the case when we compared income and education levels as determinants of intake of significant food groups in Portuguese adults. Our data shows, in both genders, a significant positive trend in the consumption of vegetables, vegetable soup, fruits, milk and fish, with higher levels of education, which did not occurred in relation to income with the exception of milk. In our study, education was adjusted for income and vice versa. While the majority of investigators use two or more indicators of socio-economic position, several [34-37] do not simultaneously adjust for the unmeasured effects of each indicator on the other. Two types of bias may result from this practice: (1) using a single indicator such as education may bias the point estimate (food choice) because the education variable is allowed to account for some of the variation that is actually the product of unmeasured socio-economic influences; as a result, if we did not simultaneously adjusted education for income and vice versa, our claims about the influence of education level on food choice probably would have been overestimated; (2) the use of a single indicator may result in the overall or total socio-economic effect being underestimated.
Data from the Portuguese Household Budget Surveys (using the DAta Food Networking – DAFNE – classification system), shows similar results to ours in relation to the positive association between education attainment and the availability of fruits, fish, milk and alcoholic beverages but some different data in regard to other foods (availability of vegetables and cereal products is fairly stable or tends to decline with education) [18]. Curiously, we found that meat consumption in men was positively associated with level of education, as in the DAFNE study [18], although our study showed a significant reduction of meat consumption with higher categories of income, in men. In Portugal [18] fish is more available among the trend-leading educated individuals which may be more advantageous to their cardiovascular health [38].
In our study, there was also a significant trend in the consumption of milk in men, being more frequently ingested with increasing income. As suggested by Axelson, [39] positive health relationships between dietary patterns and income may reflect a growing concern about health in the higher socio-economic groups.
The association between milk consumption and socio-economic position is sometimes contradictory. Cristofar and Basiotis [40], for example, reported lower intake of milk among low-income women, while Roos et al. [41] found that higher educational and income groups from both genders consumed less milk.
Consumption of alcoholic beverages, such as wine and spirits, in both genders, and beer in men, exhibited significant decreases in their frequency of intake, with increasing education levels. In Portugal, alcoholic beverages consumption is a major public health problem [42]. In DAFNE study, [18] using Portuguese Household Budget Surveys, alcoholic beverages availability was also higher in the lower educated households.
Interestingly, in our study, the consumption of bread (in both genders) and other starchy foods (in women), decreased with increasing number of years of education; men seem to abandon starchy foods (other than bread) consumption under condition of higher incomes. It is possible that higher educated individuals tend to avoid foods that are considered as being more fattening or rich in energy, such as bread and other starchy foods [43,44]. Research has demonstrated that for a given body size, higher educated women are more dissatisfied with or concerned about their bodies and are more likely to have dieted in the past than lower educated women [45,46].
One of the most interesting findings in terms of economic constraints and food consumption relationship in our study, is the few significant associations between income and food choices, even though the well established links between economic and material resources, food availability and dietary quality [47]. By contrast to our results, Turrell et al. [23] showed household income to be the strongest and most robust independent predictor of food purchasing behaviour, and the effects of education to be substantially attenuated (to non-significance or marginal significance). In our study, the specificity of the relationship between education and food choice probably reflect each respondent's individual contribution to food choice, whereas household income was possibly capturing the combined contextual effects of numerous individuals, as well as many other within-household processes [23], and thus showed a weaker relationship with food choice. Our results may also reflect lesser difficulties faced by low-income groups when selecting the food groups that we studied. In several urban and rural areas of Portugal, there are many people who own plots of land that are too small to make a living, but allow them to work the land and produce foods (e.g., fruit, vegetables and poultry) for their own consumption. Although they produced a limited range of foods that is not accounted in official agricultural statistics, probably, if they stopped working the land they would experience greater difficulties in obtaining access to those particular foods. A potential limitation of our study and most nationwide population surveys is that the poor are usually not well presented. We know from previous research into survey participation that population-based samples typically under-represent the most socio-economically disadvantaged and over-represent the advantaged [48,49], because homeless and unemployed may be difficult to reach, and this may debilitates the interpretation of our results.
In our study, it remains to be explained the different pattern of associations between income and important food groups (milk versus fruit and vegetables, for example) and the different pattern of associations between food choices and income in each gender (e.g., milk and starchy foods). Several reasons may explain specific differences in the findings of our study compared with those of previous mentioned studies, including differences in populations sampled (e.g., both genders versus women only, different cultural backgrounds ranges), differences in assessment of education or income, differences in dietary assessment (e.g., qualitative food data versus 24-h dietary recalls or food frequency questionnaires) and differences in analytic methods (e.g., covariates included in statistical models). Nonetheless, results from our study indicate that the associations between food choice were stronger in relation to educational attainment than income categories. Differences in food choices according the level of education reflect that more knowledge may influence the perceived relationship between diet and health as well as the perceived outcomes of following a healthy diet [33].
Despite differences in food consumption according education and income, in our study we could not assess if these differences were also evident on the energy and nutrient level, which was a limitation. British data point to micronutrient and antioxidant intakes as the most likely nutritional influences on health inequalities [50]. Nevertheless, according Galobardes et al., [10] it is also possible that despite differences in food consumption, nutrient intake is similar among socio-economic groups, as these may not be substantial enough to translate into differences in nutrient intake.
If a country like Portugal wants to change the adult food choice behaviour, or in other words, wants to reach certain dietary goals, the support of applied research like ours is needed in order to plan the right strategies for promoting healthy diets. Confidence in a significant positive causal link between per capita national income and individual nutrition reinforces the importance of economic growth [51] but also implies that public policy should stress education as a mean for improving healthy food choices. Education might influence food choice by facilitating or constraining one's ability to understand the information communicated in nutrition education or in food labels [52,53].
Whereas income-related dietary differences suggest ameliorative responses through the potential of the economic system, differences based on education point to initiatives such as nutrition education programmes [22,54]. According to Geraldes,[55]in Portugal it may sometimes be more appropriate to correct inequalities in the domains of education or nutrition than that of health. Given the poor education level of the majority of Portuguese adults, a move towards an increased acquisition of general knowledge and personal development through compulsory and higher education, lifelong learning and improved qualifications of the population, is desirable to promote the development of a knowledge society and improve the level and quality of national education which, in turn, may relate to healthier food choices. It is well recognized that changes in dietary behaviour may be brought about, not by direct modification of food habits, but by alteration or manipulation of the education and culture [8].
Conclusions
Regardless of the reasons explaining the complex and diversified patterns of economic and educational associations of food consumption found in Portugal, the findings of this study suggest that education and income have distinct associations with food choice. The low and high income groups are or tend to be similar in regard to the majority of food choices, and access to education appears to be the key element to a better food pattern as indicated by higher frequency of milk, vegetable soup, vegetables, fruit, and fish consumption.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
PM and PP designed the study. PM and PP did the statistical analysis, and PM wrote the paper. PM and PP reviewed the final version of the paper.
Table 5 Odds ratios for food consumption according level of income, adjusted for age, BMI, smoking habits, physical activity and education
Women Men
OR IC(95%) P trend OR IC(95%) P trend
Vegetable soup Vegetable soup
Income Income
≤314 euros (reference) ≤314 euros (reference)
315-547 euros 1.02 0.91-1.14 315-547 euros 0.95 0.85-1.05
548-815 euros 1.01 0.90-1.14 548-815 euros 0.95 0.85-1.06
>815 euros 0.96 0.86-1.07 0.281 >815 euros 0.92 0.83-1.02 0.195
Vegetables Vegetables
Income Income
≤314 euros (reference) ≤314 euros (reference)
315-547 euros 1.11 0.96-1.29 315-547 euros 1.22 1.06-1.39
548-815 euros 1.07 0.92-1.24 548-815 euros 1.16 1.01-1.33
>815 euros 1.05 0.92-1.21 0.768 >815 euros 1.06 0.93-1.20 0.796
Fruit Fruit
Income Income
≤314 euros (reference) ≤314 euros (reference)
315-547 euros 0.94 0.81-1.21 315-547 euros 0.99 0.84-1.15
548-815 euros 0.98 0.80-1.19 548-815 euros 0.95 0.81-1.12
>815 euros 1.01 0.84-1.23 0.983 >815 euros 1.03 0.89-1.20 0.769
Bread Bread
Income Income
≤314 euros (reference) ≤314 euros (reference)
315-547 euros 1.16 0.92-1.47 315-547 euros 1.05 0.80-1.39
548-815 euros 1.06 0.84-1.34 548-815 euros 1.05 0.79-1.40
>815 euros 1.00 0.81-1.25 0.502 >815 euros 0.85 0.66-1.10 0.139
Other starchy Other starchy
Income Income
≤314 euros (reference) ≤314 euros (reference)
315-547 euros 0.86 0.69-1.06 315-547 euros 0.91 0.71-1.17
548-815 euros 0.95 0.77-1.19 548-815 euros 0.84 0.65-1.07
>815 euros 0.88 0.72-1.08 0.466 >815 euros 0.72 0.57-0.90 0.002
Fish Fish
Income Income
≤314 euros (reference) ≤314 euros (reference)
315-547 euros 0.86 0.77-0.96 315-547 euros 0.99 0.89-1.10
548-815 euros 0.84 0.75-0.93 548-815 euros 0.95 0.85-1.06
>815 euros 0.92 0.83-1.03 0.409 >815 euros 0.98 0.88-1.08 0.457
Meat Meat
Income Income
≤314 euros (reference) ≤314 euros (reference)
315-547 euros 0.98 0.86-1.12 315-547 euros 0.95 0.82-1.09
548-815 euros 1.09 0.95-1.25 548-815 euros 0.92 0.80-1.06
>815 euros 1.04 0.92-1.19 0.299 >815 euros 0.87 0.76-0.99 0.026
Table 6 Odds ratios for beverage consumption according level of income, adjusted for age, BMI, smoking habits, physical activity and education
Women Men
OR IC(95%) P trend OR IC(95%) P trend
Milk Milk
Income Income
≤314 euros (reference) ≤314 euros (reference)
315-547 euros 0.94 0.83-1.05 315-547 euros 1.01 0.90-1.12
548-815 euros 1.02 0.91-1.15 548-815 euros 1.05 0.94-1.18
>815 euros 0.99 0.89-1.11 0.575 >815 euros 1.12 1.01-1.24 0.017
Wine Wine
Income Income
≤314 euros (reference) ≤314 euros (reference)
315-547 euros 0.97 0.81-1.17 315-547 euros 1.07 0.93-1.22
548-815 euros 0.92 0.76-1.11 548-815 euros 1.00 0.87-1.14
>815 euros 0.86 0.72-1.03 0.065 >815 euros 0.89 0.79-1.01 0.757
Beer Beer
Income Income
≤314 euros (reference) ≤314 euros (reference)
315-547 euros 0.89 0.67-1.19 315-547 euros 0.95 0.83-1.09
548-815 euros 0.89 0.66-1.20 548-815 euros 1.05 0.92-1.21
>815 euros 1.00 0.76-1.31 0.671 >815 euros 1.06 0.93-1.21 0.139
Spirits Spirits
Income Income
≤314 euros (reference) ≤314 euros (reference)
315-547 euros 2.18 0.74-6.43 315-547 euros 1.14 0.92-1.41
548-815 euros 2.52 0.86-7.43 548-815 euros 1.12 0.89-1.40
>815 euros 1.28 0.44-3.78 0.968 >815 euros 1.09 0.88-1.34 0.229
Port Wine Port Wine
Income Income
≤314 euros (reference) ≤314 euros (reference)
315-547 euros 1.15 0.78-1.70 315-547 euros 0.98 0.77-1.24
548-815 euros 1.28 0.87-1.89 548-815 euros 1.20 0.94-1.53
>815 euros 1.09 0.75-1.59 0.704 >815 euros 1.05 0.84-1.33 0.483
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgement
We thank the National Health Systems Observatory, National Institute of Health – Dr. Ricardo Jorge, Ministry of Health staff for their support, particularly Dr. Carlos Dias and Dr. Maria João Branco.
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| 15575953 | PMC544354 | CC BY | 2021-01-04 16:28:48 | no | BMC Public Health. 2004 Dec 2; 4:58 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-58 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-641561559210.1186/1471-2458-4-64Research ArticleIntestinal parasites prevalence and related factors in school children, a western city sample-Turkey Okyay Pinar [email protected] Sema [email protected] Berna [email protected] Ozlem [email protected] Erdal [email protected] Department of Public Health, Adnan Menderes University Medical Faculty, Aydin, Turkey2 Department of Microbiology and Clinical Microbiology, Adnan Menderes University Medical Faculty, Aydin, Turkey3 Department of Microbiology and Clinical Microbiology, Adnan Menderes University Medical Faculty, Aydin, Turkey4 Department of Public Health, Adnan Menderes University Medical Faculty, Aydin, Turkey5 Department of Public Health, Adnan Menderes University Medical Faculty, Aydin, Turkey2004 22 12 2004 4 64 64 6 7 2004 22 12 2004 Copyright © 2004 Okyay et al; licensee BioMed Central Ltd.2004Okyay et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Intestinal parasitic infections are amongst the most common infections worldwide. Epidemiological research carried out in different countries has shown that the social and economical situation of the individuals is an important cause in the prevalence of intestinal parasites. Previous studies in Turkey revealed a high prevalence of intestinal parasitic infection. The objectives of the current study were to determine the prevalence of intestinal parasitic infections in Aydin among 7–14 years old school children and to identify associated socio-demographic and environmental factors, behavioral habits and also related complaints.
Methods
Multistage sampling was used in the selection of the study sample. A questionnaire, cellulose adhesive and a stool specimen examination were done.
Results
A total of 456 stool specimens were collected. 145 students (31.8%) were infected with one or more intestinal parasites. 29 (6.4%) of the students were infected more than one parasite, 26 (5.7%) with two parasites and 3 (0.7%) with three parasites. The three most common were E. vermicularis, G. intestinalis and E. coli. Intestinal parasite prevalence was higher in rural area, in children with less than primary school educated mother, in children who use hands for washing anal area after defecation, and in children who use toilet paper sometimes or never. The relation between child health and mother education is well known. Children were traditionally taught to wash anal area by hand. Toiler paper usage was not common and might be due to low income or just a behavioral habit also. Most of the complaints of the study population were not significantly related with the intestinal parasitic infection.
Conclusions
Intestinal parasitic infection is an important public health problem in Aydin, Turkey. Rural residence, mother education less than primary school, sometimes or never usage of toilet paper, and washing anal area by hands after defecation were the significant associations. Interventions including health education on personal hygiene to the students and to the parents, especially to mothers are required. The ratio of uneducated women should be declined with specific programs. A multisectoral approach is needed.
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Background
Intestinal parasitic infections are amongst the most common infections worldwide. It is estimated that some 3.5 billion people are affected, and that 450 million are ill as a result of these infections, the majority being children. These infections are regarded as serious public health problem, as they cause iron deficiency anemia, growth retardation in children and other physical and mental health problems [1].
Epidemiological research carried out in different countries has shown that the social and economical situation of the individuals is an important cause in the prevalence of intestinal parasites. In addition, poor sanitary and environmental conditions are known to be relevant in the propagations of these infectious agents [2-4].
Previous studies at various institutions in Turkey revealed a high prevalence of intestinal parasitic infections among the following populations: 6–16 year olds (10.8%), 12–16 year olds (48.0%), 7–15 year olds (55.1%), and 6–12 year olds (88.0%) [5-8]. However, almost all of the studies were performed in isolated groups, such as collecting all samples from children attending the same school. Furthermore the majority of the studies were performed in the eastern part of Turkey. They have limitations in regards to giving an idea about all age groups in the region. Although there are studies on interactions between infection and socio-demographic, environmental factors, and behavioral habits from eastern regions, to our knowledge, there is lack of adequate information for the western part of Turkey [7,9].
The objectives of this current study were to determine the prevalence of intestinal parasitic infections in Aydin among 7–14 year old school children and its relation to socio-demographic factors, environmental factors, behavioral habits and complaints related to intestinal infections.
Methods
Study population
The data for the present study was acquired from the primary schools in urban and rural areas of Aydin, a city in the western part of Turkey. The study design was cross-sectional.
The sample size was calculated on a prevalence of 30%, d = 0.05 at a confidence level of 95%. A design effect of 2 was used to allow for multistage sampling [10]. The calculated study population size was 639. Multistage sampling was used in the selection of the study sample. Aydin was separated into four regions according to the socio-economic and health data taken from Directorate of Health. From these regions two schools were selected randomly, one from the urban communities and one from rural. The children were selected from the classes randomly based on the age, gender and weight for the population. First to eighth graders were included in the study, and the total numbers of the students enrolled in the classes were 74, 60, 70, 72, 72, 107, 104, and 81 students, respectively. Permission was obtained from Directorate of Education.
The questionnaire and family information form
The questionnaire contained four sections: 1. Socio-demographic data: age, gender, residence, education and occupation of parents, number of adults and children in the family and birth order of the child; 2. Environmental factors: housing conditions (ownership of the house, number of rooms and bathrooms) and water supply; 3. Behavior habits: type of toilet commonly used, hand washing (no washing/washing with only water/washing with soap), washing anal area by hands after defecation (yes/no), usage of toilet paper (always/sometimes/never); 4. Complaints: abdominal pain, nausea/vomiting, lack of appetite, abdominal distention, intestinal dismotility, salivation during sleeping, headache, irritability in sleeping, perianal itching, teeth grinding, and history of parasitic infections.
An informational document about the study, including how to supply a stool specimen and a cellulose tape slide, was given to each participant for their family.
Intestinal parasitic examination
Mothers were asked to perform one cellulose tape test on their child who was participating in the study. Laboratory slides were provided with cellulose tape attached to them. The mother collected material for examination in the early morning prior to bathing or defecation. On the same morning a field worker collected these slides to be microscopically examined.
The stool specimens (0.5–1.5 gr) were collected in labeled plastic vials without preservatives and transported to the laboratory within four hours after collection. They were examined for the presence of parasites by direct wet mount, Lugol's iodine solution and modified formaline-ethyl acetate sedimentation techniques.
The presence of parasites was confirmed when observed by any of the methods above.
Statistical analysis
A computer program was used for data analysis. The descriptive data was given as a mean ± standard deviation (SD). The chi-squared test and Student t-test were used for the analytic assessment. The differences were considered to be statistically significant when the p-value obtained was less than 0.05.
Results
A total of 456 (71.4%) samples for both stool specimens and cellulose tape slides were collected. The response rate to the questionnaire was lower with 367 (57.4 %). The mean age was 10.34 ± 2.27, 10.17 ± 2.30 for girls and 10.51 ± 2.23 for boys. Important socio-demographic characteristics, housing conditions and hygienic habits of children are given in Table 1.
Table 1 Important socio-demographic characteristics, housing conditions and hygienic habits of children
Characteristics No %
Socio-demographic characteristics
Residence Urban 258 56.6
Rural 198 43.4
Gender Female 232 50.9
Male 224 49.1
Education of mother No education/primary school incomplete 68 19.0
Primary/secondary school 269 75.4
High school and more 20 5.6
Education of father No education/primary school incomplete 21 5.8
Primary/secondary school 292 81.4
High school and more 46 12.8
Housing conditions
Owner 239 66.0
3 rooms and less 154 43.5
4 rooms and more 200 56.5
1 toilet 180 49.9
2 and more toilet 181 50.1
5 and less people living in 267 74.4
6 and more people living in 92 25.6
Municipal water network 232 68.6
Hygienic habits
Type of toilet commonly used Modern style 86 26.5
Traditional style 135 41.5
Both 104 32.0
Toilet paper Always 206 57.1
Sometimes 105 29.1
Never 50 13.9
Washing anal area by hands after defecation Yes 138 39.4
No 212 60.6
Washing hands with soap after toilet Always 308 85.3
Sometimes 51 14.1
Never 2 0.6
Taking a bath Once a day 18 5.2
Three times a week 161 46.1
Once a week or less 170 48.7
In all, 145 students (31.8%) were infected with one or more intestinal parasites. 29 (6.4%) of the students were infected with more than one parasite, 26 (5.7%) with two parasites and 3 (0.7%) with three parasites. The most common was Enterobius vermicularis (E. vermicularis) with 63 (13.8%) pure and 20 (4.4%) with multiple infections, in total 83 (18.2%) infected children. The second was Giardia intestinalis (G. intestinalis) with 28 (6.1%) pure and 21 (4.6%) with multiple infections, in total 49 (10.7%) infected children. The third one was Entamoeba coli (E. coli) with 21(4.6%) pure and 15 (3.3%) multiple infections, in total 36 (7.9%) infected children. G. intestinalis was the most commonly found parasite in multiple infections. The distribution of parasites is given in Table 2.
Table 2 The parasites distribution of the study population
Parasites No. %
Single
E. vermicularis 63 13.8
G. intestinalis 28 6.1
E. coli 21 4.6
H. nana 4 0.9
Total 116 25.4
Multiple
E. vermicularis+G. intestinalis 11 2.4
E. vermicularis+E. coli 8 1.8
G. intestinalis+E. coli 4 0.9
G. intestinalis+H. nana 2 0.4
G. intestinalis+Taenia spp. 1 0.2
G. intestinalis+E. coli+H.nana 2 0.4
E. vermicularis+G. intestinalis+E. coli 1 0.2
Total 29 6.4
Overall total 145 31.8
No statistically significant difference was observed between presence of intestinal parasites and gender, (p = 0.805), and also age (p = 0.916). The prevalence of intestinal parasites were significantly higher (p = 0.042) in the rural area (36.9%) than in the urban area (27.9%). A summary of significant relations observed in overall intestinal parasitic infections for the study population are given in Table 3.
Table 3 Significant relations for the intestinal parasitic infection in the study population
Risk Factor Overall infection χ2 p
n %
Residence
Urban 72 27.9 4.415 0.042
Rural 73 36.9
Mother education
Less than primary school 29 42.6 4.436 0.035
Primary school and more 85 29.4
Toilet paper
Always 50 24.3 13.596 0.000
Sometimes/never 66 42.6
Washing anal area by hands after defecation
Yes 55 39.9 5.503 0.019
No 59 27.8
Parasite-specific significant relations were the following: The prevalence of E. coli infections was significantly higher (p = 0.010) in the rural area (11.1%) than in the urban area (5.0%). Mother education less than a primary school education (p = 0.012), washing anal area by hands after defecation (p = 0.013) were the significant relations for G. intestinalis, where sometimes or non-usage of toilet paper was significant for G. intestinalis (p = 0.008) and for E. vermicularis (p = 0.024) both. Family size was larger in the group infected with G. intestinalis (p = 0.029). The prevalence of intestinal parasitic infection was 31.0% in the group using a municipal water network and 34% in the group lacking a municipal system (p = 0.592). The prevalences of G. intestinalis in these two groups were 9.9% and 16.0% (p = 0.106) respectively. No further significant relationships were found between intestinal parasitic infection and environmental or behavioral factors.
The most frequent complaint related with any parasite infection was intestinal dismotility (40.0%). Nausea/vomiting (37.7%) was second and abdominal distention (37.1%) was third. All of the complaints were seen in higher prevalence for E. vermicularis infections than G. intestinalis and E. coli.
Discussion
It was found that approximately one-third (31.8%) of the students, ages 7 to 14, in Aydin were infected by intestinal parasites. In a sample within the same age group in Izmir, a city also in the western part of Turkey, the prevalence for intestinal parasites was 22.4%, with E. vermicularis (16.0%) and G. intestinalis (11.9%) being the two most common infections, as was observed in Aydin [11]. In another study performed in Izmir, the prevalence of infection was 45.3% for E. vermicularis, 21% for G. intestinalis, 10% for H. nana, 4.3% for E. coli, 0.03% for Ascaris lumbricoides (A. lumbricoides), and 0.03% for Trichuris trichiura (T. trichiura). No Taenia was found [12]. In this current study; the most frequently observed parasites were E. vermicularis, G. intestinalis, and E. coli, 18.2%, 10.7% and 7.9%, respectively. Higher prevalence was found in the studies from the eastern part of Turkey, where the socio-economic and environmental conditions were lower. Additionally, it was observed that the types of parasites found in this study where different than those found in the eastern part of Turkey. A survey conducted among 1001 children in four elementary schools in Sanliurfa found parasites in 88% of the stool samples examined (50% A. lumbricoides, 53% T. trichiura, 22% G. intestinalis, and 11% E. coli); unfortunately, no data on E. vermicularis was given, because samples with cellulose tape slides were not taken [7]. In an another study from the eastern region, in an elementary school age group, 48.12% A. lumbricoides, 4.43% T. trichiura and 15.35% G. intestinalis were found [13]. In the last study, samples with cellulose tape slides were not taken.
Geohelmint infections (Ascaris, Strongyloides and Trichuris) were of lower prevalence in the western part of Turkey, but of higher prevalence in the eastern part. This difference may be due to improper toilet facilities which require individuals to defecate in areas around their homes as well as the use of fecal material for fertilizer in gardens in the eastern parts of Turkey [13-15]. In a study from the eastern region, 42.2% of children were found to be working in gardens watered with contaminated sewage and eating the vegetables of those gardens. There were 44.7% A. lumbricoides, 11.7% T. trichiura infections in these children while there was 12.2 %A. lumbricoides and 6.6% T. trichiura infections in the control groups [14].
Enterobiasis occurs worldwide, usually involving school-aged children [16]. In general E. vermicularis infection is transmitted by hand to mouth and/or person to person directly. High prevalence of E. vermicularis in the current study might be due to improper hygiene including not washing hands with soap after defecation, before eating and preparing foods. In the study area, there are two traditional methods of cleaning anal areas after defecation: (1) washing the anal area by hand with tap water (2) a piece of cloth is used to clean the anal area after defecation. The cloth is used multiple times until the person decides that it has become too dirty after which it is washed and reused. These improper cleaning practices after defecation could be the probable causes behind autoinfection. The higher prevalence of E. vermicularis could also be explained by the highly infectious nature of the parasites.
G. intestinalis and E. coli were the most common intestinal protozoa among the study population. Both can be transmitted orally by drinking infected water and both are environmental contaminants of the water supply. The water supply is really an important risk factor for the Giardiasis, and several large outbreaks of giardiasis have resulted from the contamination of municipal water supplies with human waste [17]. The ingestion of contaminated water is a common problem in Turkey countrywide due to the lower quality of water and faulty sewage lines. The problem is greater in the rural areas that do not have a municipal water network or sewage system [18]. Contamination of drinking water with Giardia spp. has been increasingly recognized over the past 10 years as a cause of water-borne diseases in humans [19]. Giardia cysts have been isolated from water supplies in different parts of the world [20,21]. Epidemic giardiasis may be related to drinking water [22]. G. intestinalis and E. coli are most common in the western part of Turkey [11]. In a study assessing giardiasis cases in Turkey published within the last 15 years, the prevalence of G. intestinalis was found 11.6% in the western part of Turkey [23]. From the study, 68.6% of the study group uses municipal water, while the others either use a chlorinated collection tank with a crude water network or purchase bottled water. Although no statistically significant difference was observed, the prevalence of G. intestinalis was lower in the group using municipal water (9.9%) than the other group (16.0%) in the current study. It is thought that an in-depth assessment should be done on the ways that drinking water becomes contaminated.
In this current study, there were no cases of Taenia spp. Since the consumption of pork and pork products are forbidden for Muslims this may account for the absence of T. solium cases in the population. It is also a common practice to eat uncooked meat in the eastern part of Turkey, but not in the western part. T. saginata infections are observed in the eastern studies, with prevalence of 13.8% and 12.9% [19,24]. However, in the western region infections by this parasite were not observed in this or in another previous study [12].
Differences due to gender were not observed in this current study. These results were similar to a study conducted in the central part of Turkey [24]. In this current study; the prevalence of intestinal parasitic infection was higher in the rural areas. A similar result was found in the central region of Turkey where the prevalence of intestinal parasites was higher in the rural area [25].
In this current study, most of the complaints by the study population were not significantly related with the intestinal parasitic infection. For example, perianal itching was noted in 15.8 % of the study population. There was no significant difference in the prevalence of this symptom in pinworm infected and non-infected children. Furthermore, no association was found between the prevalence of pinworm infection and a history of teeth grinding, colic, enuresis, and irritability. The complaints may not have been assessed effectively through the use of only a questionnaire without an interview, or they might not be useful for every individual's diagnosis.
The prevalence of intestinal parasites was higher in groups where the mother in the household had less than a primary school education, where the hand is habitually used for the cleaning of the anal area and where toilet paper is seldom or never used. The relation between a child's health and the mother's education is well known. Health indicators of children whose mother's education level is lower are always worse [26]. In the last two groups, the habits of the children are a factor, along with a cultural dimension. They were taught to clean the anal area by washing with the hand. Toiler paper usage was not common, possibly due to low income. Usage of a piece of cloth instead of toilet paper was also common.
The major limitation of the current study was a low response rate. The assessment would have been more valuable if a higher response rate could have been obtained. But, it was thought that the results were still important because there is little knowledge on the data of the region. Additionally, the current study was the first population-based study for the region. Although an important number of risk factors were discussed, a few risk factors (e.g.: shoe wearing) were not evaluated in the current study. This might have been another limitation of the study. The important risk factors for the region were evaluated in the study.
Conclusion
As a conclusion, intestinal parasitic infection is an important public health problem in Aydin, Turkey. Rural residence, households where the mother has less than a primary school education, periodic or non-usage of toilet paper, and the washing of the anal area by hand after defecation were the significant associations. Interventions including health education on personal hygiene to the students and to the parents, especially to mothers are required. The ratio of uneducated women should decline with specific programs. A multisectoral approach is needed.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
PO planned the research, performed the sampling and statistical analyzes and wrote draft and final version of the manuscript. SE contributed in planning the research, performed parasitic examinations and contributed discussing the results and writing manuscript. BG performed parasitic examinations and contributed discussing the results. OO organized the work in the schools and participated in its design. EB participated in initial study design, coordinated the study and revised the manuscript. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgement
Research Fund of Adnan Menderes University (TPF-01001) supported this study.
==== Refs
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Ulukanligil M Seyrek A Demographic and parasitic infection status of schoolchildren and sanitary conditions of schools in Sanliurfa, Turkey BMC Public Health 2003 3 29 12952553 10.1186/1471-2458-3-29
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| 15615592 | PMC544355 | CC BY | 2021-01-04 16:28:47 | no | BMC Public Health. 2004 Dec 22; 4:64 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-64 | oa_comm |
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BMC UrolBMC Urology1471-2490BioMed Central London 1471-2490-4-161560146610.1186/1471-2490-4-16Case ReportDelayed bowel perforation following suprapubic catheter insertion Ahmed Shwan J [email protected] Ajay [email protected] Peter [email protected] Department of Urology, Royal Surrey County Hospital, Egerton Road, Guildford, UK2 Department of Urology, Eastbourne District General Hospital, Kings Way, Eastbourne, UK2004 15 12 2004 4 16 16 19 7 2004 15 12 2004 Copyright © 2004 Ahmed et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Complications of suprapubic catheter insertion are rare but can be significant. We describe an unusual complication of a delayed bowel perforation following suprapubic catheter insertion.
Case presentation
A gentleman presented with features of peritonitis and feculent discharge along a suprapubic catheter two months after insertion of the catheter.
Conclusion
Bowel perforation is the most feared complication of suprapubic catheter insertion especially in patients with lower abdominal scar. The risk may be reduced with the use of ultrasound scan guidance.
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Background
Suprapubic catheterization is a common urological procedure. Complications of catheter insertion are uncommon but can be serious including bowel perforation or obstruction. We describe an unusual complication of delayed bowel perforation after suprapubic catheter insertion.
Case presentation
An 86 year old gentleman had a suprapubic catheter inserted for bladder outlet obstruction. This was done under a local anaesthetic using the standard Lawrence Add-a-cath® trocar with ultra sound guidance to measure the bladder volume which was estimated as 500 ml. He had been diagnosed with poorly differentiated carcinoma of the prostate six years earlier and had undergone radical radiotherapy, bilateral sub-capsular orchidectomy and transurethral resection of prostate gland during the interim period. He had bilateral ureteric stents inserted for obstructive uropathy six months earlier. He had a past history of abdomino-perineal resection for rectal carcinoma fourteen years earlier as a curative procedure.
He returned for the first change of supra pubic catheter to the Urology suite in two months time. The catheter was changed easily by the specialist nurse and the patient was discharged home. He returned about ten hours later with features of peritonitis and feculent discharge along the supra pubic catheter. He underwent an emergency explorative laparotomy. A loop of small bowel – adhered to the scar – was placed between the anterior abdominal wall and the bladder. The supra pubic tract was seen to pass through and penetrate the loop in two places before going into the urinary bladder [figure 1]. There was excessive fibrosis of the bowel segment in the area surrounding the perforation sites. Resection of the affected bowel segment and end-to-end anastomosis was undertaken. An indwelling urethral catheter was left in situ. He made a complete recovery and has been left with the urethral catheter.
Conclusions
Perforation of the abdominal viscera is well documented as a rare but important major complication of suprapubic cystostomy [1,2]. To our knowledge, only one case of delayed bowel perforation has been reported [3] three months after the actual catheter insertion. The likely mechanism is the injury occurred during the original insertion. The catheter and the ensuing inflammatory fibrosis sealed the perforation. On removal of the catheter during the change, the sealed perforation opened up.
Our case explains the increased risk of bowel damage during suprapubic catheterization in patients with history of previous lower abdominal surgery as the bowel frequently adheres to the scar. In one study, it was found that 59% of patients with midline laparotomy incision have anterior abdominal wall adhesions [4]. Therefore, patients with lower abdominal scar should only have suprapubic catheter placement under ideal conditions to reduce risk of bowel perforation. Patients must have adequately distended bladder and placed in Trendelenburg position. We do recommend that the procedure to be performed by a skilled operator using ultrasound scan to look for bowel loops between the bladder and anterior abdominal wall. If bowel loops are present or if ultrasound facilities are not available, then open cystostomy method should be considered. The first change of the catheter should be done in the urology department rather than in the community. Patients returning after having their first catheter change with features of localised peritonitis (lower abdominal pain, high temperature and raised White Blood Cell count) should alert the urologist for the possibility of bowel perforation.
Authors' competing interests
The author(s) declare that they have no competing interests.
Author's contributions
SA Collected the data; drafted and revised the manuscript and drew the illustration. AM helped to draft and revise the manuscript and PR was the main surgeon and helped to revise the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Figures and Tables
Figure 1 Suprapubic catheter traverse through a small bowel loop between the bladder and the abdominal wall.
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Noller KL Pratt JH Symmonds RE Bowel perforation with suprapubic cystostomy. Report of two cases Obstet Gynecol 1976 48 675 695
Cundiff G Bent AE Suprapubic catheterization complicated by bowel perforation Int Urogynecol J Pelvic Floor Dysfunction 1995 6 110 113
Witham MD Martindale AD Occult transfixation of the sigmoid colon by suprapubic catheter Age and Ageing 2002 31 407 408 12242207 10.1093/ageing/31.5.407
Levrant SG Bieber EJ Barnes RB Anterior abdominal wall adhesions after laparotomy or laparoscopy J Am Assoc Gynecol Laparosc 1997 4 353 356 9154785
| 15601466 | PMC544356 | CC BY | 2021-01-04 16:03:53 | no | BMC Urol. 2004 Dec 15; 4:16 | utf-8 | BMC Urol | 2,004 | 10.1186/1471-2490-4-16 | oa_comm |
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BMC BiotechnolBMC Biotechnology1472-6750BioMed Central London 1472-6750-4-281556373310.1186/1472-6750-4-28Methodology ArticleSingle-track sequencing for genotyping of multiple SNPs in the N-acetyltransferase 1 (NAT1) gene Soucek Pavel [email protected] Camilla Furu [email protected] Marit [email protected] Tom [email protected] Elin H [email protected] Vessela N [email protected] Group for Biotransformations, Center of Occupational Diseases, National Institute of Public Health, Praha 10, Czech Republic2 Department of Environmental and Health Studies, Faculty of Arts and Sciences, Telemark University College, Norway3 Department of Occupational and Environmental Medicine, Telemark Central Hospital, 3710 Skien, Norway4 Department of Biochemistry, University of Oslo5 Department of Pathology, Ullevål University Hospital, Oslo, Norway6 Department of Genetics, Norwegian Radium Hospital, Montebello 0310, Oslo, Norway7 Advanced Technology Center National Cancer Institute, NIH, NCI, Bethesda2004 25 11 2004 4 28 28 5 3 2004 25 11 2004 Copyright © 2004 Soucek et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Fast, cheap and reliable methods are needed to identify large populations, which may be at risk in relation to environmental exposure. Polymorphisms in NAT1 (N-acetyl transferase) may be suitable markers to identify individuals at risk.
Results
A strategy allowing to address simultaneously 24 various genetic variants in the NAT1 gene using the single sequencing reaction method on the same PCR product is described. A modified automated DNA sequencing using only one of the sequence terminators was used to genotype PCR products in single-track sequencing reactions of NAT1 and was shown to be universal for both DNA sequencing using labeled primers and labeled nucleotides. By this method we detected known SNPs at site T640G, which confers the NAT1*11 allele with frequency of 0.036, further T1088A and C1095A with frequency of 0.172 and 0.188, respectively and a deletion of TAATAATAA in the poly A signal area with a frequency 0.031. All observed frequencies were in Hardy Weinberg equilibrium and comparable to those in Caucasian population. The single-track signatures of the variant genotypes were verified on samples previously genotyped by RLFP.
Conclusions
The method could be of great help to scientists in the field of molecular epidemiology of screening of large populations for known informative biomarkers of susceptibility, such as NAT1.
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Background
We have previously described the single sequencing reaction (SSR) protocol for assessment of a known polymorphism in the 3'utr region of CYP19 (aromatase) [1,2]. Here we have extended and optimized the use of the method for multiple polymorphisms in the NAT1 gene. The increasing number of detected mutations in the NAT1, makes genotyping using conventional restriction fragment length polymorphism (RFLP) or allele-specific amplification complicated. We developed a rapid and universal strategy based on single-track DNA sequencing analysis of a unique PCR product encompassing the entire NAT1 coding region (contains no introns) along with the flanking 5' and 3' untranslated regions. It allows a rapid and economic characterization of NAT1 alleles. Our method brings reproducible results on both Alf Express™ (Pharmacia) and ABI310 PRISM sequencing instruments and may be adopted for majority of epidemiological studies with relevance of NAT1 in environmentally related diseases.
N-acetyltransferases (NAT, EC 2.3.1.5) are implicated in the biotransformation of primary arylamines (e.g. 2-naphthylamine and aminobiphenyls), heterocyclic amines, hydrazines, and their N-hydroxylated metabolites present in tobacco smoke and food [3-7]. An increased activity for p-aminobenzoic acid acetylation (marker for NAT1 activity) was observed in the breast malignant tissues compared to benign and control tissues [8,9]. Human NATs may have adapted a common catalytic mechanism from cysteine proteases for acetyl-transfer reactions [10,11]. The NAT1 gene is highly polymorphic (for listing of known variant alleles ). Expression of NAT1*16 but not NAT1*10 and NAT1*11 caused a 2-fold decrease in the amount and catalytic activity of NAT1 in COS-1 cell cytosol [12-14]. All available data suggest that slow NAT1 phenotype results from NAT1 allelic variants that encode reduced expression of NAT1 and/or less-stable NAT1 protein [15].
Epidemiological studies suggest that the NAT1 and NAT2 acetylation polymorphisms modify the risk of developing urinary bladder, colorectal, breast, head and neck, lung, and possibly prostate cancers [16-18]. Interactions between NAT2*4 and NAT1*10 were suggested by the increased frequency of the NAT2*4/NAT1*10 haplotype [19-21]. The individual risks associated with NAT1 acetylator phenotypes/genotypes are usually small, but they increase when considered in conjunction with other susceptibility genes and/or aromatic and heterocyclic amine carcinogen exposures. Because of the relatively high frequency of the variant NAT1 genotypes in the population, the attributable cancer risk may be high. Large-scale molecular epidemiological studies that investigate the role of NAT1 genotypes and/or phenotypes together with other genetic susceptibility gene polymorphisms and biomarkers of carcinogen exposure are necessary to expand our current understanding of the role of NAT1 acetylation polymorphisms in cancer risk [22].
Results
We outlined a strategy allowing to address simultaneously 13 genetic variants in the coding area of NAT1 with two sequencing tracks: A and G and 11 variants in the 3' flanking area with two tracks: A and T (Table 1). Using single-track sequencing of the NAT1 coding region, we have detected 11 individuals carrying polymorphisms at site T640G, which characterizes NAT1*11 alleles (Figure 1C,1D,1E). Identical results were obtained when performing the single-track sequencing on an Alf Express™ system as on ABI310 PRISM (Figure 2E,2F). The frequency of this polymorphism, 0.036 found by single-track sequencing in our study was comparable to the frequency 0.032 found by PCR-RFLP method in 396 German control individuals [23]. Polymorphisms T1088A and C1095A (Figure 3A,3B,3C and Figure 4A,4B,4C, representing each genotype analyzed by both Alf Express™ and ABI310 PRISM) were in perfect linkage disequilibrium. The frequency of these polymorphisms was 0.172 and 0.188 respectively and was comparable to frequency 0.206 reported by Bruhn et al. [24].
A-track sequencing of the 3'-untranslated region of NAT1 revealed a deletion of TAATAATAA (Figure 3D,3E on Alf Express™ and Figure 4E on ABI310 PRISM). It was found in 7 individuals accounting for the frequency 0.031, which is comparable to the frequency 0.033 observed in the study on 314 control individuals by Bruhn et al. [24]. This change is also unique for the NAT1*11 alleles and co-segregates with T640G polymorphism. Very good co-segregation of T640G and 1067-1090delTAATAATAA polymorphisms in 7 informative samples was observed (4 heterozygotes and 3 homozygotes). Despite the different chemistry, Figures 1, 2, 3, 4 demonstrate perfect concordance between the results obtained by Alf Express™ and ABI310 PRISM analysis, verified on samples with previously characterized genotypes by RFLP.
Discussion
A modified automated DNA sequencing with a fluorescent label was used to genotype PCR products spanning through the whole NAT1 gene by single-track sequencing reactions in 192 control individuals. Previously, we have shown that single-track sequencing reactions performed on PCR products, with subsequent analysis on an Alf Express™ DNA Sequencer, can be as informative, sensitive, and accurate as complete sequencing reactions but more economical option for the genotyping of known polymorphisms in the human aromatase gene [1]. Compared to analysis by full gel-based sequencing, four times more samples can be analyzed per gel in considerably shorter time. The method could be particularly efficient in cases of high density of polymorphic sites residing in a common area as in the case of NAT1.
Conclusions
This paper shows that single-track sequencing reactions can be used as a tool for screening for all types of known polymorphisms in field laboratories with limited or overloaded sequence capacity. Compared to standard sequencing, this single track sequencing may have better signal to noise ratio. This approach is time and cost effective and can be accommodated for high-throughput analyses in epidemiology studies.
Methods
Materials
Chemicals for PCR and sequencing were purchased from ABI (Applied Biosystems, Foster City, CA, USA) and Amersham Biosciences (Uppsala, Sweden).
Subjects
The study included 192 DNA samples from Norwegian healthy individuals obtained through Norwegian Population Registry as a population-based series of residents in the Oslo area. Test samples from healthy Norwegian controls with known genotypes (previously assessed by RFLP) were available.
PCR amplification of NAT1
A single PCR amplification of the entire coding region and the 3' flanking area of NAT1 (923 bp) using forward primer: 5'-tactgggctctgaccactat-3' and reverse primer: 5'-tgctttctagcataaatcacc-3' was performed. PCR mix contained 16.9 μl dH2O, 10 × PCR buffer (2.5 μl), 1.25 mM MgCl2 (4 μl), 2.0 mM dNTP (1.0 μl mixture of each), 10 μM oligonucleotide primer, 0.2 μl Taq DNA polymerase (Perkin Elmer, 5 U/μl) and 1.0 μl of genomic DNA (100 ng/μl) in a final volume of 25 μl. Thermal cycling (GeneAmp 2400, PE, Foster city, CA) included initial denaturation 2 min at 94°C, 10 cycles of 30 sec at 94°C, 30 sec at decreasing annealing temperature 58 to 48°C, 1 min at 72°C, and 25 cycles of 30 sec at 94°C, 30 sec at 50°C and 1 min at 72°C. Quality of PCR products was checked on 6% acrylamide gels.
Single-track sequencing
The SNPs studied by this assay are summarized in Table 1. Single A-, G- and T- track sequencing using 1 given terminator at a time was performed using both Alf Express™ system (Pharmacia Biotech, Uppsala, Sweden) and ABI310 PRISM capillary sequencer. Alf Express™ Cy5-labeled primers NAT1A-F-CY5: 5'-gggagggtatgtttacagca-3' and NAT1A-POLYA-CY5: 5'-gcataaatcaccaatttcca-3' were used for genotyping the coding region and 3'untranslated area of NAT1, respectively. The single sequencing reactions (7 μl) contained: 1.25 μl of dH2O, 0.5 μl of 10-times concentrated FS polymerase buffer (125 mM Tris-HCl, pH 9.5, 50 mM (NH4)2SO4, 150 mM MgCl2), 2 μl of ddNTP termination mix (containing 10 mM dNTPs and either ddATP or ddTTP (10 μM) for A-track or T-track sequencing respectively, 1 μl of 2 μM primer, 0.25 μl of FS polymerase (5 U/μl), and 2 μl of NAT1 PCR product. The following conditions were used for thermal cycling: initial denaturation for 5 min at 95°C, 35 cycles of 30 sec at 95°C, 30 sec at 50°C, and 1 min at 68°C. The single-tracks were evaluated using conventional software AlfWin, Fragment Analyzer, v. 1.02 supplied with the sequencer (Alf Express™). For the single-track sequencing by ABI310 PRISM we used the same sequencing primers as for Alf Express™ system however labeled with the fluorophor 6-FAM. Sequencing reactions (10 μl) contained: 5.0 μl dH2O, 1.0 μl of 10-times concentrated Thermo Sequenase buffer (260 mM Tris-HCl, pH 9.5, 65 mM MgCl2), 2.0 μl of ddNTP termination mix (containing dNTPs and either ddATP or ddGTP for A-track or G-track sequencing respectively – for composition see Table 3), 0.5 μl of 1 μM primer, 0.5 μl of Thermo Sequenase DNA polymerase (with pyrophosphatase) 3.2 U/μl and 1.0 μl NAT1 PCR product (described above). Thermal cycling conditions were as follows: initial denaturation 20 sec at 95°C, 25 cycles of 20 sec at 95°C, 20 sec at 55°C and 1 min at 72°C, and then 10 cycles of 20 sec at 95°C, 1 min at 72°C. Results were evaluated using GeneScan Analysis software, v. 3.7.
Abbreviations
ABPs – aminobiphenyls, NAT1 – N-acetyltransferase 1, SSR (Single Sequencing Reaction)
Author's contributions
PS performed the SSR analysis on an Alf Express™ and prepared the first draft of the paper, CFS and MS optimized the SSR analysis for an ABI system in the lab of EHK, TK designed primers and contributed with reagents, materials, and advice, VNK brought the idea, organized the study and was responsible for the revisions of the paper.
Acknowledgements
The work at this project was partly supported by grants of Grant Agency of the Czech Republic, no.: 310/01/1537 and Internal Grant Agency of the Czech Ministry of Health, no.: 6747-3. The analysis is supported by grants E01085/001 and 122772/310 by the Norwegian Cancer Society, and grant D99061/004 of the Research Council of Norway. We thank David Ryberg, National Institute of Occupational Health, Oslo, for supplying samples for testing genotypes.
Figures and Tables
Figure 1 Genotyping of the coding region of NAT1 by T-track sequencing on Alf Express™. (A) Displayed area from T382 to T417 (B) Displayed area from T531 to T582 (C) Displayed area from T618 to T661, with genotypes 640T/T; (D) 640T/G; (E) 640G/G; (F) – Displayed area from T735 to T797, A752T and T777C alleles not found.
Figure 2 NAT1 variants in the coding region assessed by G-track sequencing on ABI310 PRISM. (A) Displayed area from G418 to G476, genotypes 445G/G and 459G/G; (B) 445G/A and 459G/A heterozygote; (C) Displayed area from G544 to G590, genotypes 560G/G; (D) 560G/A; (E) Displayed area from G598 to G655, genotypes 613A/A and 640T/T; (F) 613A/A and 640A/G.
Figure 3 NAT1 alleles in 3'-untranslated region assessed by single A-track sequencing on Alf Express™. Displayed area from A1038 to A1104. (A) NAT1*4/*4 – wild type; (B) 1088T/A and 1095C/A heterozygote; (C) 1088A/A and 1095A/A homozygote; (D) NAT1*4/*11 allele; (E) NAT1*11/*11 allele.
Figure 4 NAT1 alleles in 3'-untranslated region by single A-track sequencing on ABI310 PRISM. Displayed area from A1104 to A1048. (A) NAT1*4/*4 – wild type; (B) 1088T/A and 1095C/A heterozygote; (C) 1088A/A and 1095A/A homozygote; (D) 1088T/T homozygote and 1095C/A heterozygote; (E) NAT1*4/*11 allele.
Table 1 Overview of NAT1 alleles and selection for single-track sequencing
coding region: track/ddNTP allele
350,351 GG → CC G NAT1*5
402 T → C T NAT1*20
445 G → A G NAT1*11
459 G → A G NAT1*11
497–499 GGG → CCC G NAT1*5
559 C → T T NAT1*15
560 G → A G NAT1*14
613 A → G G NAT1*21
640 T → G T/G NAT1*11
752 A → T T NAT1*22
777 T → C T NAT1*23, *27
781 G → A G NAT1*24
787 A → G G NAT1*25
poly A region: track/ddNTP allele
884 A → G A NAT1*5
976 delA A NAT1*5
1025 delT T NAT1*29
1067–1090 delTAATAATAA A NAT1*11
1067–1090 delTAATAA A NAT1*28
1067–1090 delTAA A NAT1*18
1067–1090 insTAA A NAT1*26
1088 T → A A NAT1*10, *14,
NAT1*18, *29
1091 ins AAA A NAT1*16
1095 C → A A NAT1*3, *10, *11,
NAT1*14,*16,*18,
NAT1*26, *29
1105 delT T NAT1*5
==== Refs
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| 15563733 | PMC544357 | CC BY | 2021-01-04 16:02:57 | no | BMC Biotechnol. 2004 Nov 25; 4:28 | utf-8 | BMC Biotechnol | 2,004 | 10.1186/1472-6750-4-28 | oa_comm |
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BMC EcolBMC Ecology1472-6785BioMed Central London 1472-6785-4-171560692110.1186/1472-6785-4-17Research ArticleModelling the impact of climate change on woody plant population dynamics in South African savanna Tews Jörg [email protected] Florian [email protected] Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, Maulbeerallee 2, D-14467 Potsdam, Germany2 Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, Maulbeerallee 2, D-14467 Potsdam, Germany2004 17 12 2004 4 17 17 7 9 2004 17 12 2004 Copyright © 2004 Tews and Jeltsch; licensee BioMed Central Ltd.2004Tews and Jeltsch; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
In Southern Africa savannas climate change has been proposed to alter rainfall, the most important environmental driver for woody plants. Woody plants are a major component of savanna vegetation determining rangeland condition and biodiversity. In this study we use a spatially explicit, stochastic computer model to assess the impact of climate change on the population dynamics of Grewia flava, a common, fleshy-fruited shrub species in the southern Kalahari. Understanding the population dynamics of Grewia flava is a crucial task, because it is widely involved in the shrub/bush encroachment process, a major concern for rangeland management due to its adverse effect on livestock carrying capacity and biodiversity.
Results
For our study we consider four climate change scenarios that have been proposed for the southern Kalahari for the coming decades: (1) an increase in annual precipitation by 30–40%, (2) a decrease by 5–15%, (3) an increase in variation of extreme rainfall years by 10–20%, (4) and increase in temporal auto-correlation, i.e. increasing length and variation of periodic rainfall oscillations related to El Niño/La Niña phenomena. We evaluate the slope z of the time-shrub density relationship to quantify the population trend. For each climate change scenario we then compared the departure of z from typical stable population dynamics under current climatic conditions. Based on the simulation experiments we observed a positive population trend for scenario (1) and a negative trend for scenario (2). In terms of the projected rates of precipitation change for scenario (3) and (4) population dynamics were found to be relatively stable. However, for a larger increase in inter-annual variation or in temporal auto-correlation of rainfall population trends were negative, because favorable rainfall years had a limited positive impact due to the limited shrub carrying capacity.
Conclusions
We conclude that a possible increase in precipitation will strongly facilitate shrub encroachment threatening savanna rangeland conditions and regional biodiversity. Furthermore, the negative effects found for positive auto-correlated rainfall support current ecological theory stating that periodically fluctuating environments can reduce population viability because species suffer disproportionately from poor environmental conditions.
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Background
In order to assess biodiversity response under climate change, Hannah et al. [1] recently emphasized the need to apply simulation models operating on a regional scale. Moreover, species respond differently to climate change because of different adaptations to their environment [2]. As a consequence, single-species models with a regional focus are essential to fully understand the manifold impact of global climate change. However, even though recent simulation tools have occasionally been applied for climate-sensitive animal species (e.g. [3-7]), spatial plant population models are extremely scarce (see review in [8]).
In this study we show how climate change may affect the long-term population dynamics of the raisin bush, Grewia flava DC, a common, fleshy-fruited, woody plant species of South African savannas. Understanding the population dynamics of Grewia flava is a crucial task, because it is widely involved in the shrub/bush encroachment process (e.g. [9,10]). Shrub encroachment, i.e. the increase in woody plant cover, is a major concern for conservation and savanna rangeland management, due to its adverse effect on livestock carrying capacity (e.g. [11-13]) and biodiversity (e.g. [14]). In the context of global climate change, increase in woody plant cover has primarily been investigated in association with elevated CO2 (e.g. [15,16]). For example, Bond et al. [16] suggested that higher rates of atmospheric CO2 will have a positive effect on the post-fire regrowth of woody plants resulting in an increase in woody plant cover. Unfortunately, appropriate data verifying this hypothesis are not yet available.
Here, we present a different approach. We used Spatial Grewia Model (SGM) (see [17,18]), a stochastic, spatially-explicit computer model to evaluate the impact of precipitation pattern change on Grewia flava population dynamics. In semi-arid and arid savannas, rain is the most important environmental parameter governing crucial life history processes in woody plants [19]. Hence, climate change related shifts in precipitation pattern will potentially have severe consequences for woody plant population dynamics. As a regional focus for our study we considered the southern Kalahari, the near centre of Grewia flava's distribution. In this area, recent climatological studies proposed either a decrease in mean precipitation of 5–15% by the year 2050 [20], or an increase by up to 30–40% (e.g. [21]. Further studies suggest an increase in the frequency and variability of extreme rainfall events (e.g. [22]), as well as alternating phases with low and high rainfall, typical for Southern Africa [23]. The large divergence between the various precipitation scenarios raises the question how woody plants would react along this spectrum. Therefore, we have set up a systematic sensitivity study to explore Grewia flava population dynamics along precipitation gradients, so as to detect possible thresholds in the response of this species to continuous variations of a crucial environmental driver.
Results
By implementing relevant ecological processes of Grewia flava population dynamics into the SGM (see Figure 1 and model description in method section), we developed a standard scenario, based on annual rainfall for the period 1940 – 2000. This standard scenario led to stable population dynamics (see Figure 2 and method section). To compare population trends between four climate change scenarios we then used simple linear regression to calculate the z-value, i.e. the slope of the year – shrub density relationship.
Figure 1 Flow chart of SGM showing causal pathways of Grewia flava population dynamics. Bold arrows indicate processes where population parameters and variables are affected by the annual type of rainfall (for detailed model description see text).
Figure 2 Typical SGM time series of the standard scenario with 500 annual time steps. Population trend is given as shrub density with simple linear regression y = 0.0032x + 15.73. Mean slope of population trend for 100 simulation runs was z = 0.000049x + 13.41 with standard deviation of +/-0.017. Further model output from top to bottom: number of high (black) and low rainfall (grey) years per decade, fire occurrence (indicated as a black bar), density of fire- and drought-killed shrubs and density of recruits. Decades with frequent high rainfall years are indicated by frequent fires and increased fire mortality. Decades with exceptionally low rainfall show low fire frequency and increased drought mortality. Peaks of recruitment occur mostly in years with high rainfall and absence of fire.
(1) Increase in precipitation
An increase in the frequency of high rainfall years resulted in an increase of the z-value (Figure 3 and Figure 4). For example, a 20% increase in high rainfall years yielded a mean z-value of 0.01 (Figure 4). With an increase of 50%, the maximum z-value was 0.02. This upper limit could be clearly assigned, because the carrying capacity of cell type T had been reached, i.e. no further recruits were able to establish in the sub-canopy of trees. Even though additional juveniles may still emerge in cell type M, they are prevented from reaching the open due to a low probability of Pmatrix. Additionally, fire mortality is higher in cell type M.
Figure 3 Typical time series for climate change scenarios with z-value of the population trend: increase in precipitation (1); decrease in precipitation (2); increase in inter-annual variation of precipitation (3); increase in temporal auto-correlation of precipitation (4) for period length PL with 20 and 250 years. Columns indicate 20%, 60% and 100% variation, respectively (see parameter values in Table 1).
Figure 4 Results of simulation experiments for climate change scenarios according to: increase in precipitation (1), decrease in precipitation (2) and increase in inter-annual variation of precipitation (3) given as mean population trend z for 100 simulation replicates. The mean population trend is given as z for 100 simulation replicates plotted against percent variation in the probability of occurrence of extreme rainfall years as compared to the standard scenario.
(2) Decrease in precipitation
A decrease in mean precipitation resulted in a negative population trend (scenario 2, Figure 3) with a lower limit of z at -0.027 (Figure 4). A 10% increase of Plow, corresponding to a probability of occurrence of low rainfall years of Plow = 0.165, yielded a mean z value of -0.008. This continued to z = -0.02 for a 40% increase of Plow. Above a 60% increase of Plow 30% of all replicate runs resulted an extinction of the Grewia flava population within the 500 year time frame (not shown, however compare Figure 3). Comparing the upper and lower limits of the z-value for scenario (1) and (2), negative effects associated with an increase in drought events were more effective than the positive effects related to higher recruitment rates associated with comparable rainfall excess (0.02 as opposed to -0.027 see Figure 4).
(3) Increase in inter-annual variation of precipitation
An increase in inter-annual variation between low and high rainfall years below 100% did not cause any significant departure from stable population dynamics. However, above a threshold value of 100%, corresponding to a reduction of Paverage from 0.72 to less than 0.44 (see Table 1), we observed a decrease in the mean z-value. For example, for an increase in variation of 150%, z was -0.02 (Figure 4). Here, rainfall almost exclusively occurred as either high or low (Plow = 0.375, Phigh = 0.325). Considering the negative and positive effects of low and high rainfall years, negative effects, such as drought, outweighed positive effects associated with enforced recruitment in high rainfall years.
Table 1 Probability values for low (first value) and high rainfall years (second value) for climate change scenarios: increase in precipitation (1); decrease in precipitation (2); increase in inter-annual variation of precipitation (3); increase in temporal auto-correlation of precipitation (4) with 'good' phase (4a) and 'poor' phase (4b). Columns indicate 20%, 60% and 100% variation in the probability of occurrence of extreme rainfall years, respectively, as compared to the standard scenario (Plow = 0.15, Paverage = 0.72, Phigh = 0.13).
Scenarios / Variation 20 % 60 % 100 %
(1) Plow 0.150 0.150 0.150
Phigh 0.156 0.208 0.260
(2) Plow 0.180 0.240 0.300
Phigh 0.130 0.130 0.130
(3) Plow 0.180 0.240 0.300
Phigh 0.156 0.208 0.260
(4a) 'good' phase Plow 0.120 0.060 0.000
Phigh 0.156 0.208 0.260
(4b) 'poor' phase Plow 0.180 0.240 0.300
Phigh 0.104 0.052 0.000
(4) Increase in temporal auto-correlation of precipitation
Despite constant mean and inter-annual variation in precipitation, increasing period length (PL) and intra-cycle variation (ICV) led to negative population trends and a decrease in z-value (Figure 5). Here, we found varying ICV thresholds for the PL scenarios tested, i.e. an abrupt departure from stable population dynamics. For example, a 10-year alternating rainfall cycle with an increase of 80 % in ICV (corresponding to a 5-year 'poor' phase with Plow = 0.27 / Phigh = 0.026 and a 'good' phase with Plow = 0.03 / Phigh = 0.234) yielded a z-value of -0.003, whereas population dynamics were stable for an ICV of 70%. Further thresholds were found above 60% ICV for a PL value of 20 years, 50% for 50 years, 40% for 100 years and 15% for 250 years. In the latter case, a large proportion of the population became extinct below 50% ICV where z was close to its lower limit. It is noteworthy, that we found no significant difference between similar PL scenarios that began with different phase conditions.
Figure 5 Results of simulation experiments for increase in temporal auto-correlation of precipitation (climate change scenario 4) according to a PL value of 10, 20, 50, 100 and 250 years. The mean population trend is given as z for 100 simulation replicates plotted against percent variation in the probability of occurrence of extreme rainfall years as compared to the standard scenario.
Discussion
Based on our simulation results, we have demonstrated that climate change predictions of precipitation pattern for the southern Kalahari may significantly affect Grewia flava population dynamics. This enables us to estimate the possible consequences for land use management and biodiversity due to the keystone function of Grewia flava in the shrub/bush encroachment process.
What are the implications of climate change related population trends for regional biodiversity and rangeland condition?
(1) Increase in precipitation
A climate change related increase in precipitation is commonly rejected [20]). However, based on a 28-year atmospheric circulation model, Mason et al. [21] proposed an increase in mean annual rainfall of 30–40% for the southern Kalahari. Yet, due to the model's insensitivity to topography, the authors also emphasize uncertainties in their predictions. This scenario would result in a strong increase in shrub density as indicated by a positive mean population trend for the SGM simulation period (Figure 4). Here, fire and drought mortality rates are too low to compensate substantial increase in juvenile recruitment associated with high rainfall years. On rangelands with domestic livestock this process may be strongly enhanced: in an earlier model version it was shown that cattle feeding on the foliage of Grewia flava may disperse seeds into the open matrix vegetation through dung deposition and thus facilitate shrub encroachment [18]. In this case we propose that an increase in precipitation will result in a catastrophic deterioration of current rangeland conditions within several decades. As a further consequence, biodiversity will most likely decrease due to the homogenization of woody plant community structure [24]. An increase in structural homogeneity due to shrub encroachment has been shown to adversely affect species diversity in both plants and animals (e.g. [14,25]).
(2) Decrease in precipitation
The majority of climatological studies propose a decrease in annual precipitation of 5–15% by the year 2050 [20]. For a 10% increase in the probability of occurrence of low rainfall years (at the cost of average years) we found a negative trend for Grewia flava population dynamics (Figure 3). High frequency of low rainfall years resulted in an increase of drought-related mortality and the reduction of shrub density. Increases in the probability of occurrence of low rainfall years of more than 40% lead occasionally to population extinction within the SGM time frame. However, we believe that under natural conditions population dynamics of Grewia flava may also stabilize at a lower level, as suggested by its distribution in arid parts of the southern Kalahari where droughts are more frequent (e.g. [26]). Even though precipitation decrease may mitigate the risk of local Grewia encroachment, a strong reduction in shrub density may lead to cascade effects in the food chain because the fruits are an important component in the diet of animals and humans in an environment where food resources are otherwise scarce [27].
(3) Increase in inter-annual variation of precipitation
The inter-annual variation in precipitation, i.e. frequency and magnitude of extreme rainfall events, has been predicted to increase with ongoing climate change within the next decades (e.g. [22,28]). Rates may range between 10–20% for the southern Kalahari [21]. For this scenario SGM produced no significant departure form stable population dynamics (Figure 4). Thus, we propose that the predicted increase in inter-annual variability will have a low impact on the natural population dynamics and shrub density of Grewia flava. However, this may differ for rangelands with high cattle grazing: an earlier model version showed that increase in the probability of extreme rainfall years can increase shrub encroachment if additional seed dispersal by cattle into the matrix is considered [18].
An increase in variability of more than 100% revealed a significant threshold behavior with a negative Grewia flava population trend. Above this threshold frequent droughts kill the offspring and inhibit emergence. Ecological theory generally suggest that population viability decreases with increase in environmental variability or stochasticity [29], because favorable events have a limited positive impact due to the carrying capacity, whereas unfavorable events have a full negative impact [7]. However, to our knowledge, the general character of this relationship has not been studied in detail yet. Thus, the pattern described here provides new insights into the relationship between environmental variability and the corresponding population dynamics.
(4) Increase in temporal auto-correlation of precipitation
Tyson [23] showed evidence of periodic, non-random rainfall oscillations with a period of ca. 18–20 years in Southern Africa. This distinct pattern has recently been associated with El Niño/La Niña phenomena and may increase under climate change [30]. Since we did not simulate actual total rainfall we used values of ICV, i.e. intra-cycle variation and PL, i.e. period length to evaluate the impact of climate change related increase in positive auto-correlation of rainfall. In our study region, Kruger [30] found an oscillation with a PL value of 22 years between 1955 and 1991. The ICV values used in our simulation experiments refer to an increase in the probability of an extreme rainfall category and are thus difficult to compare with the cyclic variability of actual total rainfall. However, we compared the ICV values from our model with departures of annual rainfall from the inter-annual mean of a typical rainfall oscillation provided by Kruger [30]. Based on this, we infer that an ICV value above 60% resembles a realistic rainfall variation between favorable and unfavorable periods of a 22-year oscillation. Although we did not find a negative impact on Grewia flava population dynamics for this scenario, a slight increase in ICV combined with an increasing length of El Niño/La Niña phases might have a significant negative impact on the population viability of Grewia flava (see Figure 5). This will have profound consequences for other organisms which depend on, for example, fruit provision.
As a further corollary, our results confirm current ecological theory that positive auto-correlation with constant average and inter-annual variation of a driving environmental parameter can reduce population viability and lead to extinction (e.g. [17,31,32]). In terms of the internal model processes of SGM, this means that negative effects in drought years (i.e. drought mortality and lack of reproduction) outweigh positive effects in wet years (i.e. increased reproduction). This is noteworthy, as mean annual rainfall and inter-annual variation was kept constant. Until now, this has been primarily shown in solely theoretical studies with under-compensating population dynamics (e.g. [33]) or individual-based animal models (e.g. [7]). To our knowledge this is the first empirical-based, plant population model that showed evidence of population persistence being negatively influenced by positive auto-correlation through a periodically fluctuating environmental driver.
Parameter sensitivity and predictive power of SGM
In order to assess the predictive power of SGM it is important to discuss the inference of actual precipitation amounts on probability values. Threshold values for extreme rainfall years are based on expert knowledge and may vary depending on the plant species considered. Moreover, percentage changes in extreme rainfall years, as applied in our model, are not necessarily equivalent to percentages changes in mean annual rainfall. For example, an increase in 'high' rainfall years by 10% represents an increase in mean annual rainfall of approximately 10%, depending on actual precipitation amounts in each 'high' rainfall year. Thus, it is important to derive principal population trends from the SGM model results rather than absolute predictions. Another issue regards the temporal dimension: long-term SGM simulation periods deviate from short-term predictions of climate change. However, population trends applied for only a few decades may yield biased results due to the cyclicity of Grewia population dynamics (see [17]). Finally, we stress that potential deviations in the annual probability of drought mortality may modify the output of the model. For example, a sensitivity analysis showed that a 100% increase in drought mortality for adult shrubs decreased the model output by 10% (see [18]).
Conclusions
In this study we have shown that climate change may have severe and sometimes unexpected implications at a regional species level. A woody plant like Grewia flava is relatively long-lived, fire and drought-resistant. Intuitively, this would suggest a low climate change impact on Grewia flava population dynamics. However, we found that despite Grewia flava's capability to survive in a harsh environment, it may be strongly affected when rainfall decreases as predicted, or increases in periodical fluctuations. This may also include possible range shifts in regional distribution which have not been studied here. Based on the model and the climate change scenarios analysed, it would be inappropiate to forecasts changes in the geographical distribution of Grewia flava, because SGM parameters have been estimated and validated for populations in the Kimberley region of the southern Kalahari. Moreover, current geographical distribution suggests that buffer mechanisms may facilitate survival in more arid parts of the southern Kalahari (e.g. through higher variability in emergence as an adaption towards lower frequency of high rainfall years).
Decrease in rainfall may reduce the severity of Grewia flava encroachment in the southern Kalahari. However, increase in rainfall will likely enhance this process. A shift from typical open grassland with solitary trees towards wide spread, homogeneous Grewia flava thickets implies negative cascade effects for other species, resulting in the loss of biodiversity [24]. As a further consequence, local rangelands are likely to be reduced in their carrying capacity of domestic livestock with negative effects for economic sustainability. Moreover, a shift in precipitation pattern may have further consequences, such as the alteration of fire regimes, grass biomass production or woody plant carbon uptake.
Methods
A detailed description of the simulation model SGM and previous results have been presented elsewhere (see [17,18]). SGM has been previously validated with empirical data and simulates population dynamics of Grewia flava under specific land use, fire and rain scenarios in southern Kalahari semiarid savannas. However, to facilitate a better understanding of the current results we will briefly describe the study species and relevant aspects of the simulation model.
The study species
In the open savannas of the southern Kalahari, Grewia flava typically grows beneath the dominant tree species Acacia erioloba [34], because bird-mediated seed dispersal predominately confines new establishments to woody plant microsites. However, occasionally large individuals may be found in the open grassland matrix at former tree sites, suggesting high longevity of Grewia flava. Under high cattle grazing a substantial proportion of seeds may be distributed into the open matrix vegetation, since cattle feed on the foliage and fleshy fruits of Grewia flava [18]. This may result in a substantial increase in Grewia cover, particularly around boreholes (e.g. [10]). Grewia flava has excellent resprouting capabilities after fire [35] and low drought mortality rates [36]. Size class distributions suggest a demographic bottleneck in early life stages due to low rates of emergence and high juvenile mortality [36].
General model structure
The computer model SGM (see [17,18]) represents a grid-based approach iteratively simulating population dynamics of Grewia flava in annual time steps for a period of 500 years. Based on empirical demographic and spatial data from the Kimberley region of the southern Kalahari (see [36]), an initial population of 15 shrubs ha-1 was distributed on a 200 × 200 cell grid, with each cell representing 5 m × 5 m of savanna vegetation. The SGM grid is developed in two layers: a landscape and a population layer. In the first layer micro-site types of the savanna vegetation may change in the course of time. In the second layer SGM simulates population dynamics of Grewia flava. A cell type was classified as either tree (T) (i.e. occupied by Acacia erioloba) or matrix type (M) (i.e. grassland vegetation). However, it switched from T to M status when a tree died after a mean life span of 200 years. The converse occurred where a new tree establishes. Initial tree distribution and spatial recruitment pattern was random with a constant density of 5 trees ha-1 for the entire simulation period. In each time step, SGM simulated important life history stages, environmental conditions and the key ecological processes of Grewia flava, the majority of which are governed by annual rainfall patterns (see Figure 1). For example in the model, rainfall directly determines the likelihood of a fire, drought and fire mortality, fruit crop size, emergence and annual shrub growth rate.
Rainfall
Annual rainfall in the southern Kalahari is highly variable, with precipitation ranging between 200 and 700 mm yr-1. Rain falls almost exclusively during summer (November to April) with an inter-annual mean of 417 mm for the period 1940 – 2000 (Kimberley airport, South African Weather Service 2001, unpublished data). For this period we defined a threshold value of 150 mm below and above the long-term mean to classify years into 'low', 'average' and 'high' rainfall years (thresholds of 267 mm yr-1and 567 mm yr-1, respectively) (see [18]). Accordingly, frequency of extreme rainfall years resulted in an annual probability of Plow = 0.15 for low and Phigh = 0.13 for high rainfall years (Paverage = 0.72). Similar classifications have been applied in other studies, e.g. in a spatial simulation model of Acacia raddiana in the Negev Desert [37].
Fruit production and seed dispersal
Fruit production rates are based on mean crop size of Grewia size classes and may vary depending on the annual rainfall (see [18]). Seed dispersal is mostly zoochorous and spatially aggregated with a large proportion of seeds deposited in woody plant microsites. It is a crucial factor for the population dynamics and long-term viability of Grewia flava [36] and represented by two parameters: probability of seeds removed from a shrub and deposited in cell type M, Pmatrix, and cell type T, Pacacia. Pmatrix varies randomly per year between 0.1–0.01% and represents occasional seed distribution through, e.g. small mammals. Pacacia refers to bird-mediated seed dispersal with a range of 1–5% in high rainfall years, 2.5–7.5% in average and 5–10% in low rainfall years. Based on estimates from empirical data [36], the assumption of relatively constant seed dispersal rates may be reasonable as the proportion of seeds removed is most likely to be higher in years of lower fruit set. Fruit production typically varies more than bird abundances, and unless birds switch in exactly compensatory fashion to other fruits to the degree that Grewia flava becomes less abundant, fruit removal may be higher in years of low population size.
Emergence
Even though microsite types differ markedly in micro-environmental conditions, they do not differ in emergence rates of Grewia flava seeds [36] suggesting a similar emergence probability for cell type M and T. However, depending on annual rainfall conditions, the probability of emergence in the model may vary between 1–2% in average and 2–4% in high rainfall years (no emergence occurs in low rainfall years). As survival of Grewia flava seeds in the soil is very low, seeds that do not emerge are assumed to die.
Fire and drought
The occurrence and intensity of a fire depends on the amount of rainfall, since precipitation determines annual grass biomass production and thus fuel load [38]. In the SGM, fire was simulated probabilistically for the entire grid and may only occur in high and average rainfall years. Average frequency of 7.9 years in the model is supported by fire intervals reported from similar savannah types [39]. The impact of fire varies spatially and demographically: a seedling in the matrix vegetation has a 95% chance of being killed in a fire, compared with 4% for adult shrubs which largely resprout in the following year. For T cells, fire mortality probability is 0% for adults and 75% for seedlings. These model assumptions are realistic, since grass fuel accumulation and fire severity beneath trees is less than in tree inter-spaces (e.g. [40,41]). In the SGM, the probability of drought mortality varies between life stages and the annual rainfall conditions (see also model description in [18]). Annual drought mortality for adults is restricted to low rainfall years with a probability of 3% for both cell types. This assumption is based on data from O'Connor [42] and Schurr [36]. For seedlings the annual drought mortality probability is 90% for average and 50% for high rainfall years (see [18]). All seedlings are assumed to die in years with low rainfall conditions.
Growth
In the model, each shrub was assigned a size class with an average canopy cover. For definition of shrub size classes we used the canopy volume to group Grewia flava individuals into categories of small (GrewiaS; <1 m3), medium-sized (GrewiaM; 1–10 m3) and large plants (GrewiaL; >10 m3) as well as seedlings (Grewiaseedling; temporary state after emergence, transforming to GrewiaS with the following year). We assumed a maximum age for each size class member with MaxAgeS = 5 years for small and MaxAgeM = 25 years for medium-sized shrubs. These estimates are based on annual shoot growth rates and aerial photograph analysis of the study area (see [18]). In each annual time step an individual can accumulate a growth year and, if MaxAge is reached, attain the next size class with Ptransition(S) = 0.2 for small and Ptransition(M) = 0.1 for medium-sized shrubs. No growth occurs in low rainfall years.
Carrying capacity and competition
To incorporate intra-specific competition we used a simple causal approach that incorporates cell-based shrub cover. Therefore, we defined a carrying capacity of K = 25 m2 as maximum total cell shrub cover. If shrub cover exceeded K, individuals in the cell died. Density-dependent mortality was simulated annually by removing the smallest individuals first, i.e. in descending order of size class, until shrub cover <K (for further details see [18]). Inter-specific competition between Grewia flava seedlings and the grass layer were neglected: empirical tests showed that emergence rates were similar within and outside of the grassy vegetation matrix (see [36]). For the adult stage, inter-specific competition with other woody plants is of low importance because Grewia flava often occurs as a mono-dominant species, particularly on bush encroached rangelands.
Simulation experiments
The standard scenario of the model, based on rainfall for the period 1940 – 2000, led to stable population dynamics (Figure 2). As standard deviation was generally high we performed 100 replicate simulation runs (± 0.0173 for the standard scenario, see Figure 2). To determine and compare population trends between the climate change scenarios we then used a simple linear regression of years vs. shrub density to calculate the z-value, i.e. the slope of the year – shrub density relationship. This is a reasonable approach for analyzing time-abundance relationships (e.g. [43]). When population dynamics were stable for 500 years, i.e. z was ≅ 0, the number of new recruits more or less resembled the number of fire- and drought-killed shrubs (Figure 2). Significant recruitment events mostly occurred in high rainfall years without fire.
Based on the standard scenario we performed simulation experiments for each of the four different scenarios of climate change with variation in inter-annual mean of precipitation, coefficient of inter-annual variation, and temporal auto-correlation (see Table 1):
(1) increase in precipitation, i.e. stepwise increase of Phigh resulting in a higher probability of high rainfall years (increase in recruitment events). Probability of low rainfall years is constant whereas frequency of average years is reduced, accordingly.
(2) decrease in precipitation, i.e. stepwise increase of Plow resulting in a higher probability of low rainfall years (increase in drought events),
(3) increase in inter-annual variation of precipitation, i.e. stepwise increase of Plow and Phigh resulting in a higher probability of low and high rainfall years at the cost of average years (unaltered inter-annual mean),
(4) increase in temporal auto-correlation of rainfall with 'good' and 'poor' phases.
For the scenario (4) we varied two parameters:
(4a) introduction of rain cycles with increasing period length PL, i.e. longer phases of favorable and unfavorable rain conditions and,
(4b) increase in intra-cycle variation ICV, i.e. increasing variation between alternating favorable and unfavorable periods within one rain cycle.
For PL we applied a period length of 10, 20, 50, 100 and 250 years, respectively, with each period subdivided into a similarly long 'good' and 'poor' phase. For example, a 10-year period length was subdivided into a 5-year period of superior and a 5-year period of poor conditions. For an increase in ICV we alternately increased and decreased high and low rainfall probabilities in each period, respectively (see 4a and 4b in Table 1). For example, for an ICV value of 100% Plow equaled 0.00 and Phigh 0.26 in a 'good' phase, whereas values were 0.30 and 0.00, respectively for a 'poor' phase. Through this procedure inter-annual mean and variation of rainfall probabilities were identical for each scenario and the default set of rainfall probabilities. Combined increase in PL and ICV resulted in an increase of positive auto-correlation. Near-decadal epochs of above- and below-normal rainfall have been identified for the period 1955–1991 [30] and may increase in ICV under predicted climate change.
Author's contribution
JT developed the SGM simulation model, conceived the study and drafted the manuscript. FJ supervised the study and the manuscript writing. Both authors read and approved the final manuscript.
Acknowledgements
This work was funded by the German Ministry of Education and Research (BMBF) in the framework of BIOTA South Africa (01LC0024). I would like to thank E. Bell and N. Hanan for useful comments on an earlier version of this manuscript.
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| 15606921 | PMC544358 | CC BY | 2021-01-04 16:29:14 | no | BMC Ecol. 2004 Dec 17; 4:17 | utf-8 | BMC Ecol | 2,004 | 10.1186/1472-6785-4-17 | oa_comm |
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BMC Med EducBMC Medical Education1472-6920BioMed Central London 1472-6920-4-321561757210.1186/1472-6920-4-32Research ArticleA survey of medical students to assess their exposure to and knowledge of renal transplantation Edwards Anusha G [email protected] Andrew R [email protected] Justin D [email protected] Department of General Surgery, Southmead Hospital, Westbury on Trym, Bristol BS10 5NB, UK2004 23 12 2004 4 32 32 28 9 2004 23 12 2004 Copyright © 2004 Edwards et al; licensee BioMed Central Ltd.2004Edwards et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Within the field of renal transplantation there is a lack of qualified and trainee surgeons and a shortage of donated organs. Any steps to tackle these issues should, in part, be aimed at future doctors.
Methods
A questionnaire was distributed to final year students at a single medical school in the UK to assess their exposure to and knowledge of renal transplantation.
Results
Although 46% of responding students had examined a transplant recipient, only 14% had ever witnessed the surgery. Worryingly, 9% of students believed that xenotransplantation commonly occurs in the UK and 35% were unable to name a single drug that a recipient may need to take.
Conclusions
This survey demonstrates a lack of exposure to, and knowledge of, the field of renal transplantation. Recommendations to address the problems with the recruitment of surgeons and donation of organs, by targeting medical students are made.
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Background
With the potential for improved quality of life and increased life expectancy, renal transplantation is the first choice treatment for most patients with end-stage renal failure [1,2]. However, in the UK there is an ever-increasing disparity between the number of patients on the waiting list and those being transplanted [3]. This is predominantly due to a rise in the incidence of renal failure amongst an aging, racially diverse society in conjunction with a shortage of donated organs [4].
The field of renal transplantation also suffers from a lack of surgeons. Indeed, it is predicted that by the year 2005 there will a shortage of over twenty consultant renal transplant surgeons [5].
Any measures to deal with these problems must include educating and attracting the doctors of tomorrow; medical students [6,7]. However, the General Medical Council's core curriculum model for undergraduate teaching has lead to significant changes in the way that specialist subjects are taught [8]. This survey was conducted to assess the exposure to, and knowledge of, renal transplantation amongst medical students at a single medical school in the United Kingdom.
Methods
In July 2003 a PRHO job fair was held for final year medical students of Bristol University, which all 140 students within the year attended. An anonymous questionnaire was distributed to every student, after the aims of the study had been explained, at the end of a series of short lectures. (Figure 1). The questionnaire consisted of six questions, three to assess their exposure to the field and three to assess their knowledge. The form was collected immediately upon completion, with no opportunity to confer.
Figure 1 Questionnaire distributed to the students.
The questionnaire had been piloted on a random sample of thirty final year medical students from the year before, whereby the form was distributed electronically to their University email accounts. This confirmed that the length and wording of the questionnaire, and the level of knowledge required to complete it were appropriate for the target population.
All statistical analysis was performed using the chi square test, with a p value of 0.05 or less taken to demonstrate statistical significance.
Results
Questionnaires were completed by seventy-six of the 140 medical students in the year (54%). Of these responding students, thirty-two (42%) had never completed either a general medical or general surgical placement at one of the two centres for renal transplantation within the region, both of which are affiliated to the University of Bristol. National data of renal transplantation activity shows that these two centres differ in the number of transplants performed each year [3]. Over the 2002/3 and 2003/4 period Southmead Hospital, Bristol performed approximately three times as many transplants per year than Derriford Hospital, Plymouth (2002/3: 101 in Bristol, 36 in Plymouth, 2003/4: 132 in Bristol, 42 in Plymouth).
Sixty-five students (86%) had never been in the operating theatre during a renal transplant. Of the eleven students that had witnessed a transplant the majority, seven, had been unscrubbed (9%). One student had been scrubbed and observing and a further three students (4%) had actually assisted with the procedure. Closer analysis of these eleven students demonstrates that only one of the eleven had been on placement at Derriford Hospital (χ2, p = 0.006).
Thirty-five (46%) of the students had examined a patient with a transplanted kidney but only thirty-three (43%) could accurately draw on a diagram the usual site of surgical incision that would be made on someone undergoing a left sided renal transplant. Interestingly, fourteen of the students that claimed to have examined a transplant recipient were unable to accurately draw the site of incision. Of the students that had examined a transplant recipient twenty-two had been on placement at one of the hospitals with transplant centres and thirteen had not (χ2, p = 0.128).
Fifty students (66%) were aware that in addition to the use of organs from brain stem dead donors, kidneys could also be transplanted from living donors. Eighteen students (24%) could not name any additional sources of organs and seven (9%) thought that xenotransplantation, using porcine kidneys, is carried out in the UK. None of the students suggested non-heart beating donation.
Nineteen students (25%) were unable to name a drug that might be taken by a patient with a renal transplant. Twenty-one students (28%) were able to suggest just one.
Discussion
This survey highlights both a low exposure to, and a lack of knowledge about, the field of renal transplantation amongst medical students. This is cause for concern as it has implications for the future recruitment of trainees to the speciality and, potentially, to the procurement of organs.
Previous work has highlighted the multiple factors that deter surgical trainees from this speciality [5]. These include the on-call commitment, unpredictable workload and a lack of exposure to the speciality, at an early stage in training. Based on this information, calls have been made to increase the exposure of surgical trainees by the inclusion of transplantation within basic surgical training (BST) rotation programmes [5]. However, as there are only 23 surgical centres performing renal transplantation within the UK it is unlikely that all trainees would be exposed to the field. In order to gain exposure to the maximum number of doctors, early in their careers, targeting medical students may yield the best results. Indeed, a recent crisis meeting regarding recruitment to renal transplant surgery identified that early positive exposure to the field is vital, and should begin at the undergraduate level [6]. This survey highlights that the potential for the promotion of renal transplantation within the undergraduate course is currently relatively unexplored.
The lack of knowledge regarding sources of organs commonly used within the UK is also of concern. In order to increase the number of kidneys available UK Transplant funds a number of non-heart beating programmes. Such initiatives can potentially increase the transplant rate by 20–40% [9]. Identifying all potential heart beating and non-heart beating donors is fundamental to providing a successful service, and reducing the gap between donors and patients on the waiting list. However, if future doctors are unaware of the existence of these programmes then such schemes are unlikely to reach their full potential.
One of the limitations of this study is the selection bias from a 54% response rate. From talking to some of the students who did not complete the questionnaire it became apparent that those who had no experience or knowledge of the speciality were less likely to participate. This means that the results are probably over reporting the exposure to and knowledge of renal transplantation. A further limitation is that this work only represents the situation at one medical school. We believe that this situation is not unique to Bristol University and recommend that a national study be performed to assess the true extent of the situation.
If transplantation rates are to be maximised and recruitment into the speciality improved, then ideally, all doctors should have some exposure to renal transplantation during the early stages of their career. Indeed a recent study has demonstrated that increased knowledge about organ donation is associated with higher levels of medical education, an increased likelihood of holding an organ donor card and feeling more comfortable in approaching relatives of potential organ donors [7]. Whilst it is realised that teaching time is of a premium at medical school this would be the ideal opportunity to promote transplantation. We believe that conventional methods such as ward based teaching, lectures and tutorials could be supplemented with a more multidisciplinary exposure. For example, involving students in patient education open days or the production of information leaflets/web pages could allow students to see from themselves the improvement in quality of life brought about by transplantation; Such learning experiences may prove more memorable for some students than those of the operating theatre.
Conclusions
This survey, carried out at a single UK medical school, has highlighted a low level of exposure to, and a lack of knowledge about, renal transplantation amongst medical students. These worrying results may influence the outcomes of any measures put in place to improve the recruitment of surgeons and the procurement of organs. If the trends within these areas are to be reversed then greater emphasis should be placed upon the promotion of renal transplantation within the undergraduate curriculum.
Abbreviations
UK United Kingdom
PRHO Pre-registration house officer
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
AGE designed the questionnaire, performed the study and was involved in the preparation of the manuscript. ARW and JDM were also involved in the design of the questionnaire and manuscript preparation.
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| 15617572 | PMC544359 | CC BY | 2021-01-04 16:30:54 | no | BMC Med Educ. 2004 Dec 23; 4:32 | utf-8 | BMC Med Educ | 2,004 | 10.1186/1472-6920-4-32 | oa_comm |
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BMC Health Serv ResBMC Health Services Research1472-6963BioMed Central London 1472-6963-4-371561323310.1186/1472-6963-4-37Research ArticlePractice activity trends among oral and maxillofacial surgeons in Australia Brennan David S [email protected] A John [email protected] Kiran A [email protected] Dana N [email protected] Alastair N [email protected] Australian Research Centre for Population Oral Health, Dental School, Faculty of Health Sciences, The University of Adelaide, Australia2 Oral and Maxillofacial Surgery Unit, Dental School, Faculty of Health Sciences, The University of Adelaide, Australia2004 21 12 2004 4 37 37 30 6 2004 21 12 2004 Copyright © 2004 Brennan et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The aim of this study was to describe practice activity trends among oral and maxillofacial surgeons in Australia over time.
Methods
All registered oral and maxillofacial surgeons in Australia were surveyed in 1990 and 2000 using mailed self-complete questionnaires.
Results
Data were available from 79 surgeons from 1990 (response rate = 73.8%) and 116 surgeons from 2000 (response rate = 65.1%). The rate of provision of services per visit changed over time with increased rates observed overall (from 1.43 ± 0.05 services per visit in 1990 to 1.66 ± 0.06 services per visit in 2000), reflecting increases in pathology and reconstructive surgery. No change over time was observed in the provision of services per year (4,521 ± 286 services per year in 1990 and 4,503 ± 367 services per year in 2000). Time devoted to work showed no significant change over time (1,682 ± 75 hours per year in 1990 and 1,681 ± 94 hours per year in 2000), while the number of visits per week declined (70 ± 4 visits per week in 1990 to 58 ± 4 visits per week in 2000).
Conclusions
The apparent stability in the volume of services provided per year reflected a counterbalancing of increased services provided per visit and a decrease in the number of visits supplied.
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Background
In Australia the majority of dentists work in private general practice [1]. Relatively few are specialists (10.8%), of which 16.8% are oral and maxillofacial surgeons accounting for 1.9% of all practising dentists. The major trends in oral health in Australia over recent decades indicate improved oral health among the population. For example, there has been a dramatic decline in the percentage of edentulous adults [2,3], and caries experience among children has declined since the 1970s [4], although in the later half of the 1990s improvements in child oral health had ceased [5]. Service trends in private general practice have reflected the trends towards improved oral health with a shift towards higher provision of services such as diagnostic, preventive and endodontic consistent with the retention and maintenance of a natural dentition [6]. Practice activity patterns among private general practitioners have shown declining levels of visits supplied but stable levels of time devoted to work [7].
Identifying trends over time in the practice activity of oral and maxillofacial surgeons is a key element in planning by informing debate on issues relevant to the speciality such as the future supply of services and training needs. Previous Australian data have shown the distribution of oral and maxillofacial surgery was dominated by dentoalveolar services [8]. However, the dominance of dentoalveolar surgery might be reduced if the practice patterns of surgeons in relation to third molar surgery were influenced by debate on the development of standards and criteria of care [9], and ongoing assessment of the risks and benefits of removal of third molars [10]. While the core of oral and maxillofacial surgery is dentoalveolar, the knowledge of the orofacial region forms the basis for the wider scope of the modern specialty [11]. Since 1996 it has been mandatory that all trainees in Australia enter dual degree programs and then exit with the College fellowship, as a result the percentage of medically qualified surgeons has increased from 2.5% to 17.1% between 1990 and 2000 [12]. It has been reported that oral and maxillofacial surgeons with medical qualifications, while maintaining a broad scope, tended to have a greater range of procedures within the major groupings [13].
Considering the trend towards oral and maxillofacial surgeons gaining medical qualifications and the potential impact that this may have on practice activity, the aims of this study are to describe practice activity trends among oral and maxillofacial surgeons in Australia in 1990 and 2000 in terms of time worked, visits supplied and services provided.
Methods
Study design/sample
All registered oral and maxillofacial surgeons in Australia were surveyed in both 1990 and 2000. A surgeon must be registered with a dental board in the state/territory in which they practice. For the purposes of this analysis trainees were excluded. Although some of the same surgeons may have responded to the survey at both points in time for this study the design and analysis is treated as two sequential cross-sectional surveys.
Data collection and analyses
Data were collected using mailed self-complete questionnaires with a primary approach letter sent initially to each surgeon, followed a week later by the survey materials, with a reminder card two weeks later, and up to four follow-up mailings of survey materials to surgeons who had not yet responded in order to ensure higher response rates [14]. Surgeon background characteristics and practice factors were described using percentages and compared using chi-square tests for 1990 and 2000. Service rates, time devoted to work and number of visits supplied by surgeons were described using means and compared between 1990 and 2000 using general linear models. All analyses were performed using SAS software [15].
Study variables
The questionnaire was designed to provide comprehensive coverage of a range of workforce issues and the analysis presented here is limited to a subset of the total number of variables that were collected. The questionnaire included surgeon demographic and background variables (e.g., year of birth, sex, place of birth, qualifications,), practice details (e.g., level of activity in private and public practice, type of practice, level of practice activity), and a log of services provided in a typical week. Surgeons recorded details of the patients they treated over a one-week period. Main areas of service were classified as dentoalveolar, trauma, pathology, orthognathic, reconstructive surgery and other/major medical compromise. An outline of the key variables collected and how measures of time worked, services provided and visits supplied were derived is presented in Figure 1. The time measures of hours per day, days per week and weeks per year worked were used to calculate hours per week and hours per year worked, and were used along with visits per week and services per week to calculate visits per year and both services per visit and services per year.
Results
Response and background characteristics by year of study
Data were available from 79 surgeons from 1990 (response rate = 73.8%) and 116 from 2000 (response rate = 65.1%). Service provision data were available for 4,847 patients from 1990 and 3,292 patients from 2000. Table 1 shows the majority of surgeons in both 1990 and 2000 were in the age group 40–49 years, were male and born in Australia. Similarly, in both 1990 and 2000 the majority of surgeons worked 80+% in private practice. The only significant difference between 1990 and 2000 in Table 1 was the higher percentage of surgeons with dual qualifications, having a medical degree and College fellowship FRACDS (OMS) in addition to a dental degree.
Service rates
The rate of provision of services is presented in Table 2. The distribution of services per visit was dominated by dentoalveolar services in both 1990 and 2000. The overall rate of services per visit increased between 1990 and 2000, reflecting increased rates of pathology and reconstructive surgery. The distribution of services per year reflected the pattern for services per visit with dentoalveolar services dominating. However, there were no significant differences over time in the rate of provision of services per year.
Practice activity
The number of hours per year devoted to work by surgeons did not change significantly between 1990 and 2000, reflecting stable levels of hours per day, days per week and weeks per year worked. However, the number of visits per week supplied by surgeons decreased between 1990 and 2000. The number of visits per year supplied by surgeons also showed a trend towards a decreased number of visits over time, but the change (P = 0.0508) was not significant at the P < 0.05 level. The relationship between the service and visit measures is presented in Figure 2, which shows that the stability in the number of services provided per year involved a counterbalancing of increased rates of service per visit and decreased numbers of visits supplied.
Discussion
While the findings of this study are based on a small sample size this primarily reflects the size of the population studied. Oral and maxillofacial surgeons comprise a small percentage of the dental workforce [1], hence all registered surgeons were included in order to maximise the sample size available for analysis. While smaller samples can reduce statistical power it is still possible to detect significant differences when the effect size is sufficiently large, and a number of statistically significant differences were observed. While small sample sizes can sometimes result in bias, the achievement of adequate response rates in this study did not suggest response bias issues were likely [16]. The use of a sequential cross-sectional design while unable to address change over time at an individual level, as in a longitudinal design that traces the same subjects, has the advantage of being representative at both points by the inclusion of new subjects that have entered the study population (assuming no bias has been introduced through other means).
The practice patterns of oral and maxillofacial surgeons have been reported to be largely stable, showing no change between 1990 and 2000 in their age and sex distribution, place of birth, practice activity level, referral sources, mix of cases and perceptions of work [12]. The distribution of main areas of service has also remained relatively stable over time [17]. However, there were signs of change in the increased percentage of dual qualified surgeons, and changes in rates of some non-dentoalveolar surgical procedures over the course of the study. While still a minority of surgeons, those with dual qualifications had a different service profile with higher rates of orthognathic surgery, dental implants, and bone graft procedures. While the mix of cases was dominated by dentoalveolar rather than major maxillofacial surgery in both 1990 and 2000, there appears to be a beginning of an expansion of some selected non-dentoalveolar surgical procedures. However, the stability in orthognathic surgery rates per year observed here indicates that the higher odds of orthognathic surgery among dual qualified surgeons [18] has not increased the total provision of this type of surgery.
The decline in patient visits supplied by oral and maxillofacial surgeons, while statistically significant for visits supplied per week, was not statistically significant at the conventional level of P < 0.05 for visits supplied per year. This partly reflects the stability in weeks per year worked, one of the component measures used to derive visits per year. However, the number of visits per year was at the borderline of statistical significance (P = 0.0508) and considerations other than a reliance on P values is recommended in the literature [19], with more emphasis on estimation through the use of confidence intervals to accompany point estimates [20]. A key guide is the measure of effect size, or the size of the difference being reported, which can be factored into considerations of clinical significance at the individual level and public health importance when aggregated across the individuals making up a population.
The trends observed in practice activity among oral and maxillofacial surgeons show parallels with private general practice dentists in Australia. Trends in private general practice have also shown a counterbalancing effect of declining numbers of visits supplied with increased rates of services per visit resulting in a stable level of provision of services per year. Medical general practitioners have shown a trend towards longer consultations [21], consistent with the trend observed for the dental labour force. Despite the divergence in length and scope of training that oral and maxillofacial surgeons are required to fulfil, the convergence in practice activity trends may indicate specific health labour force or even broader labour force issues influencing both groups similarly. The Australian health and community services labour force in general has shown a trend towards working less hours per year with an increase in the percentage working part-time between 1996 and 2002 [22], which is different to the stable number of hours per year worked by both dentists and oral and maxillofacial surgeons. However, the reported decline in hours worked per week for medical specialists brings their average clinical hours (41.5 hours per week) close to 39.5 hours per week reported for oral and maxillofacial surgeons [23].
Possible explanations for the lower levels of patient visits per hour among private general practice dentists include increased numbers of older patients [24], who may have complex treatment needs which require more services or take longer to complete. Changes have been observed in the distribution of the characteristics of patients treated and visits supplied by oral and maxillofacial surgeons over the study period [17], with the most marked difference being a shift to an older age distribution of patients consistent with demographic trends projecting an increase in middle-aged and older adults in the Australian population. Data from New Zealand have shown increases in the rate and number of injuries among older people and a general increase in the contribution of falls to the occurrence of trauma [25]. It may also be that as Australians retain more teeth into older age trauma services will expand in these age groups of patients. However, most trauma treated by oral and maxillofacial surgeons is related to the jaw rather than teeth and despite the increased percentage of patients aged 45 years and older, the majority of patients treated by oral and maxillofacial surgeons remained in the age groups 18–24 and 25–44 years.
Historical records have indicated that average length of dental appointments changed little over the period 1960–61 to 1974–75, but there was an increase since 1974–75 that was quite marked across the 1977–78 to 1982–83 period [26-29], and continued to increase through to 2001 [30]. Cross-infection control procedures may be another possible source of influence on productivity associated with either increased appointment or change-over times. The operation of such effects on productivity has implications for planning the delivery of services.
Conclusions
Estimates of the capacity to supply services and projections of labour force requirements need to consider that while the rate of services per visit has increased this has been counterbalanced by decreases in the number of visits supplied. This has resulted in a stable volume of services provided by oral and maxillofacial surgeons per year over the study period.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
DSB performed data analyses and drafting of the manuscript. AJS provided overall supervision of the project. KAS performed data processing and preliminary analyses. DNT was involved in data collection. ANG provided specialist advice on oral and maxillofacial surgery. All authors contributed to and approved the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was supported by a grant from the Australian and New Zealand Association of Oral and Maxillofacial Surgeons (ANZAOMS) Research and Education Foundation and Trust. The views expressed are those of the authors, and do not necessarily reflect those of ANZAOMS. The authors wish to acknowledge the cooperation of responding surgeons.
Figures and Tables
Figure 1 Derivation of practice activity measures
Figure 2 Relationship of service and visit measures. Data show mean (95% confidence interval).
Table 1 Distribution of background and practice characteristics
1990 2000
Age of surgeonns % %
20–29 years 1.3 0.0
30–39 years 27.9 14.6
40–49 years 39.2 36.6
50–59 years 19.0 34.2
60+ years 12.7 14.6
Sex of surgeonns
Male 94.9 91.3
Place of birthns
Australia 72.4 67.1
Practice activity levelns
80+% private 64.1 71.4
Qualifications **
Dental plus medical degree & FRACDS (OMS) 2.5 17.1
**(P < 0.01), ns (not statistically significant) χ2 test
Table 2 Rate of provision of services by time of study
Services per visit Services per year
1990 2000 1990 2000
Mean (SE) Mean (SE) P Mean (SE) Mean (SE) P
Dentoalveolar 1.070 (.065) 1.258 (.073) 0.0603 3400 (265) 3397 (318) 0.9937
Trauma 0.073 (.014) 0.046 (.010) 0.1593 239 (44) 145 (36) 0.1176
Pathology 0.096 (.012) 0.141 (.021) *0.0490 289 (36) 374 (57) 0.1858
Orthognathic 0.073 (.014) 0.076 (.016) 0.8745 260 (51) 220 (48) 0.5756
Reconstructive 0.059 (.009) 0.099 (.014) *0.0178 226 (41) 272 (46) 0.4528
Total services 1.425 (.051) 1.662 (.055) **0.0023 4521 (286) 4503 (367) 0.9695
*(P < 0.05), **(P < 0.01) GLM
Table 3 Time and visit measures by time of study
1990 2000
Mean (SE) Mean (SE) P
Time measures
Hours per day 7.69 (0.23) 7.84 (0.34) 0.7071
Days per week 4.62 (0.12) 5.04 (0.28) 0.1378
Weeks per year 46.35 (0.48) 46.65 (0.70) 0.7181
Hours per year 1682.50 (75.37) 1681.17 (94.91) 0.9912
Visit measures
Visits per week 69.80 (3.94) 57.85 (4.28) *0.0455
Visits per year 3241.31 (187.62) 2684.24 (207.88) 0.0508
*(P < 0.05) GLM
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| 15613233 | PMC544360 | CC BY | 2021-01-04 16:03:29 | no | BMC Health Serv Res. 2004 Dec 21; 4:37 | utf-8 | BMC Health Serv Res | 2,004 | 10.1186/1472-6963-4-37 | oa_comm |
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Int J Equity HealthInternational Journal for Equity in Health1475-9276BioMed Central London 1475-9276-3-121558505710.1186/1475-9276-3-12ResearchThe effect of ethnicity on outcomes in a practice-based trial to improve cardiovascular disease prevention Nietert Paul J [email protected] Steven M [email protected] Ruth G [email protected] Loraine F [email protected] Lori M [email protected] Chris [email protected] Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, 135 Cannon St., Suite 403, P.O. Box 250837, Charleston, SC 29425 USA2 Department of Family Medicine, Medical University of South Carolina, 295 Calhoun Street, Charleston, SC 29425 USA3 Department of Family Medicine, University of Southern California, 1000 S. Fremont Ave., Bldg. A7, Rm. 7419, Alhambra, CA 91803 USA2004 7 12 2004 3 12 12 7 4 2004 7 12 2004 Copyright © 2004 Nietert et al; licensee BioMed Central Ltd.2004Nietert et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Health disparities are a growing concern. Recently, we conducted a practice-based trial to help primary care physicians improve adherence with 21 quality indicators relevant to the primary and secondary prevention of cardiovascular disease and stroke. Although the primary concern in that study was whether patients in intervention practices outperformed those in control practices, we were also interested in determining whether minority patients were more, less, or just as likely to benefit from the intervention as non-minorities.
Methods
Baseline (fourth quarter 2000) and follow-up (fourth quarter 2002) data were obtained from 3 intervention practices believed to have at least 10% minority representation. Two practices had a black (non-Hispanic) population sufficient for analysis, while the other had a sufficient Hispanic population. Within each practice, changes in the 21 indicators were compared between the minority patient population and the entire patient population. The proportion of measures in which minority patients exhibited greater improvement was calculated for each practice and for all 3 practices combined, and comparisons were made using non-parametric methods.
Results
For all black patients, the observed improvement in 50% of 22 eligible study indicators was better than that observed among all white patients in the same practices. The average changes in the study indicators observed among the black and white patients were not significantly different (p = 0.300) from one another. Likewise for all minority patients in all 3 practices combined, the observed improvement in 14 of 29 (43.3%) eligible study indicators was better than that observed among all white patients. The average changes in the study indicators among all minority patients were not significantly different from the changes observed among the white patients (p = 0.272).
Conclusions
Among 3 intervention practices involved in a quality improvement project, there did not appear to be any significant disparity between minority and non-minority patients in the improvement in study indicators.
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Introduction
In 2002, the Institute of Medicine (IOM) issued a report suggesting that minorities are more likely than non-minorities to receive a lower quality of healthcare [1]. Because of the issues such as those raised in the IOM report, health disparities are a growing concern. This concern is reflected in many ways, including the development by National Institutes of Health of a program of action to confront these disparities and the Healthy People 2010 goal of eliminating these disparities.
Disparities are particularly evident in the area of chronic diseases. Although blacks are more likely than whites to have blood pressure monitoring, cholesterol screening, and smoking counseling, coronary heart disease is more prevalent among blacks than among whites [2]. Additionally, among all ethnic groups, blacks experience the highest mortality rates associated with heart disease, cancer, cerebrovascular disease, and HIV/AIDS. Although the overall mortality rate among blacks has been declining over the past 50 years, rates for cancer and diabetes were actually higher in 1995 than in 1950. On a similar note, Hispanics are significantly more likely as non-Hispanic whites to die from diabetes and HIV/AIDS [3].
In hopes of improving health outcomes and prevention practices for all patients, much focus has recently been given towards the improvement in quality of healthcare. For example, the 7th report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure outlines specific guidelines for preventing and managing hypertension, hyperlipidemia, and coronary heart disease [4]. The 2nd report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults makes specific recommendations on the prevention and care of hyperlipidemia and coronary heart disease [5]. Other quality indicators also exist for the prevention and management of other chronic diseases, including heart failure, atrial fibrillation, and diabetes, diseases which were all targeted in the current study.
Recently, there have also been a number of practice-based interventions aimed at improving the quality of healthcare for patients. For example, researchers have shown that a practice-based intervention (the Healthy Steps for Young Children Program) can enhance the quality of care for families of young children [6]. Additionally, a practice-based telephone intervention was proven to improve pneumococcal vaccine immunization for seniors [7]. We have also reported on a practice-based intervention to help primary care physicians improve adherence with 21 quality indicators relevant to the primary and secondary prevention of cardiovascular disease and stroke [8,9].
What these earlier interventions have lacked, however, are analyses examining whether the interventions have improved the quality of care for all patients, regardless of ethnicity. Because these types of interventions are heavily dependent on physician and/or clinical staff interaction with patients, because ethnic minorities may have less trust in their healthcare providers [10], and because barriers in the patient physician relationship may contribute to the ethnic disparities in the quality of the healthcare experience [11], there exists the possibility that poor cultural competency could result in a lack of effectiveness of the intervention among ethnic minorities. If such quality improvement efforts do not improve care for all ethnic groups equally, then there may be significant healthcare policy implications related to the refinement of existing interventions and to the development of future interventions.
The aim of this study was to examine whether or not a multi-method quality improvement (QI) intervention was equally successful among patients of different ethnicities. Some of the findings from this QI intervention have been previously published [8,12], and they suggest that primary care practices that use electronic medical records and receive regular performance reports can improve their adherence with clinical practice guidelines for cardiovascular disease and stroke prevention.
Methods
The multi-method QI intervention added practice site visits (for academic detailing and QI facilitation) and network meetings (for sharing of best practices) to the approach of guideline dissemination and audit and feedback, employed in a less intensive intervention. Ten sites received the intensive multi-method QI intervention, and ten sites received the less intensive intervention. The study was conducted in a practice-based research network (PPRNet) among users of a common electronic medical record (Practice Partner Patient Records, Seattle WA), which historically provided audit and feedback to its practice members.
As a supplement to the original study, we were also interested in whether minority patients were more, less, or just as likely to benefit from the intervention as non-minorities. The study presented here focused on outcome and process measures for minorities within 3 primary care practices, all of which received the intensive intervention. These 3 practices (labeled A, B, and C) were selected because they each had a significant (i.e. > 10%) proportion of minority patients and had recorded patient ethnicity in their electronic medical record. Practice A is an urban internal medicine practice in the Midwestern U.S. with 5 healthcare providers. Practice B is a rural family medicine practice in the Northeastern U.S. with 8 healthcare providers. Practice C is an urban family medicine practice in the Southeastern U.S.
A total of 21 study indicators (see Table 1) were obtained from each practice at baseline (fourth quarter 2000) and at the end of the study (fourth quarter 2002). These indicators were derived from published sources [4,5,13-16] and were deemed to be the most appropriate indicators for measuring quality of prevention and management of cardiovascular disease and stroke. Fourteen of the study indicators are process measures, reflecting whether recommended tests were done, appropriate diagnoses made or medication prescribed. Seven indicators are outcome measures, reflecting whether patients achieved recommended treatment goals. Some of the measures represent primary prevention, e.g., screening for hypertension or hyperlipidemia. Others represent secondary prevention, e.g., reaching treatment goals for glycosylated hemoglobin, low-density lipoprotein (LDL) cholesterol, and blood pressure in patients with diabetes. The institutional review board at the Medical University of South Carolina approved the study.
Table 1 Study indicators
CONDITION MEASURES
Hypertension1 Process measures:
• BP measurement in prior 12 months
• Diagnosis of hypertension for 3 measurements >= 140/90 in prior 12 months
• BP measurement in prior 3 months for patients with diagnosis of hypertension
Outcome measures:
• Most recent BP measurement < 140/90 for all patients
• Most recent BP measurements < 140/90 for patients with diagnosis of hypertension
Hyperlipidemia (General Population screening)2 Process measures:
• Measure of total cholesterol in prior 36 months
• Measure of HDL-C in prior 36 months
Coronary Heart Disease 1,2,3 Process measures:
• Measurement of LDL-cholesterol in prior 12 months
• Recorded diagnosis of hyperlipidemia for LDL-cholesterol > 130 mg/dl
• Medication for hyperlipidemia for LDL-cholesterol > 130 mg/dl
• Prescription of beta-blocker in patients with history of myocardial infarction
Outcome measures:
• Most recent LDL-cholesterol < 100 mg/dl
• Most recent BP measurement < 140/90
Heart Failure4 Process measure:
• Prescription of angiotensin converting enzyme inhibitor or angiotensin receptor blocker
Atrial Fibrillation5 Process measure:
• Prescription of oral anticoagulant
Diabetes Mellitus6 Process measures:
• Measurement of glycosylated hemoglobin in prior 12 months
• Measurement of LDL-cholesterol in prior 24 months
• BP measurement in prior 3 months
Outcome measures:
• Most recent glycosylated hemoglobin < 7 %
• Most recent LDL-cholesterol < 100 mg/dl
• Most recent BP measurement < 130/85
1 Adapted from The Sixth Report of the Joint National Committee on Detection, Evaluation and Treatment of High Blood Pressure (JNC VI). National Institute of Health, National Blood Pressure Education Program, NIH publication 98-4080, November 1997.
2 Adapted from Summary of the Second Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel II). JAMA 1993; 269(23): 301523.
3 Adapted from American Heart Association Scientific Statement: Smith SC, Blair SN, Criqui MH, etal: Preventing Heart Attack and Death in Patients with Coronary Disease. Circulation. 1995;92:2–4. Although the AHA recommendation is to measure total cholesterol at least every 60 months, we were forced to restrict our measure to 36 months due to restrictions of the historical data.
4 Adapted from American Heart Association Scientific Statement: Williams JF, Bristow MR, Fowler MB, etal: Guidelines for the Evaluation and Management of Heart Failure. Circulation. 1995;92:2764–2784
5 Adapted from American Heart Association Scientific Statement: Prystowsky EN, Benson DW, Fuster, V, etal: Management of Patients with Atrial Fibrillation. Circulation 1996;93:1262–1277.
6 Adapted from American Diabetes Association, Diabetes Quality Improvement Project, Initial Measure Set (Final Version) August 14, 1998
To determine practice performance on the study indicators, participating practices ran a computer program to extract patient activity during the previous quarter from their electronic medical record. To protect patient confidentiality, the extract program assigned an anonymous numerical identifier unique to each patient. The extract program obtained demographic information such as age, ethnicity, and gender, and diagnoses, medications, laboratory data, and vital signs. Text of consultation reports, progress notes, and discharge summaries were not extracted. The data were copied to diskettes and mailed to PPRNet or sent electronically via a secure server. In the PPRNet offices, data were bridged to standard data dictionaries and converted to SAS® (Statistical Analysis System, Cary NC) data sets on standard microcomputers for analyses.
In each patient's electronic medical record, ethnicity was recorded as white, black/African American, American Indian/Alaskan native, Asian, native Hawaiian/other Pacific islander, and "some other ethnicity", while ethnicity was recorded as Hispanic/Latino and non-Hispanic/Latino, all in concordance with the 2000 U.S. Census ethnicity categories. Currently, these physician practices allow the patient to designate their ethnicity categorization. However, because this process for collecting ethnicity data began in the middle of our study, some ethnicity categorizations were made by the office staff within each of the practices. Ethnicity data was only available on approximately 42% of patients, due to the fact that the electronic medical record software program did not require physicians to enter patients' ethnicity data until its most recent version was released, which occurred during the study time frame. Improvements in process and outcome measures were compared between minority and non-minority patients. Minority was defined as any ethnic designation other than white non-Hispanic.
Changes in the process and outcome measures were of primary interest in this study. Within each practice, these changes were compared between the minority patient population and the white patient population. Measures were deemed eligible for comparison if at least 10 minority patients were included in the rate calculations. For example, if practice A only had 8 minority patients with a diagnosis of having had myocardial infarction (MI), then the measure of the percentage of MI patients who had been prescribed a beta blocker could not be compared between the minority and white patient population. The proportion of eligible measures in which minority patients exhibited greater improvement was calculated for each practice and for all 3 practices combined. A Wilcoxon signed rank test (the non-parametric equivalent of the paired t-test) was used to test the hypotheses that minority patients exhibited changes similar to those of the non-minority patients. This study had approximately 80% power (2-sided hypothesis testing, α = 0.05) to detect a 6.6 percentage point difference between average improvement in the study indicators among all minority and non-minority patients.
Results
Baseline characteristics of the patients from the 3 practices are listed in table 2. In practice A, black (non-Hispanic) patients were the only sizable minority. Although practice B did contain 10 black non-Hispanic patients, this sample was not large enough for substantive comparisons. There were enough Hispanic patients in Practice B to compare with the entire groups of patients within that practice. In practice C, there were 117 black patients used for comparison. There were several significant differences of note between the minority patients to the overall population of patients within that same practice. Compared to the white patient population in practice A, the minority patients were significantly younger and significantly less likely to be diagnosed with hyperlipidemia. Compared to the white patient population in practice B, the minority patients were significantly more likely to be male and to have diabetes. In practice C, the minority patients were significantly younger, more likely to be female, and less likely to have a diagnosis of hyperlipidemia.
Table 2 Baseline characteristics of the patients within the 3 practices of interest
Characteristic Practice A Practice B Practice C
Black patients (n = 179) White patients (n = 1,079) Hispanic patients (n = 254) White patients (n = 2,526) Black patients (n = 117) White patients (n = 491)
Demographics
Age (mean ± s.d.) 54.2 ± 15.2**** 60.7 ± 17.8 34.5 ± 17.9 33.7 ± 18.9 42.0 ± 16.7*** 51.2 ± 16.9
Gender (% female) 68.7 69.2 46.1*** 57.9 67.5* 56.4
Medical conditions
Hypertension (%) 50.8 51.1 7.1 7.8 21.4 21.0
Hyperlipidemia (%) 33.5*** 47.5 5.5 4.1 0.9* 5.3
Diabetes (%) 12.9 9.7 4.3* 2.0 12.0 7.5
Coronary disease (%) 7.8 11.7 1.2 1.0 0.0 0.0
Heart failure (%) 2.2 4.9 0.0 0.2 1.7 0.6
Atrial fibrillation (%) 1.1 3.9 0.0 0.1 0.0 0.2
* p < 0.05 when compared to white patients within the particular practice
** p < 0.01 when compared to white patients within the particular practice
*** p < 0.001 when compared to white patients within the particular practice
**** p < 0.0001 when compared to white patients within the particular practice
Additional file 1 lists the baseline and end-of-study measurements for each of the 21 study indicators, for minority patients and white patients within each of the 3 practices. In practice A, the improvement in 7 of 16 eligible study indicators was better among black patients than among white patients in that practice. (For 9 of these 16 indicators, the improvement was worse among the black patients.) In practice B, the improvement in 3 of 7 eligible study indicators was better among Hispanic patients than among white patients in that practice, and worse for 4 of the 7 indicators. In practice C, the improvement in 4 of 6 eligible study indicators was better among black patients than among white patients in that practice. Thus for all black patients in practices A and C, the observed improvement in 11 of 22 (50.0%) eligible study indicators was better than that observed among white patients. On average, indicators improved 4.4 and 9.3 percentage points among black and white patients, respectively. These changes were not significantly different (p = 0.300) from one another. Likewise for all minority patients in all 3 practices combined, the observed improvement in 14 of 29 (48.3%) eligible study indicators was better than that observed among non-minority (white) patients. On average, indicators improved 4.6 and 8.3 percentage points among minority and non-minority patients, respectively, and these changes were not significantly different (p = 0.272) from one another.
Discussion
In these 3 physician practices, all of which were in the intervention arm of a randomized trial aimed at improving primary and secondary prevention of cardiovascular disease and stroke, we found that results for minorities were relatively similar to the results experienced by the overall practice populations. Change from baseline was greater among minority patients than among white patients for 48.3% of the 29 eligible study indicators, and the average changes in the study indicators among all minority patients were not significantly different from the changes observed among the white patients.
There are some limitations of this study which should be noted. As noted earlier, the ethnicity status was only available on 42% of patients within the practices of interest; thus the results may not truly represent what occurred in these practices overall during the study. Given the relatively small number of eligible indicators for comparisons across ethnicities, this statistical power to detect subtle differences was not optimal. Nevertheless, the overall findings suggest that any true differences in this intervention's effectiveness across ethnicities are small.
These findings are encouraging, and they suggest that the quality improvement strategies that have been developed to date for physician practices that use electronic medical records have a similar impact on minorities and non-minorities. Future studies should continue to address whether the effectiveness of interventions such as ours is cross-cultural, and whether interventions tailored to be more culturally appropriate can improve the overall effectiveness of interventions.
List of abbreviations used
IOM: Institute of Medicine
HIV: Human Immunodeficiency Virus
AIDS: Acquired Immunodeficiency Syndrome
QI: Quality Improvement
LDL: Low-Density Lipoprotein
MI: Myocardial Infarction
Competing Interests
The author(s) declare that they have no competing interests.
Authors' Contributions
PJN helped design the study, perform the analyses, and write the manuscript. SMO helped design the study, perform the site visits, and edit the manuscript. RGJ helped design the study, perform the analyses, and write the manuscript. LFR helped design the study, assisted with data acquisition, and edited the manuscript. LMD helped design the study, perform the site visits, and edit the manuscript. CF helped perform site visits and edit the manuscript. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Study indicators as measured at baseline (B) and the end (E) of the study for all patients and minority patients within each of the 3 practices.
Click here for file
Acknowledgements
Source of Support: This study was supported by a grant from the Agency for Healthcare Research and Quality, US Department of Health and Human Services, Public Health Service. Grant No. 1 U18 HS11132-01.
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| 15585057 | PMC544361 | CC BY | 2021-01-04 16:39:30 | no | Int J Equity Health. 2004 Dec 7; 3:12 | utf-8 | Int J Equity Health | 2,004 | 10.1186/1475-9276-3-12 | oa_comm |
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Respir ResRespiratory Research1465-99211465-993XBioMed Central 1465-9921-5-271558832610.1186/1465-9921-5-27ResearchNicotine signals through muscle-type and neuronal nicotinic acetylcholine receptors in both human bronchial epithelial cells and airway fibroblasts Carlisle Diane L [email protected] Toni M [email protected] Autumn [email protected] Michele J [email protected] James D [email protected] Neil A [email protected] Jill M [email protected] Department of Pharmacology, University of Pittsburgh, Pittsburgh, PA, USA2 University of Pittsburgh and Lung and Thoracic Malignancies Program, University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA2004 10 12 2004 5 1 27 27 27 8 2004 10 12 2004 Copyright © 2004 Carlisle et al; licensee BioMed Central Ltd.2004Carlisle et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Non-neuronal cells, including those derived from lung, are reported to express nicotinic acetylcholine receptors (nAChR). We examined nAChR subunit expression in short-term cultures of human airway cells derived from a series of never smokers, ex-smokers, and active smokers.
Methods and Results
At the mRNA level, human bronchial epithelial (HBE) cells and airway fibroblasts expressed a range of nAChR subunits. In multiple cultures of both cell types, mRNA was detected for subunits that constitute functional muscle-type and neuronal-type pentomeric receptors. Two immortalized cell lines derived from HBE cells also expressed muscle-type and neuronal-type nAChR subunits. Airway fibroblasts expressed mRNA for three muscle-type subunits (α1, δ, and ε) significantly more often than HBE cells. Immunoblotting of HBE cell and airway fibroblast extracts confirmed that mRNA for many nAChR subunits is translated into detectable levels of protein, and evidence of glycosylation of nAChRs was observed. Some minor differences in nAChR expression were found based on smoking status in fibroblasts or HBE cells. Nicotine triggered calcium influx in the immortalized HBE cell line BEAS2B, which was blocked by α-bungarotoxin and to a lesser extent by hexamethonium. Activation of PKC and MAPK p38, but not MAPK p42/44, was observed in BEAS2B cells exposed to nicotine. In contrast, nicotine could activate p42/44 in airway fibroblasts within five minutes of exposure.
Conclusions
These results suggest that muscle-type and neuronal-type nAChRs are functional in airway fibroblasts and HBE cells, that prior tobacco exposure does not appear to be an important variable in nAChR expression, and that distinct signaling pathways are observed in response to nicotine.
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Background
Nicotine, the addictive component of tobacco smoke, signals through its family of receptors, the nicotinic acetylcholine receptors (nAChR). Acetylcholine is the endogenous ligand for these receptors, and has been found in many tissues outside of the nervous system. Non-neuronal nAChR have also been identified in tissues such as the skin, vasculature, and nasal mucosa [1]. nAChR are pentamers that form ion channels permeable to either calcium or sodium. There are several types of nAChR, which are defined by the subunit composition of the receptor. Receptors contain all α-subunits, a combination of α and β subunits, or α, β, ε/γ, and δ subunits [2]. Heteropentamers have been classified as either muscle nAChR, which were first identified at the neuromuscular junction, or as neuronal type, which were discovered in the central nervous system. Homopentamers were also discovered in the CNS and are also considered to be neuronal-type receptors. The adult muscle type receptor contains the α1/β1/ε/δ subunits, with the β1 subunit occurring twice to make the pentamer. The neuronal heteropentamers occur with several different specific subunits, but always with three α and two β subunits, numbered α2 through α6 and β2 through β4. The homopentamer that has been characterized most completely is the α7 pentamer, although recently others have been identified (α8, α9, and α10). The α9 and α10 subunits are unique in that they can form functional homopentamers or can combine together to form a heteropentamer without a β subunit. They are also different from the other subunit combinations examined because nicotine acts as a competitive antagonist to receptors containing the α9 subunit [3].
The ionic permeability of nAChR is dependent upon the subunit composition of the receptor, with some receptors showing preference to either calcium or sodium [4]. However, regardless of initial preference, stimulation of all nAChR by agonist are thought to lead to a calcium influx, either directly through the nAChR channel or due to a change in membrane potential that leads to the opening of calcium L-channels [4]. Extended exposure of nAChR to agonist can lead to receptor inactivation [5]. Again, the degree and severity of inactivation depends upon the subunit composition of the receptor [5].
The primary route of exposure to nicotine is through inhalation, either by active smokers or non-smokers exposed to environmental tobacco smoke. Through inhalation, the lung, in particular, would be exposed to pharmacological doses of nicotine. In addition, receptor inactivation is likely to occur in sensitive receptors, due to the extended length of time that smokers use tobacco [2,5]. This could lead to changes in receptor expression over time; in the brain, it has been noted that the type of nAChR expressed is different in smokers than in never smokers.
Using radiolabeled agonist, we have shown that saturable nicotine binding sites exist in the lung [6]. Other previous studies of the airways exposed to nicotine have shown changes in expression of collagen [7]. In vitro data has indicated that airway epithelial cells release GM-CSF upon exposure to nicotine, and activate Akt, a signaling molecular important in cell survival [8,9]. However, although a number of different nAChR subunits are reportedly expressed at the mRNA in human airway cells, [9-12] airway tissue has not been examined for the presence of neuronal-type nAChR at the protein level or for the muscle-type nAChR at the mRNA or protein levels. It is also not known if particular nAChR are more likely to signal through particular downstream pathways when more than one receptor type is present, if the nAChR present in the airway changes after long-term exposure to nicotine, or if calcium influx is responsible for downstream signaling.
In this study, a series of 37 short-term human bronchial epithelial cultures, 25 airway fibroblast cultures, and 2 immortalized bronchial epithelial cell lines were examined by RT-PCR for nAChR expression. We also examined protein expression by immunoblot to determine which subunits are most highly expressed and to determine if appropriate combinations are present at the protein level to form functional receptors. We determined that the nAChR present are functional by examining calcium influx after agonist exposure, and blockade by antagonists. We also show that exposure of airway cells to nicotine leads to activation of downstream signaling pathways. Finally, we examined the nAChR present in HBE and airway fibroblasts derived from smokers, ex-smokers, and never smokers to determine if alterations in nAChRs based on tobacco exposure can be detected.
Methods
Primary Airway Cell Culture
HBE cells are cultured from airway biopsies using standard methodology in serum-free medium [13]. Briefly, biopsies are taken from the carina in an area that is normal by appearance by white light bronchoscopy and confirmed using LIFE bronchoscopy. Biopsies are teased apart with forceps and HBE cells are cultured in BEGM (Cambrex Biosciences, Walkersville, MD) for a maximum of two passages on collagen IV coated flasks. This medium is selective for bronchial epithelial cells and at the first passage, cultures are examined and contain 95% or greater epithelial cells. Human airway fibroblasts are derived from bronchial tissue that is minced with scalpels and cultured in DMEM with 10% bovine serum. HBE cells cannot be propagated in this medium. Biopsies were obtained during surgical thoracic resection or bronchoscopy procedures from normal areas of the upper airway. All tissue donors gave informed consent under an approved IRB protocol and answered questionnaires regarding tobacco exposure.
Airway Cell lines
BEAS2B were all purchased from ATCC and cultured according to ATCC instructions. IB3-1 cells were derived from a cystic fibrosis patient and cultured in Hams medium with 10% serum [14].
Supplies
All chemicals used were from Sigma (St. Louis, MO) and all supplies were from either Fisher Scientific (Pittsburgh, PA) or PGC Scientific (Frederick, MD), unless otherwise indicated.
RNA and Protein Isolation
RNA is isolated from cultures using standard guanidinium thiocyanate method [15]. RNA is quantitated using the absorbance at 260 nM and purity is determined using the A260/A280 ratio. After RNA is isolated from the aqueous phase of the solution, protein is extracted from the organic phase, using the method recommended by Invitrogen for the isolation of protein from Trizol. The protein pellet is resuspended in 1% SDS with protease and phosphatase inhibitors added and stored at -80°C. Alternatively, if RNA was not taken from the sample, protein was analyzed from whole cell lysates. Airway cells were scraped into RIPA buffer (150 mM NaCl, 50 mM Tris, pH 7.4, 5 mM EDTA, 1% igepal, 0.5% SDS, 1% deoxycholate) with protease inhibitors 10 μg/ml PMSF, 30 μl/ml aprotinin, and phosphatase inhibitor 1 mM sodium orthovanadate. Lysates were store at -80°C. All protein was quantitated using the BCA assay (Pierce, Rockford IL).
RT-PCR of nAChR
Primers were developed to unique regions of each subunit and were tested on human muscle and brain RNA purchased from Clontech (Palo Alto, CA). Optimized protocols were then used on RNA from airway cells. All primers span introns and do not amplify DNA. GAPDH or actin is always used as a positive control for RNA integrity. Oligo dT12–18 (Invitrogen) was annealed to 1 μg total RNA and reverse transcribed with Superscript II (Invitrogen). The reaction contained 1 μg RNA, 500 ng Oligo dT12–18, 50 mM Tris-HCl, pH 8.3, 75 mM KCl, 3 mM MgCl2, 10 mM DTT, 1 mM each dNTP, 200 U superscript. Briefly, total RNA was incubated with oligo dT12–18 at 70°C for 10 min. The cDNA produced was then used as a template for PCR using specific primers. Table 1 indicates the primer used and optimized conditions for each subunit. PCR amplification was performed in a 20 μl reaction containing 2 μl of the RT reaction, Taq DNA polymerase (Perkin Elmer), 1X PCR buffer, 1.5 mM MgCl2, 1 mM each dNTP and 1 μM primer. PCR was carried out in a Perkin-Elmer 9700 Thermocycler with 2 min, 95°C denaturation, followed by 30 cycles of 94°C for 30 s, 55°-62°C (see table 1) for 30 s and 72°C for 30 s. Final extension was at 72°C for 5 min. 10 μl of each reaction was run on a 1% TBE gel for analysis. β2 and δ subunits were detected using nested PCR. Primary PCR reactions were carried out as described above. 2 μl of the primary reaction was used as the template for the secondary PCR reaction/second round PCR. Thirty rounds of PCR were carried out at the temperatures listed in Table 1.
Table 1 RT-PCR Primers and Conditions
Primer Optimal Annealing Temperature Product Size Sequence
alpha 1* 55 580/505 CGT TCT GGT GGC AAA GCT
CCG CTC TCC ATG AAG TT
alpha 2* 55 466 CCG GTG GCT TCT GAT GA
CAG ATC ATT CCA GCT AGG
alpha 3 58 464 CTG GTG AAG GTG GAT GAA GT
CTC GCA GCA GTT GTA CTT GA
alpha 4 58 444 GGA TGA GAA GAA CCA GAT GA
CTC GTA CTT CCT GGT GTT GT
alpha 5* 55 525 GAT AAT GCA GAT GGA CGT
TGA TGG TAT GAT CTC TTC
alpha 6 58 372 GTG GCC TCT GGA CAA GAC AA
CCT GCA GTT CCA AAT ACA CA
alpha 7 58 375 GGA GCT GGT CAA GAA CTA CA
CAG CGT ACA TCG ATG TAG CA
beta 1 58 479 CTA CGA CAG CTC GGA GGT CA
GCA GGT TGA GAA CCA CGA CA
beta 2 62 453 CAA TGG CTC TGA GCT GGT GA
CCA CTA GGT GTG AAG TCG TCC A
420 GGC TCT GAG CTG GTG ACA GTA
CAC CTC ACT CTT CAG CAC CA
beta 3 62 439 TGGAGA GTA CCT GCT GTT CA
CGA GCC TGT TAC TGA CAC TA
beta 4 58 524 GTG AAT GAG CGA GAG CAG AT
GGG ATG ATG AGG TTG ATG GT
delta 58 471 CAG ATC TCC TAC TCC TGC AA
CCA CTG ATG TCT TCT CAC CA
426 CAA CGT GCT TGT CTA CCA CTA C
GGT AGG TAG AAG ACC AGG TTG A
gamma 546 CGC CTG CTC TAT CTC AGT CA
GGA GAC ATT GAG CAC AAC CA
epsilon 55 432 GTA ACC CTG ACG AAT CTC AT
GTC GAT GTC GAT CTT GTT GA
* From Maus et al. [12]
Unique primers to each nAChR subunit were used in PCR reactions. All results were sequenced and compared to the known subunit sequences to confirm that the correct subunit was being amplified.
Immunoblotting of nAChR
50 μg protein with loading buffer was denatured using 50 mM DTT and heated at 80°C for 15 min. Protein was loaded onto 10% Bis-Tris gels (Invitrogen). Brain lysate (US Biologicals) and lysates from myotubes cultures (gift, Z.Z. Wang, U. Pittsburgh) were used for positive control. Protein was transferred to PVDF (Biorad, Hercules, CA) or Multiblots (ISC Bioexpress, Kaysville, UT). PVDF membranes were blocked for 1 hr with 5% blocker (Biorad) in TBS-T (2.7 mM KCl, 138 mM NaCl, 20 mM Trizma, pH 7.4, 0.1% Tween-20 (Biorad)). Primary antibody was diluted in carrier protein, 5% blocker for PVDF or 0.5% casein (Pierce) for Multiblots and incubated at 4°C overnight, see Table 2 for details. After 4 washes with TBS-T, blots were incubated with appropriate HRP-linked secondary antibody (Santa Cruz, Santa Cruz, CA) at the dilution recommended by the manufacturer for 1 hr in carrier protein as previously. Following 4 more washes with TBS-T, ECL was then done (ECL kit, Amersham Biosciences).
Table 2 nAChR Antibodies and Conditions
Subunit Company Dilution
alpha-1 Sigma 1:30,000
alpha-2 Santa Cruz 1:1000
alpha-3 Santa Cruz 1:2000
alpha-4 Santa Cruz 1:1000
alpha-5 Sigma 1:15,000
alpha-7 Sigma 1:30,000
beta-1 Sigma 1:10,000
beta-2 Santa Cruz 1:1,000
delta Wang et al. [30] 1:4000
Antibodies specific to particular nAChR subunits were purchased from either Santa Cruz or Sigma. Antibody specific to the δ subunit was a gift from Dr. Zhu-Zhong Wang, University of Pittsburgh.
Calcium influx assay
Airway cells were grown in 24-well dishes, with 10,000 cells per well. After 24 hours, medium was replaced with serum-free, prewarmed medium spiked with calcium-45, with a final specific activity of approximately 60 μCi/mM calcium. Drug was added to the wells as indicated, in triplicate. If nAChR antagonists or channel blockers were used, they were added 20 min before the addition of agonist. The calcium ionophore A23187 (Molecular Probes, Eugene OR) was used as a positive control for calcium influx. After incubation at 37°C, plates were put on ice and washed 3 times with ice-cold PBS. Lysis buffer (1% SDS, 0.3 N NaOH) was added. Lysates were transferred to scintillation vials, scintillation fluid added, and counted. Results are presented as percent control, with untreated control normalized to 100%.
Phospho-protein Immunoblotting
For phospho-PKC, phospho-p42/44, and phospho-p38, 10 minute exposure to 20 ng/ml EGF was used as the control. These conditions have been published as optimal for signaling by EGF in several published articles [16,17]. Immunoblotting for phospho-proteins was done by running 30 μg protein, reduced by heating to 80°C for 15 min in the presence of 50 mM DTT, on a 10% Tris-Bis gel (Invitrogen). Protein was transferred to PVDF then blocked with 5% blocker (Biorad) in TBS-T 1 hr. Antibody was diluted 1:1000 in 5% blocker as directed by Cell Signaling, Inc. and rocked overnight, 4°C. Membranes were then washed four times and probed with appropriate HRP-linked antibody (Santa Cruz) 1:5000 for 1 hr. After four TBS-T washes, ECL was done (Amersham). Results were normalized for loading differences by stripping with ImmunoPure IgG elution buffer (Pierce, Rockford IL) for 3 hours at 37°C, then probing for β-actin using an HRP-linked anti-β-actin antibody (Santa Cruz).
Statistical Analysis
Differences in expression frequency of nAChR subunits at either the RNA or protein level were analyzed by Fisher's Exact Test. In all other experiments, differences from control were determined using Student's T-test. All p-values reflect two-tailed tests.
Results
Neuronal and muscle-type nAChR are present on HBE cells and airway fibroblasts
Using RT-PCR, we repeatedly detected mRNA for nAChR subunits in short-term cultures of human airway cells from bronchial biopsies. Figure 1 shows representative PCR products from two HBE cultures (Panel E and F), one airway fibroblast culture (Panel D), and immortalized HBE BEAS2B cells (Panel C). Brain mRNA was used as a positive control for neuronal-type nAChR (Figure 1 Panel A) and muscle mRNA as a positive control for muscle-type nAChR (Figure 1 Panel B), and GAPDH is included on each panel to indicate mRNA quality. A water-only sample was used as a negative control in each experiment. PCR products were found at the expected size for each subunit and were sequenced and compared to the known gene sequences, and confirmed to be the expected sequence for each subunit. HBE cells cultured from airway biopsies from a series of never smokers, ex-smokers, and active smokers were examined by RT-PCR and the results are summarized in Table 3. Due to limited RNA yield, some samples were not examined for all possible subunits. We found that seven different nAChR subunits were expressed in over 50% of the sample examined, including α5, α6, α7, α9, β1, δ and ε. Three additional subunits, α1, α3, and β4 were expressed in at least 30% of cultures examined. The α2 and α4 subunits were also examined, but were never present (data not shown).
Table 3 Expression of nAChR in HBE and Bronchial Epithelial Cells Lines as determined by RT-PCR
HBE α1 α3 α5 α6 α7 α9 β1 β2 β3 β4 * δ ε
Never smoker 2/9 (22%) 2/8 (25%) 6/9 (67%) 5/9 (56%) 7/9 (78%) 8/8 (100%) 9/9 (100%) 4/7 (57%) 0/8 (0%) 6/9 (67%) 7/8 (88%) 5/9 (56%)
Active smoker 3/10 (30%) 0/3 (0%) 9/10 (90%) 3/4 (75%) 7/10 (70%) 3/3 (100%) 9/10 (90%) 5/9 (56%) 0/5 (0%) 1/10 (0%) 6/8 (75%) 7/10 (70%)
Ex-smoker 7/18 (39%) 8/19 (42%) 18/19 (95%) 9/19 (47%) 11/18 (61%) 16/18 (89%) 17/19 (89%) 6/16 (38%) 1/19 (5%) 9/19 (47%) 11/18 (61%) 10/19 (53%)
Total 12/37 (32%) 10/30 (33%) 33/38 (87%) 17/32 (53%) 25/37 (68%) 27/29 (93%) 35/38 (92%) 15/32 (47%) 1/32 (3%) 16/38 (42%) 24/34 (70%) 22/38 (58%)
BEAS2B + - + + + + + + - + + +
IB3-1 + - + - + + + + - + + +
nAChR subunits present in HBE and bronchial epithelial cell lines as determined by RT-PCR. The table indicates the number of positive cultures out of the total number of cultures examined, as well as the percentage of positive cultures for each subunit. * indicates statistical significance when comparing cultures from never smokers to those of active or ex-smokers.
Figure 1 Expression of nAChR subunits from cell types by RT-PCR. A) Brain; B) Muscle; C) BEAS2B cell line; D) normal airway fibroblasts; E) human bronchial epithelial cells; F) human bronchial epithelial cells. On each panel, the brightest band on the 100 bp ladder represents 600 bp.
The subunits that are expressed by HBE cells could potentially combine to form muscle-type (α1/ β1/ δ /ε) heteropentamers, neuronal α7 or α9 homopentamers, and neuronal heteropentamer receptors α3/ α5/ β2 or β4 and α6/β2 or β4. By examining the pattern of mRNA expression in each individual culture, combinations were observed that would produce a functional muscle-type receptor in 7 of 33 (21%) of cultures, a functional α3-containing neuronal type receptor in 10 of 28 (35%) of cultures, α6-containing neuronal type receptor in 13 of 28 (46%), α7 homopentamer receptors in 25 of 37 (68%), and homopentamer α9 receptors in 27 of 29 cultures (93%). Only 2 of 35 (6%) HBE cultures did not express at least one functional nAChR subunit combination.
Two immortalized airway epithelial cell lines also expressed mRNA for many of these nAChR subunits, including muscle-type subunits (Figure 1, Table 3). BEAS2B, derived from normal HBE cells, and IB3-1, derived from the bronchial epithelial cells of a cystic fibrosis patient, were examined. Both immortalized epithelial cultures expressed the subunits required for a functional muscle-type nAChR (α1/β1/δ/ε) and the homopentamers α7 and α9, and BEAS2B cells express mRNA for subunits that may combine to form functional neuronal nAChRs (α6/ β2 or β4).
Airway fibroblasts also expressed nAChR (Table 4). We found that mRNA for α1, α5, α6, α7, α9, β1, β2, δ and ε were all expressed more than 70% of the time. All other subunits were expressed less than one-third of the time. As for HBE cells, we examined the pattern of mRNA expression in each airway fibroblast culture. nAChR subunits could potentially combine to form muscle-type (α1/ β1/ δ / ε) receptors in 77% of fibroblast cultures and both neuronal α7 or α9 homopentamers in 100% of cultures. Neuronal heteropentameric nAChR containing the α6 subunit might be formed in 59% of cultures, and neuronal heteropentamers containing the α3 subunit, with or without the α5 subunit, could combine to form a functional receptor in 27% of cultures.
Table 4 Expression of nAChR in Airway Fibroblasts as determined by RT-PCR
NLFB α1 α3 α5 α6 α7 α9 β1 β2 β3 β4 δ ε
Never smoker 5/5 (100%) 2/5 (40%) 5/5 (100%) 5/5 (100%) 5/5 (100%) 5/5 (100%) 5/5 (100%) 5/5 (100%) 2/5 (40%) 2/5 (40%) 5/5 (100%) 5/5 (100%)
Active smoker 10/11 (91%) 3/11 (27%) 11/11 (100%) 8/11 (73%) 10/11 (91%) 11/11 (100%) 11/11 (100%) 8/11 (73%) 4/11 (36%) 2/11 (18%) 10/11 (91%) 10/11 (91%)
Ex-smoker 9/9 (100%) 0/9 (0%) 8/9 (89%) 5/9 (56%) 5/9 (44%) 8/9 (89%) 6/9 (67%) 6/9 (67%) 2/9 (22%) 3/9 (33%) 9/9 (100%) 8/9 (89%)
Total 24/25 (96%) 5/25 (20%) 24/25 (96%) 18/25 (72%) 20/25 (80%) 24/25 (96%) 22/25 (88%) 19/25 (76%) 8/25 (32%) 7/25 (28%) 24/25 (96%) 23/25 (92%)
nAChR subunits present in airway fibroblasts as determined by RT-PCR. The table indicates the number of positive cultures out of the total number of cultures examined, as well as the percentage of positive cultures for each subunit.
The mRNA expression of four nAChR subunits is significantly different when comparing airway fibroblasts and HBE cells. The muscle-type receptor subunits α1, δ, and ε are all expressed more frequently in airway fibroblasts (present in 96%, 96%, and 92%, respectively, of airway fibroblast cultures) than in HBE cells (α1 present in 32% of cultures, δ in 70% of cultures, and ε present in 58% of cultures, p < 0.02 for each subunit). In addition, the β3 subunit is also expressed more frequently in fibroblast cultures than in HBE cells (8/25 compared to 1/28, p < 0.01). The subunit combinations that could form functional receptors were also examined and compared between cell types. Consistent with the individual subunit data, the combination of all subunits for the muscle-type receptor is expressed significantly more frequently in human airway fibroblasts (74%) than in HBE cultures (21%)(p = 0.0001).
The nAChR subunit α9 was frequently expressed by both HBE cells and airway fibroblasts. Although we find this nAChR frequently and it may have physiological significance, it is unlikely that signaling through this receptor would be responsible for the immediate downstream effects seen in our studies, which focus on the effects of nicotine, since nicotine does not act as an agonist for this receptor type.
We next examined nAChR subunit protein expression using immunoblotting. All the cultures examined expressed protein for nAChR subunits, in general agreement with mRNA expression data (for representative results, see Figure 2, Figure 3). In these experiments, whole brain and muscle tissue lysates as positive controls (Figure 2, Figure 3). For each subunit, the culture was considered positive when a band matching the size of the positive control at the expected kilodalton size was found. A doublet band just above the expected size on the blots strongly suggests that both glycosylated and non-glycosylated forms of the protein are present, and that protein processing is in progress (Figures 2,3). Glycosylation is essential for nAChR folding and expression on the cell membrane, and the higher bands are expected. For the α3 subunit, bands below the expected size were observed which might represent cross-reactivity of the antibody with other nAChR subunits, or protein degradation products, but only those that matched the size of the brain control were considered a positive result. In HBE we found that α1, β2, and δ were usually present (Figure 2A,2B). Although there are also lower cross-reacting bands for these nAChR subunits, a band of the correct size was also frequently seen, and upper bands suggest that the glycosylated form of the subunits are present (Figure 2A,2B). Protein for the subunits α4 and α7 were not observed in HBE, and α3, α5, and β1 were sometimes present, although α5 was expressed at a barely detectable level compared to other subunits (Figure 2B).
Figure 2 HBE and airway fibroblasts immunoblotting for nAChR subunits. Whole brain tissue lysate was used as a positive control. A) nAChR in HBE cells from never smokers; B) nAChR in HBE cells from ex-smokers; C) nAChR in airway fibroblast cells from one ex-smoker (1089), two never smokers (1133,1190), and three active smokers (1215, 1225, 1233). For each subunit, the positive control band was seen at the following size: α1 55 kD, α3 60 kD, α4 70 kD, α5 53 kD, α7 56 kD, β1 59 kD, β2 57 kD, and δ 55 kD.
Figure 3 Expression of nAChR subunit protein by BEAS2B and IB3-1. Immortalized bronchial epithelial cell lines BEAS2B and IB3-1 were immunoblotted for nAChR. For each subunit, the positive control band was seen at the following size: α1 55 kD, α2 60 kD, α3 60 kD, α4 70 kD, α5 53 kD, α7 56 kD, β1 59 kD, β2 57 kD, and δ55 kD.
To examine these findings more generally, nAChR protein was examined in additional HBE cultures from never smokers, active smokers, and ex-smokers (Table 5). Only some of the cultures examined by immunoblot were derived from the same individual as those we had examined by RT-PCR, so a direct comparison with the mRNA expression results was not usually possible. Subunit protein expression is generally consistent with our previous results, showing that the muscle-type receptor subunits α1, β1, and δ are detected at the protein level and theoretically form a functional receptor together with ε. Although we have not found an antibody for ε that will detect this protein in our positive controls, RT-PCR results indicate that it is frequently expressed (Table 3).
Table 5 Expression of nAChR in HBE and Bronchial Epithelial Cell Lines as determined by Immunoblot
HBE α1 α3 α5* α7 β1 β2 δ
Never smoker 5/8 (63%) 1/4 (25%) 0/4 (0%) 0/4 (0%) 1/4 (25%) 3/5 (60%) 3/4 (75%)
Active 2/2 (100) n.d. 2+/2 (100%) 0/2 (0%) 2/2 (100%) 2/2 (100%) n.d.
Ex-smoker 5/7 (71%) 1/5 (20%) 5+/7 (71%) 0/7 (0%) 4/7 (57%) 5/5 (100%) 5/5 (100%)
Total 12/17 (71%) 2/9 (22%) 7/13 (54%) 0/13 (0%) 7/13 (54%) 10/12 (83%) 6/6 (100%)
BEAS2B + - + - + - +
IB3-1 + - + - + + +
nAChR subunit protein present in HBE and bronchial epithelial cell lines as determined by immunoblot. The table indicates the number of positive cultures out of the total number of cultures examined, as well as the percentage of positive cultures for each subunit. * indicates statistical significance when comparing never smoking samples to combined active and ex-smoker samples. + The α5 subunit was present in these samples, but at low levels compared to other subunits and control.
One difference between our RT-PCR results and our immunoblotting is in the frequency of α7 expression. α7 mRNA was expressed in 68% of our HBE cultures, but the protein for this subunit was never detected, although the protein was detected in positive controls and in airway fibroblasts. This subunit is either transcribed but not translated, or the protein may be expressed below the limit of detection for immunoblotting in HBE cells.
We analyzed our protein data for combinations of subunits that would form functional receptors. We found that all three of the subunits needed for a functional muscle receptor were highly expressed at the protein level in 44% of the HBE cultures examined. The other functional combinations analyzed at the protein level are the neuronal α3-containing heteropentamers that were present in 33% of the cultures.
We also determined that nAChR subunit protein is expressed in airway fibroblasts (Figure 2). We found that subunit expression had less variation among cultures from different individuals in fibroblasts than HBE cultures. The α1, α7, β2, and δ subunits were expressed in 100% of cultures. The α3, α4, and α5 were never expressed, and β1 was expressed in 83% of cultures. The frequency of expression of α7 was significantly higher in fibroblasts than HBE cultures (100% versus 0%, p < 0.0001), and α5 is more frequently expressed in HBE cultures (54% versus 0%, p < 0.05).
We also found that nAChR subunit protein is expressed on two cell lines derived from normal bronchial epithelial cells described above (IB3-1, BEAS2B). Figure 3 is a representative immunoblot and Table 5 contains the complete table of results that have been repeated in independent experiments. We find that both cell lines express α1, β1, and δ protein. Therefore, the muscle-type (α1/ β1/ δ / ε) is most likely the major functional nAChR on BEAS2B and IB3-1. Neither cell line expresses protein for the α7 homopentamer. BEAS2B cells also express protein for α5 and β2 but do not appear to have another appropriate heterodimer partner, such as α3, to produce a functional neuronal nAChR. Based on the protein results, the only pentameric nAChR receptor detected in the BEAS2B and IB3-1 epithelial cell lines is the muscle-type heteropentamer.
NAChR are functional in lung epithelial cells
We examined functionality of the AChR on HBE cells by measuring calcium influx. After treatment with nicotine, extracellular radioactive calcium (Ca-45) is internalized by BEAS2B cells and short-term HBE cultures (Figure 4). BEAS2B cells were chosen for these experiments instead of IB3-1 due to the derivation of the cells. Since IB3-1 cells were derived from a cystic fibrosis patient with the classic sodium-channel defect, and nAChR can also act as sodium channels, their expression or function may be altered in this cell type due to abnormal ion levels. A derivative of the calcium ionophore myomycin was used as a positive control. Calcium-45 levels were 5 times over control levels using the positive control (data not shown). This high level of calcium influx is associated with loss of membrane integrity caused by ionophore.
Figure 4 Calcium influx after exposure of HBE or BEAS2B cells to nicotine. Intracellular calcium-45 was measured after exposure to nicotine for 5 minutes. * statistically significant as compared to untreated control.
The calcium influx seen in our experiments occurs within 5 minutes of treatment with nicotine and at concentrations from 1 μM to 100 μM in both cell types. Calcium influx was dose-dependent and reached 131% of control in BEAS2B cells and 137% of control in HBE cells. At all doses, influx of calcium-45 was statistically greater than control in BEAS2B cultures (p < 0.05).
To test the specificity of this response, we used the nicotinic antagonists α-bungarotoxin and hexamethonium with BEAS2B cells and nicotine. α-Bungarotoxin will block muscle-type nAChR as well as α7 homopentamers and hexamethonium will block neuronal heteropentamer nAChR such as α3- and α6- containing receptors. We used ionophore alone and with the nicotinic antagonists as a control in these experiments. Antagonists had no effect on ionophore-induced calcium influx (data not shown). In this experiment we found that α-bungarotoxin could completely prevent the calcium-45 influx seen after nicotine treatment, while hexamethonium could only slightly inhibit the effect of nicotine in BEAS2B cells (Figure 5). Based on this data, the neuronal nAChR α3- and α6- containing receptors do not appear to play a significant role in controlling calcium influx after nicotine treatment since the inhibitor of this receptor type, hexamethonium, could not significantly inhibit influx. Consistent with our immunoblot data, the influx of calcium after exposure of BEAS2B cells to nicotine is likely mediated through muscle-type receptors (α1/β1/δ/ε), and can be blocked by the inhibitor specific to these receptor types, α-bungarotoxin.
Figure 5 Calcium influx after exposure of BEAS2B cells in the presence of antagonist. BEAS2B cell were exposed to 1 μM nicotine in the presence or absence of nAChR antagonist. Intracellular calcium-45 was measured after exposure to nicotine for 5 minutes. * statistically significant as compared to untreated control.
Signaling pathways are activated in response to nicotine
We determined if protein kinase C (PKC) responds to nicotine because it is commonly activated by calcium influx. Calcium influx is an immediate effect of nicotine exposure to BEAS2B cells (Figures 4 and 5). The mitogen-activated protein kinase (MAPK) family members p38 and p42/44 were also examined because previous data showed that nAChR may be involved in regulation of apoptosis and growth [1,9]. In examination of PKC and MAPK, cells were treated with epidermal growth factor (EGF) as a positive control. We chose actin to correct for total protein for densitometry so that we could probe blots for a number of signaling pathways without compromising the protein on blots with unnecessary stripping procedures.
Using phosphorylation as a marker of activation, we find that PKC and p38, but not p42/44 are activated after treatment with nicotine in BEAS2B (Figure 6). As shown in the time course in Figure 6, phosphorylation of PKC is above control levels at 15 minutes of continuous treatment and stays above control through the longest time point, 60 minutes of continuous treatment. After correction for total protein using actin, densitometry shows that PKC levels are 120% of control after 1 minutes of exposure, and after 60 minutes are 221% of untreated control. In this experiment, a pan-phospho-PKC antibody was used that detects six isoforms of phosphorylated PKC between 78 and 85 KD. P38 was also phosphorylated immediately after nicotine treatment, with band intensity of 126% of control at the first timepoint examined, 5 minutes. Unlike PKC, phosphorylation of p38 was only briefly present, and phosphorylation drops to below control by 15 minutes of treatment. When probed for phospho-p42/44, phosphorylation state was never above control in BEAS 2B through 60 minutes of treatment (Figure 6).
Figure 6 Phosphorylation of proteins after nicotine exposure in BEAS2B cells. Nicotine causes phosphorylation of PKC and p38, but not p42/44 in BEAS2B cells. Cells were exposed to 100 μM nicotine for the times indicated. Panel A) represents the effect of nicotine on phosphorylation of PKC; B) represents the effect of nicotine on phosphorylation of p42/44; C) represents the effect of nicotine on phosphorylation of p38 D) is densitometry for the immunoblots expressed as percent of untreated control after correction with actin.
In contrast, signaling experiments done with short-term airway fibroblast cultures show that nicotine caused phosphorylation of p42/44 within 10 minutes of exposure (Figure 7). After densitometry and correction for loading differences, phosphorylation was increased to 198% of control at the 10 minute timepoint. Similarly to phosphorylation of p38 in BEAS2B cells, phosphorylation is tightly controlled, and returns to below control levels by 30 minutes. Use of α-bungarotoxin showed that phosphorylation of p42/44 can be blocked by this nAChR antagonist (data not shown), indicating that muscle-type and/or α7 receptors are involved in this response.
Figure 7 Nicotine causes phosphorylation of p42/44 in airway fibroblasts. Cells were exposed to 100 μM nicotine for the times indicated. Panel A) represents the effect of nicotine on phosphorylation of p42/44; B) is densitometry for the immunoblots expressed as percent of untreated control after correction with actin.
Together, these data indicate that the muscle-type (α1/β1/δ/ε) nAChR is consistently present on airway epithelial cells, while airway fibroblasts consistently demonstrate both a muscle-type and an α7 homomeric nAChR. Normal airway epithelial cells may also sometimes express the neuronal α3/α5/β2 or α6/β2 nAChR. The nicotinic receptors are functional, regulate calcium influx upon ligand binding, and lead to downstream activation of the signaling pathways MAPK or PKC when bound by nicotine. Downstream effects can be blocked by use of nicotinic antagonists.
Long-term nicotine exposure and nAChR expression
We examined the relationship of prior smoking to nAChR expression on airway cells. To do so, we compared subunits expressed at the mRNA and protein level in HBE cultures from never smokers, active smokers, and ex-smokers to determine if long-term exposure to nicotine was a factor in the type of nAChR expressed. As shown in Table 3, comparison of HBE cultures from 9 never smokers and 10 active smokers indicates that active smoking was associated with a significant decrease in mRNA expression of the β4 subunit (6 of 9 never smokers expressed this subunit compared to 1 of 10 active smokers (p = 0.02). This difference was not significant when comparing active to ex-smokers. In addition, frequency of expression in HBE cells of other subunits that make up the neuronal nAChR was not significantly different between active and never smokers. At the protein level, our data show that α5 may be upregulated in HBE cells at the protein level in response to chronic nicotine exposure (Table 5). Fewer never-smokers express α5 protein than ever-smokers (p < 0.05), although at lower levels than its receptor partners. An available antibody to the β4 subunit was not found to be specific to that subunit in our controls, therefore we could not determine if the β4 protein is modulated by tobacco exposure, as observed at the mRNA level.
Interestingly, in airway fibroblasts mRNA patterns for combinations of neuronal heteropentamers containing the α3 subunit were downregulated with smoking (Table 4). Cultures containing mRNA for all the subunits to form α3/ β2 or β4 receptors, with or without α5, were observed in 80% of never-smokers, but only 25% of active smokers and none of the ex-smokers examined. This difference was significant when comparing never smokers to ex-smokers or comparing never-smokers to ex- and active smokers together (p < 0.01). However, when analyzing protein results, no differences were found among active-, ex-, and non-smokers in the projected receptor type.
Discussion
Recent evidence suggest that endogenous acetylcholine is a local signaling molecule in non-neuronal tissue, and that nicotinic acetylcholine receptors are found outside the nervous system. Our data show that HBE cells can express mRNA for the neuronal α3/α5/β2 or β4, α6/β2 or β4, and α7 and α9 pentamers, as well as the muscle type α1/β1/δ/ε nAChR. Previous data in the human airway examined small numbers of HBE cultures for a limited number of nAChR subunits. Maus et al. [12] used RT-PCR and binding studies to show that the α3/α5/β2 nAChR were present on HBE cells. West et al. [9] also characterized nAChR on three non-immortalized bronchial epithelial cell lines and found the α3/α5/β2 nAChR subunits as well as α7, α9, and α10. However, neither investigation examined HBE cultures for the presence of the muscle-type nAChR. West et al. [9] examined α1 mRNA expression by RT-PCR, which was negative, consistent with our data that indicate that the mRNA for the α1 subunit is expressed in approximately one-third of HBE cultures from different individuals. In the present study, we have determined that several types of nAChR are consistently present on many primary cultures of HBE cells. Using a panel of 38 HBE cultures for RT-PCR analysis, we find that although not every nAChR subunit RNA is present in every culture, the mRNA for seven different nAChR subunits are consistently expressed in combinations that could combine to form both muscle and neuronal-type receptors. Immunoblotting confirmed that the nAChR mRNA for these subunits was translated into detectable protein. Not all subunits that are expressed at the mRNA level are highly expressed at the protein level, indicating that some regulation of nAChR expression may occur at the translational and post-translational levels. This has been previously shown in neuronal cells, where transcripts for the α7 subunit are present even on cells without functional α7 receptors [18]. Our data indicate that this also occurs in HBE cells, where α7 transcripts are frequently found, but protein of the correct size is not. Thus the conclusion in prior literature that various neuronal nAChR receptors, including α7, are responsible for actions of nicotine in HBE may not be definitive.
Instead, our results suggest that the muscle-type nAChR present in HBE cells may have a functional role in HBE cells that has not previously been considered. The muscle-type receptor was more recently characterized than the neuronal type and previous literature never examined HBE cultures for the muscle-type receptor [9,10,12]. This finding was further supported by the observation that two immortalized cell lines derived from HBE also expressed protein for the muscle-type receptors, while lacking the α7 homopentamer protein.
Similarly to HBE, mRNA and protein for nAChR subunits are commonly expressed by airway fibroblast cultures. Airway fibroblasts have never been examined for the presence of nicotinic receptors. Dermal fibroblasts have been shown to express mRNA for some receptors, although they were not examined for muscle-type receptors [19], and gingival fibroblasts respond to nicotine by decreasing expression of integrins and increasing expression of c-fos, but they have not been characterized for nAChR [20-22]. We find that airway fibroblasts express all the subunits required for muscle-type nAChR. In contrast to HBE, these cells also express readily detectable protein for the α7 receptor in all the cultures examined. Therefore, in these cells, the muscle-type receptor and the α7 neuronal homopentamer are the major functional nAChR that may be responsible for signaling initiated by nicotine. α7 receptors are less susceptible to inactivation in the long-term presence of agonist, while muscle-type receptors are more likely to undergo inactivation [23]. Therefore, in cells with these different receptors, there may be differences in the initiation of downstream signaling pathways especially in response to extended exposure to nicotine, such as occurs in a chronic smoker.
Calcium influx is a hallmark of the opening of the nAChR ion channel. An increase in intracellular calcium from the extracellular milieu can occur either by a direct influx of calcium through the nAChR channel, as occurs in α7 receptors, or by an influx of sodium that leads to depolarization of the cell and the opening of L-channels, as occurs after agonist binding to heteropentamer nAChR [24,25]. We determined that nAChR are functional in both BEAS2B and HBE cells; treatment of cells with nicotine leads to an influx of extracellular calcium. The influx of calcium seen after nicotine treatment was similar in magnitude to calcium influx seen in neuronal cells after activation of L-channels (144%) [26]. Additionally, we differentiated between functional muscle-type receptors and neuronal heteropentamer receptors. In BEAS2B cells, calcium influx stimulated by nicotine was significantly inhibited by a muscle/ α7 antagonist, but only slightly inhibited by a neuronal heteropentamer antagonist. This confirms the observation that the neuronal heteropentamer nAChRs do not play a major functional role in the response of BEAS2B cells to nicotine in our hands. Since α7 protein was not detected in HBE or BEAS2B cells, the muscle-type nAChR is more likely the major functional receptor type.
Nicotine has previously been shown to affect signaling in human airway cells, and acetylcholine, the endogenous ligand, causes proliferation of HBE cells [1,9]. In our experiments, nicotine initiated signaling pathways involved in cell growth and apoptosis in both HBE cells and airway fibroblasts. In BEAS2B cells, immediate phosphorylation of PKC is likely due to calcium influx. Calcium is a known cofactor for classical PKC activation and is required along with binding to diacylglyerol (DAG) for functional conformation. Phosphorylation of PKC, consistent with activation, increases over time. This may indicate that upregulation of other PKC cofactors, such as DAG, may occur as downstream events after calcium influx, leading to an enhanced PKC signal over time.
Over the same time period, the MAPK family member p38 but not p42/44 is phosphorylated, a required step for activation. The activation of p38 is associated with regulation of apoptosis in response to cellular stress. The MAPK family kinases, such as p38, are not known to be directly activated by calcium; however there are several indirect pathways that lead to rapid phosphorylation of these pathways. These include signaling through the calcium/calmodulin-dependent protein kinases CaMKI, CaMKII, and CaMKIV as well as the calcium-activated signaling molecule PYK2.
This finding is in contrast to signaling activated by nicotine in airway fibroblasts. These cells phosphorylate p42/44 immediately upon treatment with nicotine. Like p38, p42/44 is not directly activated by calcium, but could be phosphorylated by calcium-activated signaling molecules. However, the pathways that lead to phosphorylation of different MAPK family members in the two cell types in response to nicotine have not been elucidated. It is possible that the nAChR type differences are responsible for the differential MAPK effects. For example, the α7 receptor is highly expressed on airway fibroblasts but not on HBE cells. The additional nAChR type on these fibroblasts may change the signaling pathways activated by cells in response to nicotine. This is consistent with a previous study by Jull et al [27]. This study showed that ligand binding to the α7 nAChR in SCLC cells leads to activation of p42/44 through Raf [27]. A similar pathway may be activated by the binding of nicotine to the α7 nAChR in airway fibroblasts.
Finally, we found that smoking had only modest effects on nAChR expression in the airway. Previous studies in the brain indicate that certain nAChR may be increased in frequency in smokers as compared to non-smokers [28], and a published experiment showed that there was an increase in the α3 nAChR expressed in respiratory epithelial cells in one smoker compared to one nonsmoker [10]. Due to the limited sample size in the study of lung cells, it is impossible to know if this difference was due to individual variation or was a smoking-induced change, especially when considering the variability of nAChR subunit expression seen among individuals in our study. In this study of HBE cultures using a panel of active smokers, never smokers, and ex-smokers, there were some statistical differences in the mRNA and protein expression of nAChR subunits in cultures derived from donors with different smoking histories, including the increased presence of protein for an α5 subunit in smokers that could combine with the α3 containing receptors and change the calcium permeability [29]. However, the α5 subunit was expressed at a much lower protein level than the α3/β2 subunits, and may only be present in some of the neuronal-type receptors. Other changes that were documented with smoking status did not occur in the major functional nAChR of the cell type. Corresponding changes also did not occur in the other subunit partners that are necessary for function, so the effect on overall functional receptor expression was probably unchanged with smoking status in HBE cells.
In contrast, there was a significant decrease in the frequency of the expression of the functional combination of subunits for the α3-containing receptors in airway fibroblasts of smokers and ex-smokers compared to never-smokers. This nAChR type is a sodium channel that undergoes inactivation upon long-term exposure to agonist, and, in airway fibroblasts, appears to be downregulated with long-term exposure to nicotine. This change in receptor expression remains even after exposure to nicotine ceases, as evidenced by the reduced frequency of expression in ex-smokers.
Conclusions
We have shown that short-term cultures of normal airway fibroblasts as well as normal human bronchial epithelial cells from a number of different human donors consistently express functional nAChR and that these cell types differ in the type of nAChR they express. It is likely that the muscle-type nAChR plays a major role in the response of HBE cells to nicotine, and that the neuronal heteropentamers play a more minor role. Calcium influx as well as initiation of downstream signaling pathways indicate that receptors are functional and that both human bronchial epithelial cells and airway fibroblasts respond to nicotine, and those signaling responses may differ due to the difference nAChR present on the cell type. Together, these data suggest that exposure of the human airway to nicotine through tobacco smoke may have physiological consequences on airway homeostasis involving both the airway mucosa and the underlying submucosal mesenchymal cells. As such, nicotine may act to promote lung disease by acting to change cell growth and apoptosis. In airway fibroblasts this may leading to thickening of the airway wall seen in the pathogenesis of COPD. In the bronchial epithelium this may lead to preneoplasia or development of frank cancer.
Abbreviations
nAChR: nicotinic acetylcholine receptor
HBE: human bronchial epithelial cell
PKC: protein kinase C
MAPK: mitogen activated protein kinase
BEGM: bronchial epithelial growth medium
EGF: epidermal growth factor
P42/44: Extra-cellular signal regulated kinase isoforms 1 and 2
Authors' Contributions
DLC designed and performed the majority of the experiments and data analysis, and wrote the manuscript. TMH designed and performed RT-PCR with the assistance of MJS. JDL and NAC contributed the tissues that were grown into primary cultures and provided information on smoking history. AGD cultured the primary cells. JMS conceived of the study and participated in its design and coordination. All authors read and approved the manuscript.
Acknowledgements
We gratefully acknowledge Z.Z. Wang, Ph.D., for the kind gift of anti-delta nAChR subunit antibody as well as his helpful discussions. We would like to thank Katherine Kielek, Janine Stumpf, and Michelle Swick for their technical assistance. This work was supported by P50 CA090440, SPORE in Lung Cancer from the National Cancer Institute
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| 15588326 | PMC544394 | CC BY | 2021-01-04 16:47:22 | no | Respir Res. 2004 Dec 10; 5(1):27 | utf-8 | Respir Res | 2,004 | 10.1186/1465-9921-5-27 | oa_comm |
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Microb Cell FactMicrobial Cell Factories1475-2859BioMed Central London 1475-2859-3-151558832310.1186/1475-2859-3-15CommentaryThe relevance of genetic analysis to dairy bacteria: building upon our heritage Vadeboncoeur Christian [email protected] Sylvain [email protected] Département de biochimie et de microbiologie, Groupe de recherche en écologie buccale, Centre de référence pour virus bactériens Félix d'Hérelle, Faculté des sciences et de génie, Faculté de médecine dentaire, Université Laval, Quebec City, Quebec, G1K 7P4, Canada2004 10 12 2004 3 15 15 8 12 2004 10 12 2004 Copyright © 2004 Vadeboncoeur and Moineau; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Lactic acid bacteria (LAB) are essential for the manufacture of fermented dairy products. Studies on the physiology, biochemistry and genetics of these microorganisms over the last century have contributed considerably to the improvement of fermentation processes and have resulted in better and safer products. Nevertheless, the potential of LAB is far from being maximized. The sophistication of biotechnologies and the availability of complete genome sequences have opened the door to the metabolic engineering of LAB. In this regard, the recent publication of the complete genome sequences of two Streptococcus thermophilus strains will provide a key tool to facilitate the genetic manipulation of this important dairy species.
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Introduction
The souring of milk by microorganisms has been used for thousand of years as a natural preservation procedure [1]. The method was domesticated to manufacture man-made edible fermented products such as cheese, which appeared in the human diet some 8,000–10,000 years ago. However, up to the beginning of the 20th century, milk fermentation was by no means a controlled process, and the search for improvements was strictly empiric and based on trial and error.
The discovery and characterization of lactic acid bacteria (LAB) has tremendously modified the way fermented milk products are made. Considerable efforts have been devoted over the last fifty years to increase our knowledge about the genetics, biochemistry, and physiology of LAB. In addition to enhancing our understanding of microbial life, these studies have allowed dairy microbiologists and cheesemakers to select better strains and improve the productivity, quality, and safety of the final products. The characterization of LAB has promoted the rational development of blends of defined bacterial strains, now known as starter cultures, which are increasingly replacing the undefined blends traditionally used by the dairy industry. This strategy makes it easier to control acid production and, to some extent, phage problems. Unfortunately, it is also widely perceived that this approach reduces the much sought-after rich, complex flavour of fermented products made with undefined cultures. Thus, the search for LAB strains that produce characteristic aroma and flavours remains an attractive challenge for the dairy industry.
The increasing awareness of the population of the importance of a healthy diet has led scientists to revisit the century-old hypothesis that some specific fermented dairy products may provide health benefits. A plethora of studies are underway to evaluate this concept and to improve the health benefits of LAB. Also, the long history of the safe use of these bacteria has made them very attractive candidates as vaccine delivery vehicles [2,3]. Lastly, thanks to their relatively small genome and simple metabolism, LAB can be exploited as food-grade cell factories to produce molecules of industrial and therapeutic interest.
To meet some of these challenges and promises, it became apparent that metabolic engineering was the best approach. Owing to the industrial interest, the sound knowledge of its genetic and the availability of genetic tools, metabolic engineering of LAB has been carried out mainly with Lactococcus lactis. The publication of the complete genome sequence of L. lactis in 2001 [4] was a boost to speed up achievements in rerouting metabolic fluxes with this LAB. For instances, it became possible to increase the production of diacetyl and to transform this mesophilic bacterium from homolactic fermenter to homoalanine fermenter. The innovative production of vitamins by L. lactis via genetic manipulation [5,6] is a convincing example of how metabolic engineering of LAB can be beneficial for consumers. A number of reviews [7-12] provide a more comprehensive discussion of the metabolic engineering of L. lactis.
Recently, the complete genome sequence of two Streptococcus thermophilus strains were published [13] and a third one is nearing completion [14]. S. thermophilus is a thermophilic LAB species widely used for the manufacture of yogurt and cheeses that require elevated cooking temperatures such as Swiss and Italian types. The availability of these genome sequences will make it possible to apply genomic-related analytic methods such as functional and comparative genomics, microarray technologies, proteomics, and bioinformatics to this species. These genomic-based technologies will undoubtedly accelerate the metabolic engineering of S. thermophilus, which is still at its infancy. We will briefly discuss current studies that will greatly benefit from these S. thermophilus genomic sequences: food-grade vectors, optimization of lactose metabolism and exopolysaccharide (EPS) production, and resistance to bacteriophages.
Discussion
Food-grade vectors
Metabolic engineering often involves inactivation or overexpression of relevant chromosomal genes or stringent control of an extrachromosomal foreign gene borne by a plasmid artificially introduced into the host. Plasmids thus play a central role in these studies because they are the primary vehicles used to manipulate target DNA sequences. However, these extrachromosomal genetic elements are naturally but infrequently observed in S. thermophilus strains[15].
Understandably, very few cloning tools are at hand, and those that are available are based on similar replication machinery. This lack of diversity poses significant problems for many genetic studies. While several types of transformation methods have been developed to introduce foreign DNA into S. thermophilus, an efficient, stable, food grade expression system is still lacking. The recent discovery of a novel theta-type replicating plasmid [15] as well as suitable selection markers effective in S. thermophilus, such as galactokinase [16] and alpha-galactosidase [17], suggest that help is on the way. The complete S. thermophilus genome sequences also provide crucial information on transcription and translation initiation signals and the codon usage of this bacterium. This may help in the design of efficient expression systems.
Lactose metabolism
The ability of S. thermophilus to rapidly take up and metabolize lactose is crucial in several fermentation processes. While S. thermophilus readily metabolizes the glucose moiety of lactose, it is unable to metabolize galactose, which is expelled into the external medium. The presence of galactose in yogurt and other dairy products might be unwanted for different reasons. Notably, galactose is poorly metabolized by humans and may cause, under certain conditions, health problems [18,19]. The release of galactose by S. thermophilus results either from poor expression of the gal operon [20] or inefficient translation of the galK gene coding for galactokinase [16,21]. The finding that the inability to grow on galactose was a consequence of poor galK translation was first suggested by a comparative analysis of the S. thermophilus gal-lac operons with the homologous genes from the phylogenetically related oral bacterium Streptococcus salivarius. This is a convincing example of how comparative genome analysis may help to decipher specific metabolic pathways of LAB. While the introduction of a functional extrachromosomal S. salivarius galK allele in S. thermophilus allows it to grow on galactose, it does not prevent the expulsion of galactose during growth on lactose [16]. In this context, it is noteworthy that 10% of S. thermophilus genes are not functional (pseudogenes) and that one third of these pseudogenes are dedicated to sugar metabolism [13]. It would be interesting to determine whether reactivation of one or several pseudogenes that were originally involved in sugar metabolism could prevent or decrease galactose expulsion during growth on lactose. In this context, the availability of the complete sequence of the S. salivarius genome would be welcome since many S. thermophilus pseudogenes involved in sugar transport are functional in S. salivarius [13].
Exopolysacharides
Some strains of S. thermophilus are widely used for the commercial manufacture of yogurt because they produce exopolysaccharides (EPS), which give a viscous texture to the fermented dairy product. Other EPS-producing S. thermophilus strains can also enhance the functional properties of some cheeses such as Mozzarella. The organoleptic properties of these products are largely due to the amount and types of exopolysaccharide produced during the fermentation process. The yield and sugar constituents of EPS are influenced by several factors and vary from strain to strain [22]. S. thermophilus does not naturally produce large amounts of EPS, which explains why considerable efforts are being directed toward understanding the cellular mechanisms of EPS biosynthesis. Since low levels of sugar precursors may limit EPS synthesis, a better understanding of S. thermophilus metabolic flux during sugar metabolism may lead to new strategies to enhance EPS production [22]. Levander et al. [23] showed that such knowledge can be used to enhance EPS production in S. thermophilus by metabolic engineering of central carbon metabolism. Moreover, because EPS yields are growth associated, efforts to increase production levels are likely to require novel strategies to enhance biomass production. This task clearly requires a comprehensive view of the cell machinery. The sequencing of a whole genome is a mandatory step to achieve this goal. As our knowledge of S. thermophilus continues to improve, novel EPS and/or applications for EPS+ cultures are likely to emerge.
Phage resistance
Bacteriophages are the most significant cause of fermentation failures in the dairy industry worldwide. Dairy microbiologists have attempted for more than 70 years to eliminate, or at least bring under better control, the bacteriophages that interfere with the manufacture of many fermented milk products [24]. The publication of the complete genomes of two S. thermophilus hosts should provide new insights in several areas of phage research.
The development of bacteriophage-insensitive S. thermophilus mutants is generally the first approach used to transform a phage-sensitive strain into a phage-resistant mutant, most likely following spontaneous chromosomal mutation in the gene encoding the phage receptor. Although progress has been made in identifying phage proteins involved in S. thermophilus host recognition [25], the identification of the phage receptors on the cell surface has remained elusive. Based on the results of genome data mining [26], a number of potential receptors can now be experimentally verified.
At least eight complete S. thermophilus phage genomes are now available [27,28]. Phage research has thus already entered into the post-genomic era. Microarrays covering the two main groups of S. thermophilus phages are already available[29], meaning that it is now possible to design a complete array containing host and phage genes to study phage-host interactions on a novel and global scale during the infection process.
Conclusions
A thorough understanding of LAB metabolism and how it is regulated by external stimuli is a prerequisite for maximizing the potential of LAB. The availability of complete S. thermophilus genome sequences will obviously facilitate our understanding of the metabolic potential of S. thermophilus. It will also make it easier to design rational genetic manipulations of this important dairy bacterium in order to produce added value cheeses and yogurts and to use it as a cell factory. In addition, knowing the complete genome sequences should lead to the development of new genetic tools that will provide insights into the evolution of microbial communities, shifts in metabolism, and how each member adapts to the environmental changes that occur during complex fermentation processes.
The exciting biotechnological developments in LAB and studies that are already underway will benefit both consumers and the dairy industry. The availability of the complete S. thermophilus genome sequence have opened up exciting, new possibilities that will build on an already rich heritage.
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| 15588323 | PMC544395 | CC BY | 2021-01-04 16:05:46 | no | Microb Cell Fact. 2004 Dec 10; 3:15 | utf-8 | Microb Cell Fact | 2,004 | 10.1186/1475-2859-3-15 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-471557921410.1186/1475-2875-3-47ResearchParticipation of African social scientists in malaria control: identifying enabling and constraining factors Ngalame Paulyne M [email protected] Holly Ann [email protected] Caroline [email protected] Isaac [email protected] Samba [email protected] Felisbela [email protected] Master of Public Health Program, Morehouse School of Medicine, 720 Westview Drive, SW., NCPC Suite 344, Atlanta, Georgia, 30310, USA2 Malaria Branch, Centers for Disease Control and Prevention, Mail Stop F-22, 4770 Buford Hwy, NE Atlanta, GA, 30341, USA3 Department for International Development (DFID) Malaria Programme, London School of Hygiene and Tropical Medicine (LSHTM), Keppel Street, London WC1E 7HT, UK4 Institute of African Studies, University of Nairobi, P.O. Box 30197, Nairobi, Kenya5 Department of Public Health, Epidemiology and Medical Anthropology, University of Bamako, BP 1805, Bamako, Mali6 National Institute of Health, P.O. Box 264, Maputo, Mozambique2004 6 12 2004 3 47 47 6 9 2004 6 12 2004 Copyright © 2004 Ngalame et al; licensee BioMed Central Ltd.2004Ngalame et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Objective
To examine the enabling and constraining factors that influence African social scientists involvement in malaria control.
Methods
Convenience and snowball sampling was used to identify participants. Data collection was conducted in two phases: a mailed survey was followed by in-depth phone interviews with selected individuals chosen from the survey.
Findings
Most participants did not necessarily seek malaria as a career path. Having a mentor who provided research and training opportunities, and developing strong technical skills in malaria control and grant or proposal writing facilitated career opportunities in malaria. A paucity of jobs and funding and inadequate technical skills in malaria limited the type and number of opportunities available to social scientists in malaria control.
Conclusion
Understanding the factors that influence job satisfaction, recruitment and retention in malaria control is necessary for better integration of social scientists into malaria control. However, given the wide array of skills that social scientists have and the variety of deadly diseases competing for attention in Sub Saharan Africa, it might be more cost effective to employ social scientists to work broadly on issues common to communicable diseases in general rather than solely on malaria.
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Introduction
Malaria control remains ineffective in many endemic areas in spite of efficacious interventions, such as combined antimalarial therapies and insecticide-treated materials. Biological, environmental, political, socio-cultural, economic and behavioural factors influence the transmission of malaria, thus requiring a multidisciplinary and integrated approach to effectively control the spread of malaria [1,2].
In recent years international initiatives, such as Roll Back Malaria, have highlighted the contributions that social scientists bring to a multidisciplinary approach for malaria control. Various studies in Africa have illustrated the benefits of collaborative research between social and biomedical scientists [3-17]. However, social science knowledge and practices are still not fully integrated into malaria research and control programmes, especially when compared to other public health areas such as HIV/AIDS prevention. While it is difficult to do a comparison on the actual number of social scientists involved in HIV/AIDS versus malaria control, a review of the literature illustrates the numerous contributions that social and behavioural research has had in HIV/AIDS control and prevention [18-27]. The early recognition and inclusion of socio-behavioural research was part of the HIV/AIDS prevention strategy at the onset of the pandemic [27]. Significant funding allowed social scientists to work directly with epidemiologists in HIV/AIDS research.
Several factors continue to limit the application of social science research in tropical public health in general, and malaria control in particular. These include: confusion among many non-social scientists and social scientists alike about the variations in social science theory and methods and the different types of contributions that each can make to public health and malaria control activities, a perception among many public health and malaria control professionals that the problems of communicable diseases can be solved with technical and clinical interventions, ignoring the social nature of illness, limited communication among social scientists working in applied health and malaria control, insufficient access to current social science and malaria literature, and the lack of trained social scientists with applied experience in the endemic countries [28,29].
Given the poor academic and professional structures found in many African countries, an increasing number of highly skilled citizens emigrate abroad for further studies but fail to return to their respective country of origin, a phenomenon commonly referred to as 'brain drain' [30-36]. As a result, many African countries now have a vast pool of highly skilled professionals who are permanently living abroad, making little to no contributions to their country of origin [37,38]. While the "brain drain" significantly contributes to the lack of social science capacity in malaria control in Africa, it is unclear the extent to which the current pool of trained social scientists that remain in Africa is being integrated into malaria control. Experience has shown that commissioning a well-trained and field-experienced applied social scientist (i.e. one with both an understanding of the theoretical and applied perspectives of the discipline) can be extremely beneficial for informing malaria programme-related decisions, as well as helping in the development of effective intervention programmes [39,40]. This study was designed to better understand the level of involvement of African social scientists in malaria control, in order to identify potential approaches to facilitating collaborative work among social scientists and malaria control programme personnel and stakeholders.
Methods
A convenience sample was derived from existing social science networks such as, the Partnership for Social Science and Malaria Control (PSSMC) Network, the Social Science and Medicine Africa Network (SOMA-Net), Pan African Anthropological Association (PAAA), as well as the CHANGE Project database of African social scientists, and the United Nations Development Program, World Bank and World Health Organization's Special Programme for Research and Training (WHO/TDR) database of social scientists trained in the last decade (1992–2000). Snowball sampling was used to elicit names of additional social scientists known personally to the participants and investigators.
Social science was broadly defined to include disciplines such as anthropology, sociology, health economics, demography and population studies, development studies and public health. Enrolment criteria included specific social science training within these disciplines, and participants had to be of African origin. Enabling factors were defined as those factors that made it easy to work in malaria control, including factors that first attracted social scientists and those that facilitated entrance into the field. Constraining factors were defined as those factors that impeded work in malaria control and factors that made malaria control unattractive to social scientists.
Data collection was organized in two phases. In Phase I, consent forms and questionnaires were sent by email and/or fax to potential participants. Participants could respond directly and/or refer other eligible colleagues. Those who were ineligible or uninterested could decline. For those emails that could not be delivered, three attempts were made to resend the survey and consent form. The questionnaire focused on demographic characteristics, work experiences, factors influencing career choices and sources of career development information. The survey instrument was piloted among five participants from different countries. Based on the feedback from the pilot survey, minor adjustments were made to improve use via email. Email communication was preferred but, in the absence of email, other methods of communication were used. If the survey was not returned within three weeks, attempts were made to follow-up.
The survey responses in Phase I provided an overview of the involvement of the participants in malaria related activities but gave only a limited understanding of the specific factors affecting career trajectories. In Phase II, we conducted in-depth phone interviews using a purposive sample derived from Phase I that was selected according to country of origin, sex, academic field (social science discipline) and interest in malaria and applied or operational work experience (both in malaria and non-malaria). A deliberate effort was made to ensure that the sample represented those with or without an interest in malaria, those who actively sought employment in malaria and those who did not.
The more descriptive data from Phase II clarified Phase I data. For example, with regards to participants' interest in malaria, some participants in Phase I indicated an interest in malaria while in school, but did not include malaria in their thesis research and did not pursue employment in malaria. The in-depth, open-ended phone interviews in Phase II provided greater detail as to other factors that might have influenced participants' career decisions in malaria control. Open-ended questions focused on key events and times in the career trajectory during which critical decisions were made, current and past work experiences, reasons for selecting specific jobs, technical experiences with proposal writing, challenges and rewards of working in malaria, and suggestions for improving the integration of social science in malaria control.
Descriptive statistics were generated using EPI-Info. Qualitative data were transcribed and content analysis was used to develop contextual themes. The study was reviewed and approved by the Institutional Review Board of the Centers for Disease Control and Prevention. Verbatim quotes from the participants are used throughout the text to illustrate contextual points of discussion.
Results
This study was conducted between May 2002–May 2003. Of the 136 surveys sent out, 40 completed surveys were found to fit our enrolment criteria (Fig. 1). Eighteen participants were interviewed in Phase II.
Figure 1 Response to surveys. Flow chart illustrating response to surveys
All 40 participants were nationals of African countries and resided in the countries of Cameroon, Ethiopia, Mali, Ghana, Kenya, Nigeria, Tanzania, South Africa and Uganda. Most participants (75%) were male between the ages of 30–39 years old (Table 1). Fifty percent (n = 20) had a masters degree, 42% (n = 17) were either doctoral degree students (n = 3) or had a doctoral degree (n = 14). Almost all participants had an undergraduate degree in the social sciences and chose various social sciences disciplines for specialization during their postgraduate training.
Table 1 Age and Sex Distribution (n = 40)
Age group Male Female Percentage
20–29 1 0 2.5%
30–39 17 5 55.0%
40–49 9 4 32.5%
50–59 2 1 7.5%
60+ 1 0 2.5%
Total 30 10 100.0%
Of the 18 participants selected for a phone interview, 55% (n = 10) were between 30–39 years and 22% (n = 4) were female. Of those with postgraduate degrees, 50% (n = 9) had a masters degree, 44% (n = 8) either had a doctoral degree or where doctoral degree students while 5% (n = 1) had a bachelor's degree. When difficulties arose in ascertaining whether the professional discipline linked to the social sciences, participants were contacted for clarification.
Eighty-five percent of the participants (34/40) received funding for postgraduate training from their national government, international organizations (such as the WHO or DFID), or other foreign governments. Sources of career development information included the Internet, friends and colleagues, local universities, conferences, journals, professional networks and local newspapers.
Enabling factors
Interest in malaria
When asked if participants were interested in malaria during their academic training, 82% (33/40) of the participants said yes. Only 36% (12/33) of those interested included malaria as a research topic in their thesis or dissertation. Three participants (7.5%) who were not interested in malaria included malaria in their thesis. Post graduation, 35% (14/40) of the participants actively sought employment in malaria control. Three of the 14 (21%) who sought employment in malaria were turned down for jobs due to insufficient technical knowledge of malaria control, while six participants (15%) returned to previous health care positions, some of which were positions in malaria control. Although some participants were not currently working on malaria-related projects, nearly all participants, at some point in their career, had worked as a consultant in malaria control in addition to their full time positions. Participants who specialized in malaria for postgraduate degrees were often funded by a national or international organization, on condition that they would return to their jobs in malaria following the completion of studies.
Eleven of the 40 participants said that the primary factor that prevented those with an interest in malaria from seeking further research and employment opportunities in malaria control was the perception that social scientists could not be employed in malaria control. As one participant remarked "Malaria is an endemic disease in my community, so I can't say I was never interested. I wanted to do a thesis on malaria, but I realized that social scientists could not get jobs in malaria so I had to look elsewhere." When followed up during the phone interviews in Phase II, it was clear that some participants turned down jobs in malaria due to competing employment in areas such as HIV/AIDS, while others declined malaria control contracts due to the competing demands of doctoral-level training.
Another issue that was revealed in Phase I was the influence of the epidemiology of malaria on participants' interest in malaria. Eleven of the 18 participants during the phone interview explained that they developed an interest in malaria after learning about the complex nature of the disease, its high prevalence and the possibility of access to research funds. "For years I thought the field of malaria was boring and I was not interested in it. I only became interested in malaria when I realized that it was a complex disease with a major public health impact and it appeared that funding for research might be available."
Factors influencing employment in malaria
All 40 participants were asked to identify factors that attracted social scientists into general employment opportunities, as well as malaria-specific opportunities (Table 2). Responses in Phase II provided further clarification on the specific factors affecting career trajectories. Of the 18 participants included for an in-depth interview in Phase II, 11 indicated that a senior lecturer or mentor during the practical training experience had encouraged participants to work with them on their research project. Mentors helped to develop participants' research and proposal writing skills and identify funding and publication opportunities, and in some cases international contracts. These contracts often resulted in participants gaining local and international visibility within the larger malaria community, which led to subsequent employment opportunities.
Table 2 Factors Important for Employment (n = 40)
Malaria Employment Frequency General Employment Frequency
Sufficient funds to complete job 11 Potential for career advancement a 26
Supportive environment that facilitates the translation of research findings into programmatic use 9 Competitive salary a 13
Ability and opportunity to contribute alternative solutions to malaria control from a social science perspective 6 Proximity to family a 12
Type of social science dimension to job 5 Social value of job b 5
Sufficient technical skills to complete jobs 5
Epidemiology of malaria 4
aIndicates factors that were identified as important for both general employment and malaria specific employment.
bThe social value of a job was defined as the positive impact that a given job made in the community, as well as the professional and community respect awarded to the position. Participants were more likely to seek out employment from organizations with proven track records of using research findings to improve the health of the community.
Opportunistic events also shaped career development. For example, the successful completion of a project, fellowship or consultancy often produced a cascade effect by opening up other opportunities in malaria control, resulting in individual professional recognition by local and international collaborators as an expert in the field. Attending workshops sponsored by international organizations, such as the World Bank and WHO, enabled participants to get their name in the organizations' database for possible consultancies.
Strong writing skills were identified as essential for job and training opportunities by most of the 18 participants in Phase II. Ten of the 18 participants (55%) in the phone interview noted that while it was initially difficult to identify and develop fundable proposals, receiving constructive feedback from rejected proposals helped improve their writing skills and led to the development of more successful proposals. "Most of the rejected proposals do not give reasons for rejection, which is unfortunate. I have been lucky to have one rejected proposal that was returned with explanations as to why it was rejected. I was told the issue we raised was very broad and vague and it made me realize that I had to be very clear in my framework. After that rejection, I was able to develop projects that had a clear framework and include a multidisciplinary team to strengthen other key areas and the next proposal we developed was accepted."
When asked why they chose employment in malaria, one of the participants echoed many others by saying, "In Africa we take what we get. One rarely ends up in a profession of their choice. People take the job that is at hand and learn to deliver as best as they can." This was true for both those interested in malaria originally and those not interested. During the in-depth interviews, we also asked participants who did not initially express an interest in malaria but sought and gained malaria related employment, why they choose to stay in malaria? Of the 18 participants eight agreed with the following view: "I would probably stay in malaria because it is a major problem in my country, and I feel I have gained significant experience in malaria over the years. This has strengthened my confidence and ability to handle current and hopefully future opportunities, and maybe I am too old right now to start learning something new."
Constraining factors
Malaria employment challenges
Participants were also asked to identify factors that constrained employment in malaria. All 40 participants were more likely to refer to employment challenges specific to malaria research, rather than malaria control, as national malaria control programmes employ very few social scientists. Ten of the 18 participants in Phase II noted that social science involvement in malaria control was a recent development in their countries, as biomedical scientists had previously dominated malaria control. Another phone interview participant remarked that "specifically focusing on malaria as the only area for job opportunities narrows ones fields for job opportunities. Although malaria is an endemic disease in my country, it is largely dominated by physicians and there are not sufficient job opportunities for social scientists in my country so it is impossible to focus only on malaria. You need something to fall back on besides malaria." Participants also noted that a paucity of research centres and limited funding for social science research also contributed to the lack of job opportunities. In situations where job opportunities existed for social scientists, they were generally limited to short-term rather than long-term career paths.
Individual perceptions of malaria as a 'normal' everyday event limited participants' understanding of the impact of malaria and, thus, diminished their interest in malaria control. As one of the phone interviewed participants remarked, "At first, I did not realize the impact of malaria in my community. Everybody was talking about HIV/AIDS. I think it is because people have lived with malaria for so long that they treat it at home. It was only after doing some training at the hospital (as part of the national training requirement), that I realized how many people, especially children and mothers, die of malaria and I got interested in working in malaria control."
Of the 40 participants in Phase I, 36 identified the lack of professional development opportunities as a constraining factor to employment in malaria. The lack of a supportive work environment with good communication and mutual respect was mentioned by those working primarily with biomedical scientists. "I prefer working on maternal and child health issues, as well as HIV/AIDS, because personnel in these areas are able to recognize the role of social science and request social science input. This makes me confident that my discipline is important and I can make a contribution."
Insufficient technical skills in proposal writing and professional isolation from other social scientists hampered the development of collaborative relationships, which made it difficult to develop competitive proposals for funding and publication. As a participant observed, "Research is largely uncoordinated. We have no knowledge of who is doing what, there is no database of social scientists and the projects that they're working on from which we can develop collaborative projects. We neither have the infrastructure nor the access to current literature, which makes it difficult to develop manuscripts for publication."
In order to meet the challenges of employment, eight of the 18 participants in Phase II stressed the importance of understanding all technical aspects of malaria control. One participant echoed the view of several others in their advice to junior social scientists, "try to learn not only about social science in malaria control, but all other aspects of malaria control as well, such as entomology, vector control and treatment. Having only a social science background without any understanding of the other areas of malaria control might make it difficult to effectively apply the social science aspect within malaria control. Besides you will always be working as part of a multidisciplinary team, therefore, you will need to understand their language in order to be able to effectively communicate the social science dimension to them."
Participants also emphasized the importance of linking students to practical training opportunities in endemic areas for fieldwork experience and possible long-term opportunities. As one participant noted, "Encourage donors to develop grants for student researchers working with senior scientists, and identify conferences where students could present research findings."
Integrating social science in malaria control
Participants in Phase II were asked to describe strategies for improving the integration of social science in malaria control. Of the 18 participants interviewed, 13 (72%) stressed the need for social science advocacy as an important step in malaria research and control. Advocacy efforts were recommended for social scientists and non-social scientists alike, as well as policy makers in health and education and those who fund research. Five participants said their involvement in malaria projects was as a result of specific requests for social scientists in the call for proposals.
All 18 participants emphasized the need to develop outreach efforts for junior social scientists in order to enhance their understanding of the potential contributions that social scientists could offer, the type of training needed, and the types of jobs they could perform in malaria control. Three of the 18 participants also suggested the revision of graduate and medical school curricula to include malaria and social science courses, but noted that support had to come from the national level for curricula change to take place.
The establishment of a sustainable network of African social scientists working in malaria control was identified as a useful tool for integrating social sciences in malaria control by six of the 18 participants. Possible functions of the network were described as developing a mentoring programme for junior social scientists, a forum to disseminate current local social science research, information relating to employment and training and funding opportunities.
To facilitate the application of social science research findings to national malaria control policies, one of the phone-interviewed participants explained how to include policy makers in the process of social science research. "Make the research process more participatory. Involve policy makers and consult with them so that eventually they are the ones who demand the research and in this way, social science research can influence policy. We developed working papers with key findings for distribution to various stakeholders. We published our findings on the Internet and conducted department seminars or short courses during which we discussed our research and presented the information in a very manageable manner. At least, in this way, we were able to get our findings into the hands of policy makers and hopefully begin a dialogue with them."
Discussion
Results of this study indicate that factors such as having an interest in malaria, a mentor during one's academic and professional career, strong writing skills, and technical experience in malaria made it possible for participants to take advantage of opportunistic events and job opportunities that led to employment in malaria control activities. As well, limited job opportunities, the lack of career development opportunities, and the lack of understanding about social scientists' role in malaria limited the involvement of social scientists in malaria control. This study revealed that many of the enabling and constraining factors for social scientists' involvement in malaria control are not unique to malaria control but are common to African researchers in general. An examination of the intrinsic and extrinsic factors regarding career trajectories of social scientists in this study revealed that the majority of participants did not specifically seek malaria as a career path but, rather, were looking for employment opportunities in general, some of which happened to be in malaria. It is unclear to what extent the career development process in many African countries contributes to the way employment is sought or if this is due to the relatively new role that social science plays in malaria control, which was noted by numerous participants. Career development is a process that ideally begins during one's formal academic training and continues throughout one's professional life. However, if career development is concentrated more towards academic training and less on postgraduate employment opportunities, it is no wonder that post graduation, students are forced to 'take whatever they can get,' as opposed to seeking opportunities that complement their interest and training.
The lack of proper career counselling could also limit a student's understanding of what courses and practical experiences are most beneficial for social science and health careers, without which social scientists cannot be hired as part of a health intervention team [39,41]. More initiatives that provide research-training grants, career development fellowships and proposal development workshops, such as those currently established by the UNDP/World Bank/WHO/TDR, are needed to strengthen the social science capacity in endemic countries. However, creative strategies need to be identified to capture those who do not easily have access to such information and opportunities.
In order to effectively design and implement malaria control interventions, it is necessary to have social scientists that are knowledgeable about the technical issues that underlie malaria control. This knowledge is acquired over time and needs to be supplemented with direct field experiences. However, given the economic constraints facing national malaria control programmes and Ministries of Health, this study shows that it might not be reasonable to expect that social scientists will be assigned solely to malaria control. Given the limited number of trained social scientists with practical experience and the variety of deadly diseases competing for attention in sub-Saharan Africa, there are few social scientists that are both willing and able to dedicate their expertise only to malaria control.
Ministries of Health and Education should work with social scientists to establish employment opportunities in communicable diseases. Given the wide array of skills that social scientists have, it would be cost effective to employ social scientists to work broadly on issues common to communicable diseases, rather than solely on malaria. This "generic" approach would also support the programmatic shift from malaria as a vertical programme to its being integrated within primary health services or the wider arena of communicable diseases. Although this may not be ideal for building strong social science capacity specifically for malaria, this offers a pragmatic approach to utilizing scarce resources. Lessons learned from behavioural interventions in other diseases can also be applied to malaria control, thus strengthening social science contributions to malaria control.
The acknowledgement by many participants of their need for a better understanding of both biomedical and technical aspects of malaria control is reinforced by data from other successful projects that are aimed at establishing collaborative multidisciplinary research teams. For example, the International Clinical Epidemiology Network (INCLEN) social science programme seeks to enhance clinicians' understanding of social science by exposure to relevant issues in social science design and measurement and evaluation issues, while teaching principles of clinical epidemiology and biostatistics to established social scientists. As a result, strong collaborative partnerships among individual clinical epidemiologists, social scientists, and biostatisticians have been developed to produce the interdisciplinary solutions required for priority health problems in society [42].
A key factor identified as important for employment in malaria was the ability to contribute social science solutions to malaria control and the integration of research findings into malaria control programmes and policies. Social scientists are more likely to work directly with members of the community therefore, it is important that strong relationships are developed with local communities and their leaders, as a trust building mechanism and a matter of social prestige for the researcher. This link to the community facilitates the application of research findings on a local level [39,42].
Limitations
While findings from this study give us greater insight into factors that affect career trajectories of African social scientists, caution must be used when interpreting the results. The use of convenience sampling and snowball sampling techniques to identify eligible participants appeared to have excluded the younger more recently qualified social scientists who might not have been members of the various networks we used to identify eligible participants for this study. It is possible that some of the junior social scientists do not have much work experience in malaria and are not yet known by the more experienced social scientists that helped with referring other participants for the study. In addition, although telephone interviewing enabled us to reach social scientists from several countries, we were not able to reach those social scientists with no access to the Internet and telephone who might have had other unique challenges not captured in this study. Further research should examine younger or more recent social science graduates as well as those with no access to the Internet.
Conclusion
Malaria control is at a point where there is good evidence-based data to demonstrate that various approaches can make a difference in malaria-related morbidity and mortality. Prompt and timely treatments with an effective drug, intermittent preventive treatment for malaria in pregnancy, and use of insecticide-treated materials are examples of interventions that have been shown to be effective. However, the success of these approaches depends, to a great extent, on human behaviour. Epidemiologists and clinicians, who work in malaria control, traditionally, do not have the necessary training to incorporate behavioural and social science knowledge into the development, implementation, and evaluation of control interventions. It is time to broaden the perspective of malaria control, which can be best achieved through employing a multidisciplinary team that includes social scientists. To support such collaboration, attention needs to be given to understanding the factors that influence job satisfaction, recruitment and retention of the social scientists.
At this point in time, social science capacity is severely limited in most African malarious areas due to insufficient funding, lack of career structure, loss of trained individuals to international postings, and a limited understanding of how social scientists can contribute to malaria control. In order to improve this situation, capacity development is needed as a first step. Joint collaborative efforts should be made to offer technical malaria knowledge to social scientists and social science methodology to epidemiologists and control personnel. Social scientists need forums and networks to exchange information, learn about existing research and educational opportunities and promote collaborative partnerships with national malaria control programmes. However, these mechanisms cannot be realized without adequate levels of funding.
In order to attract skilled social scientists to Ministries of Health, a new line of thinking needs to emerge that situates social scientists more broadly within the ministry. Rather than focusing on how to integrate social scientists solely within malaria, the emphasis should shift to thinking creatively about how to best utilize social science expertise across an array of specific programmatic areas. Novel approaches within malaria control interventions are starting to emerge, such as integrating the distribution of insecticide-treated nets with measles immunizations. This same type of non-traditional thinking should be applied when developing career tracks for social scientists within Ministries of Health.
However, incentives for employment must rival or exceed those offered in other areas. Pay scales must be equitable and the value of multidisciplinary perspectives must be appreciated and desired by national malaria control programmes and Ministries of Health in order for career structures for long-term employment of social scientists to be established. For contractual employment, the same considerations should be shown. Short-term salaries for malaria-related projects should be competitive with salaries offered for similar work being conducted with other diseases, such as HIV/AIDS.
In the last five years, there has been a noticeable increase in the collaboration among social scientists and others engaged in malaria control. The interest in malaria by social scientists is clearly present. What are needed now are structures for training and employment that offer a professional career path over time.
Authors' contributions
HAW and CJ obtained funding for the project. All authors contributed to the study conception and design. PN coordinated fieldwork, data collection and analysis with ongoing supervision from HAW and CJ. PN wrote the first draft of the article with critical revisions from HAW and CJ. PN, HAW, CJ, IN, SD and FG all read and approved the final manuscript.
Acknowledgements
Funding for this project was provided by the United States Agency for International Development (USAID) through the Academy for Educational Development under the terms of Cooperative Agreement No. HRN-A-00-98-00044-00 (the CHANGE Project) with the Academy for Educational Development and its subcontractor, the Manoff Group Inc. The views expressed within this paper are solely those of the authors and do not reflect those of the funding agency.
We wish to recognize the following agencies for their support and contributions to this research: The CHANGE Project, UNDP/World Bank/WHO/TDR, CDC Malaria Epidemiology Branch, PSSMC, SOMA-Net and PAAA. Most importantly, we sincerely wish to thank all the colleagues who participated either directly in the survey or indirectly by helping us identify potential participants for the survey, as well as the anonymous reviewers of the manuscript.
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| 15579214 | PMC544396 | CC BY | 2021-01-04 16:37:27 | no | Malar J. 2004 Dec 6; 3:47 | utf-8 | Malar J | 2,004 | 10.1186/1475-2875-3-47 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-361558828110.1186/1476-4598-3-36ResearchDexamethasone protected human glioblastoma U87MG cells from temozolomide induced apoptosis by maintaining Bax:Bcl-2 ratio and preventing proteolytic activities Das Arabinda [email protected] Naren L [email protected] Sunil J [email protected] Swapan K [email protected] Department of Neurology, Medical University of South Carolina, Charleston, USA2 Department of Neurosurgery, Medical University of South Carolina, Charleston, USA2004 8 12 2004 3 36 36 5 10 2004 8 12 2004 Copyright © 2004 Das et al; licensee BioMed Central Ltd.2004Das et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Glioblastoma is the deadliest and most prevalent brain tumor. Dexamethasone (DXM) is a commonly used steroid for treating glioblastoma patients for alleviation of vasogenic edema and pain prior to treatment with chemotherapeutic drugs. Temozolomide (TMZ), an alkylating agent, has recently been introduced in clinical trials for treating glioblastoma. Here, we evaluated the modulatory effect of DXM on TMZ induced apoptosis in human glioblastoma U87MG cells.
Results
Freshly grown cells were treated with different doses of DXM or TMZ for 6 h followed by incubation in a drug-free medium for 48 h. Wright staining and ApopTag assay showed no apoptosis in cells treated with 40 μM DXM but considerable amounts of apoptosis in cells treated with 100 μM TMZ. Apoptosis in TMZ treated cells was associated with an increase in intracellular free [Ca2+], as determined by fura-2 assay. Western blot analyses showed alternations in the levels of Bax (pro-apoptotic) and Bcl-2 (anti-apoptotic) proteins resulting in increased Bax:Bcl-2 ratio in TMZ treated cells. Western blot analyses also detected overexpression of calpain and caspase-3, which cleaved 270 kD α-spectrin at specific sites for generation of 145 and 120 kD spectrin break down products (SBDPs), respectively. However, 1-h pretreatment of cells with 40 μM DXM dramatically decreased TMZ induced apoptosis, decreasing Bax:Bcl-2 ratio and SBDPs.
Conclusion
Our results revealed an antagonistic effect of DXM on TMZ induced apoptosis in human glioblastoma U87MG cells, implying that treatment of glioblastoma patients with DXM prior to chemotherapy with TMZ might result in an undesirable clinical outcome.
ApoptosisDexamethasoneGlioblastomaProteolysisTemozolomide
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Background
Glioblastoma patients usually receive steroids for alleviation of vasogenic edema and pain prior to treatment with chemotherapeutic drugs. Steroids, however, may modulate the sensitivity of tumor cells to chemotherapeutic drugs. Dexamethasone (DXM), a synthetic glucocorticoid, is commonly used to reduce inflammation and pain associated with glioblastoma [1]. However, DXM has been reported to make human glioblastoma cells resistant to ionizing radiation and chemotherapeutic agents that otherwise cause DNA damage [2-5]. Execution of cells by apoptosis usually requires the activation of cysteine proteases such as calpains and caspases [6]. Diverse stimuli may cause an increase in intracellular free [Ca2+], which is absolutely required for activation of calpain [7]. Activation of caspases may occur via different mechanisms [8,9]. Mitochondria mediated pathway of apoptosis may be activated in course of cell death. This involves the regulation of apoptosis by the Bcl-2 family proteins via controlling the release of cytochrome c from mitochondria [10,11], and subsequent formation of the cytosolic 'apoptosome' complex [12,13], which ultimately activates caspase-3 for execution of cells. Thus, the members of the Bcl-2 family modulate the mitochondrial pathway of apoptosis [14]. The pro-apoptotic (e.g., Bax, Bcl-xS) and anti-apoptotic (e.g., Bcl-2, Bcl-xL) members of this family, respectively, promote and inhibit the translocation of cytochrome c from mitochondria to cytosol [15].
Glucocorticoids are steroid hormones, which are secreted in response to stress and can modulate the ability of cells to undergo apoptosis [16]. For example, glucocorticoids induce apoptosis in thymocytes [17] and also increase the sensitivity of hippocampal neurons to cell death [18]. In contrast, DXM has been reported to induce resistance to certain drugs in glioblastoma cell lines [3-5]. Although an association with p21WAF1/CIP1 protein accumulation has been reported [19], the exact mechanism of DXM mediated protection of glioblastoma cells from apoptosis is still largely unclarified. Exposure of human astrocytoma D384 and rat glioblastoma C6 cells to staurosporine induced apoptosis but pretreatment of those cells with DXM caused reduction in staurosporine mediated apoptosis [20]. In addition, DXM also conferred protection against the induction of apoptosis by anti-cancer agents including camtothecin and etoposide [20]. It has also been shown that exposure of glioblastoma cells to glucocorticoids induces partial resistance to anti-cancer agents such as cisplatinum, methotrexate, vincristine, cytarabine, adriamycin, and teniposide [3-5]. DXM appears to interfere with p53-dependent pathways of drug toxicity since the glioblastoma cell lines (LN-229 and U87MG) with wild-type p53 status were protected from drug toxicity by DXM to a greater extent than the cell lines (LN-18, LN-308, and T98G) with mutant p53 [3-5]. It has been reported earlier that DXM mediated protection from cancer chemotherapy occurs via a p53-independent pathway of regulating p21WAF1/CIP1 expression in glioblastoma cells but this effect appears to be cell-type specific [19]. Thus, there remains a concern of modulatory effects of DXM on the mechanism of action of any chemotherapeutic agent for treatment of glioblastoma. Therefore, we have initiated this investigation to examine the modulatory effect of DXM on temozolomide (TMZ) induced apoptosis of glioblastoma cells. In an in vitro model using the human glioblastoma U87MG cells, we have investigated whether DXM confers resistance to TMZ action via inhibition of apoptosis.
TMZ is an alkylating chemotherapeutic drug that readily crosses the blood-brain-barrier in glioblastoma patients [21]. It is chemically related to decarbazine and is the 3-methyl derivative of the experimental anti-cancer drug mitozolomide. It has shown anti-tumor activity and relatively low toxicity in Phase I and Phase II clinical trials in patients with various advanced cancers, including malignant glioblastomas [21]. TMZ is spontaneously hydrolyzed under physiological conditions to its active metabolite 5-(3-methyltriazen-1-yl) imidazole-4-carboxamide (MTIC) [22]. The mechanism of action of MTIC is proposed to be methylation of DNA at the O6 position of guanine, with an additional methylation at its N7 position [23,24]. However, O6-methylguanine (O6-meG) may be removed by O6-methylguanine methyl transferase (MGMT) [25]. Cells deficient in MGMT do not repair O6-meG. Replication of DNA introduces a T instead of C opposite to O6-meG, resulting in GT mismatches [26]. Activation of mismatch repair system (MMRS) may remove T during DNA repair synthesis. However, ineffective MMRS causes growth arrest and eventually apoptotic death [27]. These studies have helped define the action of TMZ in cancer cells of mostly hemopoietic origin. However, the action of TMZ in glioblastoma cells remains largely undefined. Glioblastomas are relatively resistant to anti-cancer agents that cause apoptosis via DNA alkylation. Therefore, we have investigated the mechanism of TMZ induced apoptosis in human glioblastoma U87MG cells.
We found that TMZ caused apoptosis in U87MG cells as detected by morphological and biochemical assays. Alterations in the levels of pro-apoptotic Bax and anti-apoptotic Bcl-2 proteins are known to regulate the commitment to apoptosis [14,15]. Therefore, we investigated the levels of these apoptosis regulatory proteins following treatment of U87MG cells with TMZ. The initiation of apoptosis in U87MG cells following exposure to TMZ requires activation of calpain, a Ca2+-dependent cysteine protease, which plays a role in the mechanism of cell death in human malignant brain tumors including glioblastoma [28]. Besides, caspase-3 activity was also increased in TMZ induced apoptosis in U87MG cells. Pretreatment of U87MG cells with DXM blocked TMZ induced apoptosis, indicating that DXM worked as an antagonistic agent in TMZ induced apoptosis in human glioblastoma cells. The knowledge gained from our investigation implies that the combination of DXM and TMZ for the treatment of human glioblastoma patient may result in an undesirable clinical outcome. Preliminary results of this investigation have previously been presented [29].
Results
Evaluation of viability and apoptotic death both morphologically and biochemically
Exclusion of trypan blue dye by viable U87MG cells was evaluated under a light microscope using a hemocytometer after all treatments. A pretreatment with DXM prevented decrease in cell viability (panel A, Fig. 1). Morphological features of apoptosis were detected following Wright staining (panel B, Fig. 1) and counted to determine the amount of apoptotic cell death (panel C, Fig. 1) based on characteristic morphological features such as condensation of the nucleus and cytoplasm, cytoplasmic blebbing, and the formation of apoptotic bodies. All treatment groups were examined under the light microscopy and cells were counted to determine the percentage of apoptotic cells (panel C, Fig. 1). Compared to control (CTL) cells, cells treated with 100 μM TMZ showed an increase in the percentage of apoptotic cells (P < 0.001). A pretreatment of cells with 40 μM DXM decreased TMZ induced apoptosis by three-fold, compared to treatment of cells with TMZ only.
Figure 1 Determination of apoptosis based on morphological features. Four treatment groups: control (CTL); 40 μM dexamethasone (DXM) for 8 h; 100 μM temozolomide (TMZ) for 6 h; pretreatment with 40 μM DXM for 2 h followed by 100 μM TMZ for 6 h. (A) DXM prevented TMZ mediated decrease in U87MG cell viability. The trypan blue exclusion assay was used to assess cell viability in U87MG cells. (B) Photomicrographs showing representative cells from each treatment group. The arrows indicate apoptotic cells. (C) Bar graphs indicating the percentage of apoptotic cells counted from each group. Significant difference between CTL and TMZ treated cells was indicated by * (P ≤ 0.05) and significant difference between TMZ treated cells and DXM plus TMZ treated cells was indicated by # (P ≤ 0.05).
Results obtained from Wright staining were further supported by the ApopTag assay (panel A, Fig. 2). Both CTL and DXM treated cells showed little or no brown color, confirming almost absence of ApopTag positive cells or apoptosis. The percentage of ApopTag positive cells was calculated (panel B, Fig. 2) and found to be highly significant (P < 0.001) in TMZ treated cells, compared to CTL cells. A pretreatment of cells with DXM considerably attenuated apoptotic DNA fragmentation in TMZ treated cells.
Figure 2 ApopTag assay for detection and determination of DNA fragmentation in U87MG cells. Four treatment groups: control (CTL); 40 μM dexamethasone (DXM) for 8 h; 100 μM temozolomide (TMZ) for 6 h; pretreatment with 40 μM DXM for 2 h followed by 100 μM TMZ for 6 h. (A) The photomicrographs showing representative cells from each treatment group. The arrows indicate apoptotic cells. (B) Bar graphs indicating the average percentage of apoptotic cells counted from each group. Significant difference between CTL and TMZ treated cells was indicated by * (P ≤ 0.05) and significant difference between TMZ treated cells and DXM plus TMZ treated cells was indicated by # (P ≤ 0.05).
Treatment with TMZ increased intracellular free [Ca2+]
Using fura-2 assay, intracellular free [Ca2+] was determined in all treatment groups (Fig. 3). No significant difference (P = 0.928) was seen between CTL cells and cells treated with DXM alone. Cells treated with TMZ showed a significant increase (P = 0.007) in intracellular free [Ca2+], compared to CTL cells. This increase was attenuated almost 100% by a pretreatment of the cells with DXM. There was no significant difference (P = 0.999) between intracellular free [Ca2+] in CTL cells and those treated with DXM plus TMZ.
Figure 3 Bar graphs indicating percentage of increase of intracellular free [Ca2+] using fura-2. These data were generated from U87MG cells grown in phenol red-free medium for 24 h prior to treatments with the drugs in freshly prepared phenol red-free medium. Four treatment groups: control (CTL); 40 μM dexamethasone (DXM) for 8 h; 100 μM temozolomide (TMZ) for 6 h; pretreatment with 40 μM DXM for 2 h followed by 100 μM TMZ for 6 h. Percent changes in intracellular free [Ca2+] were shown at nM levels. Significant difference between CTL and TMZ treated cells was indicated by * (P ≤ 0.05) and significant difference between TMZ treated cells and DXM plus TMZ treated cells was indicated by # (P ≤ 0.05).
TMZ induced apoptosis with an increase in Bax:Bcl-2 ratio
A commitment to apoptosis was measured by examining any increase in the ratio of Bax (pro-apoptotic protein) expression to Bcl-2 (anti-apoptotic protein) expression. The bax gene encodes different isoforms. The antibody we used in this investigation could recognize 21 kD Baxα and 24 kD Baxβ bands (panel A, Fig. 4). Here, we considered both bands in our estimation of total Bax expression. We also examined the level of Bcl-2 expression in all treatment groups (panel B, Fig. 4). Almost same level of β-actin expression in each treatment ensured that equal amount of protein was loaded in each lane (panel C, Fig. 4). Based on the Western blot experiments (panels A and B, Fig. 4), the Bax:Bcl-2 ratios were measured in all treatment groups (panel D, Fig. 4). There was no significant difference (P = 0.983) in Bax:Bcl-2 ratio between CTL and DXM treated cells (panel D, Fig. 4). Compared to CTL cells, a rise in Bax:Bcl-2 ratio (panel D, Fig. 4) in cells exposed to TMZ was influenced more by a change in Bax expression (panel A, Fig. 4) than a change in Bcl-2 expression (panel B, Fig. 4). Compared to CTL cells, cells treated with TMZ showed a significant increase (P = 0.007) in the Bax:Bcl-2 ratio (panel D, Fig. 4). There was a significant difference (P = 0.019) in Bax:Bcl-2 ratio between cells treated with TMZ alone and those treated with DXM plus TMZ, indicating a loss of commitment to apoptosis due to a pretreatment with DXM. There was no significant difference (P = 0.871) in Bax:Bcl-2 ratio between CTL cells and cells pretreated with DXM and then treated with TMZ.
Figure 4 The Bax:Bcl-2 ratio measured by Western blot analysis. Four treatment groups: control (CTL); 40 μM dexamethasone (DXM) for 8 h; 100 μM temozolomide (TMZ) for 6 h; pretreatment with 40 μM DXM for 2 h followed by 100 μM TMZ for 6 h. (A) A representative gel picture showing level of expression of Bax. (B) A representative gel picture showing level of expression of Bcl-2. (C) A representative gel picture showing level of expression of β-actin. (D) Densitometric analysis showing the Bax:Bcl-2 ratio in all treatment groups. Significant difference between CTL and TMZ treated cells was indicated by * (P ≤ 0.05) and significant difference between TMZ treated cells and DXM plus TMZ treated cells was indicated by # (P ≤ 0.05).
Calpain and caspase-3 activities as determined by α-spectrin degradation
Calpain and caspase-3 activities were assessed by Western blot analysis of the calpain-specific 145 kD SBDP and the caspase-3-specific 120 kD SBDP, respectively (panel A, Fig. 5). Level of β-actin expression, which was almost uniform in all treatments, was used as a loading control (panel B, Fig. 5). There was no significant difference (P = 0.911) between CTL cells and DXM treated cells in generation of 145 kD SBDP, indicating similar levels of calpain activity in these two cases (panel C, Fig. 5). The generation of 145 kD SBDP in cells treated with TMZ was about 1.5-fold more intense (P = 0.003) than CTL cells, indicating that the level of calpain activity was increased in cells due to treatment with TMZ. Cells pretreated with DXM and then treated with TMZ showed a significant decrease in the generation of 145 kD SBDP, indicating an inhibitory effect of DXM on TMZ mediated increase in calpain activity (panel C, Fig. 5).
Figure 5 Determination of calpain and caspase-3 activities using Western blot analysis of α-spectrin breakdown products (SBDPs). Four treatment groups: control (CTL); 40 μM dexamethasone (DXM) for 8 h; 100 μM temozolomide (TMZ) for 6 h; pretreatment with 40 μM DXM for 2 h followed by 100 μM TMZ for 6 h. (A) A representative gel picture showing generation of 145 kD and 120 kD SBDPs. (B) A representative gel picture showing level of expression of β-actin. (C) Densitometric analysis showing percent changes in optical density of the calpain-specific 145 kD SBDP over CTL. (D) Densitometric analysis showing percent change of optical density of the caspase-3-specific 120 kD SBDP over CTL. Significant difference between CTL and TMZ treated cells was indicated by * (P ≤ 0.05) and significant difference between TMZ treated cells and DXM plus TMZ treated cells was indicated by # (P ≤ 0.05).
Caspase-3 activity was also measured by Western blot analysis in the generation of caspase-3-specific 120 kD SBDP (panel D, Fig. 5). Compared to CTL cells, treatment of cells with DXM alone did not cause a significant change (P = 0.983) in caspase-3 activity. Caspase-3 activity in cells treated with TMZ was almost 1.5 times more (P = 0.001) than CTL cells (panel D, Fig. 5). Thus, pretreatment of cells with DXM prior to treatment with TMZ appeared to decrease the upregulation of caspase-3 activity. Furthermore, there was no significant difference (P = 0.785) between CTL cells and cells that were pretreated with DXM and then treated with TMZ (panel D, Fig. 5).
Caspase-3 activation as determined by generation of caspase-3-p20 fragment
Caspase-3 activation was also measured by Western blot analysis of the production of active 20 kD caspase-3 fragment (panel A, Fig. 6). Again, almost uniform expression of β-actin in all treatments served as an internal standard and indicated equal amounts of protein loadings in all lanes (panel B, Fig. 6). The intensities of active 20 kD caspase-3 band were almost similar in CTL cells and cells treated with DXM alone (panel C, Fig. 6). Treatment of cells with DXM alone did not cause a significant change (P = 0.613) in caspase-3 activation over CTL cells. But there was a significant increase (P = 0.001) in production of active 20 kD caspase-3 fragment in cells treated with TMZ, compared to CTL cells. Treatment of cells with DXM prior to TMZ appeared to significantly decrease the activation of caspase-3 (panel C, Fig. 6), indicating an inhibitory effect of DXM on TMZ induced caspase-3 activation.
Figure 6 Determination of caspase-3 activation using Western blot analysis of caspase-3-p20 active band. Four treatment groups: CTL; 40 μM DXM for 8 h; 100 μM TMZ for 6 h; pretreatment with 40 μM DXM for 2 h followed by 100 μM TMZ for 6 h. (A) A representative gel picture showing caspase-3 activation. (B) A representative gel picture showing level of expression of β-actin. (C) Densitometric analysis showing percent change in optical density of the caspase-3-p20 active band over CTL. Significant difference between CTL and TMZ treated cells was indicated by * (P ≤ 0.05) and significant difference between TMZ treated cells and DXM plus TMZ treated cells was indicated by # (P ≤ 0.05).
Discussion
Our studies indicated that pretreatment of human glioblastoma U87MG cells with DXM did not support chemotherapeutic action of TMZ. Treatment of U87MG cells with TMZ induced apoptosis (Figs. 1 and 2) to a significant extent by increasing intracellular free [Ca2+] (Fig. 3), interfering with the expression of apoptosis regulatory proteins of the Bcl-2 family resulting in upregulation of Bax:Bcl-2 ratio (Fig. 4), and increasing the activities of calpain and caspase-3 (Figs. 5 and 6). But a pretreatment of the cells with DXM prevented all these pro-apoptotic mechanisms (Figs. 3, 4, 5, 6). Our data also suggested that pretreatment with DXM can play a critical role in inhibiting Ca2+ influx into the cells due to treatment with TMZ, and thus preventing the progression of apoptotic process.
Several in vitro studies documented a role for calpain in apoptosis of neuronal [30,31] as well as non-neuronal cells [32]. However, the mechanisms of calpain mediated cell death are not yet fully understood. Pro-apoptotic Bax is translocated to mitochondria and has shown to be activated by calpain [33]. Increased expression of calpain concurs with elevated expression of Bax relative to Bcl-2, suggesting that calpain overexpression plays an important role during cell death [34,35]. Because changes in expression of pro-apoptotic Bax and anti-apoptotic Bcl-2 control the mitochondrial pathway of apoptosis [14,15], we examined the levels of expression of Bax and Bcl-2 proteins in U87MG cells following treatment with TMZ (Fig. 4).
Our findings support a relationship between an increase in intracellular free [Ca2+] (Fig. 3) and cell death with an elevation of calpain activity (Fig. 5) following exposure of U87MG cells to TMZ. Pretreatment of cells with DXM showed a significant decrease in both intracellular free [Ca2+] and calpain activity in a subsequent exposure to TMZ. Increased intracellular free [Ca2+] causes activation of calpain and degradation of cytoskeletal proteins [36] with destabilization of the cellular integrity leading to cell death [37]. Our results indicate that DXM plays an important role in the prevention of calpain activation following treatment of glioblastoma cells with TMZ. DXM has been shown to inhibit apoptosis by induction of transcriptional expression of anti-apoptotic proteins Bcl-2 and Bcl-xL [37,38]. Upregulation of Bcl-2 either directly or indirectly can repress the Ca2+ flux across the membrane of endoplasmic reticulum, thereby abrogating apoptosis via Ca2+ signaling [39].
Treatment of U87MG cells with TMZ caused increase in calpain and caspase-3 activities as evidenced from the cleavage of α-spectrin at specific sites generating 145 kD SBDP and 120 kD SBDP, respectively (Fig 5). A pretreatment with DXM decreased calpain and caspase-3 activities in U87MG cells. Overall, the results from this investigation showed that DXM pretreatment interfered with proteolytic activities and apoptotic death in U87MG cells exposed to TMZ. A previous report from our laboratory indicated that corticosteroids could inhibit the proteolytic activity of calpain [40].
Our study suggests that pretreatment of glioblastoma with DXM should be avoided if there is a plan to treat the glioblastoma patients subsequently with TMZ. Some recurrent glioblastomas remain resistant to almost all current therapeutic endeavors, with low response rates and survival rarely exceeding six months. As there are no clearly established chemotherapeutic regimens for drug resistant glioblastomas, obviously the only aim of therapy is palliation with improvement in the quality of life. In such cases, use of DXM or other glucocorticoids may not be controversial. However, promising therapeutic activity of TMZ against newly diagnosed anaplastic astrocytomas and glioblastomas warrants continued evaluation of this agent in combination settings [41]. Delaying disease progression by treatment with TMZ is beneficial to the patients with recurrent glioblastomas [42]. Therefore, the use of this drug should be explored further in an adjuvant setting and in combination with other agents [43].
We showed that a pretreatment of human glioblastoma U87MG cells with a low dose of DXM abolished the chemotherapeutic action of TMZ (Figs. 1, 2, 3, 4, 5, 6), raising a renewed concern about the validity of DXM as a supportive therapy in the treatment of glioblastomas. We acknowledge that pharmacological studies with a glioblastoma cell line may not always yield results that are easily transferred to the in vivo situation for cancer therapy. Also, clinical recommendations should not be based on in vitro data alone. Nevertheless, our data strongly suggested that DXM treatment could well interfere with therapeutic efficacy of chemotherapy in human glioblastoma patients. In fact, this hypothesis is in line with the results from a 1983 clinical trial where the combination of bis(chloroethyl) nitrosourea (BCNU) plus high dose methylprednisolone, a steroid, tended to be less effective than BCNU alone in patients with poor prognosis [44].
The data reported here and the previous reports by others [45], taken together, provide enough reason to call for steroid withdrawal during investigative clinical trials of chemotherapeutic agents in glioblastoma patients. This should be a serious concern at the initial clinical situation when glioblastoma patients are enrolled. In the case of tumor progression during chemotherapy, steroids may still be life-saving agents, and individual decisions concerning a continuation of chemotherapy with concurrent steroid treatment must be made. We also suggest limiting steroid treatment in glioblastoma patients who are receiving chemotherapy outside a controlled clinical trial, because the benefit of chemotherapy for glioblastoma patients is still with limited efficacy and should not be further compromised by co-medication with steroid. Further investigations in xenografted and allografted animal models of glioblastoma as well as in human glioblastoma patients may shed new light in the controversial use of DXM in palliation of human glioblastoma patients.
Materials and methods
Cell culture and treatments
Human glioblastoma U87MG cells were purchased from the American Type Culture Collection (Manassas, VA). Cells were grown in 75-cm2 flasks containing 10 ml of 1 × RPMI 1640 supplemented with 10% fetal bovine serum (FBS) and 1% penicillin and streptomycin in a fully-humidified incubator containing 5% CO2 at 37°C. Prior to drug treatments, the cells were starved in 1 × RPMI 1640 supplemented with 0.5% FBS for 24 h. Dose-response studies were conducted to determine the suitable doses of the drugs for using in the experiments. Cells were pretreated with 40 μM DXM for 1 h. The DXM pretreated or untreated cells were subsequently treated with 100 μM TMZ for 6 h. Cells were washed with drug-free medium and allowed to grow for 48 h. Then, cells were collected for determination of viability, apoptosis, or Western blot analysis. DXM and TMZ were obtained from Sigma Chemical (St. Louis, MO) and Schering Corporation (Kenilworth, NJ), respectively. The drugs were dissolved in dimethyl sulfoxide (DMSO) to make stock solutions, which were then stored at -20°C until used for treating cells.
Trypan blue dye exclusion test for cell viability
Following all treatments the viability of attached and detached cell populations was evaluated by trypan blue dye exclusion test [46]. Viable cells maintained membrane integrity and did not take up trypan blue. Cells with compromised cell membranes took up trypan blue, and were counted as dead. At least 800 cells were counted in four different fields and the number of viable cells was calculated as percentage of the total cell population.
Wright staining for morphological analysis of apoptosis
The cells from each treatment were washed with PBS, pH 7.4, and sedimented onto the microscopic slides using Cytobucket and Centra CL2 centrifuge (IEC) at 1200 rpm for 5 min. Cells were fixed in 95% (v/v) ethanol before examination of morphology with Wright staining [46]. The morphology of the apoptotic cells as detected by light microscopy included such characteristic features as chromatin condensation, cell-volume shrinkage, and membrane-bound apoptotic bodies. Four randomly selected fields were counted for at least 800 cells. The percentage of apoptotic cells was calculated from three separate experiments.
ApopTag assay for biochemical detection of apoptotic DNA fragmentation
ApopTag Peroxidase kit (Intergen, Purchase, NY) was used to assess the extent of cell death following drug treatments. Briefly, cells for each treatment were grown on six-well cell culture plates (Corning Corporation., Corning, NY) and were treated as described above. Following treatments, cells were washed with PBS and then centrifuged to sediment onto the microscopic slides. Residual PBS was then removed and cells were fixed using 95% (v/v) ethanol and allowed to dry overnight. Slides were pretreated with a protein-digesting enzyme for 15 min and then washed with distilled water for 2 min. Cells were quenched with 3% (v/v) hydrogen peroxide for 5 min followed by washing with PBS. Terminal deoxynucleotidyl transferase (TdT) enzyme was added to the pre-equilibrated cells and incubated for 1 h at 37°C. Stop-buffer was added to the slide and agitated for 15 sec followed by 10 min incubation at room temperature. After washing three times with PBS for 1 min each, anti-digoxigenin peroxidase conjugate was added to the slides and incubated for 30 min. After slides were washed twice with PBS, freshly prepared peroxidase substrate 3,3'-diaminobenzidine was added to the slides and kept for 6 min and then slides were washed with water two times. Slides were counterstained with 0.5% (w/v) methyl green for 10 min followed by washing with water and then 100% n-butanol. After 10 min, cells were dehydrated in xylene for 2 min and then mounted with glass coverslip. Experiments were conducted in triplicates and the percentage of ApopTag-positive cells was determined by counting cells under light microscopy.
Determination of intracellular free [Ca2+] using fura-2
We recently reported this method [47], which was modified for determination of intracellular free [Ca2+] in U87MG cells. Briefly, cells were grown to 80% confluency in phenol red-free medium for 72 h, suspended in the culture medium, centrifuged at 2000 rpm for 5 min to obtain pellet, and washed twice in phosphate buffered saline (PBS, pH 7.4). Cells were resuspended in culture medium, and incubated at 37°C for 2 h with gentle shaking. Following incubation, cells wee washed twice in Ca2+-free Locke's buffer [48] and then counted on a hemocytometer. Cells (2 × 107 cells/ml) were dispersed in Locke's buffer with 10% FBS. Fura-2 (Molecular Probes, Eugene, OR) was dissolved in DMSO and diluted in Ca2+-free Locke's buffer containing 10% FBS. Cells were mixed with 5 μM fura-2, incubated at 37°C for 30 min, washed twice and diluted to 1 × 106 cells/ml in Ca2+-free Locke's buffer. The intracellular free [Ca2+] was calculated spectrofluorometrically using the equation [Ca2+] = Kdβ(R-Rmin)/(Rmax-R), where β is the ratio of F380max, fluorescence intensity exciting at 380 nM for zero free Ca2+ to F380min, and fluorescence intensity at saturating free [Ca2+] as reported previously [49]. The determination of fluorescence ratio (R) was performed using an SLM 8000 fluorometer (Thermospectronic, Shelton, CT) at 340 and 380 nm wavelengths. The maximal (Rmax) and minimal (Rmin) ratios were determined using 200 μl of 250 μM digitonin (Sigma) and 500 mM EGTA (Sigma, St. Louis, MO), respectively. The value of Kd, a cell-specific constant, was determined experimentally to be 0.476 μM using standards of the Calcium Calibration Buffer Kit with Magnesium (Molecular Probes, Eugene, OR).
Antibodies
Monoclonal antibody against α-spectrin (Affiniti, Exeter, UK) was used to measure calpain activity as well as caspase-3 activity. Caspase-3 polyclonal antibody (MBL International, Woburn, MA) was used to determine caspase-3 activation. Bax and Bcl-2 monoclonal antibodies (Santa Cruz Biotechnology, Santa Cruz, CA) were used to assess apoptotic threshold by determining the Bax:Bcl-2 ratio. Antibody to β-actin (monoclonal clone AC-15, Sigma) was used to standardize protein loading in Western blot experiments. The secondary antibody was horseradish peroxidase (HRP)-conjugated goat anti-mouse IgG (ICN Biomedicals, Aurora, OH), except in case of calpain and α-spectrin where HRP-conjugated goat anti-rabbit IgG was used (ICN Biomedicals).
Western blotting and ECL detection
Cells were washed in culture flasks using Hank's balanced salt solution without Ca2+ (GIBCO, Grand Island, NY). The cells were then washed twice in phosphate buffered saline (PBS, pH 7.4) and centrifuged in Eppendorf 5804R (Brinkmann Instruments, Westbury, NY) at 106 × g for 10 min. Cells were resuspended in a homogenizing buffer composed of 50 mM Tris-HCl (pH 7.4), 1 mM PMSF (Bethesda Research Laboratories, Gaithersburg, MD), and 5 mM EGTA (Sigma). A polypropylene pestle (Kontes Glass, Vineland, NJ) was placed inside the microcentrifuge tube (1.5 ml) containing cells and the tube was placed inside an ice bucket for 2 min to let the pestle cool to 4°C. Cells were completely homogenized for 30 sec (the homogenization was performed while holding the microcentrifuge tube in ice with one hand). Following homogenization, protein concentration was determined using Coomassie Plus Protein Assay Reagent (Pierce, Rockford, IL) and spectrophotometric measurement at 595 nm (Spectronic, Rochester, NY). Samples were then diluted (1:1) in sample buffer (62.5 mM, Tris pH 6.8, 2% SDS, 5 mM β-mercaptoethanol, 10% glycerol) and boiled for 5 min. Samples were then loaded onto the 4–20% gradient gels for electrophoresis at 200 V for 30 min (Bio-Rad, Hercules, CA). For detection of α-spectrin bands, a 5% gel was used for electrophoresis at 100 V for 2 h. Following electrophoresis, gels with the resolved proteins were electroblotted to nylon membranes (Millipore, Bedford, MA) in an electroblotting Genie apparatus (Idea Scientific, Minneapolis, MN). The membranes were blocked for 1 h in blocking buffer (5% powdered non-fat milk, 20 mM Tris pH 7.6, 0.1% Tween 20 in saline). Primary antibody was diluted (1:100 for Bax, Bcl-2, caspase-3, and 1:500 for calpain, 1:2,000 for α-spectrin, and 1:15,000 for β-actin) in blocking solution and then added to the blots for 1 h. The blots were washed three times with a wash buffer (Tris/Tween solution) and covered with secondary antibody (goat anti-rabbit for calpain and α-spectrin and goat anti-mouse for all others) at a 1:2000 dilution for 1 h. Blots were incubated with enhanced chemiluminescence (ECL) detection system (Amersham Pharmacia, Buckinghamshire, UK) and exposed to X-OMAT AR films (Eastman Kodak, Rochester, NY). The films were scanned on a UMAX PowerLook Scanner (UMAX Technologies, Fremont, CA) using Photoshop software (Adobe Systems, Seattle, WA), and optical density of each band was determined using Quantity One software (Bio-Rad, Hercules, CA). Estimation of the degradation products of α-spectrin indicated calpain and caspase-3 activities. The 145 kD spectrin breakdown product (SBDP) is specific for calpain activation [30], and the 120 kD SBDP is specific for caspase-3 activation [50]. Also, Western blot analysis was performed to examine the generation of active 20 kD caspase-3 fragment from 32 kD caspase-3, indicating activation of caspase-3.
Statistical analysis
Data from various experiments were analyzed using StatView software (Abacus Concepts, Berkeley, CA). Results were compared using one-way analysis of variance (ANOVA) with Fisher's protected least significant difference (PLSD) post hoc test at a 95% confidence interval. All results were presented as mean ± standard error of mean (SEM) of separate experiments (n ≥ 3). A difference between two values was considered significant at p ≤ 0.05.
Authors' contributions
AD performed the experiments and participated in writing the manuscript. NLB contributed to the interpretation of results. SJP participated in the discussion of the study and provided comments on clinical importance of this study. SKR conceived the study, planned experimental design, supervised the study, and helped writing the manuscript. All authors read and approved the final version of the manuscript.
Acknowledgements
This investigation was supported in part by the R01 grant (CA-91460) from the National Cancer Institute of the National Institutes of Health (Bethesda, MD).
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| 15588281 | PMC544397 | CC BY | 2021-01-04 16:36:34 | no | Mol Cancer. 2004 Dec 8; 3:36 | utf-8 | Mol Cancer | 2,004 | 10.1186/1476-4598-3-36 | oa_comm |
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Med ImmunolMedical Immunology1476-9433BioMed Central London 1476-9433-3-21558828410.1186/1476-9433-3-2CommentaryNew approaches to eliciting protective immunity through T cell repertoire manipulation: the concept of thymic vaccination Fridkis-Hareli Masha [email protected] Ellis L [email protected] Laboratory of Immunobiology, Department of Medical Oncology, Dana-Farber Cancer Institute, USA2 Department of Medicine, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA2004 8 12 2004 3 2 2 3 12 2004 8 12 2004 Copyright © 2004 Fridkis-Hareli and Reinherz; licensee BioMed Central Ltd.2004Fridkis-Hareli and Reinherz; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Conventional vaccines afford protection against infectious diseases by expanding existing pathogen-specific peripheral lymphocytes, both CD8 cytotoxic effector (CTL) and CD4 helper T cells. The latter induce B cell maturation and antibody production. As a consequence, lymphocytes within the memory pool are poised to rapidly proliferate at the time of a subsequent infection. The "thymic vaccination" concept offers a novel way to alter the primary T cell repertoire through exposure of thymocytes to altered peptide ligands (APL) with reduced T cell receptor (TCR) affinity relative to cognate antigens recognized by those same TCRs. Thymocyte maturation (i.e. positive selection) is enhanced by low affinity interaction between a TCR and an MHC-bound peptide in the thymus and subsequent emigration of mature cells into the peripheral T lymphocyte pool follows. In principal, such variants of antigens derived from infectious agents could be utilized for peptide-driven maturation of thymocytes bearing pathogen-specific TCRs. To test this idea, APLs of gp33–41, a Db-restricted peptide derived from the lymphocytic choriomeningitis virus (LCMV) glycoprotein, and of VSV8, a Kb-restricted peptide from the vesicular stomatitis virus (VSV) nucleoprotein, have been designed and their influence on thymic maturation of specific TCR-bearing transgenic thymocytes examined in vivo using irradiation chimeras. Injection of APL resulted in positive selection of CD8 T cells expressing the relevant viral specificity and in the export of those virus-specific CTL to lymph nodes without inducing T cell proliferation. Thus, exogenous APL administration offers the potential of expanding repertoires in vivo in a manner useful to the organism. To efficiently peripheralize antigen-specific T cells, concomitant enhancement of mechanisms promoting thymocyte migration appears to be required. This commentary describes the rationale for thymic vaccination and addresses the potential prophylactic and therapeutic applications of this approach for treatment of infectious diseases and cancer. Thymic vaccination-induced peptide-specific T cells might generate effective immune protection against disease-causing agents, including those for which no effective natural protection exists.
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Introduction
Vaccination has improved healthcare by providing the most cost effective means to prevent disease on a global basis [1,2]. Since the first safe vaccine against smallpox infection was introduced by Sir Edward Jenner more than 200 years ago [3], a myriad of killed or live viral and bacterial vaccines as well as subunit (i.e. component) vaccines have been developed and proven to be highly effective [2]. The traditional approach to vaccine development from the early 1950's until today has been based most commonly on administration of weakened versions of disease-causing agents or certain of their components with appropriate adjuvants. In this way, successful vaccines against key viruses that cause acute infectious diseases of childhood (e.g. poliovirus, measles virus, mumps, rubella, chicken pox, etc.) have been developed. These vaccines induce peripheral T and B lymphocyte memory responses, affording protection against any future attack by disease-causing agents should it occur.
To date, the fundamental principles of vaccination have remained unchanged. The overriding concept for each vaccine has been the establishment of protective immunity largely due to antigen-specific T cell expansion, facilitating subsequent proliferation and differentiation of CD8 cytotoxic effector T cells and CD4 helper T cells capable of producing antiviral cytokines and chemokines (Fig. 1A). CD4 T cells activate B cells to generate neutralizing antibodies, offering protection against viral attachment/translocation or bacterial toxins, etc. [4,5]. As neutralizing antibodies have been the subject of recent reviews [6-9], they will not be considered further here.
Figure 1 Thymic vaccination versus conventional vaccines. A. Conventional vaccines act on the mature peripheral lymphoid pool, in particular expanding existing T cells directed against the immunogen (blue square) derived from the disease-causing agent. Following subsequent infection, the T cell recognizes the pathogen, proliferates, mediates effector function and cytokines leading to immune response and elimination of the disease. For simplicity, the B lymphocyte response is not shown. B. Thymic vaccination offers a way to alter the primary T cell repertoire through exposure of immature thymocytes to APL with reduced TCR affinity relative to cognate antigens recognizing those TCRs. Thymocyte maturation (i.e. positive selection) is enhanced by the low affinity interaction between a TCR and an MHC-bound APL (green ribbon) in the thymus, with subsequent emigration of mature cells into the peripheral T lymphocyte pool. Those peripheral T cells can respond to cognate antigen (red triangle). Thus, variants of cognate antigens derived from infectious agents, tumors, etc. could be employed for peptide-driven maturation of thymocytes bearing pathogen-specific TCRs.
However, conventional vaccines have their pitfalls. Microorganisms including HIV and malaria, among others, may alter their antigenic proteins through rapid mutagenesis, thereby hindering cytotoxic T lymphocyte (CTL)-based immunity, exploiting holes in the T cell repertoire, and/or misdirecting both cellular and humoral responses away from key cell-binding receptors to pathogen components which cannot provide epitopes for neutralizing antibodies [10-13].
The fundamental ways in which the immune system recognizes and responds to antigen are identical, irrespective of the source of molecules; microbes, allografts, allergens, autoantigens, or tumor antigens are approached in a similar manner. It follows that immune-based therapies that focus on promoting the quantity and quality of the immune response should be beneficial in the treatment of a range of diseases, especially persistent viral infections and cancer. Finding ways to increase the pool of mature, primed T cells that are able to fend off disease is a goal for future vaccine development. In this respect, a novel strategy for vaccine design, termed "thymic vaccination" has been considered to alter the T cell antigen receptor repertoire centrally via altered peptide ligands (APL). APL derived from infectious agents or tumor antigens, with low affinity to the TCR could, in principle, mediate positive selection and export of specific T cells from the thymus [14]. As such, these APL might be candidates for manipulating the thymic repertoire in vivo, controlling the generation of naive T cells and hence, subsequent memory development within the peripheral lymphoid compartment. Through repertoire manipulation, it should be possible to sculpt the specificity and diversity of disease-fighting cells. This thymic vaccination approach aims to deliver, by parenteral administration, positively selecting APL of cognate antigens into the thymus, eliciting maturation of thymocytes with desired TCR specificities at the level of thymic repertoire development (Fig. 1B). Note how engendering T cells with anti-viral specificity requires administration of an APL, a weaker affinity ligand for a given TCR to encourage maturation and emigration from the thymus.
Expanding T cell repertoires has enormous potential in aiding the organism's fight against infections or in affording tumor immunity. This strategy is principally different from conventional "peripheral" vaccination, which leads to proliferation of pre-existing mature T cells but does not alter the repertoire through creation of T lymphocytes with new T cell specificities. Thymic vaccination, by contrast, will alter the thymic repertoire to create desired T cell specificities. Moreover, a key feature of thymic vaccination is that it should be capable of directing the immune response towards those non-mutable components of proteins derived from infectious agents and tumors and away from misguiding cues that are part of pathogen or cancer chicanery. The rationale for this approach and the advantages over traditional vaccines are described below.
Discussion
Generation of T cell repertoire
T cells bearing a highly diverse αβ T cell receptor (TCR) repertoire develop in the thymus from stem cells originating in the hemopoietic tissues [15-17]. On entering the thymus through the cortico-medullary junction, these cells migrate to the subcapsular epithelium and undergo a complex differentiation process in the thymic cortex and then in the medulla, involving proliferation, expression of accessory molecules, rearrangement of TCR genes and selection of the TCR repertoire (reviewed in [18,19]). T cell development does not occur autonomously but requires signals from non-hematopoietic stromal cells including various types of thymic epithelial cells (TECs) which show profound phenotypic differences between cortex and medulla. The thymic epithelium provides a broad spectrum of signals for thymocyte proliferation, differentiation and selection. Thymic nurse cells, expressing high levels of MHC class I and II molecules and also containing antigen processing machinery, are involved in thymocyte selection, mediated by peptide/MHC (pMHC) ligands. Pools of self-peptides bound to MHC molecules control both positive and negative selection (reviewed in [20]). Thymocytes that carry TCRs having low-affinity interactions with MHC-bound self-peptides are positively selected, and are exported into the pool of mature peripheral lymphocytes. In contrast, thymocytes bearing those TCRs that recognize self-peptides with "high" affinity are eliminated primarily upon interaction with dendritic cells [19,21]. A schematic representation of thymocyte development [DN (CD4-CD8- double negative) → DP (CD4+CD8+ double positive → SP (CD4+CD8-, CD4-CD8+ single positive)] is depicted in Fig. 2.
Figure 2 General scheme of thymocyte selection and emigration to the peripheral lymphoid compartment. CD4-CD8- (DN) cells expressing pre-TCR undergo divisions and become αβTCR+CD4+CD8+ (DP) at which stage they interact with self-peptides presented by class I and II MHC molecules expressed on thymic stromal cells. Those thymocytes whose TCRs interact with high affinity to pMHC undergo apoptosis, while those bound to pMHC with low affinity mature to become MHC class I-restricted CD8+ SP or class II-restricted CD4+ SP cells. These mature thymocytes then emigrate to the periphery aided by different egress-related mechanisms.
TCR/MHC/peptide interactions control thymocyte selection
The avidity of pMHC/TCR interactions plays a major role in T cell recognition [14]. Crystal structure analyses have revealed fine details about peptide conformation inside the peptide binding groove of MHC molecules and the amino acid residues interacting with the TCR Vα and Vβ domains including their CDR3 loops [22-25]. Peptide analogs of antigenic peptides with substitutes at amino acid residues, APL, have been shown to generate qualitatively different T cell responses compared with those produced by the antigenic peptides themselves [26]. Some APL act as TCR antagonists capable of positively selecting [27,28], negatively selecting [29], or otherwise altering [30] selection of thymocytes.
TCR-transgenic mice provide useful tools for studies of peptide-based thymocyte selection. For example, in N15 transgenic mice carrying a TCR specific for the vesicular stomatitis virus nucleoprotein octapeptide N52–59 (VSV8), VSV8 triggers negative selection of DP thymocytes in the context of H-2Kb. In contrast, a weak agonist peptide variant, identical to the VSV8 peptide except for substitution of leucine for valine at the p4 peptide residue, termed L4, induces positive selection [31]. Similarly, in the P14 TCR transgenic mouse which expresses a TCR specific for the Db-restricted immunodominant LCMV epitope gp33–41 [32], the cognate gp33–41 peptide causes negative selection due to high affinity pMHC/TCR interactions. However, certain mutations of amino acid residues in the gp33–41 peptide affects the fate of thymocyte development in fetal thymic organ culture (FTOC) leading to positive selection P14-bearing thymocytes [33,34]. In yet a third TCR transgenic mouse model, F5, where the TCR recognizes a nucleoprotein peptide of the influenza virus NP366–379 in the context of H-2Db, a peptide antagonist mediates positive selection in FTOC [35,36], whereas the cognate peptide itself leads to deletion of DP thymocytes [37].
Positive selection and emigration of antigen-specific thymocytes in vivo is mediated by APL of viral CTL epitopes
Design and initial characterization of APL derived from the viral epitopes gp33–41 and VSV8
To experimentally test the concept of thymic vaccination, we have designed variants of gp33–41 and VSV8 peptides with substitutions at residues interacting with the TCR aimed at reducing TCR-pMHC affinity via diminution of the number of atomic contacts between the peptide and the TCR. Subsequently, we examined the effects of these APL on thymocyte maturation and emigration in vivo in two well-defined TCR-transgenic mouse systems. In the case of gp33–41 cognate peptide, amino acids at the peptide positions p4 (Tyr, Y) and p6 (Phe, F) were modified to Ser (S) and Ala (A), respectively, based on the crystal structure of the gp33–41/H-2Db complex showing exposure of the side chains of these amino acid residues to the solvent and hence, TCR accessibility [38,39]. No change was made in the peptide anchor residues that occupy the binding pockets of H-2Db, thus ensuring proper peptide presentation in the context of MHC. In the other less extreme peptide variant of gp33–41, Ala (A) at p7 was substituted with Glu (E). For the VSV8 peptide, the weak L4 agonist with the substitution of Leu (L) for Val (V) at the p4 peptide residue has been employed. The crystal structure of the N15 TCR-VSV8/Kb complex as well as the space-filling models of Kb in complex with VSV8 and L4 peptides are shown in Fig. 3. The centrally positioned p4 peptide residue, whose atoms are shown in green in the space-filling model, faces up to the solvent and interacts with the N15 TCR. In spite of the subtle differences in the structure of VSV8/Kb as compared to L4/Kb, these focal changes (p4 and Lys 66 on the α1 helix of H-2Kb) determine the outcome of thymic selection [31]. In a similar way, Db/ gp33–41 vs. APL in which amino acid residues at p4 and p6 are altered, differentially affect development of thymocytes expressing the P14 TCR (data not shown). Experimental data using these APL (Y4S/F6A and A7E) for studies of thymocyte selection and emigration as applied to the thymic vaccination approach are summarized below (for the original work, see [40]).
Figure 3 Structural basis of APL design. Crystal structure of N15 TCR-VSV8/Kb (left panel) [57]. The figure was rendered in MOLSCRIPT [58]. The TCR β chain is shown in gold, the TCR α chain in blue, Kb in magenta and β2M in red. Note the VSV8 peptide in green with the arrow pointing to the p4 valine side chain. Space-filling models of Kb in complex with VSV8 peptide and with its L4 variant (right panel) [31]. The Kb is shown as a GRASP surface [59] in magenta with peptide in CPK format and p4 residue atoms in green.
The binding of the APL to the MHC class I molecules using RMA-S cells confirmed that amino acid substitutions at peptide residues interacting with the TCR did not affect peptide binding and, by extension, peptide presentation to T cells. A series of experiments was next performed to evaluate the functional potential of the APL to stimulate peripheral T cells in mice injected with the variant peptides. Results of both proliferation and cytokine secretion assays using mature T cells from P14Rag2-/- lymph node and spleen showed a response to the high TCR affinity cognate peptide gp33–41, but not to the Y4S/F6A variant peptide. In support of this observation, tetramers of H-2Db in complex with the Y4S/F6A peptide did not bind to SP CD8 thymocytes from P14 Rag2-/- mice at any tetramer dilution, as judged by immunofluorescence analysis, whereas high fluorescence intensity staining was detected using tetramers of H-2Db in complex with the gp33–41 peptide. The A7E/H-2Db tetramer gave intermediate staining. These data suggested that the Y4S/F6A mutant must interact with the P14 TCR with extremely weak affinity, if at all.
Effect of APL administration on thymocyte development in vivo
Injection of the cognate viral peptides gp33–41 and VSV8 leads to negative selection of P14- and N15TCR-bearing thymocytes, respectively, due to relatively high affinity pMHC/TCR interactions [31,34]. In vivo administration of gp33–41 in P14Rag2-/- mice and VSV8 injection into N15Rag2-/- mice resulted in pronounced depletion of DP thymocytes. Surprisingly, injection of P14 Rag2-/- mice with the Y4S/F6A peptide mutant resulted in a significant increase in the total number of thymocytes as well as the DP thymocyte subpopulation, while the A7E variant had no effect on thymocyte counts. As with Y4S/F6A in the P14Rag2-/- system, injection of L4 in the N15Rag2-/- mouse preserved the DP thymocytes and led to an increase in total thymocyte counts. The unusual increase in the number of DP thymocytes following exposure to Y4S/F6A peptide was not due to cellular proliferation and attendant DNA synthesis as examined by in vivo BrdU incorporation assay. Rather, Y4S/F6A peptide administration prevented apoptosis as confirmed by staining of thymocytes with anti-Annexin V mAb. To more directly test this hypothesis, we injected P14 Rag2-/- mice with mixtures of the negatively selecting cognate peptide gp33–41 plus the Y4S/F6A APL. Increasing the amount of Y4S/F6A peptide in the injection mixture resulted in a higher number of total and DP thymocytes. Thus, we infer that the Y4S/F6A variant may compete with other endogenous negatively-selecting peptides for binding to H-2Db molecules expressed on thymic stroma either by binding to "empty" surface MHC class I molecules or, perhaps, by a cross-presentation mechanism [41]. That A7E fails to afford positive selection and interacts significantly with the P14 TCR in Db/A7E tetramer binding assays suggest that this APL does not reduce TCR binding affinity sufficiently to stimulate positive selection.
Y4S/F6A and L4 peptides mediate positive selection and emigration of thymocytes in irradiation chimeras
The numerically small population of antigen-specific recent thymic emigrants (RTE) makes thymic selection/emigration studies difficult even with the use of TCR transgenic mice. To resolve this issue, we employed irradiation chimeras of congenic mouse strains (expressing the CD45.1 marker in B6 and CD45.2 in P14 and N15 transgenic mice) to determine whether interactions between the low affinity ligands, Y4S/F6A and L4, and their specific TCRs would result in thymic positive selection and subsequent emigration from the thymus. For this purpose, lineage-minus BM precursors of P14 – or N15- TCR transgenic Rag2-/- mice (donor) were injected into irradiated congenic B6 mice (recipient) and the development of donor-type cells was monitored weekly by immunofluorescence staining and multicolor FACS analysis [40]. Following determination of the parameters related to the time period of appearance and the number of donor-type T cells in the chimeric thymus we administered the APL to the recipients at 3–4 wks after donor BM injection and assessed whether such exposure might influence the subsequent selection and emigration processes of donor thymocytes.
The numbers of donor DP and SP CD8+ thymocytes in irradiation chimeras injected with Y4S/F6A were greatest, suggesting that this ligand mediated positive selection of P14 Rag2-/--specific T cells. Similarly, in N15 Rag2-/--B6 irradiation chimeras injected with L4, the numbers of both DP and SP CD8 donor thymocytes were highest, consistent with positive selection. In contrast, injection of either gp33–41 or VSV8 cognate viral peptides into irradiation chimeras led to thymocyte depletion by negative selection. Analysis of the peripheral lymphoid organs in these chimeras by triple color immunofluorescence with anti-CD45.2, anti-CD8α and anti-TCR-specific mAbs showed the greatest number of donor-type CD45.2+CD8+Vα2+ T cells in the lymph nodes of Y4S/F6A -injected chimeras (2–3 fold over PBS-injected control mice), suggesting that donor-type thymocytes expressing the P14 TCR had developed in the presence of Y4S/F6A, matured and emigrated to the lymph nodes. A similar increase in the donor cell numbers were observed up to 9 weeks after injection of Y4S/F6A peptide. Positive selection was also evident in N15 Rag2-/- -B6 irradiation chimeras injected with the L4 variant. In this case, higher CD8+Vβ5.2+ N15 TCR transgene donor-type T cell numbers were observed both in lymph nodes and spleens. The functional analysis of donor-type CD8+ lymph node T cells in irradiation chimeras injected with the positively selecting Y4S/F6A or L4 peptides showed approximately two-fold higher proliferation levels in response to the cognate peptides gp33–41 and VSV8, respectively, in vitro, compared to cells from PBS control-injected chimeric mice, reflecting the two-fold difference in the number of donor-type CD8+ T cells in lymph nodes of chimeras injected with the APL. However, importantly, these mature donor-type T cells did not proliferate in response to Y4S/F6A or L4 variant peptides in vivo. Injection of the viral peptides and their APLs in vivo led to reduction of CFSE+ staining in the case of gp33–41 and VSV8, suggestive of proliferation and/or activation-induced cell death (AICD). In contrast, no change in CFSE+ staining was observed upon injection of Y4S/F6A or L4 peptides, implying that these APLs do not facilitate T cell expansion per se. In sum, the cognate peptide ligands gp33–41 and VSV8 which interact with the TCR with relatively high affinity compared to their respective APL, induce activation of peripheral T cells, whereas peptide variants Y4S/F6A and L4, which bind TCR with low affinity and mediate positive selection, do not stimulate mature T cell divisions.
Significance of APL-driven T cell emigration for the thymic vaccination approach
The data described above and previously [40] represent the first examination of the direct effects of amino acid substitutions at the P14 and N15 TCR contact residues on thymocyte selection and emigration in vivo. In addition, we show that thymocyte emigration is dependent on the affinity/avidity of pMHC/TCR interactions. These results suggest that although the low affinity pMHC/TCR interactions are insufficient to trigger cell divisions in mature cells, differentiation of immature thymocytes nevertheless follows. Affinity measurements support the idea that positively selecting peptide ligand affinities are lower than those of negatively selecting ligands for TCRs, but additionally linked to their MHC binding/stability properties [42]. Our report is consistent with the notion that weak pMHCI/TCR interactions promote positive selection of SP CD8 thymocytes. Certainly the 10,000 fold weaker functional stimulation of N15-bearing T cells by L4 versus VSV8 peptide is in line with the view [14]. Two recent studies in class II MHC-restricted TCR transgenic mouse systems also argue that weak pMHC ligands may foster positive selection [43,44].
Collectively, our data show that cognate peptides can be modified at key TCR recognition positions to create variants that result in selection, directly or indirectly, of desired TCR specificities at the level of thymic development. This exogenous peptide administration offers a potential of expanding repertoire generation in vivo in a manner useful to the organism. Whether these peptide-specific T cells generate stronger defense mechanisms to fight viral infection or tumors in normal, non-transgenic mice remains to be investigated. The magnitude of the APL-driven increase in thymocytes and subsequent egress is only 2–3 fold, however. This level of change likely reflects the tightly regulated thymocyte egress process. In this respect, exploring peptide-based means of enhancing differentiation of thymocytes bearing desired TCRs together with the modulation of mechanisms controlling thymocyte emigration to the periphery would be of a great importance. To this end, various pathways regulating egress from the thymus are described below and should be considered as potential targets for such manipulation in conjunction with APL administration. Although not discussed further here, thymic vaccination followed by conventional cognate antigen immunization may be the best way to insure a robust memory T cell response.
Regulation of thymocyte egress
Lymphocyte migration plays an important role in regulating the localization and orchestration of immune responses. As thymocytes progress through the developmental stages, they migrate from the cortico-medullary junction, the site of entry of T cell progenitors from the BM, to the subcapsular region of the thymus, then to the cortex and to the medulla [18,19]. Finally, functionally mature thymocytes exit the thymus and seed the peripheral lymphoid tissues. The processes that regulate trafficking of lymphoid precursors to and within the thymus, and that mediate emigration of mature T cells from the thymus to the periphery remain poorly understood. Several mediators, including chemokine receptors [45], adhesion molecules [46], extracellular matrix proteins [47], neuroendocrine factors [48] and G-protein coupled receptors (GPCR) [49] have been shown to regulate thymocyte export (Fig. 2). Recently, a role for the early activation marker CD69, transiently expressed on activated mature T cells and on thymocytes undergoing positive selection, in controlling thymocyte export, has also been suggested [50]. Cellular mechanisms involved in thymocyte egress are discussed in the following sections.
Chemokine pathways (reviewed in ref. [45,51,52])
Chemokines are basic polypeptides of about 100 amino acids, usually containing four Cys residues linked by disulphide bonds, which are produced by certain thymic stroma cells and are abundantly expressed in the thymus. Specifically, thymic epithelial, medullary epithelial and dendritic cells have been shown to secrete various chemokines. Growing evidence suggests that chemokines and their receptors, expressed differentially on thymocytes during discrete maturational stages, control homing of T cell progenitors to the thymus, their intrathymic migration, and exit to the periphery. Chemokines deliver signals for lymphocyte proliferation and survival, and regulate thymocyte trafficking by functioning in concert with other adhesion molecules such as selectins and integrins. Chemokines stimulate responding cells by activating pertussis toxin-sensitive Giα protein-coupled seven-transmembrane receptors (GPCR), leading to activation of intracellular secondary mediators which control directional cell migration. To date, 43 human chemokines have been identified, acting via binding to 19 different GPCR.
Some chemokine receptors are expressed in DP and SP thymocytes, e.g. CCR9, with its ligand CCL25 secreted by TEC and DC. Others, e.g. CCR5 and CCR8, expressed on mature SP thymocytes, have been suggested to play a role in mediating thymocyte emigration. In particular, CCR7 has been demonstrated to mediate homing of naïve T cells to peripheral lymphoid organs via ligands CCL19 and CCL21.
Extracellular matrix proteins (reviewed in [47])
Extracellular matrix (ECM) proteins laminin and fibronectin are produced by TECs, fibroblasts and MHC class II+ macrophages in the thymus. Other ECM proteins including nidogen, associated with laminin, and galectins -1, -3, and -5 as well as glycosaminoglycans are produced by thymic epithelium. ECM proteins form molecular bridges between thymocytes and the thymic microenvironment, mediating adhesion of thymocytes via their ECM receptors VLA-4, -5 and -6, and their disassembly from the cell complexes. In the absence of ECM proteins, normal thymocyte development and migration are severely perturbed, both in in vitro cultures of TEC and in in vivo knockout mouse models, suggesting a crucial role of the ECM protein network in the thymic function.
S1P pathway (reviewed in [53,54])
Sphingosine 1-phosphate (S1P), a member of sphingolipid family, is an important signaling molecule present in high concentrations in body fluids. SIP binds to members of a family of G protein-coupled receptors (S1P1–5/Edg) with up to nanomolar affinity, triggering diverse effects, including proliferation, survival, migration, morphogenesis, adhesion molecule expression, and cytoskeletal changes. S1P receptors are widely expressed during embryonic development and in the adult. The tissue distribution shows that lymphoid organs express high levels of S1P1 and S1P4. Thus, these receptors may be potential targets for pharmacological drug design aimed at effecting thymocyte migration.
The expression of S1P1 on T cells controls their exit from the thymus and entry into the blood, and, thus, has a central role in regulating the numbers of peripheral T-cells [55]. Interestingly, S1P1 knock-out mice show a block in the egress of mature T-cells into the periphery. The regulated expression of S1P1 receptor levels, which is increased in mature SP thymocytes and peripheral T cells, may control responsiveness to the high levels of sphingosine 1-phosphate in the blood, which selectively induces mature T-cell migration to the periphery. Recently, S1P1 receptors have been implicated in lymphocyte trafficking and homing based on studies using FTY720, a potent immunosuppressive agent, which is an agonist ligand for S1P1,3,4,5 receptors blocking egress of T cells from the thymus. Studies of thymocyte egress mechanisms through the S1P receptor pathway may aid in facilitating emigration from the thymus to the periphery and provide additional means of enriching the mature T cell pool with desired specificities.
Therapeutic applications of thymic vaccination
Currently available vaccines unquestionably represent a success story in modern medicine and have had a dramatic effect on morbidity and mortality worldwide. Nonetheless, it is clear that improvements are required to enable the development of vaccines against infectious diseases that have so far proven difficult to control with conventional approaches (HIV-1, malaria, tuberculosis, etc.).
Thymic vaccination might offer promising clinical applications as a way of immunization against these infectious diseases and cancers, enabling "designer" thymic development to produce suitable and long-lasting protective T cell immune specificities. In a converse role, deletion of unwanted T cell specificities in the case of autoimmunity by agonist administration early in life could be considered. Assuming thymic vaccination proves clinically viable, immune responses against invariant components of infectious agents, such as HIV and malaria, which otherwise utilize their intrinsic mutational capacity to evade human immune recognition, can be targeted. Design of novel vaccines based on the thymic vaccination approaches will benefit from the information gained in the recently completed Human Genome Project, particularly as genetic polymorphism associated with high risk of developing certain diseases later in life including cancers, infectious disease susceptibility and autoimmunity are uncovered. For example, the ability to manipulate the T cell repertoire to elicit anti-tumor responses early in life may prevent clinical disease evolution later. Powerful bioinformatic tools such as computer-based identification of HLA-allele specific binding epitopes and structural insight into TCR-pMHC interactions will aid in the epitope-based APL design process [56].
Conclusions
A thymic vaccination strategy has been conceived based on the current knowledge of thymocyte differentiation and repertoire generation. This approach differs substantially from conventional vaccination since it aims to shape T cell responses through thymic repertoire manipulation, exposing developing thymocytes to positively selecting APL derived from infectious agents or tumors. Experimental data to date suggest that this strategy is possible in in vivo mouse models using irradiation chimeras reconstituted with bone marrow progenitors from TCR-transgenic animals. Increasing emigration of antigen-specific T cells from the thymus to the periphery is a challenging goal. In the future, a combined approach of exposing the subject to a positively selecting APL plus a thymic export-enhancing agent might generate practical and efficient protective repertoire manipulations. Potential applications may include design and administration of APL against cancer, infectious and autoimmune diseases.
List of abbreviations
APL, altered peptide ligand; BM, bone marrow; CMJ, cortico-medullary junction; DC, dendritic cells; DN, double negative; DP, double positive; GPCR, G-protein-coupled receptors; HEV, high endothelial venule; MMP, matrix metalloproteinases; RTE, recent thymic emigrants; SP, single positive; S1P, sphingosine 1-phosphate; TCR, T cell receptor; TEC, thymic epithelial cells.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
MFH carried out the study, including experimental design and data acquisition, drafted and revised the manuscript. ELR conceived of the study, participated in its design and coordination, and helped to draft and revise the manuscript. The authors read and approved the final manuscript.
Acknowledgements
This work was supported by NIH grant AI50900 and the Molecular Immunology Foundation.
We thank our colleagues Dr. Pedro Reche for preparation of Fig. 3 and Dr. Linda Clayton for careful review of the manuscript.
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| 15588284 | PMC544398 | CC BY | 2021-01-04 16:39:07 | no | Med Immunol. 2004 Dec 8; 3:2 | utf-8 | Med Immunol | 2,004 | 10.1186/1476-9433-3-2 | oa_comm |
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BMC Health Serv ResBMC Health Services Research1472-6963BioMed Central London 1472-6963-4-401562500610.1186/1472-6963-4-40Research ArticleAudit of therapeutic interventions in inpatient children using two scores: are they evidence-based in developing countries? Carreazo Nilton Y [email protected] Carlos A [email protected] Juan P [email protected] Luis [email protected] Universidad Nacional Mayor de San Marcos and Instituto de Salud del Niño, Lima, LI 05, Peru2004 29 12 2004 4 40 40 6 8 2004 29 12 2004 Copyright © 2004 Carreazo et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The evidence base of clinical interventions in paediatric hospitals of developing countries has not been formally assessed. We performed this study to determine the proportion of evidence-based therapeutic interventions in a paediatric referral hospital of a developing country
Methods
The medical records of 167 patients admitted in one-month period were revised. Primary diagnosis and primary therapeutic interventions were determined for each patient. A systematic search was performed to assess the level of evidence for each intervention. Therapeutic interventions were classified using the Ellis score and the Oxford Centre for Evidence Based Medicine Levels of Evidence
Results
Any dehydration due to diarrhoea (59 cases) and pneumonia (42 cases) were the most frequent diagnoses. Based on Ellis score, level I evidence supported the primary therapeutic intervention in 21%, level II in 73% and level III in 6% cases. Using the Oxford classification 16%, 8%, 1% and 75% therapeutic interventions corresponded to grades A, B, C, and D recommendations, respectively. Overall, according to Ellis score, 94% interventions were evidence based. However, out of the total, 75% interventions were based on expert opinion or basic sciences. Most children with mild to moderate dehydration (52 cases) were inappropriately treated with slow intravenous fluids, and most children with non-complicated community acquired pneumonia (42 cases) received intravenous antibiotics
Conclusions
Most interventions were inappropriate, despite the availability of effective therapy for several of them. Diarrhoeal dehydration and community acquired pneumonia were the most common diagnoses and were inappropriately managed. Existing effective interventions for dehydration and pneumonia need to be put into practice at referral hospitals of developing countries. For the remaining problems, there is the need to conduct appropriate clinical studies. Caution must be taken when assigning the level of evidence supporting therapeutic interventions, as commonly used classifications may be misleading
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Background
Previous studies have shown that medical interventions based on scientific evidence range from 10 to 80% [1-4]. These studies were performed in a broad spectrum of patients and settings in developed countries.
In paediatric practice, the proportion of evidence based interventions reported ranges from 75 to 91% [5-8]. These studies are clearly relevant to the quality of care in developed countries, with strong health systems, widely available high technology and qualified human resources. However, health services in developing countries are frequently weak, and they face too often severe lack of expensive technology. In addition, the level of qualification of health personnel may vary greatly within health facilities, even at referral level and in urban areas. Moreover, the prevalent childhood illnesses in developed countries are not necessarily those prevalent in developing countries.
The assessment of quality of care at referral level for severely ill children is an important component of the efforts for reducing the child mortality rate in poor countries, for reducing the burden on health systems and for investing money in favour of high priority health interventions [9,10]. Thus we were prompted to assess the proportion of interventions based on sound scientific base in a paediatric referral setting of a developing country.
Methods
Referral care provided to children hospitalized in the paediatric department of the Instituto Especializado de Salud del Niño (IESN) was assessed for evidence base. IESN is a national paediatric hospital with more than 500 inpatient beds, serving mostly patients from deprived socioeconomic areas of Lima and inner cities of the country.
The clinical records of 195 children aged 1 month through 16 years old and hospitalized during January 2003 were initially revised.
One of the investigators (NYC) assessed the clinical records of children and assigned to each one a primary diagnosis and one or more primary therapeutic interventions, on the basis of the main clinical features and/or definitive diagnostic laboratory aids. Patients in whom a primary diagnosis was not possible to determine or those without a clear diagnosis were excluded.
The primary intervention was defined as the treatment or other manoeuvre that represented the most important attempt to cure, alleviate, or care for the patient in respect of his or her primary diagnosis [3].
To determine the level of evidence for each primary intervention, Cochrane reviews were searched. If there was not such a systematic review, a search through PubMed (MEDLINE) was performed by one of the investigators (NYC). All Cochrane reviews were searched through their own search tools. For PubMed, the period of search was 1966 through 2002. The key words used included those related to the primary diagnosis (e.g., pneumonia). Limits: "Title", "All child: 0–18 years". Publication type was sequentially searched for "Practice Guideline", "Meta-Analysis", "Randomized Controlled Trial" and "Review". Articles in English or Spanish were included. The National Guideline Clearinghouse was additionally visited for additional references. Published recommendations for judging the quality of guidelines were used for deciding the selection of the guidelines [11]. In addition, Clinical Evidence was used whenever deemed pertinent.
The level of evidence assigned to interventions was based on Ellis score (levels I, II and III) and the Oxford Centre for Evidence Based Medicine Levels of Evidence (grades A, B, C and D) [3,12]. Ellis score considers one or more interventions for a given diagnosis as one primary intervention. For comparison purposes we ranked each individual intervention through Oxford classification.
As a next step to our study, we planned the dissemination of the results among the hospital policy makers and the suggestion of corrective courses of action for those interventions needing improvement.
Results
Results of the search strategies for PubMed are included as an appendix [See Additional File 1]. A guideline on management of pain in sickle cell disease was found in the National Guideline Clearinghouse website and the results of the search are also shown at the end of the appendix [See Additional File 1].
Overall, one hundred and ninety five clinical records were revised. Twenty eight clinical records were excluded because of undefined primary diagnosis (Table 1). One hundred and sixty seven remaining clinical records were further assessed. The most frequent primary diagnoses are shown in Table 2, being diarrhoeal dehydration and pneumonia the main causes for hospitalization. The childhood prevalent diseases are quite constant throughout the year at our hospital and thus it is unlikely that the results would have been different if we had chosen another study period.
Out of 167 primary interventions, 21% were supported by level I evidence, 73% were level II, and 6% were ranked as level III, according to Ellis classification [3]. Table 3 shows that most interventions classified as level I are referred to acute asthma exacerbations. Nebulized beta-agonists and systemic corticosteroids in bronchiolitis, and antibiotics for acute otitis media were considered level I according to Ellis. They were classified as D{5} according to Oxford Centre for Evidence Based Medicine Levels of Evidence (Table 3) [12].
Most assessed interventions were considered as level II (Table 4). Diarrhoeal dehydration and community-acquired pneumonia were the predominant diagnoses. Considering each prescription separately (fluid restriction, furosemide, spironolactone and captopril in heart failure, for example) we obtained 146 interventions. When we assessed them through the Oxford classification, 11% interventions were classified as grade B, 1% as grade C, and 88% as grade D. Appropriate interventions for the same diagnoses presented in Table 4 and ranked by Oxford classification are shown in Table 5.
Overtly unsubstantiated therapy according to Ellis classification is shown in Table 6. Considering levels I and II evidence-based therapy, 94% of therapeutic interventions were evidence based through Ellis classification. Using the Oxford classification, we obtained 193 individualized therapeutic interventions, that were classified as Grades A (16%), B (8%), C (1%), and D (75%).
Comparison of grade of recommendation of the prescribed intervention with the appropriate one is shown in Table 7. It will be used as summary evidence documenting our current hospital health care quality standard.
Discussion
In this study 94% of therapeutic interventions were evidence-based by Ellis score. It may seem encouraging that more than 90% of therapeutic decisions in a referral paediatric hospital of a developing country are evidence based. However, the level II of evidence from Ellis includes interventions based in cohort studies, case-control studies, case series, expert's opinion, and even those based in basic sciences. We attempted therefore to classify the primary interventions according to more specific criteria. Using the Oxford classification, 75% of therapeutic interventions were based in expert opinions or in basic sciences (Grade of Recommendation D).
Some limitations of our study include the possible author' bias when assigning the primary diagnosis and primary intervention. The assignment of diagnosis by the clinician may have been influenced by both the choice of treatment and the available evidence. Only one of us classified the primary intervention. In addition, we evaluated only a primary intervention for a single primary diagnosis. Actually, many patients had more than one diagnosis and obviously more than one therapeutic intervention.
We used as evidence-base for rating the interventions assessed in our study, guidelines and evidence-based resources published in the developed world. This raises the issue of whether they are fully applicable to our setting. The most prevalent conditions found were diarrhoeal dehydration and community acquired pneumonia. For both of them we used British produced guidelines [20,21] because there were not Cochrane reviews on them and because the guidelines fulfilled recommended criteria for methodological quality of published guidelines [11]. The main recommendation of the guidelines on diarrhoea favours rapid oral rehydration over intravenous rehydration for children with mild to moderate dehydration [20]. This recommendation is based on several studies performed in both developed and developing countries and thus it can be applicable to both settings. The only concern on the applicability from setting to setting is that related to the osmolarity of the oral rehydration solution (ORS). The guidelines recommend a solution with 60 mmol/l of sodium, whereas a recent expert consensus found sufficient evidence to recommend the universal use of an ORS containing 75 mmol/l of sodium [49].
Regarding community acquired pneumonia, the British guidelines recommend antibiotic treatment for all children with pneumonia, due to the difficulties in identifying the aetiology, and they also specify criteria for hospitalization [21]. These recommendations are in agreement with the World Health Organization published guidelines [50]. The main difference is that the WHO guidelines rest on fast breathing and chest retraction for the diagnosis of pneumonia, whereas the British guidelines emphasize the role of chest x-rays. Chest x-rays are widely available in referral hospitals in developing countries and thus they should be used in addition to the clinical findings.
We acknowledge that the evidence derived from studies performed in developed countries should be translated with caution to developing settings. However, when the native research is scarce or of low quality, we think that the transfer of knowledge from the developed countries is an acceptable approach, as far as the particular characteristics of patients in developing countries are considered on an individual basis.
Dehydration due to diarrhoea and pneumonia were the most frequent diagnoses. Oral rehydration for diarrhoeal dehydration and antibiotics for pneumonia are considered as interventions with sufficient evidence for implementing them widely [9,10]. In our study, all children with mild to moderate dehydration were treated with slow intravenous infusion, and most children with uncomplicated community acquired pneumonia received intravenous antibiotics.
In addition to their enormous potential for saving lives, outpatient antibiotic therapy for pneumonia and outpatient oral rehydration can drastically reduce the rate of hospitalizations, the hospital stay, the hospital mortality rate, and the costs incurred. At our hospital, the mean stay time for hospitalized children is 4.7 days, and the mean crude mortality rate is 3.6%. We estimated the cost of managing hospitalized children with pneumonia and diarrhoeal dehydration as US$ 10.6/day and US$ 8.6/day, respectively. These costs are referred only to hospital bed and laboratory tests. A substantial amount of money could be saved treating these conditions on an outpatient basis.
We planned the dissemination of our results among the hospital policy makers. The tools that will be suggested for improving the standards include the development and systematic application of locally produced guidelines and/or the adaptation of published guidelines. A useful alternative that has been experienced for several years at our inpatient ward unit is to make available personal computers connected to Internet for attending physicians, residents and interns, and to encourage the use of online evidence-based resources. This last alternative may work better, particularly where there are motivated physicians who are able to lead the efforts for improving the health care standards. However, the ultimate decision to systematically introduce and monitor the suggested changes will rest on hospital managers. Such changes should also depend on taking into account the role of several other determinants of the clinical decision making by individual practitioners, such as continuous training, motivation, time, availability of drugs, equipments and supplies, supervision, and long-term health system strengthening strategies.
Conclusions
Caution must be taken when assigning the level of evidence that supports therapeutic interventions, as commonly used classifications may be misleading. Existing effective interventions for dehydration and pneumonia are not being implemented in developing countries, even at referral level, and thus there is the need to change the current medical interventional behaviours. For the remaining diagnoses, the majority of assessed interventions were based on weak or non-existent evidence, highlighting the need to conduct appropriate clinical studies.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
NYC and LH conceived and designed the study. All authors analyzed and interpreted the data and contributed to earlier drafts of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
"Appendix: Literature search results" details of the Medline (PubMed) search strategy and of the results are provided in this additional file. The selected articles are in black, bold characters. In addition, the search results from the National Guideline Clearinghouse website are provided for painful crisis in sickle cell anaemia.
Click here for file
Acknowledgements
We gratefully acknowledge the contribution of Drs. Felipe Lindo Pérez and Carlos Alamo Solís for their suggestions to earlier manuscript drafts. Mr. Armando Barrientos provided statistical support.
Figures and Tables
Table 1 Patients excluded because of undefined diagnoses
Diagnosis N° of patients
Convulsive syndrome 4
Acute obstructive bronchial syndrome 2
Acute rhinopharyngitis 2
Recurrent obstructive bronchial syndrome 2
Severe dehydration (referred from other hospitals)* 2
Acute lymphocytic leukaemia** 2
Unclassified dehydration 1
Emetic syndrome 1
Acute haemolytic anaemia of unknown cause 1
Anaemia of unknown cause 1
Thrombocytopenic purpura of unknown cause 1
Thrombocytopenia of unknown cause 1
Histiocytosis (clinical diagnosis) 1
Secondary epilepsy of unknown cause 1
Simple partial epilepsy of unknown cause 1
Munchausen's Syndrome (unclear criteria) 1
Congenital dyskeratosis 1
Neurocysticercosis (presumptive diagnosis) 1
Mental retardation (unspecified cause) 1
Operated hydrocephalousof unknown cause 1
Total 28
*Patients were fully hydrated upon arrival to IESN.
**Patients were referred to other hospital for definitive management.
Table 2 Definitive diagnoses
Diagnosis N° of patients
Moderate diarrhoeal dehydration 48
Community acquired pneumonia 42
Acute asthma 22
Severe diarrhoeal dehydration 7
Congestive heart failure 6
Bronchiolitis 5
Mild diarrhoeal dehydration 4
Cellulitis 3
First simple febrile convulsion 3
Hypoxic crisis secondary to cyanotic congenital heart disease 3
Haemophilia A 3
Dilated cardiomyopathy 2
Nosocomial pneumonia 2
Post-infectious cerebellitis 1
Sickle cell anaemia vaso-occlusive crisis 1
Croup 1
Acute dysenteric diarrhoea 1
Invasive acute diarrhoea 1
Kawasaki disease 1
Anal fissure 1
Hydrocarbon poisoning 1
Recurrent urinary tract infection 1
Acute bacterial meningitis 1
Aspiration pneumonia 1
Acute otitis media 1
Mild acute pancreatitis 1
Immune thrombocytopenic purpura 1
Status epilepticus 1
Supraventricular tachycardia 1
Trichuriasis 1
Total 167
Table 3 Level I primary interventions (Ellis score) ranked by Oxford classification
Diagnosis Patients Prescribed therapeutic intervention Oxford classification Appropriate therapeutic intervention Oxford classification Reference
Acute asthma 22 Beta 2 agonists-Systemic corticosteroids A{1a} Beta 2 agonists-Systemic corticosteroids A{1a} 13
Bronchiolitis 5 Beta 2 agonists-Systemic corticosteroids D{5} Supportive therapy A{1a} 14
Simple febrile seizure 3 Observation A{1a} Observation A{1a} 15
Cellulitis 2 Oxacillin A{1b} Oxacillin A{1b} 16
Acute otitis media 1 Amoxicillin D{5} Antipyretic/analgesic therapy A{1a} 17
Moderate croup 1 Dexamethasone L-Adrenaline A{1a}, A{1b} Dexamethasone L-Adrenaline A{1a}, A{1b} 18
Trichuriasis 1 Albendazole – Mebendazole A{1b} Albendazole – Mebendazole A{1b} 19
Table 4 Level II primary interventions (Ellis score) ranked by Oxford classification
Diagnosis Patients Prescribed therapeutic intervention Oxford classification Reference
Severe dehydration 1 IV ClNa 0.9% bolus, Rehydration in 04 hours D{5}, B{2b} 20
Severe dehydration 6 IV ClNa 0.9% bolus, Rehydration in 24 hours D{5}, D{5} 20
Moderate dehydration 1 IV 80 ml/Kg in 04 hours B{2b} 20
Moderate dehydration 1 IV 80 ml/Kg in 06 hours D{5} 20
Moderate dehydration 43 IV Rehydration in 24 hours D{5} 20
Mild dehydration 4 IV Rehydration in 24 hours D{5} 20
Severe dehydration 6 IV ClNa 0.9% bolus, Rehydration in 24 hours D{5}, D5} 20
CA Pneumonia (<5 years) 33 Ampicillin/Ceftriaxone/Chloramphenicol D{5} 21
CA Pneumonia (<5 years) 2 Ceftriaxone-Oxacillin/Ceftazidime-Vancomycin D{5} 21
CA Pneumonia (<5 years) 1 Ampicillin-Amikacin D{5} 21
CA Pneumonia (>5 years) 4 Penicillin G sodium D{5} 21
CHF 2 Fluid restriction, Furosemide, Captopril, Spironolactone D{5}, D{5}, B{2b}, B{1a} 22, 23, 24
CHF 3 Fluid restriction, Furosemide-Captopril D{5}, D{5}, B{1a} 22, 23, 24
CHF 1 Fluid restriction, Hydrochlorothiazide, Captopril D{5}, D{5}, B{1a} 22, 23, 24
Haemophilia A 3 Cryoprecipitate/Frozen fresh plasma D{5} 25
Cyanotic crisis 2 Meperidine – Ethylephrine D{5} 26
Nosocomial pneumonia 2 Ceftazidime-Vancomycin-Clindamycin D{5} 27
Vaso-occlusive crisis (SCA) 1 Lysine-clonixinate D{5} 28
Dilated cardiomyopathy 1 Lanatoside C-Furosemide-Captopril D{5}, B{2b} 29, 30
Dilated cardiomyopathy 1 Furosemide-Captopril-Spironolactone D{5}, B{2b}, D{5} 29, 30
Status epilepticus 1 Diazepam – Phenytoin B{2a} 31, 32
Bacterial meningitis 1 Ceftriaxone C 33
Dysenteric acute diarrhoea 1 Ceftriaxone B{3b} 34
Invasive acute diarrhoea 1 Ceftriaxone B{3b} 34
Recurrent UTI 1 Ciprofloxacin C 35
Mild acute pancreatitis 1 Soft diet (low in fat) – Ranitidine D{5} 36, 37, 38
Hydrocarbon ingestion 1 Nil per os – Soft diet D{5} 39, 40
PSVT 1 Adenosine B{1b} 41
Aspiration pneumonia 1 Chloramphenicol D{5} 42
Post infectious cerebellitis 1 Acetaminophen D5 43
CA: community acquired; CHF: congestive heart failure; SCA: sickle-cell anaemia; IV: intravenous; ORS: oral rehydration solution; PSVT: paroxysmal supraventricular tachycardia.
Table 5 Level II primary interventions (Ellis score) and their corresponding appropriate interventions ranked by Oxford classification*
Diagnosis Appropriate therapeutic intervention Oxford classification Reference
Severe dehydration IV 20 ml/Kg, 30–80 ml/Kg 3–4 hours D{5}, B{2b} 20
Severe dehydration IV 20 ml/Kg, 30–80 ml/Kg 3–4 hours D{5}, B{2b} 20
Moderate dehydration 30–80 ml/Kg 3–4 hours (ORS) B{2b} 20
Moderate dehydration 30–80 ml/Kg 3–4 hours (ORS) B{2b} 20
Moderate dehydration 30–80 ml/Kg 3–4 hours (ORS) B{2b} 20
Mild dehydration 30–50 ml/Kg 3–4 hours (ORS) B{2b} 20
Severe dehydration IV 20 ml/Kg, 30–80 ml/Kg 3–4 hours D{5}, B{2b} 20
CA Pneumonia (<5 years) Ampicillin/Chloramphenicol/Ceftriaxone D{5} 21
CA Pneumonia (<5 years) Ampicillin/Chloramphenicol/Ceftriaxone D{5} 21
CA Pneumonia (<5 years) Ampicillin/Chloramphenicol/Ceftriaxone D{5} 21
CA Pneumonia (>5 years) Penicillin G/Ceftriaxone/Cefuroxime D{5} 21
CHF Fluid restriction, Spironolactone, Captopril D{5}, B{2b}, B{1a} 22, 23, 24
CHF Fluid restriction, Spironolactone, Captopril D{5}, B{2b}, B{1a} 22, 23, 24
CHF Fluid restriction, Spironolactone, Captopril D{5}, B{2b}, B{1a} 22, 23, 24
Haemophilia A Factor VIII/Cryoprecipitate D{5} 25
Cyanotic crisis Meperidine D{5} 26
Nosocomial pneumonia Broad spectrum antibiotic therapy D{5} 27
Vaso-occlusive crisis (SCA) Ketorolac C{2b} 28
Dilated cardiomyopathy Digoxin-Furosemide, Captopril D{5}, B{2b} 29, 30
Dilated cardiomyopathy Digoxin-Furosemide, Captopril D{5}, B2b} 29, 30
Status epilepticus Diazepam – Phenytoin B{2a} 31, 32
Bacterial meningitis Ceftriaxone C 33
Dysenteric acute diarrhoea Ampicillin/Ceftriaxone B{3b} 34
Invasive acute diarrhoea Ampicillin/Ceftriaxone B{3b} 34
Recurrent UTI Empirical antibiotic therapy C 35
Mild acute pancreatitis Enteral feeding, Antibiotic, H2 Antagonists D{5} 36, 37, 38
Hydrocarbon ingestion Nil per os – Low fat diet D{5} 39, 40
PSVT Adenosine B{1b} 41
Aspiration pneumonia Penicillin/Clindamycin A{1b} 42
Post infectious cerebellitis Intravenous immunoglobulin C4 43
*Diagnoses are the same as in Table 4. CA: community acquired; CHF: congestive heart failure; SCA: sickle-cell anaemia; IV: intravenous; ORS: oral rehydration solution; PSVT: paroxysmal supraventricular tachycardia.
Table 6 Level III primary interventions (Ellis score) ranked by Oxford classification
Diagnosis Patients Prescribed therapeutic intervention Oxford classification Appropriate therapeutical intervention Oxford classification Reference
Moderate dehydration 3 IV 0.9% ClNa bolus – Time of rehydration: 24 hours D Oral rehydration solution, 30–80 cc/Kg in 3–4 hours B{2b} 20
Community acquired pneumonia (<5 years) 2 Antibiotic therapy <3 days D Ampicillin/Chloramphenicol/Ceftriaxone D{5} 21
Cyanotic crisis due to cyanotic congenital heart disease 1 Meperidine, Ethylephrine, Oxygen D{5}, D Meperidine D{5} 26
Facial cellulitis 1 Ceftazidime, Vancomycin D{5} Oxacillin, Cephalosporin D{5} 20
Idiopathic thrombocytopenic purpura* 1 Platelets, Dexamethasone D{5} Systemic corticosteroids A{1b} 44
Anal fissure 1 Zinc oxide D Topical anaesthetic or glyceryl trinitrate A{1b} 45, 46
Kawasaki disease 1 Acetylsalicylic acid D Intravenous immunoglobulin, acetylsalicylic acid A{1a} 47, 48
*Presenting as haematoma with 15,000 plateles/mm3
Table 7 Therapeutic interventions graded according to Oxford classification
Prescribed intervention Appropriate intervention
Grade of recommendation N° % N° %
A 30 16 38 20
B 16 8 77 41
C 2 1 4 2
D 145 75 70 37
Total 193 100 189 100
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| 15625006 | PMC544399 | CC BY | 2021-01-04 16:03:28 | no | BMC Health Serv Res. 2004 Dec 29; 4:40 | utf-8 | BMC Health Serv Res | 2,004 | 10.1186/1472-6963-4-40 | oa_comm |
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Health Res Policy SystHealth Research Policy and Systems1478-4505BioMed Central London 1478-4505-2-81558506410.1186/1478-4505-2-8ResearchTransfer of Health for All policy – What, how and in which direction? A two-case study Tervonen-Gonçalves Leena [email protected] Juhani [email protected] Tampere School of Public Health, 33014 University of Tampere, Finland2004 7 12 2004 2 8 8 9 9 2004 7 12 2004 Copyright © 2004 Tervonen-Gonçalves and Lehto; licensee BioMed Central Ltd.2004Tervonen-Gonçalves and Lehto; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
This article explores the transfer of World Health Organization's (WHO) policy initiative Health for All by the Year 2000 (HFA2000) into national contexts by using the changes in the public health policies of Finland and Portugal from the 1970's onward and the relationship of these changes to WHO policy development as test cases. Finland and Portugal were chosen to be compared as they represent different welfare state types and as the paradigmatic transition from the old to new public health is assumed to be related to the wider welfare state development.
Methods
The policy transfer approach is used as a conceptual tool to analyze the possible policy changes related to the adaptation of HFA into the national context. To be able to analyze not only the content but also the contextual conditions of policy transfer Kingdon's analytical framework of policy analysis is applied.
Conclusions
Our analysis suggests that no significant change of health promotion policy resulted from the launch of HFA program neither in Finland nor in Portugal. Instead the changes that occurred in both countries were of incremental nature, in accordance with the earlier policy choices, and the adaptation of HFA program was mainly applied to the areas where there were national traditions.
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Introduction
The World Health Organization (WHO) launched a policy framework called Health for All by the Year 2000 (HFA2000), in 1978, and has since then been advocating this framework for health policy making to all its member states [1,2]. This paper explores the transfer of HFA policy into national contexts by using the changes in public health policy of Finland and Portugal from the 1970's onward and their relationship to WHO policy development as test cases. Finland and Portugal were chosen to be the cases observed as they represent different welfare state types and as the paradigmatic shift from the old to new public health is assumed to be related to the wider welfare state development. The development of the welfare state constitutes the frame of reference for the analysis of transfer of HFA policy.
Policy transfer is a theoretical perspective that has been used to describe the spread of policy ideas from one political setting to another [3]. Most studies have concentrated on studying the transfer between countries, here the transfer is assumed to be mediated through an international organization (WHO) to its member states. Our aim is to locate the transfer of HFA policy in a broader conceptual framework. This entails clarifying the theoretical and political assumptions inherent in HFA policy as well as studying the transfer process in the historical context of broader welfare state development.
In order to analyze the transfer of HFA policy it is necessary to recognize that HFA policy is not a totally coherent health strategy that can be defined in one compact and consensual manner. The ambiguous nature of HFA policy stems from fact that it is constructed in various policy documents drawn up in temporally and contextually different situations. We refer to the following four central documents and the ideal model of public health policy they construct when we speak of HFA policy: the Declaration of Alma-Ata (1978) [1], Targets in support of the European regional HFA strategy (1985) [4], the Ottawa Charter (1986) [5], Health21 for Europe (1999) [6]. There are points of convergence in the picture of the ideal policy model these documents transfer, but also differences linked to the evolution of temporal macro-political cycles (collapse of colonialism, new international economic order, expansion of the welfare state, collapse of communist regimes in Eastern Europe, globalization and the crisis of the welfare state) or to the regional characteristics (European vs. global). Thus when we speak of HFA we refer to the HFA policy constructed in the aforementioned documents. To be able to analyze the adaptation of HFA in national contexts we have concentrated on examining three aspects of it: primary health care, community approach and healthy public policies. Based on the analysis of these aspects the study aims to explore how since the 1970's a number of essential aspects of health promotion policy have changed in Finland and in Portugal in relation to the ideas of HFA.
While a few studies have addressed the spreading of the HFA policy to the member states [7-11], this is quite seldom based on any theory of policy change or policy transfer. Also, while most of these studies have either been descriptive in nature or focused on evaluating national policies in the program level by verifying program's outcomes or situational validity of its objectives, we aim to analyse the policies in a broader societal context by taking into account the societal-level vindication as well as the political context of health policies [12].
In the policy transfer literature past policies, present policy complexity and the question of policy feasibility are seen as possible policy constraints. Likewise factors such as identical past policies or similar ideology can be seen to facilitate the transfer [3]. Locating the transfer of HFA policy in the context of existing public health policies and the wider political and social contexts of the countries in question offers one means to identify essential capacities, constraints and conditions for the adaptation of this particular policy innovation.
To be able to analyze not only the content (What was transferred?) but also the contextual conditions (How/why did this happen?) of policy transfer we use Kingdon's (1986) analytical framework of policy analysis [13]. According to Kingdon, a policy change process is conditioned by three analytically distinct streams: problem, policy and politics stream. Problem stream brings issues to the political agenda, while policy stream, which consists of experts, produces solutions and alternatives to policy problems. From these alternatives the politics stream then determines what, if any, are politically feasible alternatives to be adapted. A window of opportunity is open for a major policy change only if these three different streams of policy making process coexist simultaneously.
Policy transfer may occur at different stages of the policy making process. In this paper we will focus mainly on the agenda-setting and policy formulation phases. These phases can be regarded as a valuable starting point for the further development and implementation of HFA at the national level. The policy transfer approach is used as a conceptual tool to analyze the possible policy changes that the adaptation of HFA into the national context may have caused.
Method and materials (see figure 1)
We aim to identify concrete examples of transfer related changes in the content of formal government documents such as laws, reports, strategies and government programs. The detailed analysis of these documents provides some evidence of policy transfer. Non-formal government documents, evaluative reports, studies and relevant discussion are also used as material. The analysis of the policy documents was supported by expert interviews conducted in both of the countries in 2003–2004 for the purposes of this study. Historical reading of the documents can provide evidence about the time frame of policy change. In Portugal the first health strategy was published in 1999 [14], and thus the primary material for the analysis of governmental health policy before 1999 is government programs [15]. Finnish health promotion policy and its relevant documents [16-18] have been evaluated twice by an international review group [19,20] and several times by Finnish public health experts and national committees [20] and thus the analysis of the Finnish case is rather based on these evaluations and reviews than on the programs.
Figure 1 The Analytical Framework of the Study
Results
HFA as a rethinking of public health policy
WHO advocated "Health for All" as a rethinking of and challenge for reform in the national public health policies of the member states. HFA was frequently understood as a policy for developing countries focusing on advocacy for linking public health aims with broad social and environmental development policy at the local level, instead of investing in hospital medicine for the elites of the country. The core idea of the Alma-Ata Declaration (1978) was to advocate such a policy under the concept of primary health care.
In most OECD countries there already was some organizational form of primary medical care. Many health policy makers thought that HFA does not apply to OECD countries. Others argued that the idea of a comprehensive social and intersectoral health policy under the banner of primary health care also challenged the OECD countries. According to this understanding, Europe, too, was to develop its own HFA policy [21].
Seven years after Alma-Ata, in 1985, the WHO Regional Committee for Europe adopted its own HFA policy. It advocated a comprehensive, intersectoral and participatory health policy aiming at health gain and equity in health [4]. The conceptual differences between the Global and the European HFA are significant. The European HFA located primary health care as one aspect of "appropriate care" and to the basic level in the organization of health services. Both the Global and the European HFA argued for a comprehensive and intersectoral health policy. However, the meaning of these concepts is dependent on the context in which they are used. It may be argued that in the European HFA, the context is a welfare state – at that time either state capitalistic or state socialistic – with its numerous institutions and administrative sectors. The context in the Global HFA was a general and broad social and economic development of so-called developing countries.
The concept of HFA was accompanied by the introduction of two other challenging concepts: health promotion [5] and new public health [22]. All three were rhetorically contrasted to something that was called old or dominant way of thinking and making health policy that was characterized as focusing on hospital and cure, following a biomedical model and applying a narrow understanding of health and determinants of health.
Each of these three concepts had their own history and points of reference. For example, health promotion was mainly developed from a critical assessment of the health education of the 1970's [23]. New public health was advocated as a response to the change in the disease panorama, which meant that instead of hygiene, physical environment and vaccinations the new focus of interventions was to be on the social, cultural and political determinants of lifestyles and health [24]. HFA advocated an outcome-oriented health policy implemented by a wide range of social and economic institutions instead of focusing on the supply of medical care inputs.
The Ottawa Charter on Health Promotion mentions peace, shelter, education, food, income, a stable ecosystem, sustainable resources, social justice and equity as basic prerequisites for health [5]. In addition to advocating reorientation in health services and the development of personal health skills, the Charter also includes in health promotion foci a wide range of public policies, communities and daily social and physical environments. The WHO Vienna Dialogue (1986) even concluded that the best health promotion policy is a good social policy [25]. Taking into consideration that OECD had declared, in 1979 [26], the "Crisis of the Welfare State", we may locate the conceptual innovation of health promotion in OECD countries as advocacy for certain aspect of welfare state reform, as one remedy for the "crisis".
The European version of HFA may also be read from a welfare state reform advocacy perspective. The European HFA strongly advocates a broad health policy managed by objectives [21]. The strong management by objectives advocacy in the European HFA [4,6] links it with the managerialistic reform agenda of the welfare state [e.g. [27,28]]. Managerialism seems to be less prominent in the Ottawa Charter. Rather, it is possible to claim that the Charter has been influenced by ideas to develop welfare states leaning on social and community movements [29].
For the purposes of this study, we may conclude that there are three related concepts, health for all, health promotion and new public health that have a lot in common in their critique of the hospital focused and biomedically oriented health policy paradigms. They all advocate a broader socio-political orientation for health policy. However, they do not have a common idea of what this broader orientation is. We find it helpful to distinguish at least three different orientations. The first is the broad social and economic development context where the Alma-Ata Declaration located the radical local development program under the concept of primary health care. The second is the managerialistic welfare state reform context where the European HFA located the proposed health policy guided by HFA targets. A third orientation has been developed under the banner of the Ottawa Charter. This third variant may also be located in a welfare state reform context, but in a different idea of reform emphasizing community development.
Finland and the HFA challenge
Finland experienced an extremely rapid urbanization phase in the 1960's and the 1970's. A large part of the population moved from the rural areas to industrial and service workplaces in the urban centers. The proportion of working people earning their main income from agriculture decreased from 26% to 10% in 1950–1980 [30]. A significant part – about 7 % of the population – even moved outside the country, to Sweden. Part of the rapid and profound socio-economic change experienced was the development of a Scandinavian type welfare state in Finland. In about 25 years the country developed universal old age, sickness, disability and unemployment benefit systems and started the expansion of a public day care system for children and long term care for the elderly. The existing public education and culture systems were rapidly expanded and a universal health care system was set up. By 1985, it was possible to include Finland in the group of small, prosperous and egalitarian Nordic countries, still somewhat poorer and less generous than the older sisters Sweden and Denmark [31].
Primary care
In the late 1960's and the early 1970's, the challenge of the Finnish health policy was often articulated by asking: "Why does a country with Europe's healthiest children have the sickest middle-aged male population?" International comparative statistics had indicated that the country was at the European top in terms of low child mortality, but the adult population, particularly males, was dying younger than most other West European adult populations. The positive health status of children was understood as an outcome of a universal, strong and preventive maternal, child and school health system. The health system for the adult population was criticized for being too hospital centered [32,33]. The context of rapid socio-economic change, left-center-coalition government and a perspective of rapid overall development of the welfare state was a fertile growing ground for extending the example of universal, public and preventive child and maternal care to the adult population as well.
The Primary Health Care Act of 1971 started the building of multi-professional and multi-functional local health centers to carry forward the idea of "people's health work" at the local level [34]. It took about 25 years to build health centers throughout the whole country. Thus, in Finland the idea was not restricted to demonstration projects or particular regions as in some other countries, from which the idea of health center was learned [35]. The North Carelia Project, which received widespread international recognition as an example of broad community action for public health initiated by the local health centers [36], was developed as a demonstration project specifically to reduce the high mortality rate from cardio-vascular diseases, in the rural and less prosperous part of Finland.
Given this background, the WHO concept of Primary Health Care as expressed in the Alma-Ata Declaration (1978) was not foreign to Finnish health policy experts. Rather, many of them felt that Finns were pioneers significantly contributing to the development of the WHO policy and demonstrating its applicability also in the Northern hemisphere [21,37].
However, transforming into practice the radical idea of the local health center carrying out "people's health work" was not a simple task. Since the initial expansion phase, the developmental activities and reforms of the health centers have mostly focused on improving the medical cure and care functions [19]. According to some evaluators, health promotion, community-based prevention and public health have largely been pushed to the margins. The emphasis of main reforms addressing the health centers have focused on the management of diseases, division of labour between health centers and hospitals and the development of the GP function in medical care [35]. Thus, the radical concept of Alma-Ata was, in practice, transformed into a normalized concept of primary medical care.
The community approach
The North Carelia project was and continues to be the best known Finnish example of community action for public health. However, the evaluators of Finnish health promotion policy have repeatedly expressed critical assessment of the leadership and implementation of community action at the local level [19,20]. It has not been mentioned as the strong or innovative part of the Finnish health promotion policy.
Finland also used to be a dissident in resisting the managerialistic idea advocated by the WHO Regional Office for Europe to manage health promotion policy by setting the policy aims in the form of numerical health improvement targets [38].
At the beginning of the 2000s, reference to the role and responsibility of local actors and local community is an essential part of health policy rhetoric [39]. The latest national health promotion programme "Health 2015" [18] is also built around numerical health improvement targets. Thus, we may conclude that both the managerialistic approach and the community approach to the redesign of health policy in the welfare state have been introduced to national health promotion policy rhetoric. However, at the same time as they are present, the evaluators have indicated that these approaches are not effectively implemented.
Healthy public policy
One aspect of the rapid expansion of the Finnish welfare state in the early 1970s was the idea of improving people's health through a comprehensive planning system of all public sectors. Health indicators were to be used to provide feedback on the health impact of developments in the various public sectors and policies in these sectors should be adjusted accordingly [40]. Alcohol taxation and restrictions on its availability had already been used in Finland, mainly to reduce alcohol related criminality and social problems, but now the same policies were motivated primarily by public health concerns [41]. A comprehensive nutrition policy to change the traditional Finnish diet rich in fatty dairy products and poor in vegetables and fruit was developed. In addition to health education, policies such as shifting the priorities in the subsidies of agricultural products and negotiating changes in the dietary practices of the catering services in the schools and workplaces were used to reduce the consumption of high fat dairy products [42,43]. Tobacco control policies were developed as a flagship of the new health promotion policy applying high excise taxation, restrictions in the availability of tobacco and a ban on advertising. This policy was continuously tightened from the Tobacco Law of 1977 to the late 1990's [44]. Environmental health was also a rapidly developing sector, both as a part of occupational health and as a part of the development of overall environmental legislation and administration, particularly in the 1980s.
The Finnish record on developing policies outside the health sector to promote health has been referred to in placing the country among the forerunners of the Health for All policy in Europe [19,20]. In any case, Finland may be taken as an example of combining ambitious and rapid welfare state building with the ambition of promoting health through the development of the health impact of other policy sectors. It is less obvious that Finland could be taken as an example of how to do this in more mature welfare states. We may, rather, argue that the maturing of the Finnish welfare state from the late 1980's on has been paralleled by growing problems in the development of healthy public policies. The most dramatic example is the dismantling of the traditional Nordic alcohol control policies in the process of redesigning the welfare state under the pressures of European single market legislation and globalization [45]. The existence and at least partly increasing inequity in health between different socio-economic population groups has also been taken as an indication of the less successful development of healthy public policies [46]. Paradoxically, the strengthening of the capacity of the sector to promote health has also separated it from the mainstream health promotion policy. Development of environmental policy and policy administration has contributed to the growing distance in policy discourse and policy communities of environmental and public health. The latest international evaluation of the Finnish national health promotion policy [20] gave a critical assessment of the capacity of health policy makers to assess and influence the policies of other sectors.
Portugal and the HFA challenge
The Carnation Revolution in 1974 ended a long period of authoritarian rule in Portugal and opened the door to the democratization of the country. As in the other Southern European countries, the democratic Constitution was of a progressive nature while conferring wide economic, social and cultural rights and duties on the citizens [47]. The Constitution that came into force in 1976 aimed at the creation of a welfare state as a political form of transition to a socialist state and society [48]. Although the goal of a socialist, classless society was removed from the Constitution in its reform in 1982, the state's responsibilities to guarantee the economic, social and cultural rights of its citizens were left untouched [49]. Welfare state remained the ultimate goal, but the socialist model was changed to the model of social protection the European Economic Community (EEC) advocated [50,15].
The Southern European welfare state is a relatively recent addition to the conceptual map of European welfare state models. Many southern countries' present day characteristics are related to the legacy of authoritarianism, as well as to the historically strong presence of the Catholic Church [51]. Leibfried sees the weak institutionalization of constitutional promises of social rights as a characteristic feature of Southern European welfare states [47]. The term semi- institutionalized welfare state can be used to describe the whole of the Southern European welfare state that has been built up in principle, yet not implemented in practice. On the other hand it is recognized that southern welfare states have during recent decades been catching up the more developed European welfare systems [47,51]. But in spite of the catching-up effect and the overall pressure towards convergence of social policies in the European Union, Southern European countries seem to maintain a relatively distinct type of welfare state [52,53]. Portuguese welfare state development seems to follow the southern pattern, and Portugal is here analyzed from the viewpoint of the Southern European welfare state type.
The notions of semi-institutionalization and catching up-effect conceptualize the Southern European welfare state on the one hand as a developing (vs. mature) welfare state and on the other hand as following a different path than the more northern European welfare states [See [47,52]]. The attempts to institutionalize welfare state in Southern Europe occurred simultaneously with the era of welfare state crisis. Consequently, the crisis rhetoric was assumed in Portugal in the initial phases of welfare state development. Thus the welfare state was declared to be in a state of crisis before it actually even existed [53]. Due to the dynamics of crisis before maturation, welfare state has remained to some extent a semi- institutionalized promise until the present day.
The development of Portuguese health policy can be broadly divided into two historical phases that are linked to the general welfare state development. The first period from the beginning of the 18th century until 1971 was dominated by preventive public health policies. Through general preventive measures, such as sanitary education, environmental sanitation, hygiene, mental hygiene and sickness prevention "sanitary police" (polícia sanitária) aimed at governing the health of the nation. Preventive policies were directed towards the collectivity and they benefited the individual citizen only indirectly. Publicly provided health care services were tied to the clientele of social assistance and were only available to poor people until 1971, when the right to health care was legally defined to be the right of every citizen [54]. The reform bill of Health and Assistance (Reforma de Saúde e Assistência) established in 1971 marked the beginning of the second phase of health policies [55]. The consolidation of the universal right to health care in the Constitution and in the National Health Service (NHS) (Sistema Nacional de Saúde) law in 1979 [56] signified the strengthening of the social citizenship rights and changed not only the nature of health policies, but also the general nature of the Portuguese welfare state. The qualitative change in the welfare policies from the distributive to productive policies happened precisely in the area of health [53].
Primary care
Maternal and child health were already part of health policy during the authoritarian era, and women's and children's health was also included into the primary health care concept established with the Reform of Health and Assistance [57]. However these programs were limited to the health education and medical monitoring of women's and children's health during and after pregnancy as family planning was prohibited for political and religious reasons until 1974. A right to family planning was legally defined in the Constitution of 1976 [58]. The integration of family planning into primary health care has widened the scope of maternal and infant health policies in Portugal. Since 1979 Portugal has been collaborating actively with WHO/UNFPA in improving services in family planning [21,59]. In Portuguese health strategy reproductive issues are included in various priority areas. The importance of social policies directed to women, children and family is recognized in the strategy as well as in the government programs. The policies concerned with maternal and child health have developed during the last three decades into policies of reproductive health. The indicators of maternal and child mortality have improved significantly and are on the level of other EU countries [60].
The reform of Health and Assistance aimed at creating a nationwide network of local level health centers that were supposed to provide primary health care services for the entire population [61]. Although the full implementation of this reform was hindered due to political and organizational obstacles, it is seen to mark the beginning of a new era of expansion in Portuguese public health policy [62,63]. This reform included most of the principles of primary health care recognized in the Alma-Ata Declaration seven years later [63,64]. The building of a primary health care network was further consolidated in the Constitution and in NHS law. The process of building up a primary health care network was on the Government's health policy agenda from the beginning of the 1970's until 1985 (15). Analysis of scientific texts and reports on the development of Portuguese public health policy as well as the expert interviews conducted for this study in 2003 indicate that although the Declaration of Alma-Ata was used to legitimise the development of the primary health care system – at least on the level of policy stream – the adaptation of the primary health care-concept presented in Alma-Ata did not change the national policy line.
A right to health care has been an essential part of the democratization process, strengthening social citizenship. Nevertheless the democratization of health care has not been linear; health was politicized following the creation of the public NHS. The critical welfare state philosophy of the liberal political cycle (1985–1995) affected the content of health policies by favoring privatizations of health care during the term of office of the Social Democrats (centre-right party) [63,64]. Due to continuing political and financial problems in the implementation of NHS, difficulties in access to health care services have persisted as a health policy problem. This situation has in its turn kept the development of the health care system and medical care approach in the center of the problem stream feeding the political agenda. According to some of the public health experts interviewed the clinical, curative approach of health care gained more control in the health sector's internal power sharing during the liberal cycle and at the same time the position of public health declined. The analysis of the government programs proves that at the same time the development of primary health care disappeared from governments' agenda. The crisis period of public health policy lasted a decade (1985–1995) [61]. However as the institutionalization of health care has signified the permanent centrality of services on the health policy agenda, not even the crisis period of public health did signified a great break in terms of health promotion in policy documents. Indeed some health education campaigns were launched during the crisis period [15].
The Portuguese Journal of Public Health (Revista Portuguesa de Saúde Pública) published a special issue dedicated to HFA in 1988. In the Editorial of the journal it is suggested that HFA2000 should in Portugal have as an objective rather "adequate health care for all" than "health for all" [65]. The general health service orientation of health promotion and disease prevention is also present on the level of government programs. The clinical, treatment-centered ethos typical of the expansion period of the health care system is dominant in the government programs 1976–2002. Health promotion and disease prevention are conceptualized as activities of primary health care and they are seen to be implemented by the medical and nursing professions [15]. Concentration on the primary health care element of the HFA-program is not only a Portuguese specialty; other Southern European countries, such as Spain and Greece, have also put weight on the development of primary health care [59]. The first health strategy is likewise disease-oriented (14 out of 27 of the priority areas are diseases), and since the health service sector is seen as the main actor of health promotion policy, the means are mainly biomedical or educative. Emphasizing rather the individual level than the structural level seems to be a more general Southern European feature in public health policies [66].
The community approach
The Ottawa Charter calls the countries to strengthen community action. However, it does not explicitly define what is meant by the concept of community. In the social policy literature the term community is often understood to refer either to the network of family members, friends and neighborhoods, or to civil society, understood as a complex of social associations and non-governmental organizations. The archetype of Southern European welfare state carries the connotation of the strong and traditional role of community in welfare provision. [67] However, most comparative studies fail to mention that during the authoritarian era the civil society element of community was repressed, as free associations were prohibited by law. In Portugal only a few religious associations connected to the Catholic Church were approved by the state. Since 1974 the number of associations acting in the field of social and health issues has expanded. [68] Often the call to strengthen community action is seen from the perspective of the welfare state crisis debates. However, in Portugal the growth of the civil society element of community was not an answer to the welfare state crisis as such, but its growth should be located in the context of the recent liberation from state repression. Yet Sousa Santos [69] argues that the state restricts true citizen participation and the functioning of those associations created after 1974 as it continues to support conservative religious organizations.
In the Portuguese health strategy (1999) private institutions of social solidarity (Instituições Particulares da Solidariedade Social) and non-governmental organizations (Organizações Não-governamentais) are recognized as the main representative categories of community. Strengthening partnerships with these organizations is seen as indispensable for achieving the goals set. Although these organizations are also identified as doing health promotion work, they are mainly actors in curing and caring. The second community level actor identified as relevant for health promotion activity is the local level of public administration. Direct citizen participation (e.g. user/consumer/patients' associations) and the need to cooperate with syndicates and health professionals are also mentioned in the strategy. However, they are not given any significant role in the program implementation. All these community categories identifiable in the health strategy seem to match the current categorizations of community actors and their partners in the social sector [See [68]].
The fourth category of community action for health promotion manifest in the form of setting-based projects of Healthy Cities (WHO), the Health Promoting Schools- network (WHO & EU) and Healthy Workplaces (EU). The first Healthy City was established in 1995 and now there are 9 cities belonging to the national network of Healthy Cities [70]. The Health Promoting Schools- network was initiated in 1994 and reaches currently one third of pupils in the public education system [71]. These projects represent the model of community action that is unique for the domain of health promotion. This kind of community based action model targets the whole population of a certain community, while the traditional actors in social and health sectors concentrate on caring for and curing those who are in need of care. Targeted solidarity of traditional community action is challenged by universal equality dominant in these health promotion projects. The model of community action adapted with these projects introduced new ideology and forms of organization into the sphere of public health. When analyzing the adaptation of HFA in a timeframe it seems that the community level adapted HFA philosophy before the national level.
Healthy Public Policies
The Ottawa Charter emphasizes the role of policy as a factor promoting healthy choices. In other words, this means that health should be taken into consideration in all public policies. When analyzing the Portuguese development in relation to intersectoral policies, there is action in conventional intersectoral issues, such as tobacco, alcohol and nutrition, but it does not seem that any major development has happened in these policy domains. The project of Healthy Schools and the overall health education campaigns are based on interministerial cooperation and pacts between the Ministry of Health and the Ministry of Education. Intersectoral work is also carried out in the field of drug addiction [14,15]. In this section we focus on one case of public policies, that of sanitation, and observe its development in the welfare state development context.
Portuguese public health indicators have shown remarkable improvements during the last three decades. The fact that public health indicators have been improving side by side with general socio- economic indicators has led researchers to conclude that although the creation of NHS and the improved access to health care have influenced the positive evolution of the health status of the Portuguese population, these improvements are greatly connected to general improvements in economic and social conditions, such as education, income and living standard, housing, sanitation, hygiene, and transport infrastructures [72,73,54]. These improvements occurred in the context of the expansion of the welfare state. In this process some of the core issues of the ancient sanitary police, such as matters of basic sanitation, have conceptualized more clearly under respective sectoral policies, out of the national health policy agenda. This reflects the administrative differentiation of state functions and sectoral differentiation of respective policies that typifies the expansion of the welfare state.
Basic sanitation (saneamento basico) has been a priority in Portuguese post- authoritarian development policy, however in the government programs (1976–2002) basic sanitation is not recognized as a priority of health policy. Although in some programs environmental conditions and habitation are seen to influence public health and the welfare of the population, basic sanitation is not explicitly considered either as a health policy problem, or as a goal or means. Basic sanitation is not conceptualized as an issue of health policy, it is not explicitly on the government's health policy agenda, neither is health used as an argument to improve it in other sectors. Only in the XIII Government Program (1995–1999) are water quality and the intersectoral action needed to reach it mentioned in the section dealing with health policy. Apart from this, the issue of basic sanitation has become conceptualized as an issue of renovation of infrastructure, and this discourse has constituted it as a policy of infrastructure and renovation. In the Regional Development Plan (2nd Community Support Framework 1994–1999) basic sanitation is conceptualized as an issue of environment and no reference is made to health [74]. In the national health strategy, healthy environments refer to social environments and basic sanitation is not conceptualized as a policy action area. The differentiation of sanitation from the domain of health policy implies that although a change clearly came about in the content of "healthy public policies", it did not happen towards new public health as the improvement of sanitation was not justified by health reasons. Some of the recent documents [75] imply that in recent years the development has begun to turn in a different direction as issues of basic sanitation are again included in the domain of health policies.
Discussion and conclusions
"Health for All" was developed as an international synthesis of emerging health policy ideas of the 1970's, sometimes conceptualized as "the new public health". Reflecting both the many roots of the concept and the many different contexts to which it was to be adapted, different interpretations of HFA have coexisted. The Alma-Ata Declaration was adapted to combining new public health with local socio-economic development in the developing countries. The HFA targets of the WHO European Region and the Ottawa Charter combine the new public health with the reform demands of state capitalistic and state socialistic welfare states. The target approach is closer to the managerial reform agenda while the Ottawa approach seems to lean more on the community empowerment agenda.
HFA was launched to contribute to the development of national health policies. Thus it may be used as a standard for evaluating national health policies and health promotion policies, as has been done in some studies inspired by the WHO [7-10]. However, understanding HFA as a synthesis of many policy tendencies and allowing different contents for different policy contexts makes such direct comparisons between national policies and WHO documents problematic. In the policy transfer perspective the role of the WHO (or, for that matter, of the EU) may not be that of an international policy leadership but, rather, that of an international policy mediator.
We have tried to trace the impact of HFA on the development of the Finnish and Portuguese health policies. The Finnish development of "people's health work" and local health centers was clearly inspired by the same ideas as the primary health care concept of the Alma-Ata Declaration. The Portuguese health policy ideology expressed in the reforms of 1970's also comprehended the ideas of Alma-Ata Declaration. However, neither of these can be seen as a transfer from WHO to the member states. Rather, the Finns claim that the direction of the transfer was from Finland to WHO. The Portuguese primary care concept also had its own national roots, e.g. in the pre-revolution development of maternal and child health.
The subsequent development of primary health care in both countries indicates that the Alma-Ata idea of broad primary care tends to contradict the welfare state reforms inspired by the ideas of the New Public Management. This context tends to reduce primary health care to primary medical care. The impact of this change in the welfare state context may be identified both in Finland and Portugal from the 1980's on as well as in comparing the primary care concepts of the Alma-Ata HFA and the HFA targets of the WHO Europe. At the same time, the aim of the Ottawa Charter of reorienting health services towards health promotion does not seem to have guided primary care development in either country.
Thus the development of primary care in both countries has been in dialogue with the HFA. However, what primary care means in the framework of HFA has changed over time and the dialogue cannot be simplified into the unidirectional transfer of HFA policy from WHO to member states.
Dialogue or interaction are also appropriate concepts to describe the relationship between WHO and the two countries with regard to developing a community approach in health promotion policy. First of all, the different variants of HFA locate "community" in different contexts. In Alma-Ata, community is the totality of local actors without making distinctions between economic, social and health actors or private and public actors. The European HFA target documents [4,6] see community as a partner or a cluster of partners to the health sector and public authorities. The Ottawa Charter seems to be build around the idea of community empowerment and increasingly participative health policy making. The Finnish community approach as expressed in the North Carelia project, in the cooperation of the public health sector with the traditional public health associations and in the emphasis on local public sector action, seems to be quite close to the approach of the European HFA targets. Both the broad community concept of Alma-Ata and the community empowerment approach of Ottawa seem more alien to Finnish health policy strategies.
A number of welfare state characterizations [e.g. [47,67]] create expectations that we should find, in Portugal, a strong role of traditional communities strongly linked to the Catholic Church in health promotion policy. Such an expectation may fail to recognize the historical legacy of the authoritarian Salazar regime, which, while keeping close linkage to the Catholic Church, was quite a state centered regime that did not allow strong independent community action. Our analysis indicates that the role of community action in health promotion is not particularly eminent in Portugal, either in governmental health policy documents [14,15] or according to the opinion of public health experts [61,76]. The activity of the Catholic Church and religiously inspired organizations in health promotion is, however, visible [77,78]. But so is also the attempt of the government to conceptualize community action through projects such as Healthy Cities and Health Promoting Schools, where community action is led or arranged by the public authorities.
Thus, whatever is meant by the community approach in health promotion policy, Finland and Portugal do not seem to be strong examples of policy development following the initiative of HFA. We could not identify policy transfer other than in participation in the Healthy Cities and other "health settings" projects.
Healthy Public Policy was our third focus in health promotion policy. The concept was raised in the European HFA document in 1985. In Alma-Ata the integration of health and other policies is extended much further and no specific concept resembling health public policy is needed.
The Finnish health promotion strategy has included a number of public policies outside the health sector, particularly with regard to alcohol, tobacco, nutrition and physical exercise. We could not identify any specific impact of the different HFAs of the WHO on these policies. Rather, there seems to be growing pressure to restrict the use of the impact of other sectors in alcohol control. At the same time, the distance between the mainstream health promotion policy and environmental policy seems to be growing, although the public health impact of environmental policies is obvious. Thus, with the exception of tobacco policy, the idea of healthy public policy may even experience increasing problems, although this is not so far reflected in the development of the health status of the population.
The rapid positive development of the health status of the Portuguese population during the last 30 years reflects the rapid improvement of the sanitary conditions as well as of the social determinants of health [72,73,54]. Sanitary policy, including both preventive services such as vaccinations and health education, as well as improvement of environmental and housing conditions, has been the most significant aspect of Portuguese healthy public policy. However, the analysis of health policy documents indicates that Portugal has also experienced a distancing of environmental policies and health policies, that is: a trend antagonistic to the ideas of the different version of HFA. Other public policies, including tobacco and alcohol control and nutrition policies are weakly developed in Portugal. Thus, we cannot identify any significant transfer mediated by the WHO in Portugal either.
At the beginning of 1970's public health indicators showed that Finland and Portugal were lagging behind the majority of Western European countries in terms of public health indicators. Both defined this distance from the Western European level as the core health policy problem [15]. This way of defining the policy problem has clearly contributed to the fact that both countries have looked to international organizations and international comparison for their policy development.
The Finnish health policy expert community has often referred to WHO and Finland has been an active member of the European region of this organization. In the 1980's, it even took the responsibility for acting as a pilot country for the national development of HFA in Europe [37]. Thus there has been much interaction between WHO and Finland in health policy development. Our analysis indicates that this interaction cannot be understood as policy transfer and that it has influenced Finnish health policy development much less than is often assumed.
For the Portuguese government documents, the EU and the idea of a "European welfare state" has been the reference much more often that the WHO [15]. However, Portugal has also been in dialogue with the WHO in health policy development, although not to the same extent as Finland.
We have also asked what conditions the adoption of HFA policy in the two countries. Our analysis indicates that the phase of welfare state development matters a lot. The ambitious welfare state development period in the late 1960's and the 1970's in Finland was a good basis for adopting the ambitious idea of "people's health work" and setting far-reaching aims for the development of the health impact of all public policies. Much of the Finnish health promotion policy development until the 1990's is rooted in the initiatives of this period. HFA, as expressed in the Alma-Ata Declaration and in the later versions of HFA were taken in Finland as international evidence in support of the policy choices already made in the country. Portugal also had courageous ambitions of developing a European welfare state, after the Carnation Revolution and the call of Alma-Ata was heard in this context. While Finland was fairly successful in building a universalistic institutional welfare state of the Scandinavian type, Portugal seems to have so far ended up in what Leibfried (1992) calls a semi-institutional welfare state. This may be a good explanation for the continuity of health promotion policy in Finland, in contrast to the discontinuity in Portugal which also is reflected in the concept "semi-institutional".
Both HFA and the two countries examined have also been influenced by the end of the "Golden Age of the Welfare State" [79]. The differences between the Alma-Ata approach and those of the Ottawa Charter and European HFA expressed in policy targets is not only the difference between global and Europe or OECD. It is also a difference between the ambitions of the Golden Age and the post-expansion period [80] of the Welfare State. Now the political agenda is dominated by the idea of reforming the (existing) welfare state. We have linked the Ottawa approach to a reform agenda emphasizing community empowerment and the European HFA targets approach to the more managerialistic reform agenda. While we can identify the impact of the managerialistic agenda in both countries to the reduction of "primary health care" to "primary medical care", we are more hesitant regarding the impact of the community empowerment agenda on the health policy development in the two countries.
The development of health promotion policy in the two countries has also been related to changes in politics, particularly to changes in the political composition and orientation of the national governments. In this regard, the Portuguese development has been stormier with a radical regime shift in the Carnation Revolution, starting a new regime inspired by socialist visions, followed by a turn to liberal conservative governments ten years later. The Socialist Party's victory in the elections of 1995 after ten years in opposition signified a change in social policy orientation once again [50,81]. However, there does not seem to be any significant changes in the health promotion policy even if the government has published health strategy. The Finnish political development has been much less stormy. The tradition of coalition governments which normally include both the Social Democratic Party and some parties of the bourgeois side has strengthened continuity rather than radical turns in Finnish health policy. However, within this continuity, an incremental movement from welfare state expansion to post-expansive welfare state reform policy may be identified [82-84].
Our analysis does not give a clear picture of the significance of politics in the adoption of HFA in the two countries. We may assume that the continuity in the Finnish politics has contributed to the continuity in the Finland-WHO dialogue and interaction as expressed, e.g., in the reviews of WHO-teams of Finnish health policy development [19,20]. The more stormy political development in Portugal may also have caused more discontinuity in the WHO relationship. However, the level of interaction was not a direct indicator of significant policy transfer. Rather, our analysis shows that the political context and its changes in countries probably impacts on which version of HFA is adopted. Thus, during the dominance of politics in support of more radical or expansionist welfare state development, Alma-Ata seems to have been the preferred version of HFA, while the European HFA target approach may be more feasible in the post-expansive welfare state politics.
In Kingdonian (1995) terms, we may sum up that the health policy problem of both Finland and Portugal, being European laggards in the 1970's, caused them to be open to transfer of policies from abroad. Thus, the "problem stream" was ready for policy transfer. The "policy stream" seems also to have been ready for a certain kind of transfer, but only for those versions or elements of HFA that could be fitted into the specific policy contexts of the countries. HFA as such was not a dynamo of policy change in either country. The "politics stream" changed in both countries so that the window for radical policy changes was closed fairly soon. After that, if any political window was open, it was only for incremental changes in line with the post-expansive welfare state reform agenda.
Competing interests
This study is a part of a research project called "Finnish National Health Promotion Policy from an International Comparative Perspective", which has been financed by the Academy of Finland.
JL acted as a scientist in WHO Centre for Health Policy, Brussels, in 1999.
Authors' contributions
JL is responsible for the analysis of Finnish policy while LTG has done the analysis on Portugal and drafted the other parts of the article.
Acknowledgements
The authors are grateful for the support of the members of the research team "Finnish National Health Promotion Policy from an International Comparative Perspective". We are also grateful to the public health experts interviewed in Finland and Portugal for the purposes of this study in 2003–2004.
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| 15585064 | PMC544400 | CC BY | 2021-01-04 16:37:15 | no | Health Res Policy Syst. 2004 Dec 7; 2:8 | utf-8 | Health Res Policy Syst | 2,004 | 10.1186/1478-4505-2-8 | oa_comm |
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BMC BiotechnolBMC Biotechnology1472-6750BioMed Central London 1472-6750-4-331561933010.1186/1472-6750-4-33Research ArticleDynamic in vivo imaging and cell tracking using a histone fluorescent protein fusion in mice Hadjantonakis Anna-Katerina [email protected] Virginia E [email protected] Department of Genetics and Development, College of Physicians and Surgeons of Columbia University, 701 West 168th St., New York, NY 10032, USA2 Developmental Biology Program, Sloan-Kettering Institute, New York, NY10021, USA2004 24 12 2004 4 33 33 5 10 2004 24 12 2004 Copyright © 2004 Hadjantonakis and Papaioannou; licensee BioMed Central Ltd.2004Hadjantonakis and Papaioannou; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Advances in optical imaging modalities and the continued evolution of genetically-encoded fluorescent proteins are coming together to facilitate the study of cell behavior at high resolution in living organisms. As a result, imaging using autofluorescent protein reporters is gaining popularity in mouse transgenic and targeted mutagenesis applications.
Results
We have used embryonic stem cell-mediated transgenesis to label cells at sub-cellular resolution in vivo, and to evaluate fusion of a human histone protein to green fluorescent protein for ubiquitous fluorescent labeling of nucleosomes in mice. To this end we have generated embryonic stem cells and a corresponding strain of mice that is viable and fertile and exhibits widespread chromatin-localized reporter expression. High levels of transgene expression are maintained in a constitutive manner. Viability and fertility of homozygous transgenic animals demonstrates that this reporter is developmentally neutral and does not interfere with mitosis or meiosis.
Conclusions
Using various optical imaging modalities including wide-field, spinning disc confocal, and laser scanning confocal and multiphoton excitation microscopy, we can identify cells in various stages of the cell cycle. We can identify cells in interphase, cells undergoing mitosis or cell death. We demonstrate that this histone fusion reporter allows the direct visualization of active chromatin in situ. Since this reporter segments three-dimensional space, it permits the visualization of individual cells within a population, and so facilitates tracking cell position over time. It is therefore attractive for use in multidimensional studies of in vivo cell behavior and cell fate.
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Background
Macro- and microscopic imaging are pivotal readouts in the field of biology both for determining the normal (baseline) course of events and for observing the effects of experimental perturbations and natural aberrations [1]. Recent advances in microscopic imaging make it possible to routinely gain visual access to samples hundreds of microns thick [2]. The emergence of green fluorescent protein (GFP) as a reporter has opened up many new experimental approaches that were not previously possible [2-4]. GFP and other genetically-encoded autofluorescent protein reporters have a number of properties that make them ideal for multidimentional imaging of living specimens: no substrate (except photons) is required to generate signal, they have a high signal-to-noise ratio, are non-toxic, stable at 37°C and resistant to photobleaching. Moreover they are available in an increasingly large compendium of spectrally-distinct variants.
To construct high-resolution anatomical models of normal, mutant and pathological situations, we must establish technologies to identify and follow individual cells in three-dimensional (3D) space and in 3D over time, in four-dimensions (4D). Unfortunately, native fluorescent proteins permit tracking the position of any given cell over time only if the population of tagged cells is distributed among non-expressing cells by virtue of lineage or in a mosaic experimental situation [5-9]. In situations where groups, or all cells in a 3D field of view express a fluorescent protein label, information on the behavior of individual cells cannot be discerned. Therefore an approach is required where 3D space is segmented at cellular resolution. This is most easily achieved if each cell can be marked with an easily identifiable tag that is visible at subcellular resolution [10,11]. Since it exhibits low autofluorescence, and is a single, universal and volumetrically constrained cellular organelle, the nucleus is ideal for such labeling [12-14].
Our goal was to take advantage of this feature and to develop a non-invasive fluorescent protein marker of the nucleus for in toto imaging (all cells within the multidimensional space being imaged – discussed in Ref. [11]) of individual cells in situ in living mice [10-12]. For the unequivocal identification of individual cells, we sought a developmentally neutral, genetically-encoded autofluorescent protein-based marker that labels DNA during all phases of the cell cycle while preserving cell morphology and behavior.
As the principal structural proteins of eukaryotic chromosomes, histones are attractive targets for fluorescent nuclear labeling. Histone tagged fluorescent protein fusions have previously been shown to incorporate into chromatin without any adverse effects on the viability of cells in culture [15]. When compared to reporters containing nuclear localization sequences (nls), histone fusions exhibit an improved signal-to-noise ratio and have the distinct advantage of signal remaining bound to the target even during cell division when the nuclear envelope has broken down. In contrast nls-tagged markers (both GFP and lacZ) become dispersed throughout a cell during division, making it difficult to distinguish individual cells during mitosis.
To date GFP fusions to several histones have been generated and used for labeling nuclei in live transgenic animals, including nematode worms, fruit flies and zebrafish [13,14,16,17]. One of these is a fusion between EGFP and human histone H2B which was developed in order to label active chromatin and used to follow the segregation of double minute chromosomes in cancer cells [15]. We have investigated the expression and germline transmission of this type of fusion in mice and established its usefulness not only for imaging cell cycle dynamics [18], but also for tracking cells in living specimens. Moreover unlike native GFP variants this subcellularly localized histone fusion was found to withstand fixation while retaining both fluorescence and subcellular localization.
Results
To evaluate histone-tagged fluorescent protein fusions in embryonic stem (ES) cells and mice, we generated constructs comprising an N-terminally positioned human H2B sequence followed at the C-terminus by sequences for various fluorescent proteins both GFP and DsRed-based. We previously reported that DsRed1 was not amenable to use in ES cells or mice [22], however several improved DsRed variants have recently become available [10]. We therefore chose to evaluate DsRed2 and DsRedExpress as part of this study.
The H2B-fluorescent protein fusions we generated were introduced into vectors utilizing the CAG promoter [19] designed to drive high-level constitutive gene expression in ES cells, embryos and adult mice [20]. Standard protocols were used to establish stable lines of ES cells constitutively expressing an H2B fusion [20-22]. Several transgenic ES cell lines were generated each expressing H2B-EGFP at strong homogenous levels [23]. However, even though we did recover lines with H2B-DsRed2 and H2B-DsRedExpress expression [24], subsequent maintenance of these lines in culture revealed a continued reduction and heterogeneity in fluorescence. We were unable to establish lines with sustained homogenous H2B-DsRed2 or H2B-DsRedExpress fluorescence. Moreover our recent data suggest that mRFP1 [10,25], a rapidly-maturing monomeric form of DsRed, is amenable to use in mice, both in its native form and as a part of functional fusion proteins (AKH unpublished observations).
H2B-EGFP expressing ES cells are shown in Fig. 1. It noteworthy that with this histone fusion we observed a high signal-to-noise ratio and so could achieve high-resolution imaging of mitotic chromosomes (pink arrowheads), various states of interphase chromatin and nuclear debris (yellow arrowheads). Moreover for cells undergoing mitosis we could also discern the stage of mitosis and the plane of cell division (Fig. 1b inset). Previous work indicated that a similar fusion protein expressed in HeLa cells did not affect cell cycle progression [15], and accordingly not only could we visualize nuclear dynamics and identify the various phases of mitosis in live ES cells [26] (Fig. 2), but in doing so, we did not observe any change in growth rate or mitotic index in the transgenic ES cells compared to non-transgenic parental ES cells (data not shown). By imaging several CAG::H2B-EGFP transgenic ES cells undergoing mitosis (n = 30) we calculated the progression from early prophase to cytokinesis to take less than one hour (Fig. 2). Furthermore imaging of embryoid bodies demonstrated that individual nuclei could be discerned from a three-dimensional population of densely packed cells all of which were expressing the H2B-EGFP marker (Fig. 1c). No loss of fluorescence was observed with prolonged in vitro passage of the ES cells expressing the H2B-EGFP fusion in the absence of positive selection in the presence or absence of LIF (t > 3 months in the presence of LIF).
Figure 1 Imaging chromatin in living transgenic ES cells constitutively expressing a H2B-EGFP fusion protein. (a) Bright-field and (b) dark-field micrographs of a CAG::H2B-EGFP ES cell colony. The inset shows a detail with three nuclei in metaphase (pink arrowheads) with the metaphase plates orientated differently. The mitotic spindle of the cell at the top is closely aligned to the z-y plane whereas those for the lower two cells are more closely aligned with the x-z planes. (c) Rendered stack (3-D reconstruction) of sequential optical slices acquired using spinning disc confocal methodology, projected as a fixed angle view of an embryoid body comprised of ES cells constitutively expressing a H2B-EGFP fusion. Pink arrowheads indicate two nuclei in late-anaphase – telophase. Yellow arrowhead points to the nuclear remnant of a cell that has necrosed or apoptosed. (d – f) High-power sequential optical sections each (1 μm apart) through ES cells constitutively expressing the H2B-EGFP fusion, taken using laser scanning confocal methodology showing interphase nuclei, a mitotic nucleus (pink arrowhead) and a pycnotic nucleus (yellow arrowhead).
Figure 2 Live imaging the progression through mitosis. Laser scanning confocal x-y images taken at a single z-plane at five minute intervals for one hour. Note that not all green fluorescence (corresponding to nuclear material) will be represented in the plane being imaged. A cell progressing from anaphase to cytokinesis (pink arrowheads). A cell progressing from prophase to telophase (blue arrowheads). The average time taken to transition from early prophase to cytokinesis was calculated to be approximately 1 hour (n = 30).
We next tested the effects of widespread expression of an H2B fusion protein in mice. We generated germ line chimeras and established transgenic lines of mice constitutively expressing H2B-EGFP. We were able to breed this transgene to homozygosity, resulting in viable and fertile animals exhibiting widespread expression with no overt morphological abnormalities. The transgene has been maintained for over three years in a breeding colony of homozygous mice with no apparent effect on viability, breeding performance or lifespan. We therefore infer that this fusion protein is developmentally neutral and does not interfere with either mitosis or meiosis. Wide-field microscopic analysis of both mouse embryos and adult organs demonstrates widespread expression of the H2B-EGFP fusion in all types of nucleated cells.
We used laser scanning confocal microscopy [10,11] to image this constitutively expressed transgenic reporter at subcellular resolution in live mouse embryos. Such non-invasive visualization of chromatin in living preparations allowed us to acquire high-magnification sequential optical sections (z-stacks) that can be used to generate high-resolution anatomical volumetric (3-dimensional) images with details of interphase chromatin in addition to mitotic chromosomes and fragmenting nuclei. To do this, stacks of sequential optical sections are reconstructed into 3-dimensional projections. This methodology can be used to generate 3-dimensional (3D) image sets not only of cells propagated in culture but also of cells in situ in living animals and is illustrated here by imaging whole mouse embryos at the 4-cell stage, the blastocyst stage, and the pre-gastrula stage (Fig. 3 and Additional Files 1 and 2). These data sets can be computationally manipulated in various ways, for example for the visualization of individual xy slices from a z-stack, rendered images from the full, or part of a z-stack, and color-coded depth projections of a z-stack (Fig. 3).
Figure 3 Live embryo imaging of preimplantation and early postimplantation mouse embryos hemizygous for a constitutively expressed H2B-EGFP fluorescent fusion. (a) Single confocal optical section fluorescence overlay on a bright-field image of a 5-cell stage pre-implantation embryo. Two of the blastomeres are dividing synchronously and are in metaphase (pink arrowheads in b). (b) Dark-field projection of the entire rendered z-stack of x-y sections (n = 19), through the entire embryo shown in panel a. (c) Color-coded depth projection of the entire z-stack of x-y images for the embryo shown in the previous panels. (d) Single confocal optical section fluorescence overlay on a bright-field image of a blastocyst stage embryo. Inner cell mass (ICM) is to the top left corner and second polar body is on the bottom left, juxtaposed to the edge of the ICM. (e) Dark-field projection of half the rendered z-stack of x-y sections (n = 40, sections 1–19 were used for generating the projection), spanning half the embryo shown in panel d. Condensed chromosomes of nuclei in prophase (pink arrowheads) can be seen in three cells of the mural trophectoderm. Cells of the polar trophectoderm (green arrowhead) and inner cell mass (blue arrowhead) can also be distinguished by position within the half-blastocyst reconstruction. (f) Color-coded depth projection of the entire z-stack of x-y images for the embryo shown in the previous two panels. (g-h) Saggital views and rendered z-stacks of x-y images of an E5.75 (pre-streak stage) embryo. (g) Single optical confocal section fluorescence overlay on a bright-field image positioned half the way through the embryo. The brackets on the left illustrate the position of the embryonic (Em) and extraembryonic (Ex) regions of the embryo. (h) The same optical section with only the fluorescence image. Cells of the epiblast (blue arrowhead) and visceral endoderm (green arrowhead) can clearly be distinguished on the basis of position and nuclear morphology. Cells in mitosis can readily be distinguished within the embryo (pink arrowhead). (i) Color-coded depth projection of the stack of serial sections (n = 60), part of the series of which is shown in the previous two panels. Color-coded z-scale (upper right) applies to all projections and denotes distances along the z-axis (0–120 μm).
Data on older embryos and adult organs illustrates that larger specimens can be imaged, however not in their entirety given current limitations in optical imaging capabilities. Instead of imaging the whole specimen, larger samples are positioned so that data can be acquired from regions of interest, which can then be acquired in a tiled manner and computationally re-aligned in image acquisition and processing software.
Our data demonstrates that nuclear morphology afforded by the H2B-EGFP fusion can be used to identify different cell types. In both the raw data, and a rotated rendered stack of an embryonic day (E) 7.5 embryo, cells of the definitive endoderm, mesoderm and embryonic ectoderm can be distinguished solely on the basis of nuclear morphology and orientation in addition to their expected position (Fig. 4b–h and Additional File 3). Low magnification rendered z-stacks taken from a transversely cut section through the head of an E10.5 embryo (Fig. 4i) reveal the stereotypical 3D organization of nuclei within the region imaged (Fig. 4j), and electronically magnified views of this image illustrate a characteristic apposition of nuclei both in and around the notochord, and within the mesenchyme and endoderm of the pharyngeal region (Fig. 4k and 4l and Additional Files 4 and 5), in addition to providing information on cell division and cell death (pink and yellow arrowheads, respectively in Fig. 4l).
Figure 4 Live imaging H2B-EGFP in postimplantation mouse embryos. (a) Lateral view of the embryonic region of an E7.5 embryo (anterior to the left) with box depicting the region imaged in b and double-headed arrow depicting the x-y layering of the z-stack. (b-d) single optical x-y sections of fluorescence overlayed on bright-field images acquired at the same focal plane. Each panel is 60 μm apart from the preceding panel. These panels comprise x-y images in the z-stack depicted in panel a. The different layers of this stage of embryo including the epiblast, mesoderm, visceral endoderm and node can be distinguished on the basis of both position and nuclear morphology. (e-h) projection of a rendered z-stack of (x-y) sections (n = 90) of the dark-field component of the sections taken in the series schematized in a and of the raw data shown in b. (e) 0° rotation, (f) 60° rotation, (g) 120° rotation, and (h) 180° rotation views. (i) low-magnification frontal view of an E11 embryo that has had a transverse cut made to remove the head. The box depicts the region (at the ventral hindbrain and 1st branchial pouch) subject to laser scanning confocal imaging, with the double-headed arrow depicting the x-y layering of the acquired z-stack. (j) rendered (z-) stack of sections (n = 200, i.e. 400 μm depth) taken through the boxed region. (k) rendered stack of top 50 x-y sections (100 μm depth) taken from the region imaged around the notochord (comprising axial mesoderm and mesenchyme cells). (l) rendered stack of top 50 sections (100 μm depth) taken around the branchial pouch region (comprising endoderm and mesenchyme cells). The sections used to generate the rendered stacks in panels k and l were electronically magnified. Pink arrowheads, mitotic nuclei; yellow arrowheads, pycnotic nuclei; ect, ectoderm, en, endoderm, hf, headfold, mes, mesoderm, noto, notochord.
Wide-field microscopic examination of organs from adult animals revealed widespread fluorescence as has been reported for animals expressing native fluorescent proteins under the regulation of the CAG promoter [20,22,27]. Laser scanning confocal imaging of various organs obtained from adult animals was used to generate high-resolution images revealing stereotypical nuclear positions, reflecting different cell types and revealing other subcellular details, such as mitosis and nuclear fragments, also observed in embryos (Fig. 5 and Additional Files 3, 4, 5).
Figure 5 High resolution live imaging of the organs of CAG::H2B-EGFP adult mice. Confocal images of freshly isolated organs from a 6 week old adult male hemizygous CAG::H2B-EGFP Tg/+ animal illustrate the widespread nuclear localized expression of the histone fusion. A transverse cut was made through each organ and the cut surface was placed closest to the objective lens and imaged. Cell tracker orange was used as a vital cytoplasmic counter stain. The panels show rendered confocal z-stacks imaged through 80 μm of the brain using a 20x plan-apo objective (a-c), 568 μm of the heart using a 5x fluar objective (d-f), 142 μm of a lung lobe using a 5x fluar objective (g-i) and 346 μm of a kidney using a 5x fluar objective low power view (j-l), and high power view (m-o). Insets in panels a and d show the region of the brain and heart imaged, respectively. High resolution images of the kidney (m-o) illustrate electronic magnification of the data shown in j-l. Bron, bronchus; glom, glomeruus; med, medulla; sept, septum; ub, ureteric bud; ven, ventricle. Areas of increased fluorescence in the red channel are an artefact due to saturated pixels in regions of the sample closest to the objective.
Finally we investigated whether we could follow cell movement, division, and death in time-lapse experiments using various imaging modalities. We cultured ES cells and embryos on the stages various each of which had been modified to permit culture under physiological conditions. The different types of data routinely generated using different optical imaging modalities that are widely used and commercially available are illustrated in Figure 6. Spinning disc confocal microscopy [1] was used for short-term high-resolution 4D imaging of CAG::H2B-EGFP ES cells (Fig. 6 and Additional File 6), wide-field microscopy was used for long-term low-resolution imaging of CAG::H2B-EGFP preimplantation stage embryos (Fig. 6 and Additional File 7). Note that development proceeds normally in most embryos, and that some of the embryos imaged are undergoing cavitation to form blastocysts [28] (arrowheads). Two-photon excitation microscopy [10,29,30] was used to image cells in a whole gastrula-stage mouse embryo without perturbing the morphogenetic movements associated with gastrulation (Fig. 6 and Additional File 8). Cells can clearly be followed through the successive time points in each of these experimental situations ranging from a few minutes (short-term) to 24 hours (long-term) time-lapse duration. These studies reflect the range of resolutions at which information can be acquired using a marker of this type. We observed normal cell proliferation throughout the course of these imaging experiments and no excessive nuclear fragmentation. Also, because the on-stage cultures were comparable to parallel cultures maintained in a tissue culture incubator, we conclude that the outcome of the cultures was not affected by the various imaging modalities.
Figure 6 Dynamic time-lapse imaging of mouse CAG::H2B-EGFP transgenic ES cells, preimplantation and postimplantation embryos using different imaging modalities. (a) Rendered confocal stacks of transgenic ES cells constitutively expressing a CAG::H2B-EGFP transgene representing a 25 minute time-lapse recording of images acquired using a spinning disc confocal scan head. x-y sections with a z-interval of 0.2 μm were taken at a rate of 10/second over a total z-stack of 40 μm. Cells can be traced through the 4D rendered stack. Cells entering or completing mitosis (pink arrowheads) and the nuclear remnant of a cell that has either undergone apoptosis or necrosis (yellow arrowhead) are clearly visible. (b) Wide-field imaging of CAG::H2B-EGFP transgenic preimplantation embryos. This 24 hour image sequence illustrates cavitation leading up to the formation of the blastocyst in several embryos (violet arrowheads). (c) Rendered two-photon stacks of CAG::H2B-EGFP transgenic gastrulation stage postimplantation embryos. This 40 minute time-lapse sequence illustrates cell division and tracking within the visceral endoderm (green arrowhead) and epiblast (blue arrowheads) and the movement of mesoderm emanating from the primitive streak, which is positioned to the right, out of the field of view. Scale bar in a = 10 μm, b = 100 μm and c = 50 μm.
Discussion
Here we report the evaluation of a chromatin localized histone fusion fluorescent reporter in vivo through the generation of transgenic embryonic stem (ES) cells and mice having widespread expression of this reporter.
The transgenic mice that we have generated provide a new tool for high-resolution live imaging of a genetically tractable mammalian model organism [10,11]. They represent a resource for analyzing development and disease at the subcellular level in cells, embryos and adult tissues. The marker used facilitates the acquisition of in vivo data and allows it to be integrated onto a high-resolution anatomical framework. This type of multidimensional data is complex and thus difficult to digitize and compile into a standardized and integratable format. In toto imaging of fluorescent protein expressing specimens on a large-scale could be used for generating high-resolution digitally recorded anatomical databases where the baseline (wild-type) cell behaviors and cell fates can be contrasted to those observed in mouse mutants. However, developing in toto imaging technologies for acquiring large amounts of data will necessitate improving the speed and throughput of microscopic image acquisition and analysis. This would also be coincident with the ongoing development of improved computational approaches to mine and integrate this type of data [discussed in ref. [11]].
Much of the information generated using a fluorescent fusion reporter such as the H2B-EGFP fusion is analogous to conventional histology [21,31] except that this mode of data acquisition optically sections a sample (circumventing the need to physical section), excels in permitting computational 3D reconstructions of spatial information, and can additionally be coupled to time-lapse imaging for the capture, processing and quantitation of 4D information (Fig. 6 and Additional Files). Also, unlike conventional GFP-based reporters [20,22], the histone H2B-EGFP fusion is resilient to fixation, so samples can be processed and stored for extended periods of time without compromising signal integrity or specificity (Fig. 7).
Figure 7 High-resolution 3-dimensional imaging of fixed CAG::H2B-EGFP transgenic embryos. Confocal images of an E8.5 CAG::H2B-EGFP transgenic embryo fixed in 4% paraformaldehyde for 72 hours, then washed, stored and imaged in PBS. Low-magnification views and reconstructions of whole embryo (a-c). Boxes in a designate region imaged in d and g. High-magnification views of the headfolds (d-f) and posterior primitive streak and proximal allantois (g-i). Single xy images (a, d and g) from the z-stacks used to computationally render the data sets. These images are overlayed onto the bright field channel so as to display the outline of the embryo. Rotations through the rendered z-stacks displayed at 45° intervals (b, e and h). Color-coded depth projections of each of the z-stacks (c, f and i).
The future development and availability of mouse strains constitutively expressing spectral variant histone H2B fusions should prove useful for visualizing anatomy and tracking different populations of cells in multiple dimensions at high-resolution in mice as has previously been demonstrated in other organisms which are classically perceived as being more amenable to in vitro culture and optical imaging [13,16]. They can also be used as tagged populations of cells in chimeras [8], in addition to transplantation and cell isolation experiments [32]. Also, since fluorescence is proportional to genome content and the fluorescence intensity reflects chromosome condensation state, the reagents we have generated should permit the study of alterations in ploidy and chromosomal condensation including determination of phases of mitosis [26]. Additionally, real-time analysis of chromatin fragmentation as well as the effects of mutations on chromosome stability during disease processes can be investigated using CAG::H2B-EGFP transgenic mice.
Conclusions
The CAG::H2B-EGFP strain that we have generated takes in vivo imaging using genetically-encoded reporters in mice to sub-cellular resolution. The development of additional strains permitting spectrally-distinct high-resolution live cell in vivo imaging, coupled to advances in optical imaging modalities and the development of improved computational methods to mine imaging data should pave the way for a multidimensional understanding of biological processes.
It is anticipated that in the future, in vivo imaging approaches using transgenic animals expressing genetically-encoded fluorescent proteins will not only provide high-resolution information on cell behavior in specific biological processes [12,33], but more importantly it may lead to an exponential increase in the available multidimensional in vivo biological information which could mirror the recent explosion of available genomic data. Therefore recent advances in live imaging will need to be paired with developments in computational biology, as appropriate informatics methods will need to be developed and implemented in order to mine, present and integrate this type of in vivo biological data.
Methods
The coding sequence for the human histone H2B gene (X57127) was amplified from genomic DNA by PCR using Pfx Polymerase (Invitrogen). The resulting product was cloned into pCR4 TOPO (Invitrogen) to generate pH2B. The H2B fragment was then cloned into plasmids pEGFP-N1, pDsRed2-N1 pDsRedExpress-N1 (BD Biosciences, Inc) in order to generate plasmids pH2B-EGFP, pH2B-DsRed2 and pH2B-DsRedExpress (oligonucleotide sequences are available upon request). The resulting fusions were then re-amplified by PCR and cloned into the XhoI site of pCAGGS [19] to generate pCX-H2B-EGFP, pCX-H2B-DsRed2 and pCX-H2B-DsRedExpress. All vectors were tested by transient transfection of Cos-7 cells and R1 ES cells using Fugene 6 Transfection Reagent as per manufacturer's recommendations (Roche) and electroporation respectively. The H2B-DsRed2 and H2B-DsRedExpress fusions failed to produce sustained homogenous levels of fluorescent signal, however the H2B-EGFP fusion gave strong nuclear-localized fluorescence throughout the extended culture period without perturbing cell morphology, the rate of proliferation, or the mitotic index (Fig. 2 and data not shown).
Transgenic ES cell lines constitutively expressing H2B-EGFP were generated by co-electroporation of the linearized reporter construct and a circular PGK-Puro-pA plasmid [34] conferring transient puromycin resistance. Puromycin selection was carried out as described previously [20,22]. Briefly, drug selection was initiated 24–36 hours after electroporation, maintained for 5 days, after which time it was replaced with non-selection media for a further 24–48 hours. Fluorescent colonies were identified and picked under an epifluorescence microscope (Nikon SMZ1500). Clones were passaged in 96-well plates, and scored for maintenance and extent of fluorescence. Those exhibiting homogeneous and robust transgene expression in vitro under both stem cell conditions and conditions employed to promote their differentiation were maintained further. For stem cell conditions ES cells were grown on gelatin in the presence of LIF. For differentiation, ES cells were grown on bacteriological Petri dishes in the absence of LIF for 2–5 days to promote embryoid body formation. Thereafter embryoid bodies were re-plated onto tissue culture dishes in the presence of factors promoting directed differentiation.
To assess whether an H2B fusion can continue to be widely expressed and transmitted through the germline of mice we used H2B-EGFP expressing ES cells for chimera generation by injection into C57BL/6 blastocysts using standard procedures [21]. Chimeras were bred to outbred ICR and inbred 129/Tac mice (Taconic, Germantown, NY) for germline transmission and subsequent maintenance of the lines. Two independent clones were taken germline giving indistinguishable results. We therefore focused on one of the transgenic lines. After germline transmission, this transgene was maintained at homozygosity, suggesting that the site of integration is not perturbing essential gene function. All animals retained widespread homogenous fluorescence for at least five subsequent generations. Homozygotes were distinguished from heterozygotes either by increased fluorescence in newborn (unpigmented) animal tails, by breeding, or by intensity of an EGFP hybridizing fragment on a Southern blot.
Embryos and organs were dissected in HEPES buffered DMEM media containing 10% fetal calf serum, then cultured either in a standard tissue culture incubator or on a microscope stage under standard conditions promoting the culture of mouse embryos [35,36] in 50% rat serum: 50% DMEM buffered with bicarbonate and maintained under physiological conditions in a closed temperature-controlled, humidified and oxygenated (95% air, 5% CO2) chamber (Bioptechs Inc. or Solent Sci Ltd. or home-made). For cytoplasmic staining, samples were incubated in Cell Tracker Orange (Molecular Probes; 1:500 dilution in dissecting or culture media) for 10–20 minutes. Embryos were kept in a standard tissue culture incubator at 37°C during staining. Samples were then washed twice with warm dissecting or culture media prior to imaging.
All images shown (except Fig. 7) are of living hemizygous (Tg/+) embryos or freshly dissected (unfixed) tissues obtained from Tg/+ adults and maintained under physiological conditions. Increased fluorescence and a higher signal-to-noise ratio was observed in homozygous (Tg/Tg) specimens. The embryo presented in Figure 7 was fixed in 4% paraformaldehyde at 4°C for 72 hours, then washed, stored and imaged in PBS. Similar results were obtained in embryos fixed for up to two weeks.
Wide-field images were acquired on a Nikon SMZ1500 stereo-dissecting microscope or Nikon Eclipse 5000 inverted microscope equipped with epifluorescent illumination. Spinning disc confocal data was acquired on an UltraView RS3 (Perkin-Elmer Systems) fitted on a Zeiss Axiovert 200M microscope with illumination from a 488 nm Argon laser (Melles Griot). Laser scanning confocal and multiphoton excitation data were taken on a Zeiss LSM510 NLO on a Zeiss Axioscop 2 FS MOT microscope. Objective lenses used on the Axiovert 200M and Axioscop 2 were plan-apochromat 63x/NA1.4, C-apochromat 40x/NA1.2, a plan-apochromat 20x/NA0.75 and a fluar 5x/NA0.25. For laser scanning microscopy GFP was excited using either a 488 nm Argon laser (Lassos, Inc) at 488 nm (for single-photon excitation) or a Titanium:Sapphire laser (Coherent Mira 900F with Verdi 5W pump laser) tuned between 860 and 890 nm (for two-photon excitation). Cell Tracker Orange was excited using a 543 nm HeNe laser (for single-photon use).
Images were acquired as z-stacks comprising sequential x-y sections taken at 0.1–2 μm z-intervals. Raw data was processed using a variety of packages including Zeiss AIM software (Carl Zeiss Microsystems at ), Image J (NIH at ) and Volocity (Improvision at ). Each image series was re-animated using software to make the time-lapse movies that are available as additional files. Both rendered volume and time-lapse movies were assembled in QuickTime Player (Apple Computer, Inc at ).
Appendix
The CAG::H2B-EGFP strain of mice generated in this study will be made available through the Jackson Laboratories Induced Mutant Resource (JAX IMR at ).
Supplementary Material
Additional File 1
Rotating 3D projection of a whole live CAG::H2B-EGFP Tg/+ blastocyst. Supplementary to stills presented in Fig. 3. This file was assembled at 6 frames/second.
Click here for file
Additional File 2
Rotating 3D projection of a (electronic) half blastocyst. Generated from the same raw data set used to compile Supplementary File 1. Supplementary to stills presented in Fig. 3. This file was assembled at 6 frames/second.
Click here for file
Additional File 3
Rotating 3D projection the node region of a live CAG::H2B-EGFP Tg/+ E7.5 embryo. Supplementary to stills presented in Fig. 4. This file was assembled at 6 frames/second.
Click here for file
Additional File 4
Rotating 3D projection of the notochord of a live CAG::H2B-EGFP Tg/+ E10.5 embryo. Supplementary to Fig. 4. This file was assembled at 6 frames/second.
Click here for file
Additional File 5
Rotating 3D projection of the branchial region of a live CAG::H2B-EGFP Tg/+ E10.5 embryo. Supplementary to stills presented in Fig. 4. This file was assembled at 6 frames/second.
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Additional File 6
Time-lapse sequence of CAG::H2B-EGFP Tg/+ ES cells. Supplementary to stills presented in Fig. 6. This file was assembled at 12 frames/second.
Click here for file
Additional File 7
Time-lapse sequence of CAG::H2B-EGFP Tg/+ preimplantation embryos. Supplementary to stills presented in Fig. 6. This file was assembled at 6 frames/second.
Click here for file
Additional File 8
Time-lapse sequence of a CAG::H2B-EGFP Tg/+ postimplantation embryo. Supplementary to stills presented in Fig. 6. This file was assembled at 6 frames/second.
Click here for file
Additional File 9
Rotating 3D projection of a fixed CAG::H2B-EGFP Tg/+ E8.5 embryo. Supplementary to stills presented in Fig. 7. This file was assembled at 6 frames/second.
Click here for file
Additional File 10
High-magnification rotating 3D projection of the headfold region of the same fixed CAG::H2B-EGFP Tg/+ E8.5 embryo as shown in Movie 7. Supplementary to stills presented in Fig. 7. This file was assembled at 6 frames/second.
Click here for file
Additional File 11
High-magnification rotating 3D projection of the allantois and posterior tail region of the same fixed CAG::H2B-EGFP Tg/+ E8.5 embryo as shown in Movie 7. Supplementary to stills presented in Fig. 7. This file was assembled at 6 frames/second.
Click here for file
Acknowledgements
We thank J.-I. Miyazaki for the pCAGGS plasmid, and T. Swayne at the Herbert Irving Comprehensive Cancer Center Optical Microscopy Facility for instruction and assistance with laser scanning microscopy data acquisition and processing. This work was supported by NIH grants GM60561 and HD33082 (VEP). AKH was a fellow of the American Heart Association during part of this work.
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| 15619330 | PMC544401 | CC BY | 2021-01-04 16:38:25 | no | BMC Biotechnol. 2004 Dec 24; 4:33 | utf-8 | BMC Biotechnol | 2,004 | 10.1186/1472-6750-4-33 | oa_comm |
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PLoS MedPLoS MedpbioplosbiolPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1574041310.1371/journal.pmed.0020039PerspectivesInfectious DiseasesHealth EconomicsHealth PolicyHIV/AIDSHIV Infection/AIDSMedicine in Developing CountriesPublic HealthHealth education (including prevention and promotion)International healthScaling Up AIDS Treatment: What Is the Potential Impact and What Are the Risks? PerspectiveLamptey Peter [email protected] David Peter Lamptey is the president of the Institute for HIV/AIDS at Family Health International, Arlington, Virginia, United States of America. David Wilson is a senior monitoring and evaluation officer at the World Bank, Washington, District of Columbia, United States of America.
Competing Interests: The authors declare that they have no competing interests.
2 2005 11 1 2005 2 2 e39Copyright: © 2005 Lamptey and Wilson.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Integrating HIV Prevention and Treatment: From Slogans to Impact
Lamptey and Wilson discuss the implications of a new study showing that combining treatment with prevention is the best approach to tackling the HIV pandemic.
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There has been a recent and dramatic rise in global funding for HIV/AIDS, from US$2.1 billion in 2001 to US$6.1 billion in 2004 [1], thanks to several new funding mechanisms (Box 1). These funds, coupled with reduced drug costs, make it feasible to roll out antiretroviral therapy (ART) even in resource-poor settings. Nevertheless, the total number of people living with HIV rose in 2004 to reach its highest level ever: an estimated 39.4 million people are living with the virus, including 4.9 million who acquired it in 2004 [1]. Therefore, the debate over the appropriate distribution of money between prevention efforts (such as voluntary counseling and testing [VCT], or behavior change) and treatment efforts (the provision of ART) is now more topical than ever.
Box 1. Initiatives to Fund ART
The WHO “3 by 5” Initiative: This initiative aims to place 3 million people in low- and middle-income countries on ART by the end of 2005.
The US President's Emergency Plan for HIV/AIDS Relief: This initiative aims to treat 2 million HIV-infected people with ART, to prevent 7 million new infections, and to care for 10 million HIV-infected individuals and AIDS orphans in five years (2004–2009).
The Global Fund to Fight AIDS, Tuberculosis, and Malaria: In the five years following its inception (2002–2007), the Global Fund aims to provide 1.6 million people with ART and 52 million people with VCT, and to support more than 1 million orphans with medical services, education, and community care.
Balancing Prevention and Treatment
The scale of the proposed increase in the number of patients receiving ART raises numerous questions about the treatment itself. Which drugs will be used? How much will it cost? How will their quality be monitored and assured? How will they be distributed? Who will be eligible? How will the desired level of treatment be sustained? Is there adequate infrastructure and human resources to support the expanded services?
The commitment of substantial funding to treatment in resource-poor countries also has implications for the prevention efforts in those same countries. In many Western countries and Brazil (the sources of the majority of the available data on the subject), the impressive drop in mortality due to HIV following increased access to ART is coupled with a disheartening rise in the number of new cases of HIV, as emphasis and funding are shifted from prevention to treatment [2]. Countries in which this pattern has been seen are evidence of the pitfalls of failing to adapt prevention efforts once life-extending treatment becomes widely available.
Of course, prevention and treatment are not mutually exclusive. Successful prevention efforts mean fewer patients will need the costly drug treatment programs, helping extend the sustainability of ART. In turn, the success of ART in prolonging healthy living helps prevention efforts by reducing the stigma associated with self-education and responsible behaviors.
Measuring Prevention and Treatment Effects
In their study in the January 2005 issue of PLoS Medicine, “Integrating HIV Prevention and Treatment: From Slogans to Impact,” Salomon and colleagues use mathematical modeling to assess the epidemiologic impact of treatment and prevention efforts, and to quantify the opportunities and potential risks of large-scale treatment roll-out. Using a variety of different scenarios, they propose methods for establishing the most effective balance between spending on prevention and spending on treatment.
Modeling is a technique used by many scientists, including epidemiologists and statisticians, to create a mathematical equation that can be used to determine which variables affect an outcome of interest, and to what extent. Once the influential variables are determined, a baseline model is established that includes those variables and reflects their relative importance to the outcome. The effect of changing the value of any of these variables, or several of them, can then be tested, and new outcomes projected. HIV modeling is inexact and requires far better data but can nevertheless provide important insights.
Salomon and colleagues used mathematical modeling to assess the effect of changing aspects of the HIV/AIDS “equation” on the future course of the HIV/AIDS epidemic. First, a baseline model was created to fit expected HIV/AIDS projections for the year 2020 if there were to be no change in the current epidemiologic trends—no ART scale-up, and no changes in prevention efforts or behavior. Heterosexual contact is the predominant mode of HIV transmission across Africa, and Salomon and colleagues' study modeled the disease only within the heterosexual population. The model was also tailored to take into account epidemiologic, demographic, and sociologic patterns in the eastern, central/western and southern regions of Africa. Using the baseline models tailored to each region, the effects of prevention and treatment efforts were then measured.
Two treatment-centered scenarios were tested in which the World Health Organization's “3 by 5” initiative (see Box 1) was achieved. In these treatment-centered scenarios, the reduction of transmissibility, the number of partners of each patient, and condom use were either optimal (reduced transmissibility, reduced partners, and increased condom use) or less than optimal. The prevention-centered scenario tested the impact of a comprehensive package of 12 prevention tools (such as VCT and peer counseling for sex workers), modeling only partial effectiveness at the population level, to reflect weaker political and social support for HIV control efforts. Finally, combined response scenarios were tested. In the first scenario, treatment efforts strengthened prevention efforts as, for example, when the availability of ART increases people's willingness to undergo testing. In the second, an emphasis on treatment led to less effective implementation of prevention efforts.
Baseline projections in Salomon and colleagues' study showed that without any behavioral change or ART scale-up, the HIV/AIDS prevalence rate would remain relatively stable, but the number of new infections would increase by 52.3 million by 2020. Treatment-centered scenarios reduced the total number of new infections through 2020 by a maximum of 3 million, or 6%, while indicating that the number of AIDS deaths through 2020 would decline by 13%, to 32.4 million. A prevention-centered strategy would provide greater reductions in incidence (36%) and similar mortality reductions by 2020, but more modest mortality benefits over the next five to ten years.
The scenarios in which all of these statistics were most improved, however, were those that combined both prevention and treatment efforts. In the scenario in which treatment enhanced prevention, Salomon and colleagues projected 29 million averted infections (55%) and 10 million averted deaths (27%) through the year 2020. However, if a narrow focus on treatment scale-up leads to reduced effectiveness of prevention efforts, the benefits of a combined response would be considerably smaller—9 million averted infections (17%) and 6 million averted deaths (16%) (Figure 1).
Figure 1 Projected New Adult Infections and Total Adult Deaths, in Millions, to 2020
This graph represents projections through 2020, and, when there was a choice, highlights the more successful iteration of a model. The treatment-centered response, therefore, shows data from the optimal-effects model, and the combined response data reflect the optimistic model.
Combining treatment with effective prevention efforts could reduce the resource needs for treatment dramatically in the long term. In the various scenarios the numbers of people being treated in 2020 ranges from 9.2 million in a treatment-only scenario with mixed effects, to 4.2 million in a combined response with positive treatment–prevention synergies.
Moving Forward
The authors have demonstrated through mathematical modeling that the integration of treatment and prevention is epidemiologically sound. However, an integrated and comprehensive program (Figure 2) is not only logical but makes sense from the service delivery point of view: it can be cost-effective and ideal for the community.
Figure 2 Components of an Integrated Comprehensive HIV/AIDS Program
PMTCT, prevention of mother-to-child transmission; STD, sexually transmitted disease.
Effective prevention makes treatment more affordable and sustainable. Effective prevention can lead to a substantial reduction in the number of new infections and therefore ultimately will lead to a reduction in the number of people who will need treatment. The reduction of adult HIV/AIDS prevalence in Uganda from 18.5% to 6% over the last several years has reduced the number of those eventually needing treatment by nearly 68% [3]. Unless the incidence of HIV is sharply reduced, HIV treatment will not be able to keep pace with all those who will need therapy [4]. Salomon and colleagues' reaffirmation that only effective prevention will make treatment affordable is critically important.
Successful treatment and care can make prevention more acceptable and effective. Widespread access to treatment could bring millions of people into health-care settings, providing new opportunities for health-care workers to deliver and reinforce HIV prevention messages and interventions [4]. Improved access to HIV testing provides an entry point to both prevention and treatment services and provides a unique opportunity to identify and target the infected, vulnerable, and uninfected with more appropriate interventions. All health-care settings, including HIV treatment sites, should deliver HIV prevention services [4].
Prevention can make treatment more accessible. The early establishment of community-based prevention services in rural Ghana was instrumental in reducing the stigma of AIDS and improving the knowledge and attitude of the community prior to the development of ART and VCT services (K. Torpey, personal communication). This process also made it easier for community and implementing agencies to identify and refer patients needing treatment services.
Expanded care and prevention activities have synergistic effects. Continued effective treatment, care, and prevention programs will reduce the number of orphans and vulnerable children, reduce mother to child transmission of the virus, and improve the lives of families and the strength of communities.
Integration ensures that prevention activities are not neglected. The world has a unique opportunity, as ART services are launched and expanded, to simultaneously bolster prevention efforts [4]. Experience in the United States indicates that availability of treatment can lead to increased risk behavior [5]. In addition, the improvement in the health, well-being, and longevity of people living with AIDS could increase the opportunities for HIV transmission. Integration can help reduce these potential negative impacts of treatment.
Integration can provide opportunities to address vulnerable groups more effectively. A commitment to providing large-scale treatment helps to focus attention on communities at greatest risk, particularly in lower prevalence contexts. This provides an opportunity to address the prevention and treatment needs of vulnerable groups more effectively.
Treatment resources can help improve infrastructure for prevention and other health services. The training of health providers and improvements in laboratory services, pharmacy, logistics, commodity management, and health information systems can benefit both treatment and prevention services. Further, in many countries, a large number of health-care workers are themselves infected. Treatment can help to preserve the lives and productivity of these critically needed AIDS prevention and treatment workers, as well as those of other health professionals.
A long-term decline in AIDS deaths may be preventing new infections. The short-term decline in AIDS deaths is driven by effective care and treatment programs, but a long-term decline may be driven by the prevention of new infections. Integrated and comprehensive strategies are more likely to lead to affordable, sustainable programs.
Success requires dramatic expansion of both ART and prevention. Globally, fewer than one in five people at high risk of infection have access to proven HIV prevention interventions [6] and less than 10% have access to ART [1]. Unless there is a substantial increase in commitment and resources for both prevention and ART, efforts to control HIV/AIDS and mitigate its impact will only meet with partial and limited success. In addition, to increase resources, intensified commitment is required to ensure every opportunity is taken to integrate prevention and treatment. Future analysis and debate should move from comparisons of prevention and treatment priorities to a sustained analysis of how we can reciprocally integrate and strengthen prevention and care and use every opportunity provided by one to reinforce the other. We must focus on the development of training, monitoring, and quality assurance systems that ensure that prevention and care are integrated whenever possible.
The results of Salomon and colleagues' model need to be validated. Further operational research is needed to validate the findings of this study.
Thanks to Rebecca Oser, at Family Health International, for her contributions to this article.
Citation: Lamptey P, Wilson D (2005) Scaling up AIDS treatment: What is the potential impact and what are the risks? PLoS Med 2(2): e39.
Abbreviations
ARTantiretroviral therapy
VCTvoluntary counseling and testing
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References
Joint United Nations Programme on HIV/AIDS, World Health Organization AIDS epidemic update 2004 2004 December Geneva World Health Organization Available: http://www.unaids.org/wad2004/report.html . Accessed 22 December 2004
Salomon JA Hogan DR Stover J Stanecki KA Walker N Integrating HIV prevention and treatment: From slogans to impact PLoS Med 2005 2 e16 15647780
United Nations Development Programme UNDP in Uganda 2004 Available: www.undp.or.ug/hivaids.htm . Accessed 22 December 2004
Global HIV Prevention Working Group HIV prevention in the era of expanded treatment access 2004 June Available: http://www.gatesfoundation.org/nr/downloads/globalhealth/aids/PWG2004Report.pdf . Accessed 22 December 2004
Voelker R What the San Francisco increase in HIV infections might foretell J Int Assoc Physicians AIDS Care 2000 November Available: http://www.aegis.com/pubs/iapac/2000/IA001101.html . Accessed 22 December 2004
World Health Organization Coverage of selected health services for HIV/AIDS prevention and care in less developed countries in 2001 2002 November Geneva World Health Organization Available: http://www.who.int/hiv/pub/prev_care/isbn9241590319.pdf . Accessed 22 December 2004
| 15740413 | PMC544403 | CC BY | 2021-01-05 10:39:27 | no | PLoS Med. 2005 Feb 11; 2(2):e39 | utf-8 | PLoS Med | 2,005 | 10.1371/journal.pmed.0020039 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1566015110.1371/journal.pbio.0030001Community PageBioinformatics/Computational BiologyDevelopmentDrosophilaBiology by Numbers—Introducing Quantitation into Life Science Education Community PageAegerter-Wilmsen Tinri Bisseling Ton [email protected] 2005 18 1 2005 18 1 2005 3 1 e1Copyright: © 2005 Aegerter-Wilmsen and Bisseling.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.An online educational module introduces students to concepts of quantitation and numerical simulations in developmental biology
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Driven by the massive datasets that are generated by “omics” research, the molecular life sciences are entering a new phase. This phase is characterised by a shift in focus from individual genes and their products to networks and whole systems [1,2,3]. For a thorough analysis of the behaviour of networks and their underlying principles, quantitative tools are often necessary. Numerical simulations can, for example, be used to explore the behaviour of a network when the values of different parameters are varied, and, in turn, mathematical analysis can help to understand a particular biological phenomenon [2].
The successful application of quantitative tools in the molecular life sciences requires a good understanding of these tools and sufficient knowledge of the biological system under study. This can be achieved by collaboration between quantitatively trained scientists such as physicists on the one hand and biologists on the other. However, cultural differences hamper such collaboration [1]: even at the undergraduate level, students in the different disciplines speak very different languages [4].
A more productive approach is therefore to prepare students better for the quantitative nature of the molecular life sciences by integrating quantitative thinking and biology in the life science curriculum. This can be achieved in various ways. For example, a curriculum could be developed in which mathematics, the physical sciences, and biology are introduced together [4]. However, we recommend that quantitative thinking also be included throughout the curriculum in the biology courses themselves, covering topics such as cell biology, developmental biology, and biochemistry. We consider this important because it will help to show students how quantitative tools can be used to address various cutting edge questions in biology.
A Modelling Module in Developmental Biology
As an example of the integration of quantitative teaching and cutting edge biology, we have implemented an educational module in which numerical simulations are used in an existing course on developmental biology (http://mbedu.fbt.eitn.wau.nl/demo_plos/). Some of the features of this module and the thinking that led to its development are quite general, and so we present the module here as a case study in the hope that this might inspire and guide others to create similar resources.
First, we wanted to illustrate to students the value of using numerical simulations to study a developmental process. Therefore, a pattern-forming mechanism was selected that can initially be rather hard to understand: the generation of the morphogen gradient formed by the extracellular signalling molecule decapentaplegic (Dpp) early during Drosophila embryogenesis [5]. The generation of this gradient results from the fact that key proteins are synthesized in different embryonic regions, from the formation of complexes of these proteins, and from the different diffusion rates of these complexes and their components, as well as from the specific degradation of some components. Students are guided through the creation of a model for Dpp gradient formation based on a set of experimental data. At several stages, students can perform simulations in a separate simulation environment. Students use simulations, for example, to check whether a number of core interactions is sufficient to yield the most important characteristics of the wild-type gradient.
Second, we designed the simulation environment in such a way that biology students with their existing mathematical background can build quantitative models and run numerical simulations themselves. In this environment (Figure 1), students do not have to program anything, or set up differential equations, themselves. Instead, they indicate which processes occur at the molecular level, and the program then shows how each of these processes is translated into a term in a differential equation. In Figure 2, for example, the program adds a diffusion term to a differential equation if the student indicates that diffusion occurs. Besides setting up the equations in this way, students specify the initial localisations and concentrations of the different proteins, as well as the constants that are used in the differential equations.
Figure 1 A Simulation That Students Can Perform
After several minutes, Dpp forms one peak in the centre of the dorsal region, as in the wild type. The various elements of the quantitative model can be entered under “protein conc. changes”, “initial localizations”, “values of constants”, and “initial concentrations”. The numerical simulation itself shows the dynamic behaviour of the designed quantitative model.
Figure 2 Illustration of How Students Can Set Up Differential Equations
If a student indicates that Dpp diffusion occurs, a diffusion term is added to the differential equation that describes the changes in Dpp concentration.
Third, we wanted to make sure that students would use the simulation environment effectively. Therefore, a clear goal is formulated when students enter the simulation environment. For example, they are asked to make a model that generates a Dpp gradient that fulfills a number of specific criteria, or simulates certain mutants. After running a simulation, students can view feedback that helps them draw conclusions or consider the next step to be taken. If a student's model, for example, generates a gradient that is too shallow, the student has to indicate which change in the model he expects to be useful for generating a steeper gradient. The student then receives an intuitive explanation of the usefulness of the given suggestion. If an increase in the synthesis of one of the proteins, Short gastrulation, is proposed, for example, feedback is given that this could indeed be useful, since there would then be more Short gastrulation available to transport Dpp, such that the gradient can become steeper. In this way, the student is stimulated to carefully consider each step and is provided with sufficient support to decide which is a useful step to follow. In addition, with this type of feedback, explanations are given that relate quantitative changes in the model to qualitative changes in its behaviour, which should increase the student's understanding of the behaviour of the biological model.
We consider it important that students, while using the module, are not distracted too much by quantitative issues from the actual biological principles and facts. These have to be mastered in order to obtain a strong biological background. If students want to learn more advanced quantitative skills, they can still follow courses that are specifically aimed at this aspect.
The Future
Quantitative analysis is already gaining importance in molecular life sciences. Therefore, it is desirable that curriculum changes are implemented in the short term. This poses challenges to faculties, especially to those whose members do not have much, if any, experience with the application of quantitative tools in their own research. Therefore, it may be useful to initially focus on the development of learning materials that are rather self-contained, such that their application requires relatively little competence in quantitative analysis from the teaching staff. If these materials are openly available they can be incorporated rapidly into existing courses, such that even the current generation of students may be better prepared to integrate quantitative thinking and biology in their future research.
We would like to thank Rob Hartog, Fred Janssen, Dik Kettenis, and Olivier Sessink for their contribution to the development of the educational material and Christof Aegerter for helpful discussions.
Citation: Aegerter-Wilmsen T, Bisseling T (2005) Biology by numbers—Introducing quantitation into life science education. PLoS Biol 3(1): e1.
Ton Bisseling and Tinri Aegerter-Wilmsen are in the Laboratory of Molecular Biology, Wageningen University and Research Centre, Wageningen, the Netherlands.
Abbreviation
Dppdecapentaplegic
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Lander AD A calculus of purpose PLoS Biol 2004 2 e164 15208717
Pennisi E Tracing life's circuitry Science 2003 302 1646 1649 14657470
Bialek W Botstein D Introductory science and mathematics education for 21st-century biologists Science 2004 303 788 790 14764865
Eldar A Dorfman R Weiss D Ashe H Shilo BZ Robustness of the BMP morphogen gradient in Drosophila embryonic patterning Nature 2002 419 304 308 12239569
| 15660151 | PMC544540 | CC BY | 2021-01-05 08:21:19 | no | PLoS Biol. 2005 Jan 18; 3(1):e1 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030001 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1566015310.1371/journal.pbio.0030012Community PageOtherNoneAccessing the Microscopic World Community PageCarlson Charles 1 2005 18 1 2005 18 1 2005 3 1 e12Copyright: © 2005 Charles Carlson.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.The Exploratorium in San Francisco offers museum visitors the opportunity to use and manipulate state-of-the-art microscopes to visualize an array of living specimens
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The Exploratorium, based in San Francisco, is a “hands on” science museum filled with interactive science and art exhibits, as well as a laboratory for the research and development of innovations in science education. In the summer of 2004, the Exploratorium launched the most ambitious microscope facility ever created for general public use. This initial phase of the project gives visitors the ability to image living specimens, as well as control the instruments themselves. Visitors can select among various specimens, move over them, change the magnification and focus, and, where appropriate, change the lighting to illuminate through the specimen, or use reflected light and fluorescence to dramatically change how it looks. They can image and explore tiny zebrafish embryos from the first stages of development to two-day-old fry with beating hearts and circulating blood cells, as well as a host of other organisms and cells from crawling amoebas to human blood cells.
Below the surface, all living things share common features. The primary goals of this facility are to open a door on the wonder of the microscopic world to a diverse range of museum visitors and allow them to explore it, and to allow them to make connections to science and biomedical research. By empowering visitors with the instruments to explore this unfamiliar universe, the Exploratorium seeks to recreate some of the excitement and wonder that the earliest researchers found as they discovered another world all around them (Box 1).
Box 1. History of Light Microscope
The light microscope falls amongst the greatest inventions of human history. Images from it in the 17th century literally revolutionized our understanding of life, providing first-hand evidence of a previously unseen or unsuspected world of organisms and cells all around us. This knowledge profoundly shaped our view of life, and of our placement in the universe. Robert Hooke used a primitive early microscope to see the walls between cells in a piece of cork (essentially discovering the cellular nature of all life), and Anton van Leeuwenhoek's simple scope revealed a previously unknown world of microorganisms living inside his own mouth. Swimming sperm were observed in semen, changing our fundamental understanding of conception. Since that time our world has become populated by marvelously beautiful and intriguing images and movies created by scientists using precise lighting and optics. Most recently, computer-controlled image-capturing techniques and digital technologies capture events and processes too small, slow, or fast for our unaided eyes to see.
Van Leeuwenhoek's first microscopes were probably about as powerful as a simple water-drop scope (see Box 2); he used his simple devices to make observations that weren't confirmed for over a hundred years. It's quite possible that early microscope-makers were inspired to make small domed lenses by observing the magnifying properties of a rounded drop of water. Further experimentation with the sizes and shapes of lenses eventually led to much greater magnifications.
Beyond the Armored Microscope
Bringing a first-hand, high-quality microscope experience to a wide range of visitors and students has been a major challenge for many science museums and classrooms.
In the expanded, frenetic classroom of the science museum exhibition floor, microscopes themselves are too delicate and precise for operation without assistance and supervision: the optics can be easily damaged, all but the most robust of specimens are easily destroyed, and the user interface with lighting, positioning, and focus compose a world unfamiliar to most novice users. Over the years, we had collected our share of failed and broken microscopes in various projects. For these reasons most microscopes in museums have been armored, stripped-down, single-magnification instruments.
Problems related to the operation of the microscope hardware, however, represent only some of the challenges created by bringing uninitiated visitors to the microscopic world. The experience has to be repeatable. It has to be engaging. It has to be both simple and complex. It needs to work for individuals and groups. It needs to support investigation as well as provide essential information.
An array of research scientists joined us as we began work to redefine and renew our existing presentation on biology, most notably Christian Sardet, a cell biologist with a passion for visualization imagery. During the summer and fall of 1999, we engaged in a series of conversations directed at defining and developing a publicly accessible microscope imaging station, and the more we explored, the more technically feasible the project became.
At the same time, research-grade microscopes with high-quality optics underwent a revolution of their own. Many manufacturers planned to introduce fully automated, computer-controllable microscopes in their next models, and the prospects of having an off-the-shelf remotely controllable microscope seemed to solve a major hardware problem. It seemed as if software programs might be used to address many other user interface issues. In 2000, we applied for and received funding from the National Institutes of Health (a Science Education Partnership Award from the National Center for Research Resources) and from the David and Lucile Packard Foundation to support the development of a microscope imaging facility.
Seven months later we obtained the first microscope and its associated imaging equipment. But the software to control the microscopes proved dauntingly problematic. Programs that worked well in the laboratory proved difficult to adapt for our educational uses. Instead, we wrote our own programs to control stage movement (for specimen positioning), focus, specimen selection, magnification, and lighting. Each of these controls needed to be selectable and limited so that visitors might operate the microscope within a defined range.
On a parallel track, with the generous help of numerous biomedical researchers and their laboratories across the United States, we explored specimens for their visitor attraction and interest. Not surprisingly, we found that familiar structures and organisms provided the best entry points. A zebrafish embryo with a beating heart, circulating blood, and twitching tail movements rated more popular than zebrafish or sea urchin embryos at earlier stages of development. The tiny transparent roundworm Caenorhabditis elegans attracted attention with its earthworm-like movements. Overall, these biomedically relevant specimens provided a treasure trove of potential educational activities for visitors.
Given a range of wonderfully attractive specimens and potential activities, we then aimed to create an educational experience that combined the observable features in the specimens, supporting information, and further activities to be chosen as desired by the visitor. To achieve this, we adopted a multimedia approach (Figure 1). The incorporation of multimedia into the user–microscope interface required a complex piece of technology: the melding of live video imagery from the microscope with an interactive multimedia touchscreen, where selected microscope controls and specimen information changed as needed. To create this information presentation device, which we fondly refer to as the Usercart, we decided to use two side-by-side monitors with physical control devices for specimen position, focus, and variable magnification. Subsequent evaluation shows that the Usercarts work well; visitors get it!
Figure 1 The Microscope Imaging Station
Plasma screens, visitor microscope control consoles, and interactive media presentations provide visitors with access to live specimens and biomedical information
Visitors can pick and choose specimens of their choice, refine their selection, change magnification, and engage in suggested activities at their own pace with integrated appropriate information. Individuals and groups engage with the microscope imagery and companion information for relatively long periods of time, ranging from several minutes to upwards of 20 minutes, a long time for a museum exhibit. To expand the use of the microscope imagery beyond that of one or two visitors, we incorporated large plasma-screen presentations in to the Microscope Imaging Station facility.
What Else Is New?
In addition to using some of the latest devices in microscope technologies, the Imaging Station also provides a window on revolutionary research techniques. For example, in 1994, Columbia University's Martin Chalfie inserted a gene for a fluorescent jellyfish protein into a bacterium (Escherichia coli) and a roundworm (C. elegans) and found that the genetically modified organisms emitted an eerie green glow under certain conditions. Other scientists built on this technique to create a powerful tool that makes hidden structures and processes easier to study. The coupling of vital staining with the green fluorescent protein gene allows scientists to observe events inside developing cells and detect the presence of diseased structures and environmental toxins with extreme sensitivity. This newly created sensitivity has sparked new insights and discoveries, re-revolutionizing the capabilities of the light microscope.
At the Imaging Station, visitors can take advantage of this technique to observe specific types of cells—such as the brain cells, sex cells, or muscle cells of roundworms. Visitors can also peer inside the developing embryo of a tropical zebrafish whose circulatory cells have been made visible by the protein made from the transplanted green fluorescent protein gene. In the immediate future, visitors will be able to closely examine living human blood cells and fruit flies that researchers use to study the genetics of a wide range of human disorders. Over the next year, we intend to add a major component on mouse stem cells and the process of differentiation. At the Imaging Station, visitors have access to seamless video footage of events that take place in a fraction of a second or occur slowly over weeks or months (Box 1). Using the latest technologies available, we have built operating software specifically for the Imaging Station that gives visitors the same kind of control that professional researchers have over their own work. The straightforward user interface is integrated with explanatory graphics to provide control and orientation simultaneously. Finally, images are brought to users on large, high-resolution video screens. But the best may be yet to come, because the Imaging Station's capabilities are continually expanding, and visitors will ultimately be able to observe specimens by logging on to the Station's Web site (http://www.exploratorium.edu/imaging_station).
Supporting Information
Video S1 A Medley of Time-Lapse Videos
Collected at the Microscope Imaging Station Many of the cells and organisms found in these movies are available via visitor-accessible microscopes on a daily basis. At the station, visitor-directed, microscope-based observations form the basis for informal education on basic and biomedically relevant research. Video microscopy and production by Kristina Yu.
(5.4 KB MOV).
Click here for additional data file.
Box 2. How to Make a Simple Microscope from a Drop of Water
Materials. You'll need the clear plastic part of a CD case; if you can't find one, any thin sheet of rigid, clear plastic will work. Remove any paper or packaging so that you can see through the plastic. (Take the CD out, too!)
What to do. (1) Put the object you want to examine—a bug, a tiny leaf or plant, the small lettering on a coin or newspaper—on a flat surface. (2) Put one large drop of water on the center of the clear plastic. (An eyedropper makes this easy.) (3) Pick up the plastic and hold it horizontally so that the water drop is directly over the object. Move the plastic slowly back and forth to center the object, and up and down to focus. The water will form a curved shape that magnifies the object!
Citation: Carlson C (2005) Accessing the microscopic world. PLoS Biol 3(1): e12.
Charles Carlson is the director of life sciences at the Exploratorium: Museum of Science, Art, and Human Perception, San Francisco, California, United States of America. E-mail: [email protected]
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1566015710.1371/journal.pbio.0030017Research ArticleNeuroscienceRattus (Rat)Neuronal Encoding of Texture in the Whisker Sensory Pathway Texture Coding in the Whisker Sensory SystemArabzadeh Ehsan [email protected]
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Zorzin Erik
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Diamond Mathew E
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1Cognitive Neuroscience Sector, International School for Advanced StudiesTriesteItalyMeister Markus Academic EditorHarvard University1 2005 11 1 2005 11 1 2005 3 1 e1730 7 2004 9 11 2004 Copyright: © 2005 Arabzadeh et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Whisker Velocity Patterns Tell Rats What They're Feeling
A major challenge of sensory systems neuroscience is to quantify brain activity underlying perceptual experiences and to explain this activity as the outcome of elemental neuronal response properties. Rats make extremely fine discriminations of texture by “whisking” their vibrissae across an object's surface, yet the neuronal coding underlying texture sensations remains unknown. Measuring whisker vibrations during active whisking across surfaces, we found that each texture results in a unique “kinetic signature” defined by the temporal profile of whisker velocity. We presented these texture-induced vibrations as stimuli while recording responses of first-order sensory neurons and neurons in the whisker area of cerebral cortex. Each texture is encoded by a distinctive, temporally precise firing pattern. To look for the neuronal coding properties that give rise to texture-specific firing patterns, we delivered horizontal and vertical whisker movements that varied randomly in time (“white noise”) and found that the response probabilities of first-order neurons and cortical neurons vary systematically according to whisker speed and direction. We applied the velocity-tuned spike probabilities derived from white noise to the sequence of velocity features in the texture to construct a simulated texture response. The close match between the simulated and real responses indicates that texture coding originates in the selectivity of neurons to elemental kinetic events.
Rats move their whiskers rhythmically across surfaces to sense their environment. Textured surfaces induce distinct patterns of vibration in the whiskers that are encoded by central neural activity
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Introduction
One goal of sensory systems neuroscience is to understand how the representations of complex, natural stimuli arise from the basic response properties of neurons. The present experiments explore the representation of textures in the rat somatosensory system. Rats have texture discrimination capacities rivaling those of humans [1]. In rats, as in humans [2], object exploration in the tactile modality derives from active palpation. Thus, rats create sensory signals by sweeping their whiskers across surfaces in a rhythmic forward-backward cycle with a frequency ranging from 5 to 15 Hz [1,3,4,5]. Several hundred primary afferent fibers—“first-order neurons”—innervate specialized receptors on each whisker shaft [6], and these are excited by whisker movement. Signals travel along the sensory nerve, past the cell body in the trigeminal ganglion, to the brain stem. Here the first synapse is located. The axons of second-order neurons cross the brain midline and travel to the thalamic somatosensory nuclei, where the second synapse is located. Thalamic neurons project to the primary somatosensory cortex, conveying information to layer IV cell populations called “barrels” [7,8].
There have been no reports concerning the cortical or subcortical neuronal activity generated by whisking along irregular surfaces, and the differences in activity associated with two surfaces remain unknown [9]. However, recent work suggests the framework for a texture coding model. For nonnatural whisker deflections such as ramps [10], sinusoids [11,12], and temporally unstructured movement [13], first-order sensory neurons and cortical neurons emit spikes with probabilities that increase in proportion to stimulus velocity. This raises the possibility that neurons represent texture by encoding the kinetics of whisker vibrations. However, key elements of the model are untested. Does whisker movement across different textures produce distinct vibrations? If so, do neurons in the central pathway reliably report these vibrations? Through what coding mechanisms?
To answer these questions, the model must be challenged under conditions where the sensory input is precisely controlled and yet resembles what occurs during natural tactile behavior. Guided by this strategy, in anesthetized rats we produced whisker movements across textures while measuring vibrations of the whisker shaft. We then played back the identical vibrations to other rats, and measured the neuronal activity at two stages of the sensory pathway—the first-order neurons that innervate the sensory receptors, and the barrel cortex neurons, which are the first site of cortical integration. Texture discrimination depends on the integrity of the cortical barrels [14]. By measuring the activity of trigeminal ganglion neurons (the cell bodies of the first-order neurons), we investigated how the sweeping motion of whiskers along a surface is converted to a neuronal impulse code. By measuring activity in the cortex, we explored the neuronal representation that rats rely on to judge the identity of external objects [15,16]. Comparing two levels of the pathway, we reveal the transformation of neuronal signals at successive levels of integration, and show how the neuronal signals emerge from elemental feature extraction.
Results
Kinetic Signatures of Textures
The experimental strategy (Figure 1) was to collect records of the natural movement of whiskers across surfaces (Figure 1A) and use them as a stimulus set to probe the neuronal representation of texture (Figure 1C). In one group of rats (n = 3), we electrically stimulated cranial nerve VII, generating 8-Hz whisking movements [17,18] that resemble whisker trajectories in awake rats [4]. Meanwhile, whisker displacements transmitted to the receptors in the follicle were measured by an optical sensor placed 1 mm from the skin. The vertical and horizontal channels of the sensor (Video S1) reported whisker position with less than 3-μm spatial and 0.13-ms temporal resolution. Movements were measured under different conditions (Figure 1B, “texture” column): whisking with no object contact (“free whisk”), whisking on compact disk surface (smooth), and whisking on sandpapers of four different grades: P1200, P400, P280, P100 (from fine-grained to coarse-grained; Table 1). The surface was oriented so that the whisker rested on it and remained in contact during the entire whisk trajectory. The proximal edge of the surface was 7 mm from the base of the whisker. The illustrated data (Figure 1) come from whisker C3 in rat EW3.
Figure 1 Collection and Playback of Texture Library
(A) Whisker vibration data were collected during “electrical whisking,” induced by stimulation of the facial nerve (1) with pulse trains (2) in rat EW3. An optical sensor, shown schematically by two orthogonal light paths (3), monitored vertical and horizontal whisker motion of whisker C3.
(B) “Texture” column: Photographs of the 5 surfaces used. “Trajectory” column: Sample whisker trajectories (first whisk of trial 50) associated with free whisking and the five surfaces. Each point, separated by 1 ms, gives the horizontal and vertical position; the trajectory begins with protraction (P) at t = 0 and terminates 125 ms later at the end of retraction (R). Speed is given by the color of each point. Note the irregularities—jumps, stops, and starts—induced by whisking on sandpaper. “Velocity profile” column: Whisker trajectories displayed according to the horizontal and vertical velocities (VH and VV, respectively). P refers to protraction phase (positive VH), and R to retraction phase (negative VH). In this and all figures, VH and VV were calculated 7,634 times per second. “Velocity spectrogram” column: Velocity spectrograms for each texture (see Materials and Methods).
(C) Playback of the whisker trajectories to a second group of rats through a piezoelectric motor (4), shown schematically by the horizontal and vertical arrows at the base of the whisker.
Table 1 Parameters of the Sandpapers
See http://www.fepa-abrasives.org
ND, no data
Under free whisking conditions the trajectory was a smooth ellipsoid [4], the principal axis aligned with protraction and retraction movements (P and R, respectively, in Figure 1B, “trajectory” column). As the rat whisked on the compact disk surface, the trajectory was similar but covered a more restricted vertical range. In contrast, whisking across grainy surfaces produced irregularities in the trajectory, and each texture was associated with a characteristic whisker shaft vibration. These distinct “kinetic signatures” are evident in the velocity profile—that is, the temporal sequence of velocity features across the course of a whisk (Figure 1B, “velocity profile” column). Each velocity profile covers 125 ms (one complete forward and backward whisk) and consists of two histograms—horizontal (VH) and vertical (VV) velocity. For VH, whisker protraction (forward movement) is positive and whisker retraction (backward movement) negative. For VV, upward movement is positive and downward movement negative. To better visualize the time-varying frequency content of the velocity profiles, the velocity spectrograms are also plotted (Figure 1B, “velocity spectrogram” column). The spectrograms were formed by computing the magnitude of all sinusoidal components (0–500 Hz) of the velocity profile in a 6-ms wide window, and then sliding the window with 0.13-ms time steps (see Materials and Methods). The velocity profile and the velocity spectrogram, taken together, illustrate the kinetic features that make each texture unique—the duration and frequency content of each velocity peak, as well as the number of peaks and the temporal spacing between them.
In a different group of rats, the texture-induced vibrations were played back to the base of a whisker (Figure 1C and Video S2) and neuronal activity was recorded. Construction of the stimulus sequence off-line allowed us to smoothly “stitch together” the vibrations: The transitions between textures and free whisks were always inserted at time zero, corresponding to the point of maximal retraction, when whisker velocity was zero. The replay was an accurate replica of the motion recorded during electrical whisking (Figure S1).
Receptor and Cortical Coding Properties
The physiological dataset (seven rats) consists of six first-order neuron recordings, five cortical cluster recordings, and seven “paired” recordings—simultaneous first-order neuron and cortical cluster. The principal result is that time-varying neuronal activity in the trigeminal ganglion and cortex captured the kinetic features of the texture-induced vibrations. From the same texture library given in Figure 1 (rat EW3, whisker C3), Figure 2A gives the velocity profile, averaged across 100 trials, for two free whisks (−250 to 0 ms) followed by two whisks on P280 sandpaper (0 to 250 ms). Note the distinct kinetic signatures of whisker movement: Unlike free whisking, the coarse (P280) sandpaper caused irregular bursts of high and low velocity, particularly during whisker retraction. The response of one first-order neuron (named Zurvan) is shown in Figure 2B as a raster plot of 100 trials and in Figure 2C as a peristimulus time histogram (PSTH). Several coding properties are evident: (i) The first-order neuron fired a greater number of spikes for the coarse texture than for free whisks; (ii) spikes were closely aligned to instants in which the whisker moved at high velocity (blue arrowheads); (iii) it fired in a reproducible manner across trials—the spikes were aligned; and (iv) it was selective to whisker retraction (did not fire for high-velocity protractions; red arrowhead).
Figure 2 Sensory Receptor and Cortical Coding Properties
(A) VH and VV for two free whisks followed by two P280 whisks. The labeling conventions are as in Figure 1B. Each presented trial was unique due to small variations in whisker trajectory even on the same surface (Figure 7); the illustrated velocity profiles are the averages of 100 trials. The red arrowhead indicates the time of the first VH peak during whisker protraction on P280; blue arrowheads indicate the times of the three VH peaks during whisker retraction on P280.
(B) Raster plot of first-order neuron aligned with the whisker trajectories, in response to 100 unique trials. Stimuli were applied to whisker E4.
(C) PSTH of first-order neuron with 0.2-ms bins. Blue arrowheads indicate the times of maximum response to the three peaks in retraction velocity. The red arrowhead indicates the expected time of response to the peak in protraction velocity; however, the neuron did not respond to whisker protraction.
(D) Raster plot for the cortical neuron cluster recorded simultaneously with the first-order neuron.
(E) Two cortical PSTHs, both with 2-ms bins. The upper PSTH corresponds to the raster plot in (D); the lower PSTH is from a second cortical neuron cluster recorded simultaneously at a neighboring electrode (distance 560 μm). Blue and red arrowheads indicate the times of maximum response to the peaks in whisker protraction and retraction velocity, carried down from (A). The cortical neuron clusters responded to high velocities for both protraction and retraction. Because the first two peaks in retraction velocity were separated by just 7 ms, the resulting peaks in cortical response were fused. All PSTHs are extended to 260 ms to show responses to the final velocity feature.
Two barrel cortex neuron clusters were recorded simultaneously with the first-order neuron, allowing direct comparison of different stations along the sensory pathway. Figure 2D shows the raster plot for one of the cortical clusters, while Figure 2E shows PSTHs for both cortical clusters. Like the first-order neuron, the cortical clusters responded to high velocities (arrowheads, Figure 2A, 2C, and 2E) and, as a result, fired a greater number of spikes for P280 sandpaper than for free whisks. Key differences from the first-order neuron are clear: (i) The cortical neuron clusters fired in a less reproducible manner across trials—there was more variability in the number of spikes per whisk and in the temporal alignment of spikes; and (ii) they fired for both whisker protraction and retraction (red and blue arrowheads, respectively).
The selectivity of first-order neurons and cortical clusters for the direction of whisker movement is described in more detail in Figure 3.
Figure 3 Directional Selectivity in First-Order and Cortical Neurons
(A) Mean spike count per whisk for ten first-order neurons separated into protraction and retraction phases. Responses to free-whisk and all textures were combined, giving a total of 8,000 whisks. First-order neurons are arranged from left to right according to their retraction:protraction spike count ratio. Five first-order neurons preferred retraction, three preferred retraction, and two responded to both phases. Principal whisker of each neuron is indicated. The neuron Zurvan is indicated by an asterisk.
(B) Same analysis for 12 cortical clusters. Individual cortical neuron clusters did not present a clear preference for either retraction or protraction. Conclusions about single unit directional selectivity cannot be drawn, however, because the directional selectivity of any cluster must always be less than that of the most selective single unit in the cluster. The neuron cluster (Figure 2D, 2E) recorded simultaneously with Zurvan is indicated by an asterisk.
Texture Coding by Firing Rate
To permit sensory discriminations, some properties of neuronal firing must vary systematically from texture to texture. Earlier work [11,12] showed that neuronal firing rate, in response to sinusoidal whisker movement, is dictated by mean vibration speed, proportional to the product of amplitude and frequency, Xω (referred to in previous publications as Af). The generalization of Xω to the natural, texture-induced vibration is 〈|X(ω,τ)|ω〉Ω,Τ, a quantity known as “equivalent noise level” (see Materials and Methods). In Figure 4, we compare response magnitude for the full population of first-order and cortical neurons to each texture's equivalent noise level. The Pearson correlation coefficient between equivalent noise level and spike count was 0.93 for the first-order neurons and 0.99 for the cortex. This finding indicates that neuronal spike count is a function of the magnitude of the composite frequency components of the whisker vibration, whether the stimulus is a simple sinusoid (where all the power is at a single frequency) or a complex, texture-induced vibration.
Figure 4 Texture Coding by Firing Rate
Equivalent noise level (plus SD) of texture-induced vibrations averaged across 100 trials of 500 ms each (see Materials and Methods). Average spike count per trial (plus SD) pooled from ten first-order neurons and 12 cortical neuron clusters. Note separate scales for spike counts of neurons recorded in Ganglion and Cortex.
Texture Coding by Firing Patterns
Since different sandpapers can induce vibrations with similar equivalent noise levels and thereby evoke similar spike counts (e.g., P400, P280, and P100 in Figure 4), we must expect additional texture coding mechanisms to be at work. We therefore examined more closely the neurons shown in Figure 2, on the hypothesis that spike patterns might carry texture-specific information. VH and VV profiles associated with two whisks on each texture are plotted (Figure 5A), together with PSTHs for the first-order neuron (Figure 5B) and the cortical neuron cluster (Figure 5C). Textures evoking similar spike counts due to similar equivalent noise levels were readily distinguished by spike patterns. The patterns arose from the alignment of spikes to the velocity profile of the input vibration. An assessment of spike alignment to other stimulus features (whisker position and whisker acceleration) is given in Figure 6 and indicates that these features were reported less reliably than whisker velocity. The first-order neuron reported the velocity profile for whisker retraction, while the cortical neuron cluster reported both protraction and retraction profiles, albeit with lower fidelity to individual velocity features.
Figure 5 Texture Coding by Firing Patterns
(A) VH and VV for two whisks on texture P400 (left), P280 (middle), and P100 (right). Each illustrated velocity profile is the average of 100 unique profiles.
(B) First-order neuron PSTHs (0.2-ms bins) aligned with the whisker trajectories.
(C) Cortical PSTHs (2-ms bins). PSTHs are extended to 260 ms. The arrowheads on the left side of PSTHs indicate mean firing rates.
Figure 6 Test for First-Order Neuron Encoding of Position and Acceleration
To investigate whether first-order neurons represented stimulus features other than velocity, we repeated the same analysis as in Figure 5, in relation to whisker position (A) and acceleration (B), because it has been suggested that neuronal activity is determined by these stimulus parameters [10,13]. Alignment between the PSTH (C) and stimulus position or acceleration revealed no consistent correlation. For texture P100, the boxes extending across A, B, and C highlight the absence of correlation. For example, two periods with similar positions produced first no spikes (red-outlined box on left) and then a large response (red-outlined box on right). Moreover, high acceleration (left box) produced no spikes, while lower levels of acceleration (right box) produced a large response. For this neuron, only velocity was encoded.
Sources of Neuronal Variability
In a number of sensory modalities, first-order neuron responses can be remarkably reliable when a stimulus is presented repeatedly [19], whereas cortical responses vary across trials [20]. It is of interest to elucidate the mechanisms that permit reliable first-order neuron responses and, by the same token, to identify the sources of trial-to-trial variability among cortical neurons. In the data shown so far, the 100 trials for a given texture were composed of 400 unique whisks (four whisks per trial). Each whisk differed in the minute details of its trajectory (Figure 7). To discover the origin of neuronal variability, we selected trial 50 for each texture and repeated the four-whisk sequence 100 times. If neuronal variability originates purely in stimulus variability, it will disappear across repeated trials; variability due to internal brain fluctuations, however, will remain.
Figure 7 Velocity Profile Variability across Trials
Ten successive trials are shown (numbers 46–55), each trial composed of the final two free-whisks (–250 to 0 ms) and the first two whisks on P280 (0 to 250 ms). Free whisk velocity profiles varied little across trials. When the whisker swept across P280 repeatedly, the fundamental kinetic signature was conserved (e.g., the three peaks in retraction velocity for P280) but minute details of the profile varied—note, for example, the velocity event (red asterisk) that occurred uniquely on trial 50.
The velocity profile for the final two free whisks (−250 to 0 ms) and the first two P280 whisks (0 to 250 ms) of trial 50 is given in Figure 8A. The response of the first-order neuron is shown as a raster plot (Figure 8B) and a PSTH (Figure 8C). These can be compared to responses of the same cell in Figure 2B and 2C. Response was nearly identical on each trial, because of the precise temporal alignment of spikes on the high-velocity events. Indeed, some stimulus features evoked 0.7–0.8 spikes per bin per trial, meaning that neuronal jitter fell within the 0.2-ms PSTH bin size.
Figure 8 Sources of Neuronal Variability
(A) VH and VV across the final two free whisks and the first two P280 whisks of trial number 50. Here, as in Figure 2, the red arrowhead indicates peak whisker velocity during protraction, and blue arrowheads indicate the peak whisker velocities during retraction.
(B and C) First-order neuron raster plot (B) and PSTH (C), aligned with the whisker trajectories, for 100 stimulus repetitions. Due to the temporal precision of neuronal responses, the vertical scale of the PSTH has been altered (compare to Figures 2 and 5) to reflect the large numbers of spikes within single bins.
(D) Cortical neuron cluster raster plot.
(E) Two cortical PSTHs from activity recorded simultaneously with the first-order neuron. The upper PSTH corresponds to the raster plot in (D); the lower PSTH is derived from a second cortical neuron cluster recorded simultaneously at a neighboring electrode (distance of 560 μm). PSTHs have 0.2-ms bins for the first-order neuron and 2-ms bins for the cortical neuron clusters. All PSTHs are extended to 260 ms to show responses to the final velocity feature. Response peaks are signaled by red and blue arrowheads according to the velocity events that evoked them.
In Figure 8D and 8E, the cortical response to repeated trials is presented. Direct, quantitative comparisons between the variability of first-order responses and that of cortical responses cannot be made, because the cortical recordings were made from multi-neuron clusters. However, the cortical response to repeated trials can be compared to the same cluster's response to 100 unique trials in Figure 2D and 2E. Eliminating trial-to-trial variability in the timing of stimulus features reduced but did not eliminate neuronal jitter.
The remarkable response locking of first-order neurons to stimulus features is further highlighted in Figure 9. Unlike Zurvan, the illustrated neuron was selective to whisker protraction. For texture P280, the velocity histograms (Figure 9A) contained well-separated peaks during protraction. With 100 repetitions of trial number 50, the raster plot (Figure 9B) and PSTH (Figure 9C) yielded discrete response peaks aligned with high-velocity protraction events. Applying the green horizontal line to the PSTH as a threshold, we extracted six separable response clusters. Every cluster contained exactly 100 spikes resulting from one spike per trial for each velocity event. The final response was evoked by a clear protraction event (red asterisk in Figure 9A) and was selected for closer inspection (red inset). Here, the 100 spikes spanned a range of 0.38 ms; standard deviation (SD) in spike time was 0.08 ms. For the five preceding response clusters, spike time SDs were 0.13, 0.09, 0.09, 0.13, and 0.11 ms. The minimum measured spike time SD in the dataset was 0.07 ms. These must be taken as underestimates of spike time precision, given that they include measurement noise inherent to the recording system (e.g., the SD in spike time caused by digitizing the action potential threshold crossing time at 30 samples per ms is nearly 0.02 ms).
Figure 9 Precision of a First-Order Neuron
(A) VH and VV across the first two P280 whisks of trial number 50 (see Figure 7).
(B and C) Raster plot (B) and PSTH (C) of the first-order neuron for 100 repetitions of the stimulus given in (A). Inset in red frame shows a magnified view of spikes emitted in response to a single velocity event (red asterisk in [A]) and their SD in time. The same measurement of jitter was carried out for each of the response peaks that surpassed the green horizontal line (see text).
From these observations we conclude that, under our experimental conditions, the trial-to-trial response variability of first-order neurons is caused exclusively by stimulus jitter, whereas that of cortical neurons results mainly from variations across time in sensory integration, and must emerge at some integration site between the trigeminal ganglion and cortex. A question of current interest is whether the variability in cortical responses results from noise and imprecision in neuronal integration [20], or else reflects functionally significant modulations in responsiveness [21,22,23].
From Response Properties to Natural Responses
We hypothesize that the firing patterns of first-order and cortical neurons during presentation of textures can be explained by their extraction of elemental features from the complex input signal, and that these elemental features are bursts of high velocity. To test this directly, we presented a “white noise” stimulus in which the two stimulus features VH and VV varied randomly across time. Responses to noise stimuli allowed us to quantify velocity sensitivity and then to generate simulated spike trains based on the sequence of velocity events in the actual texture-induced vibrations. Finally, comparison between simulated and observed responses reveals the extent to which the responses to natural stimuli are explained by neuronal selectivity to velocity features: If simulated responses closely resemble real responses, we can conclude that neurons are in fact operating on natural texture stimuli according to their tuning to elemental kinetic events.
Typically, neuronal tuning curves are mapped out using an “unbiased” stimulus—a stimulus that avoids the temporal correlations present in natural stimuli. The first step, therefore, was to map out how first-order and cortical neurons encode whisker kinetic features when these features are extracted from the context of the natural stimulus. We applied a stimulus that varied randomly in velocity—Gaussian velocity noise—and therefore was not constrained by the velocity patterns present in texture trajectories (see Materials and Methods). Figure 10A tracks VH and VV (white circles) across 5 ms of filtered white noise.
Figure 10 Velocity Tuning Curves and Simulated Texture Responses
(A) A 5-ms trajectory of velocity white noise. Radial coordinates give VH, VV. Velocity space was subdivided such that each segment included the same number of events (3,435,300). One segment (red outline) is selected for further explanation (see text).
(B) 100-ms ganglion and cortical spike train aligned below occurrences of the velocity event of interest (red bar). After each such event, spike times were accumulated to build up a spike probability profile.
(C) First-order neuron spike probabilities, given by color scale, in relation to joint A,R events. To estimate the tuning curve in finer detail, the number of angles was increased to 20. Each segment now contains about 1,374,120 velocity events. One P280 whisk trajectory is superimposed.
(D) Spike probabilities for cortical neuron cluster, given by color scale, in relation to joint A,R events. One P280 whisk trajectory is superimposed.
(E) Simulated raster plot for first-order neuron and simulated (black) and real (red) PSTHs.
(F) Simulated raster plot for cortical neuron cluster and simulated (black) and real (red) PSTHs.
We then constructed firing probability profiles in relation to millions of occurrences of each velocity event, such as the velocity event in the fifth angular sector and ninth radial sector, or A5,R9 (red outline in Figure 10A). Figure 10B shows how response probabilities were constructed in relation to this particular velocity event. The first trace shows the occurrences of A5,R9 across a 100-ms window—the first occurrence of the event (asterisk in Figure 10A) corresponds to the crossing of A5,R9 in Figure 10A (also marked by an asterisk). Below, first-order sensory neuron and cortical spike times are shown. After 10 min of stimulus noise, the postevent spike probability profiles associated with A5,R9 and all other events could be constructed.
For the first-order neuron Zurvan, spike probabilities in the 1–2-ms postevent interval are given in Figure 10C for all joint events (A,R). To construct the neuron's “tuning curve” in finer detail, velocity space was subdivided into 20 angular and 10 radial segments. The neuron emitted spikes with increasing probability as velocity increased, but only for restricted directions, preferring high speeds that combined retraction (negative horizontal velocity) and upward movement (positive vertical velocity). For the simultaneously recorded cortical neuron cluster, spike probabilities in the 5–20-ms poststimulus interval are given in Figure 10D; like the first-order neuron, the cortical cluster emitted spikes with increasing probability as speed increased, but its directional selectivity was less pronounced and was radially symmetric.
Can the neurons' responses to complex, natural stimuli be explained as the outcome of these elemental tuning properties? To find out, we projected whisk velocity trajectories upon the white noise-derived tuning curves. One whisk (first whisk of trial 50) on P280 sandpaper is depicted on both tuning curves. The first observation is that the intersection of the velocity trajectory with the tuning curves explains why the first-order neuron was selective for whisker retraction while the cortical cluster was directionally nonselective (see Figures 2 and 5).
Figures 10E and 10F show the simulated responses of the first-order neuron and cortical cluster to two P280 whisks, delivered 100 times. The simulation was of 100 unique trials (see Figure 2) rather than repeated trials (see Figure 8). Thus, on each trial the minute details of the whisks gave rise to a unique sequence of P(t) and a corresponding raster plot for that trial. The 100-trial raster for one run of the simulation was summated to form a PSTH. The close match between the simulated PSTHs (black) and the real PSTHs (red lines, reproduced from Figure 2C and 2E) indicates that the real responses to natural stimuli could be explained by neuronal selectivity to velocity features. The Pearson correlation coefficients between the predicted and observed PSTHs were 0.94 for the first-order neuron and 0.84 for the cortical cluster. Because these correlation values fall within the range obtained by comparing two real PSTHs generated from separate sets of 50 trials, we conclude that simulated spike trains are as similar to real spike trains as real spike trains are to each other. Thus, the velocity feature extraction properties of the neurons are sufficient to explain texture responses.
Discussion
Texture coding appears to derive from two fundamental processes: First, the whisker transmits a “kinetic signature” of the palpated surface to the receptors in the follicle. Second, the first-order neurons relay to the whisker region of cortex (through intervening stations) precise information about the kinetic features transmitted to the follicle. One kinetic feature is the “equivalent noise level”: Spike counts per whisk both for first-order neurons and for cortical neurons are proportional to the equivalent noise level of the texture-induced vibration. Thus, when texture vibrations differ in this quantity, neuronal spike counts also differ and thereby carry information that could, by itself, separate the textures. By the same token, when texture vibrations have similar equivalent noise levels, spike counts per whisk appear not to carry sufficient information. The second kinetic feature, then, is the temporal sequence of velocity events—distinctive velocity profiles induce distinctive temporal patterns in the spike trains with spike alignment of better than 0.2 ms in the first-order neurons and a few ms in the cortex.
The stimulus playback method used here might not produce the identical input to the sensory receptor as occurs during active whisking [18]. The optical sensor at the base of the whisker did not register the bending nor tension (pulling) of the whisker. Moreover, active muscle contractions might affect sensory receptors at the interface between the whisker shaft and the inner membrane of the follicle. Thus, additional information related to surface texture might be available to the sensory system. In the present dataset, even after the possible loss of some texture-dependent information due to passive stimulation, the neuronal responses afforded a high degree of discriminability. We interpret the dataset as showing that vibration patterns, by themselves, must be a fundamental feature supporting the neuronal coding for texture. This awaits confirmation in experiments in actively whisking rats.
A classical approach to investigating sensory coding is to map the relationship between well-controlled artificial sensory stimuli and evoked neuronal activity. This can provide a complete description of neuronal feature extraction properties [24,25], but it sheds little light on the brain activity underlying normal perceptual experiences. Moreover, the processing mechanisms that have evolved to extract behaviorally relevant information may operate inefficiently during artificial stimulation [26]. Another approach [27,28] is to measure neuronal activity during natural stimuli (i.e., visual scenes or animal calls). Here, the drawback is that the features evoking spikes during natural stimulation can be multidimensional, complex, and difficult to quantify, offering only limited insight into sensory processing mechanisms [27].
In principle, one can bridge the gap between artificial and natural stimuli by (i) measuring neuronal activity during ecologically relevant stimuli, stimuli that are collected from an animal's normal interaction with the environment, (ii) constructing tuning curves under artificial stimulation (usually white noise), and (iii) applying the tuning curves to the natural stimuli to test whether they account for the observed response. Because artificial stimuli only partially cover dimensions of stimulus space present in natural stimuli, and because of neuronal nonlinearity, this procedure typically provides neuronal simulations that match real neuronal output with a correlation of less than 0.5 [27,29,30]. Yet, the present experiments followed this same procedure and generated simulated PSTHs that were highly correlated with real responses (first-order neuron, 0.94; cortex, 0.84), approaching the upper bound set by the trial-to-trial variability that limits the correlation even between two real PSTHs. The simulations were successful for two reasons. First, recent work [11,12] uncovered the critical physical dimension—velocity—encoded by neurons. Second, receptor and cortical stimulus integration is linear to a first approximation—ongoing responses depend upon an integration process where preceding events affect the neurons independently of one another.
As an alternative to the temporal model outlined here, a spatial model for texture coding has recently been proposed. It begins from the observation that whisker length varies systematically across the anterior-posterior dimension of the rat's snout [31]. When stimulated near the distal end, the posterior whiskers resonate at lower frequencies than do the shorter, anterior whiskers [32]. If different textures cause systematically different vibration frequencies, there might be texture-specific differences in the magnitude of vibration of posterior versus anterior whiskers [33]. Because whisker position is relayed in a somatotopic manner to the cerebral cortex [7], texture could be encoded by the location of the focus of activity in barrel cortex, much like frequency is encoded by the tonotopic organization of the cochlea and the auditory cortex.
The resonance frequency hypothesis predicts that rats would fail to discriminate between surfaces using just a single whisker, yet they have been shown to perform texture discriminations after progressive clippings down to one or two whiskers [34]. Our results help explain the behavioral findings by emphasizing that, although additional information might be available due to differences in the mechanical properties among whiskers, even a single whisker can transmit large amounts of texture-specific information to its central neural circuits. This occurs because of the match between the feature of the whisker output signal that best distinguishes one texture from another (the kinetic signature of the vibration) and the tuning properties of first-order neurons. Cortical neurons conserve this kinetic signature in their firing patterns.
Materials and Methods
Recording the texture library and construction of the stimulus set
Experiments were conducted in accordance with NIH and institutional standards for the care and use of animals in research. Subjects were ten adult male 250–350-g Wistar rats. In one set of anesthetized rats (urethane, 1.5 g/kg), “electrical whisking” [17,18] was generated by stimulating the right facial nerve (see Figure 1A) with 1–2-V pulses of 100 μsec at 200 Hz for 60 ms to produce whisker protraction, followed by a passive 65-ms whisker retraction. For a selected whisker, horizontal and vertical movements at the base were registered by a two-channel optical sensor, each channel consisting of an LED light source and phototransistor (Video S1). The two voltage signals were digitized (7,634 samples per second). Whisker movement was studied for 10 min under each of six conditions (see Figure 1B). The angle traversed at the whisker base, averaged across all trials and all textures, was 25 degrees (SD ± 1 degree). Average translation across the edge of the textured surface was 3.08 mm (SD ± 0.14 mm).
For each texture, a 50-s continuous record was extracted and sliced into 100 trials of 500 ms, each trial composed of two-dimensional position signals across four 125-ms whisks. For free whisks, a 250-s record was sliced into 500 unique trials. The stimulus set was constructed by splicing trials together at the point of maximum retraction (VH = 0), avoiding the introduction of any position or velocity discontinuity. A free whisk trial always separated two successive texture trials. A block was composed of five different texture trials (t1–5) with free whisk trials (fw) interspersed, e.g., fw-t3-fw-t5-fw-t1-fw-t2-fw-t4. Before stimulus delivery, signals were low-pass filtered at 500 Hz. All data were manipulated in MATLAB software (http://www.mathworks.com).
Analysis of vibration kinetics
We sought to quantify the kinetic features that characterized each texture-induced vibration. We refer to the whisker trajectory in one dimension as x(t) and the whisker velocity profile as
For each texture, we computed the spectrogram |V(ω,τn)| of the velocity profile
where Δt is the 6-ms interval within which each spectrum was computed and τn represents the series of N sequential time windows. Thus, each spectrogram (see Figure 1, “velocity spectrogram” column) was composed of N = 906 spectra.
Considering the duality between the direct and inverse Fourier space
we can substitute Equation 1 into Equation 2, to rewrite Equation 2 as
Note that |X(ω,τn)| also corresponds to the spectrogram of the trajectory in position.
Previous studies have shown that, when the stimulus set consists of sinusoidal whisker movements, the spike count per stimulus for cortical neurons is proportional to the product of the sinusoid's amplitude and frequency, Xω [11,12]. To test whether this coding principle extends to natural stimuli, we generalized the measure of Xω to the texture-induced vibration. X becomes a time-varying spectrum X(ω,t)
where T is the entire time domain and Ω is the entire domain of frequency ω.
This quantity—known as the “equivalent noise level”—represents the average amplitude of white noise velocity that dissipates the same average power as the signal of interest. It serves to characterize the entire kinetic signature by a single quantity, equivalent to mean value of the product Xω across all time intervals and all values of ω.
Our experiments measured velocity in two dimensions, VH and VV. Equations 1–5 were generalized to a second dimension by adding, for each time window τn, the separately measured spectrograms for the two dimensions. The “velocity spectrogram” column in Figure 1B gives two-dimensional spectrograms as above. Similarly, in Figure 4 the equivalent noise level was calculated in two dimensions, averaged across 100 trials of each texture vibration, and then plotted in relation to neuronal spike count.
Measurement of neuronal responses
In a second set of urethane-anesthetized rats, neuronal recordings were made simultaneously from two sites. First-order neurons were recorded by advancing a single electrode (FHC, Bowdoinham, Maine, United States; http://www.fh-co.com) into the right trigeminal ganglion according to stereotaxic coordinates. Cortical recordings were obtained by inserting a 100 microelectrode array (Cyberkinetics, Foxborough, Massachusetts, United States; http://www.cyberkineticsinc.com) to a depth of 700–1,000 μm in the left barrel cortex [35,36]. Ganglion recordings were always single units, whereas cortical recordings consisted of a multiunit cluster at each channel.
The principal whiskers (receptive field centers) in the first-order-only recordings were C3, E1, D6, E6, and γ (twice). The principal whiskers in the cortex-only recordings were A1, B4, E5, and E3 (twice). The principal whiskers in the paired first-order neuron-cortex recordings were δ (twice), E3 (twice), C2 (twice), and E4.
Texture stimuli were delivered to a single whisker using a motor constructed from two orthogonal pairs of parallel piezoelectric wafers driven independently by horizontal and vertical signals (Video S2). The whisker was inserted into a metal tube (0.33 mm inner diameter) with opening 1 mm from the skin. By optically monitoring the whisker shaft, we verified that movements precisely reproduced the previously recorded signals (Figure S1). The second set of rats thus received whisker vibrations identical to those previously recorded during active whisking in the first set of rats.
Construction of neuronal tuning curves and response simulations
We selected VH and VV independently from a Gaussian distribution 7,634 times per second. The stimulus was then low-pass filtered (Chebyshev type II) at 500 Hz so as to not exceed the physical capacities of the piezoelectric wafer stimulator (instantaneous reversals of velocity cannot be achieved by any device). The noise stimulus was presented for 10 min after conclusion of the texture stimuli.
We adopted a method of “forward correlation” between stimulus and response where, for all occurrences of a particular stimulus event, the ensuing neuronal spike trains were averaged to construct a response probability profile for that event. This required subdividing velocity space into discrete segments. In Figure 10A, velocity space was partitioned into eight 45-degree angular sectors (A1–8) and ten radial sectors (R1–10). At each time point, angle corresponds to the direction of whisker movement, while radial distance corresponds to instantaneous speed. The positions of the radial boundaries were chosen to make each segment contain an equal number of instantaneous velocity events: Because velocity had a Gaussian distribution with a mean of zero, high-velocity events were less common, and consequently the radial boundaries were increasingly widely spaced as distance from zero increased.
To test whether the complex, texture-induced spike patterns followed directly from the tuning curves, a more elaborate analysis was necessary. Each instantaneous velocity (A,R) during the whisk gave an ensuing spike probability profile. To simulate a spike train, a spike was generated in each time bin t (size 0.13 ms) with probability P(t), given by the average of the overlying spike probabilities associated with velocity events in the time window before t (time window was 1–2 ms before t for first-order neurons and 5–20 ms before t for cortical neurons). After completion of the simulation, P(t) was normalized so that the overall simulated spike count matched that in the real data; this normalization did not affect the temporal profile of the simulated PSTH.
Supporting Information
Figure S1 Comparison of Recorded and Played Back Whisking Trajectories
The whisker movements presented as sensory stimuli were accurate reproductions of the movements recorded during electrical whisking in other rats.
(A) Trajectories of two whisks (only horizontal channel shown) recorded during contact with sandpaper P280.
(B) To test playback, a whisker was inserted in the piezoelectric motor guide tube and whisker displacements were measured by the optical sensor placed adjacent to the insertion point of the whisker. The trace shows recordings of the playback of the same two whisks of part A.
(C) Magnified view of the traces indicated in the rectangle in (A) and (B).
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Click here for additional data file.
Video S1 Two-Dimensional Optic Sensor and Electrical Whisking
Each optic sensor channel consisted of a pair of light tubes (one is a light source, the other leads to a photodiode). The two channels were mounted normal to each other in a metallic ring. Electrical stimulation of cranial nerve VII with 1-V pulses of 100 μs at 200 Hz for 60 ms produced whisker protraction that was followed by a passive 65-ms whisker retraction. The film shows four 125-ms free whisks in the air (8-Hz whisking), consisting of protraction (right to left) and retraction (left to right). Recorded at 500 frames per second with a Motion Scope 500 digital camera (Redlake, San Diego, California, United States; http://www.redlake.com). Rat EW3, whisker C3.
(9.1 MB MOV).
Click here for additional data file.
Video S2 Texture Playback with the Two-Dimensional Piezoelectric Motor
The motor consists of two pairs of piezoelectric wafers with axes meeting at a mobile joint. The whisker to be stimulated was placed inside the metal tube, which is orthogonal to the joint. The film shows playback of four 125-ms free whisks in the air, consisting of protraction (left to right) and retraction (right to left). Recorded at 500 frames per second.
(9.0 MB MOV).
Click here for additional data file.
This paper is dedicated to the memory of Irving T. Diamond. We thank Moritz von Heimendahl, Miguel Maravall, and John Nicholls for valuable discussions. Research was supported by European Community IST-2000–28127, Telethon Foundation GGP02459, J. S. McDonnell Foundation 20002035, and the Human Frontiers Science Programme RG0043/2004-C.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. EA and MED conceived and designed the experiments. EA and EZ performed the experiments. EA, EZ, and MED analyzed the data. EZ designed and built custom instrumentation. EA and MED wrote the paper.
Citation: Arabzadeh E, Zorzin E, Diamond ME (2005) Neuronal encoding of texture in the whisker sensory pathway. PLoS Biol 3(1): e17.
Abbreviations
A[number]angular sector [number]
fwfree whisk trial
msmillisecond
PSTHperistimulus time histogram
R[number]radial sector [number]
SDstandard deviation
t[number]texture trial [number]
VHhorizontal velocity
VVvertical velocity
μmmicrometer
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References
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Gamzu E Ahissar E Importance of temporal cues for tactile spatial-frequency discrimination J. Neurosci 2001 21 7416 7427 11549752
Sachdev RN Sellien H Ebner F Temporal organization of multi-whisker contact in rats Somatosens Mot Res 2001 18 91 100 11534778
Bermejo R Vyas A Zeigler HP Topography of rodent whisking - I. Two-dimensional monitoring of whisker movements Somatosens Mot Res 2002 19 341 346 12590835
Berg RW Kleinfeld D Rhythmic whisking by rat: Retraction as well as protraction of the vibrissae is under active muscular control J Neurophysiol 2003 89 104 117 12522163
Ebara S Kumamoto K Matsuura T Mazurkiewicz JE Rice FL Similarities and differences in the innervation of mystacial vibrissal follicle-sinus complexes in the rat and cat: A confocal microscopic study J Comp Neurol 2002 449 103 119 12115682
Woolsey TA Van der Loos H The structural organization of layer IV in the somatosensory region (SI) of mouse cerebral cortex. The description of a cortical field composed of discrete cytoarchitectonic units Brain Res 1970 17 205 242 4904874
Welker C Microelectrode delineation of the fine grain somatotopic organization of SmI cerebral neocortex in albino rat Brain Res 1971 26 259 275 4100672
Prigg T Goldreich D Carvell GE Simons DJ Texture discrimination and unit recordings in the rat whisker/barrel system Physiol Behav 2002 77 671 675 12527017
Shoykhet M Doherty D Simons D Coding of deflection velocity and amplitude by whisker primary afferent neurons: Implications for higher level processing Somatosens Mot Res 2000 17 171 180 10895887
Arabzadeh E Petersen RS Diamond ME Encoding of whisker vibration by rat barrel cortex neurons: Implications for texture discrimination J Neurosci 2003 23 9146 9154 14534248
Arabzadeh E Panzeri S Diamond ME Whisker vibration information carried by rat barrel cortex neurons J Neurosci 2004 24 6011–6020 15229248
Jones LM Depireux DA Simons DJ Keller A Robust temporal coding in the trigeminal system Science 2004 25 1986 1989
Guic-Robles E Jenkins W y Bravo H Vibrissal roughness discrimination is barrel-cortex dependent Behav Brain Res 1992 48 145 152 1616604
Whitfield IC The object of the sensory cortex Brain Behav Evol 1979 16 129 154 435970
Hutson KA Masterton RB The sensory contribution of a single vibrissa's cortical barrel J Neurophysiol 1986 56 1196 1223 3783236
Brown AWS Waite PME Responses in the rat thalamus to whisker movements produced by motor nerve stimulation J Physiol 1974 238 387 401 4840852
Szwed M Bagdasarian K Ahissar E Coding of vibrissal active touch Neuron 2003 40 621 630 14642284
Adrian ED The physical background of perception 1946 London Oxford University Press 95
Shadlen MN Newsome WT The variable discharge of cortical neurons: implications for connectivity, computation, and information coding J Neurosci 1998 18 3870 3896 9570816
Petersen CC Hahn TT Mehta M Grinvald A Sakmann B Interaction of sensory responses with spontaneous depolarization in layer 2/3 barrel cortex Proc Natl Acad Sci U S A 2003 100 13638 13643 14595013
Sachdev RN Ebner FF Wilson CJ The effect of subthreshold up and down states on the whisker-evoked response in somatosensory cortex J Neurophysiol 2004 92 3511 3521 15254074
Erchova IA Diamond ME Rapid plasticity fluctuations in rat barrel cortex J Neurosci 2004 24 5931 5941 15229241
Carandini M Heeger DJ Movshon JA Linearity and normalization in simple cells of the macaque primary visual cortex J Neurosci 1997 17 8621 8644 9334433
Adelman TL Bialek W Olberg RM The information content of receptive fields Neuron 2003 40 823 833 14622585
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| 15660157 | PMC544542 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2005 Jan 11; 3(1):e17 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030017 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030019SynopsisDevelopmentEvolutionGenetics/Genomics/Gene TherapyVertebratesDanio (Zebrafish)MammalsUnexpressed but Indispensable—The DNA Sequences That Control Development Synopsis1 2005 2 12 2004 2 12 2004 3 1 e19Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Highly Conserved Non-Coding Sequences Are Associated with Vertebrate Development
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Amidst the hoopla over the exact number of genes we have in our genome—more than a fruitfly, fewer than a rice plant—a more fundamental genetic truth has often been obscured. The expression of 20,000–30,000 genes is under the control of an uncounted host of non-coding sequences, which bind transcription factors and thereby regulate when and where genes are expressed. Unlike coding sequences, whose signatures are easy to spot, the characteristic features of non-coding regulatory elements are largely unknown, making their discovery by simple sequence analysis difficult. In this issue, Greg Elgar and colleagues attack this problem by comparing the non-coding sequences of the human and the pufferfish.
Since the last common ancestor of these two species existed 450 million years ago, the authors reasoned that non-coding sequences conserved between them are likely to be fundamental to vertebrate development. Through sequence alignment with increasingly strict criteria, they identified 1,373 highly conserved non-coding elements (CNEs), with an average length of about 200 base pairs. The average sequence match is 84%: not perfect, but much higher than for coding regions shared by humans and pufferfish. A quick check showed that virtually all the sequences also occurred in rodents, chickens, and zebrafish, but not in the nematode, fruitfly, or even the sea squirt, a primitive non-vertebrate chordate.
Highly conserved vertebrate non-coding elements direct tissue-specific reporter gene expression
CNEs are not spread uniformly throughout the genome. Instead, they are bunched together in fewer than 200 clusters, most of them in close proximity to genes implicated in transcriptional regulation or development. This clustering of CNEs suggests they may not only attract transcription factors, but may also influence the local topology of the DNA, thereby controlling access to their associated gene. Several clusters also appear in regions without any known genes—the identification of these clusters might lead to the discovery of new developmentally significant genes.
While “in silico” discoveries such as this can be the jumping-off point for whole new areas of investigation, their validity must be tested “in aqua,” in the wet biology of real organisms. For this Elgar and colleagues chose the zebrafish, because its transparent embryo is ideal for observing developmental events. They injected individual CNEs into embryos, along with a green fluorescent protein (GFP) reporter. By day two of development, 23 out of 25 CNEs injected had upregulated GFP expression, indicating interaction of these sequences with endogenous transcription factors. Different CNEs caused different regional patterns of expression, in keeping with their presumed roles in distinct developmental processes.
The discovery of these developmentally important sequences opens several avenues of new research. For example, analyzing the sequence and location of these CNEs may help point the way to other non-coding elements that remain undiscovered. It is also likely that mutations in these critical sequences cause human diseases. Studying how such mutations drive development astray may lead to better understanding not only of these diseases, which are likely to be rare, but also of normal human development.
| 0 | PMC544543 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2005 Jan 2; 3(1):e19 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030019 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1566015810.1371/journal.pbio.0030021PrimerDevelopmentEvolutionAnimalsDrosophilaNematodesSex Determination across Evolution: Connecting the Dots PrimerHaag Eric S [email protected] Alana V 1 2005 18 1 2005 18 1 2005 3 1 e21Copyright: © 2004 Haag and Doty.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Sexual differentiation appears to be an ancient, and potentially homologous, feature of animal biology, and yet the pathways that underlie the process exhibit bewildering variety
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Evolutionary developmental biology is motivated by the premise that the differences we see between species are caused by changes that have occurred in the genes that regulate their developmental programs. Beginning in the 1980s, general principles began to emerge about the evolution of development in animals. The identification of the Hox genes in Drosophila melanogaster and the subsequent discovery of their conservation and similar expression in different Metazoans led to the revolutionary realization that many of the mechanisms critical to basic animal development have been conserved across more than 500 million years of evolution. Many other developmental pathways, such as those specifying the heart and the central nervous system, have since been elucidated and promptly subjected to successful comparative analysis.
These celebrated discoveries illustrate ways that very different organisms are, at a fundamental level, similar to one another. But not all developmental processes are so conservative; an outstanding example is sex determination. The majority of animal species produce two sexes, and current phylogenies (e.g., [1]) suggest that sexual dimorphism was likely a feature of the last common ancestor of the coelomate bilaterians, a vast clade of animals that excludes only sponges, ctenophores, cnidarians, and acoel flatworms. However, though critical for development and reproduction, the mechanisms that specify sex determination are among the least-conserved known. Marked variation exists in both the primary sex determination signal and in the downstream genetic pathways that interpret the signal. We are thus presented with our first conundrum: sexual differentiation appears to be an ancient, and potentially homologous, feature of animal biology, yet its genetic specification suggests multiple origins.
Bewildering Variety
The variety of primary sex determination cues was appreciated long before the advent of molecular genetics [2]. The two broadest categories are genetic sex determination (GSD), in which the sex of offspring is set by a sex chromosome or an autosomal gene, and environmental sex determination (ESD), in which sex is determined by temperature (as with turtles), local sex ratio (as with some tropical fish), or population density (as with mermithid nematodes). Though little is known about the molecular mechanisms of ESD, within the GSD systems many different mechanisms have been uncovered. Dual sex chromosome systems, in which either the female (ZW/ZZ) or the male (XX/XY) is heterogametic, are common, as are systems set by the ratio of the number of X chromosomes to sets of autosomes (X:A). There are also systems in which heterozygosity at a single locus is required for female development (known as complementary sex determination; [3]), as well as systems involving sex determination via multiple genes with additive effects.
Molecular genetic investigations of GSD in model systems such as Drosophila, Caenorhabditis, and mice have revealed a clear lack of conservation, underscoring the diversity. For example, although the primary sex determination signal in both D. melanogaster and C. elegans is the X:A ratio, the fruit fly pathway consists of a cell-autonomous cascade of regulated mRNA splicing, while that of the nematode follows a Hedgehog-like intercellular signaling pathway [4]. GSD in mammals depends (with some interesting exceptions—see [5]) upon a Y-specific dominant gene (Sry) encoding a transcription factor. In the face of such impressive differences, perhaps we should question our assumption of homology: could it be that sex determination in different taxa has arisen independently over and over again in evolution? Until 1998, this seemed like a good bet.
The discovery of the homology of the key sex-determining genes doublesex in Drosophila and mab-3 in C. elegans provided the first evidence for a common evolutionary basis of sex determination in animals [6]. Soon, related doublesex-mab-3 (DM)-family genes with roles in male sexual development were discovered in vertebrates and even cnidarians [7,8]. Here at last was a smoking gun that could link the diverse metazoan sex determination systems (Figure 1). But as satisfying as the result was, it immediately gave birth to another mystery: if the enormous diversity of sex determination systems are all derived from a common ancestor, how could they possibly have been modified so radically? After all, sexual differentiation and reproduction are hardly unimportant developmental processes!
Figure 1 Diverse Genetic Factors Converge on a Conserved Regulator
The primary sex determination mechanisms are shown, from left to right, for Drosophila, Caenorhabditis, the box turtle Terrapene carolina, and humans. These proximate signals are then relayed by diverse signal transduction pathways that ultimately converge on a DM-family gene. The left image is from Muller [23]; the center-left image appears courtesy of Dr. Barbara Conradt, the center-right image appears courtesy of J.D. Willson, and the right image is from a plaque mounted on the NASA spacecraft Pioneer 11.
Focusing on Close Relatives
To understand how such diversity came to be, we need to look at the differences between closely related species. This approach allows the discovery and interpretation of small-scale sex determination changes before they are obscured by subsequent changes. The processes discovered in this way might then be reasonably extrapolated to explain the seemingly unrelated systems of more deeply diverged taxa. Work in dipterans [9] and nematodes [10] has revealed three evolutionary phenomena that characterize shorter-term sex determination evolution.
The first of these is the often astounding rate of molecular evolution at the level of nucleotide and aminoacid sequences. Although some sex-determining genes are well conserved, many show unprecedented substitution rates [11]. An extreme example is the central integrator of the X:A ratio in Caenorhabditis, xol-1. The xol-1 orthologues of the closely related nematodes C. elegans and C. briggsae are a mere 22% identical [12], even though genes surrounding xol-1 are much better conserved (Figure 2A). Remarkably, the 3′ neighbor of xol-1, the immunoglobulin dim-1, is only 5 kb away and is essentially identical between species.
Figure 2 Evolutionary Dynamics of Sex-Determination Pathways
(A) Rapid sequence evolution. Shown are the genes in xol-1 region of C. elegans that have syntenic homologues in C. briggsae, with the amino-acid-level identity between them indicated below.
(B) Pathway evolution and primary signal swapping (modified from Graham et al. [9]). In Drosophila (L), the X:A ratio indirectly regulates tra splicing through a requirement for Sxl. In the medfly Ceratitis (R), Sxl is not a sex determination gene, and the female-promoting positive regulation of tra is instead autonomous. Its inhibition by the dominant M gene allows an XX/XY system to replace one based on the X:A ratio.
(C) Convergent evolution of nematode hermaphroditism in C. elegans and C. briggsae. fog-2 exists only in C. elegans, and although all species use the fem genes for male somatic development, only C. elegans requires them for hermaphrodite spermatogenesis.
A second phenomenon, best exemplified by dipteran insects, is the modification of genetic control pathways through the gain or loss of key pathway components (Figure 2B). In Drosophila, the first gene to respond to the X:A ratio is Sxl, whose transcription is regulated by both autosomal and X-linked factors very early in development [4,13]. When X: A = 1 (i.e., in female embryos), Sxl transcription occurs and produces Sxl protein. Later in development, transcription from a second promoter occurs in both sexes, but these transcripts cannot be productively spliced without the earlier burst of Sxl expression. As a result, only females sustain Sxl expression, and in turn only females can productively splice the mRNA of tra, its downstream target. Productive splicing of tra is required to produce the female-specific form of dsx, a founding member of the DM family mentioned above.
In a series of groundbreaking papers, Saccone and colleagues investigated the pathway in the more distantly related heterogametic Mediterranean fruit fly Ceratitis capitata. The first surprise was that although a highly conserved Sxl homologue exists in Ceratitis, it does not undergo sex-specific regulation similar to that of Drosophila, which suggests that it does not play a key switch role (Saccone et al. 1998). Similar results have also been found for the housefly, Musca domestica [14], indicating that the role of Sxl in sex determination may be restricted to Drosophila and its closest relatives. In contrast, tra and dsx are key sex regulators in all dipterans examined thus far.
A further surprise came when the Ceratitis tra homologue was characterized [15]. In the case of this gene, clear evidence for sex-specific regulation was found, and as with Drosophila, only females productively splice tra mRNA. However, this splicing difference can be explained nicely by a positive feedback, similar to that seen in Drosophila Sxl, in which Tra protein regulates its own splicing. In 2002, Pane et al. proposed that the dominant, male-specifying M factor on the Y chromosome inhibits this autoregulation [15]. As a result, males cannot make functional Tra protein, and the male form of Dsx is produced. These experiments show not only how a pathway can evolve, but also, importantly, how X:A and heterogametic GSD systems can be interconverted by modifying the cue that regulates a conserved molecular switch gene (the splicing of tra mRNA). A detailed scenario for how this might occur has recently been proposed [16].
Finally, recent studies of Caenorhabditis nematodes have shed light on the genetic basis of the convergent evolution of sex determination related to mating system adaptations. An important factor in this area are new phylogenies of the genus [17,18], which consistently suggest the surprising possibility that the closely related hermaphroditic species C. elegans and C. briggsae acquired self-fertilization independently, from distinct gonochoristic (male/female) ancestors (Figure 2C). Although this scenario is somewhat uncertain purely on parsimony grounds, recent work on the genetic control of the germline bisexuality that defines hermaphroditism has tipped the balance toward parallel evolution.
Working with C. elegans, Clifford et al. [19] cloned fog-2, a gene required for spermatogenesis in hermaphrodites but not in males. Upon doing so, it became clear that fog-2 is part of a large family of F-box genes and was produced by several recent rounds of gene duplication. The C. briggsae genome sequence suggested that while C. briggsae possesses a similarly large family of F-box proteins, the duplication event giving rise to fog-2 was specific to the C. elegans lineage. In this issue of PLoS Biology, Nayak et al. [20] extend this work by rigorously demonstrating that fog-2 is indeed absent in C. briggsae. The authors also identify a short, C-terminal domain that makes FOG-2 uniquely able to perform its germline sex-determining function. This domain is probably derived from a frame-shifting mutation in an ancestral gene. Working with C. briggsae, Stothard et al. [21], Haag et al. [22], and Hill et al. (unpublished data) have also found evidence of important species-specific regulation of germline sex determination. RNA interference and gene knockout approaches have shown that while C. elegans requires the male-promoting genes fem-2 and fem-3 to produce sperm in hermaphrodites, C. briggsae requires neither. Given that both genes have conserved roles in male somatic sex determination, this suggests that C. briggsae evolved hermaphroditism in a way that bypasses these genes.
The long-standing mystery of sex determination and its diversity began by comparisons between distantly related species. Recent work on closer relatives has uncovered processes that through a reasonable extrapolation enable the connection of these disparate dots into a fascinating picture of developmental evolution. Though the divergence is extreme, it is likely that a better understanding of the evolution of sex determination genes and pathways holds lessons about the evolution of development in general. The next major challenge will be to integrate the comparative developmental data with the ecological and population processes that are driving the evolution of sex determination. Only then will we be able to say that the picture is complete.
Citation: Haag ES, Doty AV (2005) Sex determination across evolution: Connecting the dots. PLoS Biol 3(1): e21.
Eric S. Haag and Alana V. Doty are with the Department of Biology and Program in Behavior, Evolution, Ecology and Systematics, University of Maryland, College Park, Maryland, United States of America.
Abbreviations
DM
doublesex-MAB-3
ESDenvironmental sex determination
GSDgenetic sex determination
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Cline T Meyer B Vive la difference: Males vs. females in flies vs. worms Annu Rev Genet 1996 30 637 702 8982468
Graves JAM Chadwick D Goode J Evolution of the testis-determining gene—The rise and fall of Sry The genetics and biology of sex determination 2002 West Sussex (United Kingdom) J. Wiley and Sons 276
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Raymond C Kettlewell J Hirsch B Bardwell V Zarkower D Expression of Dmrt1 in the genital ridge of mouse and chicken embryos suggests a role in vertebrate sexual development Dev Biol 1999 215 208 220 10545231
Miller SW Hayward DC Bunch TA Miller DJ Ball EE A DM domain protein from a coral, Acropora millepora , homologous to proteins important for sex determination Evol Dev 2003 5 251 258 12752764
Graham P Penn JK Schedl P Masters change, slaves remain Bioessays 2003 25 1 4 12508274
Stothard P Pilgrim D Sex determination gene and pathway evolution in nematodes Bioessays 2003 25 221 231 12596226
Civetta A Singh R Sex-related genes, directional sexual selection, and speciation Mol Biol Evol 1998 15 901 909 9656489
Luz JG Hassig CA Pickle C Godzik A Meyer BJ XOL-1, primary determinant of sexual fate in C. elegans , is a GHMP kinase family member and a structural prototype for a class of developmental regulators Genes Dev 2003 17 977 990 12672694
Saccone G Peluso I Artiaco D Giordano E Bopp D The Ceratitis capitata homologue of the Drosophila sex-determining gene sex-lethal is structurally conserved, but not sex-specifically regulated Development 1998 125 1495 1500 9502730
Meise M Hilfiker-Kleiner D Dubendorfer A Brunner C Nothiger R Sex-lethal, the master sex-determining gene in Drosophila, is not sex-specifically regulated in Musca domestica
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Pane A Salvemini M Delli Bovi P Polito C Saccone G The transformer gene in Ceratitis capitata provides a genetic basis for selecting and remembering the sexual fate Development 2002 129 3715 3725 12117820
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| 15660158 | PMC544544 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2005 Jan 18; 3(1):e21 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030021 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030023Book Reviews/Science in the MediaPsychologyHomo (Human)Turning to Science Book Review/Science in the MediaParthasarathy Hemai 1 2005 18 1 2005 18 1 2005 3 1 e23Brockman J editor ( 2003 ) Curious minds: How a child becomes a scientist .
New York : Pantheon Books . 236 p (hardcover) 0-375-42291-9. US$23.95 Copyright: © 2005 Hemai Parthasarathy.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.A review of John Brockman's new book Curious Minds: How a Child Becomes a Scientist.
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Do you want your child to grow up to be a scientist? If so, then by all means follow the roadmaps set down by the 27 scientists who contributed their personal histories to John Brockman's Curious Minds: How a Child Becomes a Scientist.
You could do worse than start with a family history rich in science, like Nicholas Humphrey whose grandfather, Nobel laureate A. V. Hill, remembers being startled by a solar eclipse while out rabbit hunting as a boy in England and having the presence of mind to smear the rabbit's blood over the glass from his pocket watch in order to watch the phenomenon in safety. Or, if you don't have a healthy lineup of scientific superstars in your family history, you could educate yourself about the wonders of science, like Murray Gell-Man's autodidactic father, and insist that your son at least try majoring in physics before settling on linguistics or archaeology. If you cannot be inspired to teach yourself quantum physics, you might also try the more commonplace achievement of a bad marriage to create an unstable emotional life for your daughter, so she might retreat into “solitary and bookish tendencies,” as the evolutionist Lynn Margulis did.
Perhaps you cannot bring yourself to actively steer your child into science, but hope this book might guide you to at least watch for the signs of future genius. You might watch for a young Freeman Dyson in his crib, bored by the stuffed animals and mobiles, occupying his time by adding infinite series of fractions. Or you might watch for your daughter sneaking into your study to read your medical reference books on the sly, as Janna Levin did before ultimately becoming a professor of physics. Then again, you could watch for your son to emulate David Buss by reaching high for a C+ grade point average in school, indulging in recreational drugs, and taking a night shift job at a truck stop, before a scientifically minded girlfriend and a fortuitous lottery-based scholarship to the University of Texas turned him to the pursuit of evolutionary psychology.
The pattern is clear: there is no pattern. And the book's strong contingent of psychologists is not shy about commenting on the dubious reliability of self-narrative. Steven Pinker writes, “Don't believe a word of what you read in this essay on the childhood influences that led me to become a scientist. Don't believe a word of what you read in the other essays, either….Recounting childhood influences is a mental process no less subject to quirks and errors than falling for the visual illusions on the back of a cereal box….None of us has taken part in the experiments that would isolate the causes of our choices in life.” Or as Nicholas Humphrey puts it more simply, “Each of us is who we are, and we must each have had some sort of childhood. Who's to say whether any particular factor carried the weight that our self-narrative now likes to attribute to it.”
This book, then, is not a guide to the prescientific childhood, but a set of memoirs bound by their common conclusion. Many of them are highly entertaining, some of them are self-conscious and pedantic. All of them highlight the passion of childish curiosity extending into adulthood, either by family tradition or because a mentor appeared at the right place and time to ensure that this curiosity was not abandoned. As the developmental psychologist Allison Gopnik notes, “I suspect that there are few reports of scientists with a childhood fascination for babies, because most of those children turned into nursery school teachers or children's librarians or just stay at home mothers….It seems to me now that I was destined to become either a psychologically minded philosopher or a philosophically minded pscyhologist. But given slightly different contingencies, I might have become a frustrated preschool teacher or faculty wife.”
We wish we could find prescriptions for guiding our children towards success, but the nature versus nurture debate has long since been replaced by the more sophisticated understanding of just how inextricably intertwined these two influences are in creating even a lowly zebrafish, let alone anything as behaviorally complex as a human being. This understanding can lead to frustration, as simple causes and cures become more elusive. Selling Mozart recordings for expectant mothers to play to their wombs can help assuage the fears and ignite the hopes of overachieving, yuppie parents, but there is little evidence that such interventions create the sought after intellectual advantage for the newborn.
Mostly what we see in these essays are stories about people—stories that people tell about themselves. Stories of childhoods interrupted by war, of mentors persecuted by McCarthyism. Would a book tracing the self-styled childhood influences of outstanding politicians or judges have been so different? Possibly fewer socially awkward children and less gadget-building in the mix, possibly not.
The contributors to this book are all outstanding, motivated scientific leaders who have chosen or stumbled into their intellectual paths. But I am not convinced that reading their brief reflections compares with the unique opportunity of interviewing Albert Einstein that John Brockman identified as one inspiration for this book—for turning what began as dinner party conversation into a methodical attempt at biographical anthology. Much of these pages are in fact dinner party conversation, whether it is Robert Sapolsky's horror of the white Southern gentlemen of Harvard's intellectual elite playing cards and drinking while dividing the spoils of sociobiology, or Allison Gopnik's personal relief that there were no prohibitions against sleeping with teaching assistants when she was a nubile college student. Some of it is heart-rendingly personal, as when Jaron Lanier talks about his futile attempt at age 11 to attract friends to his homemade electronic Halloween haunted house. All it really tells us is that there are as many ways of forming a scientist as there are of forming a human being.
Citation: Parthasarathy H (2005) Turning to science. PLoS Biol 3(1): e23.
Hemai Parthasarathy is a senior editor at the Public Library of Science, San Francisco, California, United States of America. E-mail: [email protected]
| 0 | PMC544546 | CC BY | 2021-01-05 08:21:19 | no | PLoS Biol. 2005 Jan 18; 3(1):e23 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030023 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1566015910.1371/journal.pbio.0030024FeatureGenetics/Genomics/Gene TherapyNeuroscienceMus (Mouse)A Golden Age of Brain Exploration FeatureGewin Virginia 1 2005 18 1 2005 18 1 2005 3 1 e24Copyright: © 2005 Virginia Gewin.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.The Allen Brain Atlas of gene expression in the mouse brain is poised to serve as an outstanding resource to neuroscience
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Armed with billions of cells, elaborate circuitry, and a seemingly animate anatomy, capable of growing as it learns, the brain is a marvelously enigmatic organ. Much to the chagrin of those that study it, the brain remains perhaps too mysterious.
Although genetic information exploded out of the Human Genome Project, it has been of little consequence to neuroscience—a discipline still grappling with the boundaries and names for distinct brain regions. According to United States National Institute of Mental Health Director Thomas Insel, over 99% of the neuroscience literature focuses on only 1% of the estimated 15,000–16,000 genes expressed in the brain. David Van Essen, a neurobiologist at Washington University in St. Louis, Missouri, likens the current genetic map of the brain to a 17th century map of Earth. A voyage around the Earth had already proven it was round, but landmass resolution was still vague at best.
Magellan's benefactors, though, never bankrolled a technical advance quite like the Allen Brain Atlas. Neuroscience's unlikely sugar daddy, Microsoft cofounder and the world's fifth wealthiest man, Paul Allen, created the $100 million dollar Allen Brain Institute in Seattle, Washington, two years ago. The first explicit goal of the institute was to create an open-access, visual, searchable online map of genes expressed in the brain, as well as of brain circuitry and cell location. Roughly one petabyte of data—equal to the memory necessary to hold the information held in about 50 Libraries of Congress—will be produced as a result.
In mid-December, the first 2,000 genes were uploaded. By 2006, the Allen team plans to have as many as 24,000 genes online. While the ultimate goal is to map the human brain, the atlas ushers in a new era of neurogenetics—an attempt to make connections between anatomical, genetic, and behavioral observations.
Of Mice and Men
The initial effort will focus on the standard, inbred lab mouse strain known affectionately as C57BL6. Like it or not, mice are remarkably similar to humans—sharing 99% of our genes. Humans, at this time, provide too many hurdles—not the least of which is a lack of willing brain donors that are the same age. Since the C57BL6 strain is inbred, the mice are also much more uniform than humans—a key to constructing the most accurate representative map possible of one species' adult brain.
In situ hybridization—A cross-section through the mouse brain shows gene expression (black dots) in specific brain regions
(Copyright: David Anderson)
With a map of mouse genes in hand, scientists will be able to develop informed hypotheses about genes that may affect human brain function and dysfunction. “People will be able to look first in the mouse atlas, then more selectively focus on human cases,” says Gregor Eichele, director of the Max Planck Institute for Experimental Endocrinology in Hannover, Germany. Indeed, researchers may do well to focus their efforts on those specific cells in which homologous genes are expressed in the mouse. Insel suggests an initial mental health application: find dyspyndin, a gene linked to schizophrenia. Insel is banking on the atlas to locate genes linked to conditions including bipolar disorder, schizophrenia, and autism. Once identified, such critical genes can be examined in detail and used in studies aimed at disease cures or drug screens. Presumably, the atlas will be a boon for drug discovery and development by providing information on drug targets present within brain cells.
Eichele points out that the Allen Brain Atlas will eliminate time wasted testing fruitless hypotheses. “If you think gene X may be involved in [hippocampal-dependent] memory, but it's not expressed in the hippocampus, you shouldn't bother following that line of research,” he says. Insel agrees that where genes are expressed in the brain will be most telling. “In the brain, more than any other organ, function follows form,” he says.
Cellular resolution of expression patterns will prove necessary to uncover as yet unknown relationships between circuitry, cell type, and gene expression in the brain, says Arthur Toga, a neuroscientist at the University of California, Los Angeles, and Allen Brain Atlas advisor. Ed Lein, a neuroscientist at the Allen Brain Institute, thinks that mapping at the cellular scale will also redefine anatomy. Traditionally, neuroanatomists have delineated brain regions pretty much by eye, identifying clusters of cells and patterns of connections that look the same. “We're starting to redefine boundaries of regions by cell type,” says Lein, defining cell type by gene expression pattern.
“Robot” used for high-throughput in situ hybridization, developed by Gregor Eichele and colleagues at the Max Planck Institute for Experimental Endocrinology
(Photo: Gregor Eichele)
The difficulty, according to Allen Brain Institute scientific advisor David Anderson, a neuroscientist at the California Institute of Technology in Pasadena, California, is in understanding how a gene mutation affects behavior. “It is impossible without a knowledge of the circuits in which a gene is expressed,” he says. Anderson himself studies innate behaviors such as fear responses. Specifically, he's most interested in understanding the function of different regions of the almond-shaped amygdala. Once he finds genes expressed in neurons within regions of the amgydala implicated in fear, he plans to determine neuron function by creating transgenic animals in which specific neuron activity has been silenced.
Thus far, Anderson's laboratory has endured the slog of using microarray techniques to identify genes with expression patterns linked to fear behavior, followed by in situ hybridization, which traditionally involves twenty-odd complex, error-prone steps, to find just some of the genes expressed in different parts of the amygdala. “The Allen Brain Atlas will identify a whole set of genes that we would have had to spend several years to find,” he says. Indeed, locating where even one gene of interest is expressed in the brain eats up valuable research time, especially when there are so many potentially interesting genes. In most cells, 10,000 genes can be expressed.
Eichele improved the slow error-prone process into an automated, fast, “high throughput” method that caught the attention of the Allen team because it was capable of meeting the needs of such an ambitious project. In situ hybridization uses labeled probes for specific messenger RNA sequences, allowing scientists to test individual brain tissue samples for gene expression.
Automated in situ hybridization data will be generated for the entire mouse transcriptome—the full complement of activated genes in a particular tissue at a particular time—on a genome-wide scale. The mouse is a young adult at 56 days old, free from the confounding factors of development. After it is sacrificed, the mouse brain is immediately frozen, then sliced very thinly—to get forty sections from each millimeter of thickness—so that the probes for hybridization can expose gene expression in individual cells.
Mining the Mind for Riches
The in situ data will be matched in a three-dimensional framework to the reference atlas developed by Allen's team. The resulting images will be turned into a virtual microscope, allowing users to focus down on genes expressed in regions of interest.
While the Allen Brain Atlas is somewhat like other genomics projects in scale, it is unique. “No one's gone into a 3-D structure like a tissue and examined it in a systematic way,” says Allan Jones, senior director of Allen Brain Atlas Operations. In that way, he adds, it's a much richer dataset than the Human Genome Project. “As we're ramping up—fully by spring of next year—we'll be generating about 1,000 microscope slides a day with four mouse brain sections on each slide,” says Jones. Each day, those sections are scanned and stitched together electronically into 300-megabyte batches. “Scaling up a lab process is currently the biggest challenge,” says Jones. “In effect, we're turning an art form into something that gives high-quality data day in and day out,” says Jones. “If you are off slightly when cutting a 2-D brain slice, it becomes very difficult to map back into a 3-D context.”
While it is a logical starting point, a spatial understanding of gene expression is just one way to mine the brain. Other avenues currently being pursued explore individual variability, development, and comparisons between species (See Box 1). The sheer comprehensive nature of the Allen Brain Atlas will be its crowing achievement, and its complement to the other ongoing atlas efforts. However, the magnitude of the project also poses its greatest hurdle—about which onlookers have expressed some concern.
Box 1. A Bevy of Brain Databases
The Allen database will provide a spatial map of neurogenetic data, specifying where the 99% of shared mammalian genes are expressed in the brain. Other online databases are striving to provide alternative axes of information that will detail individual variability, species comparisons, and changes during development.
Going deep on the genetic variability axis of understanding is Web QTL, a collection of images from 800 brains of 35 different strains of mice. “We're interested in genetic sources of variation,” says Rob Williams, a neurobiologist at the University of Tennessee in Memphis. “We study many strains of mice and map the upstream modulators that control expression differences.”
Van Essen's online atlas strives to map the structural and functional areas of the cerebral cortex, believed to be the seat of thought, learning, emotion, sensation, and movement, for humans, macaques, rats, and mice. In constructing the SuMS (for “surface management system”) database, they've put most of their effort into comparing datasets between species.
Finally, GenSAT (Gene Expression Nervous System Atlas) follows gene expression as it changes through the development of an organism. Using a method that manipulates “bacterial artificial chromosomes” to insert, change, or delete parts of large gene sequences, one transgenic mouse is created for each gene. When reporter genes are added to the bacterial artificial chromosomes, cells with selective gene activity glow. This advance takes a step towards relating gene expression patterns to connectivity between brain regions.
“It's clear that they want to do a first-rate job of working with all this information, but I'm not sure they entirely appreciate how hard it's going to be to manage the staggering amount of information they're getting,” says Van Essen. As manager of his own comparative anatomy database, he understands the magnitude of the task. He points out that properly digitizing the data in electronic form and registering one particular slice to a standardized reference with meaningful coordinates is not trivial.
“In an atlas, there will be considerable variability from one individual to the next—even in inbred mouse strains,” says Van Essen. Even if it's only 20% variability, it is still going to pose challenges for managing experimental data. Van Essen acknowledges that this isn't just an impediment for Allen's team, but for neuroscience as a whole. “These tools, in general, don't emerge from a vacuum. They emerge best when rich, challenging datasets are staring people in the face,” he says.
An even larger problem is communicating the data effectively. Figuring out how to navigate the tremendous morass of data will be a bioinformatic stumbling block. Lein acknowledges that figuring out how to best annotate the data is one of the bigger challenges for the future—particularly in cases where gene expression doesn't match the agreed-upon boundaries of anatomical regions.
For now, users will be able to mine the data by gene name only. The initial release consists of an image viewer to view the in situ hybridization data for one or several genes at a time, along with a reference atlas to determine the structures in which genes are expressed. Future releases will allow the user to conduct more sophisticated searches, such as by anatomical structures.
“Not only is there this 3-D structure, but there are lots of studies where people are trying to understand what drives the turning on and off of genes,” says Jones. “At the end, if the atlas has a big impact, it will be in providing the precise coordinates for those people to tease apart what specific DNA elements drive expression within regions or structures.”
And while the Allen Brain Atlas will provide a fine level of detail, there are limitations. Much like the Human Genome Project, the information will be the starting point and not the end point of understanding brain function. “It won't change strategy for doing experiments,” says Nobel laureate and Columbia University neuroscientist Eric Kandel. “The atlas will be a catalyst rather than a direction setter.”
The Final Frontier
Overall, neuroscience is entering a new era. Insel notes that recent work has proven the brain to be extraordinarily dynamic, birthing neurons throughout a lifespan. Brain functions seem more modular than global. And there is no real separation between the mind and brain. “Mental disorders are brain disorders,” he says.
Over the next 5–10 years, neurogenomics will fuel a golden age of discovery in neuroscience. In fact, scientists may even reach an overarching goal—understanding how the wild card of environment impacts brain function. “We want predictive genetics able to accommodate environmental differences,” says Rob Williams, neurobiologist at the University of Tennessee in Memphis.
For Kandel, one thing is certain. “Most of the mysteries of the brain lie ahead of us.”
Using WebCaret and SumsDB to visualize functional magnetic resonance imaging activations and visual areas on a flat map of the human “Colin” cortical atlas
(Image: David Van Essen; dataset with publication sources available: http://sumsdb.wustl.edu:8081/sums/directory.do?dir_id=702541)
Citation: Gewin V (2005) A golden age of brain exploration. PLoS Biol 3(1): e24.
Virginia Gewin is a science writer based in Portland, Oregon, United States of America. E-mail: [email protected]
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Further Reading
Allen Brain Atlas www.brainatlas.org
Reymond A Marigo V Yaylaoglu MB Leoni A Ucla C Human chromosome 21 gene expression atlas in mouse Nature 2002 420 482 586 12466837
| 15660159 | PMC544547 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2005 Jan 18; 3(1):e24 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030024 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1566016010.1371/journal.pbio.0030032FeatureScience PolicyHomo (Human)Help Wanted: Science Manager FeatureHubbard Kirsten A 1 2005 18 1 2005 18 1 2005 3 1 e32Copyright: © 2005 Kirsten A. Hubbard.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.The recently created professional science master's degree may be the answer to the increasing need for science-savvy employees in the business world
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“I didn't want to be just another MBA,” says Pascal Herzer, one of the first recipients of a new graduate credential known as the professional science master's, or PSM. “Not many people have the ability to understand science and business, and [the PSM] program was designed for that very purpose.”
PSMs are two-year American master's degrees financed in large part by the Alfred P. Sloan Foundation to cultivate science managers. Sloan's ultimate goal is to make science careers more attractive to talented young people like Herzer, a 2003 PSM graduate in Applied Biosciences from the University of Arizona, who believes his PSM makes him more marketable to science-based businesses. “I am at the true junction of science and business,” he says.
The Missing Degree
Fortunately for Herzer, the business of science is booming. Jobs for scientists and engineers grew four times faster than the United States national average since 1980, and should outpace the market until at least 2010. Surprisingly to many academics, most of these jobs are in industry. In 1999, the last year with complete data, two out of three employed science and engineering (S&E) graduates worked in industry, including the great majority of bachelor's and master's degree holders, and 40% of doctorates. In other words, industry, not academe, now drives American S&E employment, and will for the near future.
Like academia, industry needs scientifically literate personnel; unlike academia, industry wants employees with business savvy as well. However, in the past, graduate students received either science or business instruction, not both. “Industry simply hired regular master's-degreed people, or MBAs, or more likely PhDs, and just expected them to learn their weaknesses on the job,” says Eleanor L. Babco, Executive Director of the Commission on Professionals in Science and Technology, a nonprofit corporation with funding from the Sloan Foundation to assess PSM graduates.
For science-based businesses, then, the American S&E doctorate—viewed by many as the worldwide gold standard for science education—is too specialized for their needs (see Box 1). But a master's degree may be just right.
Box 1. Is There a Doctorate in the House?
The length of time to obtain a biological science doctorate has increased…
…The number of postdocs and part-time faculty in the biological sciences is increasing…
…And the proportion of doctorates in academia is decreasing…
(Statistics taken from the National Science Board's “Science and Engineering Indicators 2004” [http://www.nsf.gov/sbe/srs/seind04/] and the National Science Foundation Science Resources Statistics Division's “1995 and 2001 Survey of Doctorate Recipients” [http://www.nsf.gov/sbe/srs/infbrief/nsf04328/table1.xls].)
Bridging the Gap
During the 20th century, the master's degree evolved as a professional credential in many fields, including business, education, and social work, and more recently, pharmacy, physical therapy, and accounting. In the 1990s, non-incidental master's in the sciences—in other words, intentionally terminal degrees, not consolation prizes for failing out of graduate school—crept into engineering and applied mathematics, too, as companies grew more reliant on computational analysis and hired accordingly. From 1981 to 2000, for example, the number of earned master's degrees in mathematics and computer science more than doubled.
With hopes of spurring a “significant movement,” in 1997 the Sloan Foundation bet big on professional master's degrees, eventually spending $11 million on almost 100 programs across the US. Sloan Foundation–backed PSM programs now operate at 45 universities in 20 states, in such fields as microbial biotechnology and applied genomics; similar programs have also developed independently of the Sloan Foundation, such as the Master of Science in Bioinformatics at Johns Hopkins. And while most PSM-style programs are currently in the US, this may soon change: the 1999 “Bologna Agreement” requires all European Union universities to adopt uniform undergraduate and graduate degrees “relevant to the European labour market”; so master's-level industry-centric degrees are sure to follow. At Leiden University in the Netherlands, for example, students can now add a “science-based business” focus to any research master of science (MSc) program.
Like all graduate programs, PSMs offer advanced coursework in a (science or math) specialization, usually in an emerging or hybrid field such as bioinformatics. Most PSMs also provide business courses—including finance, project management, regulatory affairs, and intellectual property law—and information technology classes as well. PSMs are “industry relevant” by design, with external advisory committees populated by local business leaders, weekly colloquia led by corporate representatives, special arrangements for employed students, and industry internships or final projects exploring realistic business scenarios (see Box 2).
Box 2. Requirements for PSM Programs
Only programs meeting most of the following requirements may earn the official moniker “professional science master's.”
Two years of science or math graduate-level coursework, taught by regular faculty, characterized by interdisciplinary studies and a focus on informatics
Training in business fundamentals—such as finance, marketing, project management, communication, and team building—and exposure to industry professionals
Final project reflecting a realistic workplace issue and/or industry internship
Advisory board of industry professionals
Targeted recruitment and admissions separate from other degree programs
Commitment to tracking graduates through first five years
Long-term sustainability
(Source: http://www.sciencemasters.com/affiliation.html.)
A key principle underlying the PSM model is interdisciplinarity. PSM students are encouraged to reach out to other departments and broaden their expertise in multiple areas, to better understand the collaborative culture of industry-style scientific enterprise. To promote such connections, PSM programs explicitly teach teamwork and effective scientific communication, with authentic case studies analyzed alongside MBA students, classroom presentations and public seminars, and open defenses of final projects. Consequently, PSM graduates, unlike many doctoral graduates, are trained to possess a wide array of interactive skills, including sizing up an audience for their ability to comprehend the presented material and adapting appropriately.
In a science-based business, ideas must flow freely between scientists and non-scientists in and out of the company—between researchers and marketers, say, or inventors and patent lawyers—to capitalize on discoveries and comply with regulations. When non-scientists misunderstand the science underpinning a business model, profits suffer. But the presence of a central employee who streams data between differently educated members of the network may boost the bottom line. PSM students are specifically trained to act as such “science translators.” “[My PSM] allows me to serve as an efficient mediator between corporate entities, university personnel, and scientists,” says Herzer.
For this reason, small companies and start-ups, which cannot afford specialists for every position, may particularly benefit from PSM-credentialed employees, able to connect different people and function in multiple roles; indeed, many PSM graduates have job descriptions expressly created for them. “We need generalists rather than specialists,” says James L. Ratcliff, Chairman and CEO of Rowpar Pharmaceuticals, a dental products company in Scottsdale, Arizona. For small companies like his, Ratcliff says, “PSM graduates have an appropriate combination of project management expertise, an understanding of business environments and priorities, and advanced knowledge in the physical and life sciences.”
Although it is too early for comprehensive assessment, employment outcomes for PSM graduates have been examined, and this result is clear: they are getting industry jobs. According to The Conference Board, an independent business management organization funded by the Sloan Foundation to survey PSM alumni, by 2002, 91% of the first PSM graduates had obtained full-time positions within their field despite a white-collar recession, two-thirds with salaries of $50,000 or more. A separate analysis by the Commission on Professionals in Science and Technology found that 61.5% of employed respondents were hired by businesses. Employment opportunities range from marketing to bioinformatics (see Box 3). “Companies need people that can work in companies,” says Lindy A. Brigham, coordinator of the Applied Biosciences PSM program at the University of Arizona.
Box 3. First Jobs Obtained by PSM Graduates
A sampling of actual first jobs obtained by students in PSM or PSM-style programs:
Coordinator of Regulatory Affairs
Associate Criminalist
Licensing Assistant
Staff Researcher
Senior Computer Database Specialist
Project Manager
E-Product Marketing Specialist
Clinical Consultant
Technical Support Specialist
Bioinformatics Programmer
Manager of Medical Affairs
(Source: personal communications from L. A. Brigham, D. Ascher, T. Tiongson Pohar, and S. Inamdar.)
Not a Perfect Cure
Although most scientific careers demand a graduate degree, a professional master's in many hard sciences still encounters entrenched academic opposition. According to Lee-Jen Wei, then acting chair of the Department of Biostatistics in the Harvard School of Public Health, quoted in the Wall Street Journal, “Harvard tries to create leadership in industry, academics and government, and our philosophy is we don't think that with a master's degree people can fill that role very easily.”
The government appears to agree with this view. While most doctoral candidates receive federal funds for tuition and other expenses, there is little money for master's students, who disproportionately end up in industry regardless of specialization. PSM students are especially affected by this problem because “interdisciplinary” equals “expensive.” Similarly, “interdisciplinary” can also mean “hard to find”—companies with targeted recruitment often miss PSM students, who are not “in” any particular department—and “confusing”—differences in these new, still somewhat vaguely defined programs can make hiring comparisons difficult. But perhaps the most conspicuous drawback to PSMs is their newness, and resulting obscurity: almost half of graduates say they are “not sure” employers will value their PSM, or the unique skill set it affords.
But Will They Succeed?
Still, many observers of higher education support the PSM concept. Judith Glazer-Raymo, author of the forthcoming book Professionalizing Graduate Education: The Master's Degree in the Marketplace, argues that converging market forces will lead to the success of the professional master's degree in science. These forces include: rapid technological change; the rise of alternative learning channels such as online and distance education, corporate universities, and hi-tech certification programs; the proliferation of degrees in general, and in multidisciplinary fields specifically; and a fundamental societal shift away from public service and toward entrepreneurship, profitability, and competition.
Kenneth R. Smith, former dean of the Eller College of Business and Public Administration at the University of Arizona, and others make the case that PSMs may protect students' careers from outsourcing to foreign countries. The American S&E labor pool is shrinking, and industry has already responded by transferring much of its research and development overseas; however, companies are mostly moving lab scientists, not strategic analysts. Cross-training in both science and business could thus provide an edge for domestic workers in the near-term employment environment; in fact, PSM programs have a higher proportion of US citizens and residents than S&E doctoral programs.
Further, in its 2003 report, the National Science Board urged the government to better align S&E graduate education with “expected national skill needs,” including “interdisciplinary skills.” The report also recommended federal funding for a wider range of educational options and more attention on the real economic concerns of students—code words for support of professional master's degree initiatives. In the same vein, top universities now advocate “interconnections” between their professional schools and traditional departments, as a way of strengthening the overall academic mission, and many countries are sponsoring initiatives to stimulate university–industry links, to maximize marketing of technological innovations.
For advocates, then, the PSM both advances the cause of science education reform and addresses changing employment conditions with one big idea: reinvention of the two-year graduate credential for an entrepreneurial age. Herzer, for one, now a technology development representative at the Scripps Research Institute, has staked his future on the potential of professional master's degrees. “Scientists rarely understand business dealings, and business personnel rarely comprehend scientific discoveries,” he says. “The overlay of the two is crucial for any successful business transaction of scientific origins.”
Median length of time to doctorate degree
Number of doctorates employed in the biological sciences, by position
Science and engineering doctorates by employment sector
Citation: Hubbard KA (2005) Help wanted: Science manager. PLoS Biol 3(1): e32.
Kirsten A. Hubbard is a writer based in Newton, Massachusetts, United States of America. E-mail: [email protected]
Abbreviations
PSMprofessional science master's
S&Escience and engineering
| 15660160 | PMC544548 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Jan 18; 3(1):e32 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030032 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030036SynopsisNeuroscienceRattus (Rat)Whisker Velocity Patterns Tell Rats What They're Feeling Synopsis1 2005 11 1 2005 11 1 2005 3 1 e36Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Neuronal Encoding of Texture in the Whisker Sensory Pathway
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Whiskers don't fossilize, so it's hard to say when they first evolved. But it's quite likely they emerged along with mammals, over 200 million years ago. To elude the eye (and feet) of ungainly dinosaurs, it's thought these shrew-like prototypes foraged at night and sought refuge underground, where the sensory advantages of whiskers would come in handy. Nocturnal animals use whiskers much like the blind use walking sticks: to navigate their surroundings, explore close objects, and avoid running into things.
Whiskers, or vibrissae, connect to nerves, blood vessels, and muscles. These special connections allow rats, for example, to actively “whisk” the surface of objects and discern fine differences in texture, just as we move our fingertips along a surface to pick up details. In the wild, whisking helps rats navigate unfamiliar terrain to find food. But how does the brain know what the animal is touching?
Rat whiskers scan surfaces in a rhythmic motion that excites sensory receptor cells embedded in their whisker pad. Receptors in each whisker shaft are innervated by several hundred “first-order neurons” that relay sensory signals to second-order neurons in the brain stem, then on to third-order neurons in the thalamus, and finally on to the cortex, where sensory stimuli are integrated in cell clusters called barrels.
Ehsan Arabzadeh, Erik Zorzin, and Mathew Diamond work with rats to investigate how sensory receptors extract fundamental features from complex and diverse stimuli to encode texture. Not much is known about how receptor and cortical neurons respond to active whisking along irregular surfaces, though responses to simple stimuli (like sinusoidal vibrations) suggest that neurons might represent texture by encoding kinetic features of whisker vibrations, in particular, velocity. In a new study, Diamond and colleagues investigate the connection between textures, whisker vibrations, and neural codes: do distinct textures produce distinct vibrations? If so, how are these vibrations encoded and reported?
Timing of neuronal activity captures sensory information
The authors first collected kinetic data of whiskers moving across different textured surfaces. Stimulating cranial nerve VII of anesthetized rats (the motor nerve) generated whisking movements akin to those seen in conscious rats; the kinetics of these movements and the vibrations of the whisker shafts were measured under different conditions, including no contact with objects (“free whisk”), contact with smooth objects, and contact with various grades of sandpaper. These vibrations were then “played back” to other rats, while measuring the neuronal activity at two critical stages in the sensory pathway: the first-order neurons that innervate the whiskers and the barrel cortex neurons that integrate the incoming signal.
Altogether, the authors collected a neural dataset consisting of first-order recordings, barrel cortical cluster recordings, and simultaneous paired recordings from both sites, all in response to playback of the library of texture-related vibrations. This approach afforded the opportunity to directly compare encoding of information at both levels in the sensory pathway. These recordings show, the authors argue, that temporally distinct firing patterns in the trigeminal ganglion (the cell bodies of the first-order neurons) and cortex captured the kinetic features of the texture-induced vibrations. Each texture's “kinetic signature” is encoded by a characteristic, temporally precise firing pattern associated with whisker movement. Compared to free whisking, coarse sandpaper produced irregular bursts of high and low velocity, and both first-order and cortical neurons fired far more impulses for coarse sandpaper than for free whisks. The authors then used stimuli consisting of random velocities to uncover the “tuning curves” of neurons, and simulations showed that these neuronal tuning curves could perfectly predict the real neural responses to textures.
Noting the close match between the simulated and natural responses, Diamond and colleagues conclude that the texture-induced firing patterns observed in the first-order and cortical neurons suggest that neurons selectively encode elemental kinetic features—namely, high velocity—to tell rats what they're whisking. This selectivity allows even a single whisker to transmit significant bits of texture-specific information to the brain. Interesting as rat whisking may be, these findings have relevance beyond the world of whiskered beings, shedding light on the underlying neural processes that translate touch into recognition.
| 0 | PMC544549 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2005 Jan 11; 3(1):e36 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030036 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030061CorrectionBioengineeringBiotechnologyCell BiologyDevelopmentMolecular Biology/Structural BiologyPhysiologyMus (Mouse)Correction: Regulation of Muscle Fiber Type and Running Endurance by PPARδ Correction1 2005 18 1 2005 18 1 2005 3 1 e61Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Regulation of Muscle Fiber Type and Running Endurance by PPARδ
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In PLoS Biology, volume 2, issue 10:
Regulation of Muscle Fiber Type and Running Endurance by PPARδ.
Wang YX, Zhang CL, Yu RT, Cho HK, Nelson MC, et al.
10.1371/journal.pbio.0020294
The following competing interest should have been indicated in the above research article. R. M. Evans wishes to acknowledge a consulting relationship with Ligand Pharmaceuticals. Under a licensing agreement with GlaxoSmithKline, Ligand receives milestone payments on the development of GW501516, a PPARδ-specific drug used in this study. For clarification, no materials or support were received from either company, and no agreements were in place concerning the execution or publication of this work.
Published January 18, 2005
Citation: (2005) Correction: Regulation of muscle fiber type and running endurance by PPARδ. PLoS Biol 3(1): e61.
| 0 | PMC544550 | CC BY | 2021-01-05 08:21:19 | no | PLoS Biol. 2005 Jan 18; 3(1):e61 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030061 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1566016410.1371/journal.pbio.0030066Research ArticleCell BiologyDevelopmentGenetics/Genomics/Gene TherapyDanio (Zebrafish)The you Gene Encodes an EGF-CUB Protein Essential for Hedgehog Signaling in Zebrafish Zebrafish you and Hedgehog SignalingWoods Ian G
1
Talbot William S [email protected]
1
1Department of Developmental Biology, Stanford University School of MedicineStanford, CaliforniaUnited States of AmericaStemple Derek Academic EditorWellcome Trust Sanger InstituteUnited Kingdom3 2005 11 1 2005 11 1 2005 3 3 e666 9 2004 17 12 2004 Copyright: © 2005 Woods and Talbot.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Fish Need You
Hedgehog signaling is required for many aspects of development in vertebrates and invertebrates. Misregulation of the Hedgehog pathway causes developmental abnormalities and has been implicated in certain types of cancer. Large-scale genetic screens in zebrafish have identified a group of mutations, termed you-class mutations, that share common defects in somite shape and in most cases disrupt Hedgehog signaling. These mutant embryos exhibit U-shaped somites characteristic of defects in slow muscle development. In addition, Hedgehog pathway mutations disrupt spinal cord patterning. We report the positional cloning of you, one of the original you-class mutations, and show that it is required for Hedgehog signaling in the development of slow muscle and in the specification of ventral fates in the spinal cord. The you gene encodes a novel protein with conserved EGF and CUB domains and a secretory pathway signal sequence. Epistasis experiments support an extracellular role for You upstream of the Hedgehog response mechanism. Analysis of chimeras indicates that you mutant cells can appropriately respond to Hedgehog signaling in a wild-type environment. Additional chimera analysis indicates that wild-type you gene function is not required in axial Hedgehog-producing cells, suggesting that You is essential for transport or stability of Hedgehog signals in the extracellular environment. Our positional cloning and functional studies demonstrate that You is a novel extracellular component of the Hedgehog pathway in vertebrates.
Genetic studies in zebrafish have identified a new protein involved in Hedgehog signaling - a key pathway required for development in vertebrates and invertebrates
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Introduction
The coordination of growth, proliferation, and differentiation during development requires transmission of information in the form of extracellular signals. Hedgehog signaling is of fundamental importance in the development of a wide variety of tissues and organ systems. Much of the initial functional analysis of Hedgehog signaling focused on the patterning of Drosophila larval segments and imaginal discs, dorsoventral patterning of the vertebrate neural tube, and anterior–posterior patterning of vertebrate limbs; in addition, many recent studies have illuminated the widespread and conserved role of Hedgehog signaling in development (reviewed in [1]). Misregulation of Hedgehog signaling has been implicated in several diseases and developmental abnormalities, including basal cell carcinoma [2,3,4], medulloblastoma [5,6,7], pancreatic cancer [8], and holoprosencephaly [9,10].
After release from signaling cells, the activity and distribution of Hedgehog proteins are modulated by a variety of factors in the extracellular environment. In Drosophila, diffusion of lipid-modified Hedgehog proteins is dependent on the action of tout velu, a gene involved in the synthesis of heparan sulfate proteoglycans [11,12]. The diffusion of Hedgehog is also attenuated via sequestration by its receptor, Patched [13]. In vertebrates, Hedgehog proteins may be further regulated by binding to the growth-arrest specific gene product Gas1 [14], and Hedgehog-interacting protein Hip1, which is itself induced by Hedgehog signaling [15]. Moreover, the ability of Hedgehog proteins to diffuse over significant distances in the developing vertebrate limb bud appears to depend on the cholesterol modification of the Hedgehog protein; this modification may facilitate the assembly of Hedgehog proteins into a multimeric structure, perhaps conferring increased stability or mobility [16,17]. Genetic and biochemical evidence suggests that the low-density receptor-related protein Megalin may also play a role in Hedgehog signaling in vertebrates, perhaps by binding to Hedgehog proteins and facilitating their endocytosis [18,19].
Hedgehog pathway function in zebrafish has been analyzed primarily in the context of skeletal muscle development and differentiation [20,21,22,23,24,25,26,27,28]. In zebrafish embryos at 24 h post fertilization (hpf), skeletal muscle can be subdivided into two distinct classes based on morphological characteristics and gene expression. Slow muscle fibers are mononucleate, express characteristic slow muscle forms of myosin heavy chain, and show strong nuclear expression of the transcription factor prox1. In contrast, fast muscle fibers can be identified via their multinucleate morphology and lack of prox1 expression [28]. Cell labeling experiments have demonstrated that slow muscle fibers derive from the adaxial cells that lie immediately adjacent to the notochord [21]. As development progresses, a subset of these developing slow muscle cells migrates laterally through the myotome to form the superficial slow fibers [21,27,28]. Slow muscle fibers that remain near the midline—the muscle pioneers—express high levels of Engrailed and organize the somites into their distinctive chevron shape [22,28,29,30]. The remaining muscle cells in the interior of the myotome form multinucleate fast muscle fibers [21,28].
Many lines of evidence indicate that Hedgehog signals from axial tissues specify slow muscle in zebrafish. Slow muscle fibers are reduced or absent in embryos with Hedgehog pathway mutations [25,26,31,32,33]. Conversely, slow muscle is expanded at the expense of fast muscle in embryos with increased Hedgehog pathway activity [20,23,24,34]. Moreover, addition of Hedgehog protein to cultured zebrafish myoblasts induces expression of slow-muscle-specific forms of myosin heavy chain [35].
Genetic screens have identified a number of mutations disrupting the Hedgehog pathway in zebrafish [32,33,36,37,38,39,40,41]. Many of these Hedgehog pathway mutants share characteristic defects, the most obvious of which is abnormal somite morphology resulting from disrupted slow muscle specification and the lack of horizontal myoseptum [22]. These mutants are thus termed “you-class” mutants because of their U-shaped somites. Five of the seven you-class mutations have been cloned, and four of these genes, syu/shh, yot/gli2, smu/smoh, and con/disp1, encode members of the Hedgehog signaling pathway [32,33,36,37,39]. The exception is ubo/prdm1, which encodes a transcriptional switch that acts downstream of Hedgehog signaling in the development of slow muscle [27,42]. Careful analysis reveals differences between the ubo and Hedgehog pathway mutant phenotypes. For example, Hedgehog pathway mutants have defects in the lateral floor plate of the neural tube and the dorsal aorta, which are apparently normal in ubo mutants [22,43]. Examination of Hedgehog-induced gene expression also reveals a clear distinction between Hedgehog pathway mutations and ubo: Hedgehog pathway mutations reduce or abolish expression of the Hedgehog target ptc1, whereas ptc1 expression is normal in ubo mutants, indicating that they can receive Hedgehog signals [22,25,26,32,33,36,39].
Previous phenotypic characterization of mutants for the eponymous you-class gene, you, has revealed delayed development of the dorsal aorta and the absence of lateral floor plate marker expression in addition to slow muscle defects [22,43]. Moreover, expression of Hedgehog target genes, including ptc1 and adaxial myod, is reduced in you mutants [25]. These results suggest that the you gene acts within the Hedgehog pathway itself rather than downstream of Hedgehog signaling in processes specific to slow muscle development. Prior to this study, the molecular identity of the you gene has remained unknown. We report the positional cloning of the you gene and show that it encodes a novel extracellular EGF-CUB (epidermal growth factor–complement Uegf Bmp1) protein required for Hedgehog signaling. Functional studies provide evidence that you is essential for the transport or stability of Hedgehog signals in the extracellular environment.
Results
you Function Is Required for Hedgehog Signaling
In wild-type zebrafish embryos, the dorsal and ventral portions of each myotome converge at a point where the horizontal myoseptum forms, giving the somites their characteristic chevron shape (Figure 1A). In contrast, you mutants lacked the horizontal myoseptum and exhibit the U-shaped morphology that defines mutants of the you class (Figure 1B; [22]). The formation of the horizontal myoseptum in zebrafish depends on the proper development of slow muscle, a process that is defective in you-class mutants [22,31]. Hedgehog activity in the context of slow muscle cell specification can be assayed by analyzing Engrailed expression in the muscle pioneers and adaxial myod expression during somitogenesis. Wild-type embryos at 24 hpf exhibit strong Engrailed staining in characteristic elongate nuclei of 2–6 muscle pioneers—slow muscle cells that develop along the prospective horizontal myoseptum—per myotomal segment (Figure 1C). In addition, weaker Engrailed expression can be observed in the typically more rounded nuclei of mulitnucleate fast muscle fibers, which are situated farther from the horizontal myoseptum (Figure 1C; [28,30]). you mutant embryos completely lacked the strong Engrailed expression in the muscle pioneers (Figure 1D; [22]), but very weak labeling was sometimes observed in a small number of cells. When weakly expressing cells were present, they were confined to the nine most anterior somites. In wild-type embryos, myod is expressed during somitogenesis in the adaxial cells of both somitic and presomitic mesoderm, and laterally along the posterior borders of developing somites (Figure 1E). you mutant embryos retained the lateral expression of myod, but the adaxial expression of myod was absent in the trunk and reduced in the tail bud (Figure 1F; [25]). In addition, expression of ptc1—both a member of the Hedgehog pathway and a sensitive transcriptional readout of Hedgehog signaling—was reduced in the adaxial cells of you mutants at 22 somites and other stages (Figure 1G and 1H; data not shown; [25]).
Figure 1
you Mutants Exhibit Hedgehog-Associated Defects in Slow Muscle and Ventral Spinal Cord
(A and B) Lateral views of live zebrafish at 22 hpf. Wild-type embryos (A) have chevron-shaped somites and a clearly visible floor plate (arrowhead), while you mutants (B) exhibit U-shaped somites and an indistinct floor plate (arrowhead).
(C and D) Lateral views of somites 8–13 in whole-mount embryos at 24 hpf. Wild-type embryos (C) show strong Engrailed expression in muscle pioneers (arrow), and weaker expression in multinucleate medial fast fibers (arrowheads). Engrailed expression in you mutants (D) is mostly absent, though very weak expression can occasionally be observed (arrowhead).
(E and F) Dorsal views of posterior trunk and tail bud in 12-somite embryos. Wild-type embryos (E) exhibit adaxial myod expression throughout the somitic (arrowhead) and presomitic (arrow) mesoderm, while you mutants (F) lack expression in the somitic (arrowhead) and in parts of the presomitic (arrow) mesoderm.
(G and H) Lateral view of somites 9–15 in whole-mount embryos at 22 hpf. Wild-type embryos (G) exhibit strong expression of ptc1, while you mutants (H) show weaker levels of ptc1 expression.
(I and J) Lateral view of spinal cord in the posterior trunk of whole-mount embryos at 24 hpf. Wild-type embryos (I) show expression of nkx2.2 in the ventral spinal cord, while in you mutants (J) this expression is strongly reduced.
(K–N) Dorsal views of whole-mount embryos at bud stage (10 hpf). Expression in you mutant embryos of both ehh (L) and shh (N) is similar to that observed wild-type embryos (K and M).
Anterior is to the left in all images. Genotypes of all embryos were determined by PCR after photography.
In addition to these disruptions in developing somites, you mutant embryos showed defects in patterning of the central nervous system. nkx2.2, a Hedgehog-induced marker of ventral cell types in the spinal cord [44], was absent in the trunk and tail of you mutants (Figure 1I and 1J). Moreover, our analysis and prior work has revealed that you embryos show delayed and weakened blood circulation in the dorsal aorta (data not shown; [45]). These results and previous phenotypic analyses support the conclusion that you gene function is required for Hedgehog signaling in development of slow muscle, ventral spinal cord, and the dorsal aorta.
In zebrafish, three hedgehog genes—ehh, shh, and twhh—are expressed at the midline in early embryonic stages. To determine whether you is required for hedgehog gene transcription, we analyzed the expression of hedgehog genes in wild-type and you mutant embryos. Expression of all three hedgehog genes appeared normal in you embryos at bud stage (10 hpf; Figure 1K–1N; data not shown).
Positional Cloning of you
As the first step toward identifying the you gene, we mapped the you mutation to a 1-cM (12 recombinants among 1,156 meioses) region of LG 7, between simple sequence length polymorphism (SSLP) markers Z11119 and Z15270 (Figure 2A). By comparing the position of the you mutation to zebrafish genetic maps ([46,47]; unpublished data), we excluded as candidate genes more than 60 zebrafish orthologs of Hedgehog pathway genes and genes known to interact with the Hedgehog pathway. We therefore adopted a positional cloning strategy to identify the gene disrupted by the you mutation. Using a marker linked with Z15270 on a contig from the Sanger Institute whole-genome shotgun assembly, we screened a pooled bacterial artificial chromosome (BAC) library and initiated a chromosome walk beginning with BAC zC172H20. After identifying polymorphisms in BAC end sequences, testing these polymorphisms on our mapping panel, and iteratively rescreening the pooled BAC library, we identified a contiguous stretch of genomic sequence spanning portions of five BACs with ends that flanked you (Figure 2A).
Figure 2 Positional Cloning of the you Locus
(A) Genetic and physical map of the you region on LG 7, showing the initial flanking SSLPs and the BACs used in the chromosome walk. The number of recombinants in 1,156 meioses is shown for the SSLP markers and for mapped BAC end sequences.
(B) Diagram of BAC zC93A15, with expressed sequence tag markers BM184987 and AI722938 shown flanking the you locus. Both BM184987 and AI722938 showed one recombinant out of 6,514 meioses, and were genetically localized on opposite sides of you.
(C) The genomic region of the you locus is depicted by horizontal black bars. Gaps in these bars represent fragments of genomic sequence that were not obtained in the sequencing analysis. Exons are depicted by blue rectangles, and the number of recombinants in 6,514 meioses is shown below each mapped exon. Single nucleotide polymorphisms in four exons always segregated with the you locus, and one of these exons harbored a single nucleotide lesion predicted to change a glutamine codon to a stop codon and truncate the open reading frame.
To reduce the critical interval that contained you, we improved the resolution of our map by increasing our mapping panel to 6,514 meioses. By scoring sequences identified from BAC zC93A15 in key recombinants from the mapping panel, we localized the you gene to a portion of this BAC between polymorphisms identified in expressed sequence tag markers BM184987 and AI722938 (Figure 2B). Further sequence analysis and mapping identified other exons of the same gene as AI722938, some of which were on the opposite side of the mutation from the original AI722938 marker (Figure 2C). We isolated a full-length cDNA clone for this gene and used this new sequence information to identify polymorphisms in other exons. In all, high-resolution mapping identified four exons that failed to recombine with you and confirmed that the two ends of the gene flanked the youty97 mutation. Sequence analysis of the four non-recombining exons in wild-type and youty97 mutant genomic DNA identified a nonsense mutation that truncates the predicted protein approximately two-thirds of the way through the open reading frame. These findings, together with others described below, indicate that this gene is disrupted by the you mutation.
you Is Orthologous to Scube2
The protein encoded by you comprises 1,010 amino acids, and comparison of the predicted you amino acid sequence against the protein database indicated that the you protein is highly similar to a family of proteins founded by mouse SCUBE1 (Signal sequence, CUB domain, EGF-related; [48]). Pairwise sequence comparisons between you and SCUBE family members revealed that the You protein most closely matched SCUBE2 in mouse (65% identity) and SCUBE2 in human (66% identity). The orthology of these genes was further supported by comparative mapping: the human genes SCUBE2, LMO1, STK33, and ST5 exhibited conserved synteny with orthologous genes in both mouse and zebrafish ([49]; unpublished data).
SCUBE proteins are characterized by a signal peptide and by two types of conserved extracellular domains: EGF and CUB [50]. In all identified SCUBE family members, the N-terminal signal sequence is followed by nine EGF repeats, a spacer region, and a single C-terminal CUB domain [48,51,52,53]. Figure 3A shows an alignment comparing the predicted you amino acid sequence with selected SCUBE proteins in mouse and human. Similarity between the You protein and mouse SCUBE2 was particularly high in the CUB domain (89% identity), the C-terminal sequence following the CUB domain (90% identity), and the EGF repeats (74% identity). A spacer region in the center of the amino acid sequence showed lower conservation (47% identity). Examination of this spacer region in the vicinity of the CUB domain revealed a repeated motif of six cysteine residues with characteristic and regular spacing, shown in yellow in Figure 3A. Conservation in amino acid sequence was notably higher in this region (66% identical) than in the remainder of the spacer domain (33% identical). This six-cysteine repeat motif does not match the general structure of EGF repeats. The functional significance of this motif is not presently known, but it does lie within a region of SCUBE1 that is required in cell culture for secretion and cell surface expression [52].
Figure 3 SCUBE Protein Alignment and Truncation of the You Protein
In (A) and (C), the signal peptide is labeled in blue, the nine EGF domains are labeled in red, and the CUB domain is labeled in green.
(A) Alignment of the predicted You amino acid sequence with SCUBE proteins in mouse and human. Identical amino acids are shaded, and similar amino acids are boxed. Conserved cysteines in these domains and elsewhere in the alignment are indicated by filled circles. A conserved six-cysteine repeat motif N-terminal to the CUB domain is labeled in yellow. The location of the glutamine residue at amino acid 644 in the zebrafish protein, which is changed to a stop codon in youty97 mutants, is boxed in bold.
(B) Sequence traces from homozygous wild-type and youty97 embryos. In youty97 mutants, a C to T transition is predicted to change a glutamine codon (CAA) to a stop codon (TAA) and truncate the open reading frame.
(C) Model of You protein domain structure. The You protein in youty97 mutants is predicted to be truncated prior to the six-cysteine repeat motifs, the CUB domain, and the conserved C-terminus.
In youty97 mutants, a C to T transition alters the coding sequence at residue 644, changing a glutamine codon to a stop codon (Figure 3A and 3B). The predicted mutant protein is truncated immediately prior to the six-cysteine repeat motif, so that these repeats, the CUB domain, and the highly conserved C-terminus are lacking (Figure 3C).
Morpholino Phenocopy and RNA Rescue of you
To confirm that this zebrafish EGF-CUB gene is disrupted in you mutants, we performed morpholino oligonucleotide (MO) injection experiments to phenocopy defects seen in you, and mRNA injection experiments to rescue the you phenotype in mutants (Figure 4). All wild-type embryos injected with a MO targeting the translational start site showed reduced expression of myod (n = 112) in the adaxial cells (Figure 4B), and an absence of strong Engrailed expression (n = 70) in the muscle pioneers (Figure 4D). Embryos injected with a mismatch control MO did not exhibit these defects in either myod (n = 65) or Engrailed (n = 48) expression (Figure 4A and 4C).
Figure 4 MO-Induced Phenocopy of you Defects and Rescue of the you Phenotype with mRNA Injection
(A, B, E, and F) Dorsal view of the posterior trunk and tail bud of 12-somite embryos.
(C, D, G, and H) Lateral views of somites 8–13 in whole-mount embryos at 24 hpf.
Anterior is to the left in all images. Injection at the 1–4-cell stage of 420 pg of a MO targeting the translational start site of the you mRNA (ATG MO) resulted in decreased adaxial expression of myod in the somitic (arrowhead) and presomitic (arrow) mesoderm of wild-type embryos (B). Injection of an equivalent amount of a mismatch control (mismatch MO) did not produce these defects (A). Similarly, wild-type embryos injected with 420 pg of the mismatch MO (C) exhibited strong Engrailed expression in muscle pioneers (arrow) and weaker expression in medial fast fibers (arrowheads). In contrast, Engrailed expression was strongly reduced in wild-type embryos injected with 420 pg of the ATG MO (D), though very weak expression was still observed (arrowhead). Genotypically you mutant embryos (E) showed rescued expression of adaxial myod in somitic (arrowhead) and presomitic (arrow) mesoderm when injected with 50 pg of synthetic you mRNA at the 1–4-cell stage, while mutants injected with 50 pg of a frameshift mutant form of you mRNA (F) did not exhibit rescue of adaxial myod expression. At 24 hpf, genotypically you mutant embryos injected at the 1–4-cell stage with 50 pg of you mRNA (G) showed rescue of strong Engrailed expression in the muscle pioneers (arrow) and weaker expression in the medial fast fibers (arrowheads). Mutant embryos injected with 50 pg of the mutant mRNA (H) did not show rescued Engrailed expression, though very weak Engrailed expression (arrowhead) was observed in some cases. Engrailed expression at the MHB was normal in all analyzed embryos (data not shown). Genotypes of embryos shown in (E–H) were determined by PCR after photography.
When injected with 50 pg of synthetic wild-type you mRNA, 98.7% (n = 665) of embryos from you/+ intercrosses showed expression of myod in adaxial cells at 12 somites (Figure 4E). Genotyping of 571 embryos with wild-type myod expression from these intercrosses showed that 137 (24%) were homozygous mutant for you. In contrast, 23.6% (n = 127) of embryos from you/+ intercrosses injected with a mutant form of you mRNA lacked expression of myod in adaxial cells (Figure 4F); 20 of the mutants were genotyped and confirmed to be you mutant homozygotes. Similarly, all embryos (n = 62) from a you/+ intercross injected with wild-type mRNA showed strong Engrailed labeling in the muscle pioneers (Figure 4G; genotyping of 32 phenotypically wild-type embryos showed that seven were homozygous youty97), whereas 24% (n = 33) of embryos injected with control mRNA lacked Engrailed expression (Figure 4H; eight phenotypic mutants were confirmed as homozygous for the you mutation). In addition, injection of you MOs resulted in loss of nkx2.2 expression in the trunk and tail of wild-type embryos at 24 hpf, and injection of 50 pg of you mRNA was sufficient to rescue nkx2.2 expression in you mutants (data not shown).
you Expression
you transcripts appear to be maternally deposited in zebrafish embryos (Figure 5A) and are distributed widely in the embryo through early gastrulation stages (Figure 5B). During late gastrulation, the distribution of you transcripts in the embryo began to be restricted, and at bud stage (10 hpf) you was expressed in the eye field, in distinct bilateral domains within the developing brain, and in the developing trunk of the embryo in broad paraxial stripes (Figure 5C). During somitogenesis, you expression continued to be refined, such that six-somite embryos exhibited expression in the eye field, in stripes in the midbrain and the midbrain–hindbrain boundary (MHB), in a complex pattern in the hindbrain, and in paraxial stripes along the anterior–posterior axis (Figure 5D). At 24 hpf (Figure 5E and 5F), you transcripts were localized to the border of the ventral telencephalon and the dorsal diencephalon and to the ventral tectum, and were strongly expressed in the MHB, the hindbrain, and along the length of the embryo in the dorsal spinal cord. In addition, you expression was observed in the ventral tail and posterior to the yolk extension at the developing urogenital opening (Figure 5E). In the following 24 h of development, dorsal spinal cord expression continued, and you transcripts persisted in a complex and dynamic pattern in the brain. At 48 hpf (Figure 5G and 5H), you expression was particularly strong in the cerebellum and in the hindbrain along the rhombic lip.
Figure 5 Expression of you Examined by In Situ Hybridization
(A and B) Maternal you transcripts were evident in cleavage-stage embryos (A) (16-cell; 1.5 hpf), and you mRNA was widely expressed into the gastrula period (B) (shield stage; 6 hpf). In addition, you mRNA was detectable by RT-PCR at 2 hpf, prior to the zebrafish midblastula transition.
(C) Toward the end of gastrulation, you transcripts began to be restricted, so that at the bud stage (10 hpf), you expression was evident in the eye field (white arrowhead), in the developing midbrain and hindbrain (black arrowheads), and in posterior paraxial stripes (arrow).
(D) During early somitogenesis (12 hpf), you expression was observed in the eye field (white arrowhead), in stripes in the midbrain and the MHB (black arrowheads), in a complex pattern in the hindbrain (white arrow), and in paraxial stripes along the developing trunk and tail bud (black arrow).
(E and F) At 24 hpf, you transcripts were observed dorsal to the hypothalamus (black arrow), at the boundary between the telencephalon and the diencephalon (white arrow), in the ventral tectum (white arrowhead), in the region of the presumptive cerebellum (asterisk), and dorsally along the length of the spinal cord. Additional expression of you at this stage and later was observed in the ventral tail and at the urogenital opening (arrowheads; data not shown).
(G and H) At 48 hpf, you transcripts were highly expressed in the cerebellum (black arrow), and were also present in the rhombic lip (white arrowhead), and continuing along the length of the anterior–posterior axis in the dorsal spinal cord (black arrowhead; data not shown).
Orientation of images: (A) lateral view; (B) lateral view, dorsal to the right; (C, D, F, and G) dorsal views, anterior to the left; (E and H) lateral views, anterior to the left.
Permissive Role of you Upstream of the Hedgehog Cellular Response
To explore the possibility that the you gene may induce a gain-of-function phenotype when overexpressed, we injected wild-type embryos with an amount (50 pg) of synthetic you mRNA that was sufficient to rescue the phenotypic defects observed in you mutants (Figure 6). When compared to embryos injected with a mutant form of you mRNA (myod, n = 81, Figure 6A; Engrailed, n = 25, Figure 6E; nkx2.2, n = 17, Figure 6I), overexpression of you in wild-type embryos did not result in obvious ectopic expression of myod (n = 394; Figure 6B), Engrailed (n = 25; Figure 6F), or nkx2.2 (n = 17; Figure 6J). This result suggests that you functions as a permissive factor in Hedgehog signaling, rather than as a potent activator of the Hedgehog pathway. When 50 pg of synthetic mRNA encoding a potent Hedgehog pathway activator (shh) was injected into embryos from a you/+ intercross, ectopic expression of myod (n = 53; Figure 6C and 6D), Engrailed (n = 78; Figure 6G and 6H), and nkx2.2 (n = 78; Figure 6K and 6L) was induced in all embryos. Genotyping a subset of these embryos indicated that both genotypically wild-type and you mutant embryos showed ectopic expression of each of these markers (myod: 41 wild-type, 12 you; Engrailed: 34 wild-type, 8 you; nkx2.2: 63 wild-type, 15 you). Because downstream targets of the Hedgehog pathway were rescued or upregulated in shh-injected you mutants, components of the Hedgehog pathway downstream of shh are most likely functional in you embryos. These results are consistent with you acting upstream of or parallel with shh in the Hedgehog pathway.
Figure 6 Early Overexpression of you in Wild-type Embryos and Rescue of you Defects by shh mRNA Injection
(A–D) Dorsal views of the posterior trunk and tail bud of whole-mount embryos at 12 somites (15 hpf).
(E–F) Lateral views of somites 2–7 in 24-hpf embryos.
(G–H) Lateral views of somites 8–13 in 24-hpf embryos.
(I–L) Lateral views of 24-hpf embryos. Anterior is to the left in all images.
When 50 pg of you mRNA was injected into wild-type embryos at the 1–4-cell stage, no obvious expansion of myod (B), Engrailed (F), or nkx2.2 (J) expression was observed when compared either with wild-type embryos injected with equivalent amounts of mutant mRNA (A, E, and I) or with uninjected embryos (see Figure 1). Muscle pioneers were counted in a subset of the embryos; there were 4.0 ± 0.8 Engrailed-expressing muscle pioneers per somite in embryos injected with the control mRNA (n = 3 embryos, 33 somites) and 4.6 ± 1.1 muscle pioneers per somite in embryos injected with synthetic you mRNA (n = 8 embryos, 88 somites). Injection of 50 pg of shh mRNA into embryos at the 1–4-cell stage resulted in expansion of myod, Engrailed, and nkx2.2 expression in both wild-type (C, G, and K) and you mutant (D, H, and L) embryos. shh injection rescued adaxial expression of myod (D), muscle pioneer expression of Engrailed (H), and ventral spinal cord expression of nkx2.2 (L) in genotypically mutant you embryos (compare with Figure 1). Genotypes of all embryos were determined by PCR after photography.
Additional evidence that You acts upstream of the Hedgehog response derived from a loss-of-function approach, in which we activated the Hedgehog pathway by knocking down patched activity with MOs (Figure 7). Expression of myod in adaxial cells was rescued or expanded in all you mutant embryos that were injected with MOs targeting ptc1 (Figure 7E and 7G; n = 8 mutants) or a combination of MOs targeting both ptc1 and ptc2 (Figure 7I and 7K; n = 13 mutants). you mutant embryos injected with a ptc1 mismatch control MO did not exhibit rescued myod expression (Figure 7A and 7C; n = 8 mutants). In similar experiments, injection of patched MOs was sufficient to rescue or expand Engrailed expression in muscle pioneers of you mutant embryos (Figure 7B, 7D, 7F, 7H, 7J, and 7L; ptc1 MO, n = 11 mutants; ptc1 + ptc2 MO, n = 11 mutants).
Figure 7 Knockdown of patched Function Rescues Slow Muscle Defects in you
After injection of 420 pg of a mismatch control ptc1 MO, adaxial expression of myod (A) and Engrailed (B) was normal in wild-type embryos, but absent in you mutant embryos (C and D). When injected with 420 pg of a MO targeting ptc1, however, myod expression in mutants (E) was rescued to levels comparable to wild-type embryos (G). Engrailed expression was slightly expanded in both wild-type (F) and mutant (H) embryos injected with 420 pg of ptc1 MOs. Both adaxial myod expression and Engrailed expression was slightly expanded in wild-type (I and J) and you mutant embryos (K and L) injected with MOs targeting both ptc1 and ptc2 (420 pg each). Embryos assayed for myod expression are shown in flat mount at the 12-somite stage, and somites 5–9 of Engrailed-expressing embryos are shown in lateral view at 24 hpf. Anterior is to the left in all panels. Genotypes of all embryos were determined by PCR after photography.
you Acts Non-Autonomously in Muscle Pioneer Differentiation
To determine whether a cell must be wild-type for you function to respond to Hedgehog signaling, we created genetic chimeras by transplanting cells from mutant embryos into wild-type hosts (Figure 8). Cells derived from you mutant embryos were able to differentiate as muscle pioneers, as defined by characteristic strong Engrailed expression in elongate nuclei of mononucleate cells at the proper position in the somite (Figure 8C–8E; n = 13 embryos). Similarly, cells from embryos in which you function had been reduced with MOs were able to differentiate as Engrailed-expressing muscle pioneers when introduced into embryos treated with mismatch control MOs (data not shown).
Figure 8
you Acts Non-Autonomously in Muscle Pioneers and Is Not Required in Cells Producing Hedgehog Signals
(A) Donor embryos were labeled at the 1–4-cell stage with Oregon Green dextran.
(B) Cells from donor embryos were transplanted into unlabeled hosts during late blastula and early gastrula stages.
(C–E) Images from a chimera made by transplanting cells from labeled mutant donors (green in C and E) into unlabeled wild-type hosts. At 24 hpf, muscle pioneer cells in chimeric embryos were labeled with anti-Engrailed antibody (red in D and E). When transplanted into wild-type embryos, mutant cells were able to differentiate as muscle pioneers, as shown by co-labeling with the anti-Engrailed antibody (E, yellow arrows).
(F–K) Images from chimeras made by transplanting cells from labeled wild-type donors (green in F, H, I, and K) into unlabeled mutant hosts. Expression of Engrailed (red in G, H, J, and K) in some mutant muscle pioneers (one marked by red arrows in G, H, J, and K) was rescued in a subset of embryos (see also Table 1). Donor cells in the embryo shown in (F–H) contributed solely to muscle and to non-floor-plate identities within the neural tube. Moreover, in a subset of chimeras, cells derived from wild-type donors differentiated as muscle pioneer cells (yellow arrows in J and K), simultaneously showing both the characteristic strong nuclear Engrailed expression and the typical flattened and mononucleate morphology of this cell type. The somite labeled with the arrows in J and K contains two muscle pioneers, one derived from the wild-type donor (yellow arrow) and another derived from the mutant host (red arrow). Donor cells in the embryo shown in (I–K) contributed primarily to muscle and to non-floor-plate identities within the neural tube; in addition, a group of seven floor plate cells derived from the wild-type donor was present in the tail of this embryo (not shown).
Table 1 Chimeras with Wild-Type Donor Cells in you Mutant Hosts
Rescue of muscle pioneers in wild-type→you mutant chimeras was evidenced by the presence of mutant (i.e., host) cells with strong Engrailed expression in elongate nuclei at the proper position in 1–8 somites. Wild-type contribution indicates structures that included cells from labeled wild-type donors
CNS, non-floor-plate cells in neural tube; FP, floor plate
Muscle Pioneer Differentiation Does Not Require you Function in Axial Hedgehog-Producing Cells
To determine which cell types must be wild-type for you function in order for target cells to appropriately respond to Hedgehog signals, we transplanted cells derived from wild-type donors into you mutant hosts. Of 91 chimeric mutant hosts, ten embryos exhibited rescue of Engrailed expression in genotypically mutant muscle pioneers, as defined by characteristic strong Engrailed expression in elongate nuclei at the proper position of posterior somites, where Engrailed is not normally expressed in you mutants (Table 1; Figure 8F–8K). In addition, wild-type cells differentiated as muscle pioneers in two of the ten chimeras with rescued mutant muscle pioneers. In these cases (Figure 8I–8K), the muscle pioneer identity of cells strongly expressing Engrailed was further confirmed by presence of the lineage tracer dye, which showed that these cells had the characteristic flattened and mononucleate morphology of muscle pioneers. In all ten embryos with rescued Engrailed expression, wild-type cells were present in the muscle and in non-floor-plate regions within the neural tube. In five of the chimeric embryos with rescued Engrailed expression, the floor plate and notochord were derived entirely from you mutant cells, indicating that you function is not required in axial Hedgehog-producing cells. Collectively, the transplantation experiments indicate that you function is not required in either the signaling or responding cells, and instead suggest that you is essential for the transport or stability of Hedgehog signals.
Discussion
Using a positional cloning approach, we have shown that the you gene encodes a novel EGF-CUB protein essential for Hedgehog signaling in zebrafish. High-resolution mapping indicates that the you locus is tightly linked to a zebrafish homolog of mouse Scube2. youty97 mutants harbor a nonsense lesion that truncates the open reading frame upstream of the CUB domain and other highly conserved sequences. MO-mediated knockdown of the gene phenocopies defects observed in you mutants, and injection of wild-type mRNA into mutant embryos rescues the you phenotype. Taken together, these experiments provide compelling evidence that the you mutation disrupts this EGF-CUB gene.
Biochemical studies of SCUBE family members have shown that they are extracellular, membrane-associated glycoproteins [52,53], but no previous work implicates these proteins in the Hedgehog signaling pathway. Although the Hedgehog pathway has been extensively studied in flies and mammals, several factors have likely obscured the connection between this Scube gene and the Hedgehog pathway. Prior to this study, no loss of function analysis had been performed on any Scube family gene; there is no known Scube gene in the fly, and mouse mutants have not been reported. Also, because overexpression of synthetic you mRNA does not significantly hyperactivate Hedgehog signaling, Scube gene function in the Hedgehog pathway would not be apparent in gain-of-function screens to identify pathway components.
you mutant embryos exhibit phenotypic defects characteristic of reduced Hedgehog signaling in zebrafish, indicating that the you gene is a positively acting component of the Hedgehog pathway. Development of slow muscle is disrupted, as shown by lack of adaxial myod expression during somitogenesis and the absence of Engrailed-expressing muscle pioneer cells. Moreover, you mutants lack nkx2.2 expression in the ventral spinal cord, showing that specification of ventral neural fates is disrupted in this region of you embryos. Additionally, expression of the Hedgehog target gene ptc1 is reduced in you mutants. Analysis of the mutant phenotype, therefore, demonstrates that the you gene is essential for Hedgehog signaling in development of slow muscle and ventral spinal cord fates.
Current evidence indicates that EGF-CUB proteins, including You, have extracellular functions. The you gene product and other SCUBE proteins contain a signal peptide sequence targeting the protein for secretion, as well as EGF and CUB domains characteristic of extracellular proteins [48,50,51,52,53]. The You homolog SCUBE1 is a glycosylated peripheral membrane protein when expressed in 293T cells, and is also present at low levels in the culture medium [52]. EGF and CUB domains are found together in a small but diverse group of extracellularly acting proteins, including the complement subunits C1s and C1r, the metalloproteinase Tolloid, the sea urchin extracellular matrix protein Fibropellin, the serum glycoprotein Attractin, and the scavenger receptor Cubilin. CUB domains have been implicated in mediating protein–protein interactions and may confer specificity to ligand binding; for example, specific CUB domains in Cubilin have been implicated in facilitating the binding and subsequent endocytosis of specific ligands (reviewed in [54]).
Although you is essential for Hedgehog signaling, our analysis indicates that you mutant cells are able to produce and respond to Hedgehog signals. hedgehog gene expression in the embryonic midline is normal in you mutants, indicating that You acts downstream of hedgehog gene transcription (see Figure 1). Further evidence that you function is not required in cells generating Hedgehog signals derives from the analysis of chimeric embryos: muscle pioneers can differentiate in chimeras in which the notochord and floor plate are formed entirely from mutant cells (see Figure 8; Table 1). Conversely, you mutants can respond to Hedgehog pathway activation, mediated by either shh overexpression or disruption of patched, demonstrating that the defect in the mutants lies upstream of cellular response mechanism (see Figures 6 and 7). In addition, you mutant cells can respond to Hedgehog and differentiate as muscle pioneers when transplanted into a wild-type host (see Figure 8), indicating that you gene function is not required in cells responding to Hedgehog signals. The analysis of chimeras also demonstrates that the presence of wild-type cells in the paraxial mesoderm and neural plate is sufficient to allow you mutant cells to respond to Hedgehog signals produced from you mutant notochord and floor plate. Thus, our epistasis and transplantation experiments provide evidence that You functions in the extracellular environment in the field of responding cells, likely acting to transport or stabilize Hedgehog signals. It is possible that You interacts with components of the extracellular matrix, some of which are known to regulate the action of Hedgehog signals in other systems [11,12,55,56].
Another possible model is that You activates the Hedgehog pathway indirectly, by inhibiting an extracellular pathway antagonist such as Hip1 or Gas1. Our results, however, do not support such a model. Overexpression of high levels of synthetic you mRNA (up to ten times the amount required to rescue you mutants) in wild-type embryos did not result in obvious phenotypes when assayed by gross morphology or by expression of Hedgehog target genes, including adaxial myod, Engrailed in muscle pioneers, or nkx2.2 in the ventral spinal cord (see Figure 6; data not shown). The finding that overexpression of you does not significantly hyperactivate Hedgehog targets argues against simple models in which you functions to counteract an endogenous repressor of the Hedgehog pathway.
An interesting aspect of the you mutant phenotype is that it encompasses only a subset of defects seen in other zebrafish Hedgehog pathway mutants. Whereas mutants for syu/shh, yot/gli2, smu/smoh, and con/disp1 have prominent midline abnormalities in the head, such as ipsilateral retinotectal projections, reduction of anterior pituitary, and defects in medial neurocranial cartilage, these phenotypes are not evident in you mutants [26,32,33,37,43,57,58,59]. Also, development of pectoral fins, which is disrupted in syu, con, and smu [26,60], appears normal in you embryos. you mutants, therefore, show characteristic Hedgehog signaling defects in slow muscle specification, patterning of ventral spinal cord, and the development of the dorsal aorta, but you is apparently not required for Hedgehog signaling in some other regions of the zebrafish embryo. Because the primary cell types disrupted in you mutants all develop in close proximity to the notochord, it is possible that you gene function may be required for the transport or stability of Hedgehog signals in the vicinity of the developing notochord but not some other regions. The notochord is a defining feature of chordates, and a notochord-associated function would explain why no you counterpart is required for Hedgehog signaling in the fly. It is not clear, however, why Hedgehog signaling near the notochord would require a special extracellular mediator. Another possibility is that maternal you function could mask earlier requirements in zygotic you mutants; future work with maternal-zygotic you mutants is needed to address this possibility. A third explanation of the requirement for you in only a subset of Hedgehog-regulated processes is that additional factors with redundant functions may substitute for you in other regions of the embryo. Intriguingly, expression of another Scube gene in mouse—Scube1—is observed in many embryonic tissues known to require Hedgehog signaling for their proper development, including the ventral forebrain, limb bud, somites, and developing gonad [48]. These results suggest that an additional zebrafish Scube gene may also play a role in the development of other areas of the embryo where Hedgehog signaling is active. Moreover, interactions between SCUBE proteins may be important for Hedgehog signaling; biochemical analysis suggests that SCUBE1 and SCUBE2 proteins can interact to form both homodimers and heterodimers [52].
In addition to its role in promoting Hedgehog signaling in the developing muscle pioneers, ventral neural tube, and dorsal aorta, the expression pattern of you suggests that the gene may act in other cell types and perhaps in other pathways. During gastrulation, when Hedgehog signaling is required for specification of muscle pioneers [28], you is widely expressed. In 24-hpf embryos, however, you is expressed strongly in specific regions in the forebrain, midbrain, and hindbrain, and dorsally along the length of the spinal cord. Some of these expression domains overlap with regions of Hedgehog signaling, whereas others do not. One region where Hedgehog activity and you expression intersect at later embryonic stages is the cerebellum, where Hedgehog signaling plays a well-defined role in the proliferation of granule cell precursors in mammals [61,62]. In the trunk and tail, however, you expression in the dorsal spinal cord corresponds neither with known sources of Hedgehog signals nor with cells that require Hedgehog signaling for their proper development. This result suggests that you may function in other pathways later in development. Future studies will define the role of you in the Hedgehog pathway and address the possibility that you and other Scube family genes also function in other signaling pathways.
Materials and Methods
Fish strains
Zebrafish embryos were maintained at 28.5 °C and were staged according to [63]. Wild-type embryos were derived from the WIK strain. All phenotypic analysis of you mutants was performed with embryos homozygous for the youty97 allele [22].
Genetic mapping
The mapping panel was generated by crossing youty97/+ individuals with wild-type fish from the WIK strain. youty97 heterozygotes in the F1 generation were intercrossed, and mutant and wild-type embryos in the F2 progeny were collected at 3–4 dpf for mapping. Genomic DNA was prepared from these embryos as described [64]. Primer sequences for SSLP markers were obtained from the MGH zebrafish database (http://zebrafish.mgh.harvard.edu). For initial localization of you, bulked segregant analysis (reviewed in [65]) was performed on DNA pools from 20 mutant and 20 wild-type embryos. Putative zebrafish orthologs of hedgehog-related genes were identified by reciprocal BLAST analysis and localized to the Heat Shock Panel as previously described [47].
BAC screening, chromosome walking, and BAC sequencing
The CHORI211 BAC library was screened by PCR to identify positive BAC clones (http://www.rzpd.de). BAC end sequences were obtained from the Sanger Institute database (http://trace.ensembl.org, and PCR primers were designed to amplify regions of these sequences. PCR amplicons were sequenced from homozygous wild-type and mutant embryos to identify nucleotide differences that generated restriction enzyme fragment length polymorphisms. These polymorphisms were tested on the mapping panel, and markers showing tighter linkage with you were iteratively screened against the BAC library until a contiguous stretch of genomic sequence with ends that flanked you was identified. The BAC zC93A15 was subcloned into pBluescript SK+ (Stratagene, La Jolla, California, United States) following double digest with either Pst I and EcoR I or Xba I and Xho I. Sequences were analyzed on a 3730 DNA Analyzer (Applied Biosystems, Foster City, California, United States). Sequences generated from this BAC were used in iterated searches against the zebrafish whole-genome shotgun assembly to nucleate contigs of genomic sequence. Sequencing primers were designed from these contigs and used to generate additional sequence data for zC93A15.
Plasmid constructs
A full-length you clone in pBluescript SK− (Stratagene) was isolated from a 15–19-hpf cDNA library (gift of Bruce Appel and Judith Eisen). A cDNA clone harboring a frameshift mutation that is predicted to truncate the you protein at amino acid residue 34 was isolated from the same library and was used as a control in overexpression experiments. A modified version of the pCS2+ expression vector was generated by cloning a 41-bp fragment into its EcoR I and Xba I sites. This stuffer fragment abolished the endogenous EcoR I, Stu I, Xho I, and Xba I restriction digest sites of pCS2+, and introduced Xba I, Sac I, Apa I, Pst I, and Xho I recognition sequences in a 5′ to 3′ orientation with respect to the SP6 promoter. Wild-type and mutant you clones were subcloned into the Xba I and Xho I sites of this modified pCS2+ vector. In situ probes for you were generated by linearizing this vector with Xba I, followed by antisense RNA synthesis with T3 polymerase. Synthetic you mRNA for injections was generated by digestion with Not I, followed by transcription using the SP6 mMessage mMachine kit (Ambion, Austin, Texas, United States).
In situ hybridization, antibody labeling, and genotyping
Probe synthesis, in situ hybridizations, and immunohistochemistry were performed using standard protocols. Embryos from you/+ intercrosses were genotyped after in situ hybridization and antibody labeling as described [66]. Other probes used were zebrafish myod [67], ptc1 [68], nkx2.2 [44], ehh [20], shh [69], and monoclonal antibody 4D9 [29]. Genotyping was performed by scoring a polymorphism in AI722938 (primer 1,
GTGAAAGCAAAAAGCAAGCA; primer 2,
GCACTGCATTATGTTTGTGGA; followed by a Hinf I digest).
Microinjections
Embryos were injected through their chorions with 500 pl of solution at the 1–4-cell stage as described [70]. RNA was diluted in 0.2 M KCl with 5 mg/ml Phenol Red prior to injection. MOs were obtained from Gene Tools (Philomath, Oregon, United States). A MO targeted to the you translational initiation site (5′-
GCCGTACAGTCCAAACAGCTCCCAT-3′) or a 5-bp mismatch control MO (5′-GCCcTAg
AGTCg
AAACAcCTg
CCAT-3′) was diluted in a 1x Danieau's solution with Phenol Red at 5 mg/ml prior to microinjection. Sequences for MOs targeting ptc1 and ptc2 were obtained from [28].
Transplantations
Cellular transplantations were done according to standard methods [22]. Embryos derived from you/+ intercrosses were injected at the 1–4-cell stage with a 1% solution of Oregon Green 488 dextran (Molecular Probes, Eugene, Oregon, United States). Approximately 50–100 cells were removed from labeled donors in late blastula and early gastrula periods (4–5.3 hpf) and transplanted near the margin of unlabeled sibling hosts. Labeled donor embryos were allowed to develop until 24 hpf. Genotypes of donor embryos derived from you/+ intercrosses were determined by PCR. Genotypes of host embryos were determined by staining with Engrailed antibody. Donor cells in chimeras with wild-type cells transplanted into you mutant hosts were obtained either from WIK intercrosses or from genotypically wild-type embryos in you/+ intercrosses.
Supporting Information
Accession Numbers
The you cDNA sequence has been deposited in GenBank (http://www.ncbi.nlm.nih.gov/Genbank/index.html) under accession number AY741664.
The LocusLink (http://www.ncbi.nlm.nih.gov/LocusLink/) accession numbers for the genes and gene products discussed in this paper are Attractin (LocusID 8455), C1r (LocusID 715), C1s (LocusID 716), con/disp1 (LocusID 378448), Cubilin (LocusID 8029), ehh (LocusID 30299), Engrailed (LocusID 30244), Gas1 (LocusID 14451), Hedgehog (LocusID 42737), Hip1 (LocusID 15245), Megalin (LocusID 14725), myod (LocusID 30513), nkx2.2 (LocusID 30697), Patched (LocusID 35851), prox1 (LocusID 30679), ptc1 (LocusID 30181), ptc2 (LocusID 30189), sea urchin Fibropellin (LocusID 373313), smu/smoh (LocusID 30225), syu/ssh (LocusID 30269), Tolloid (LocusID 42945), tout velu (LocusID 36614), twhh (LocusID 30444), ubo/prdm1 (LocusID 323473), yot/gli2 (LocusID 30154), human LMO1 (LocusID 4004), human SCUBE1 (LocusID 80274), human SCUBE2 (LocusID 57758), human ST5 (LocusID 6764), human STK33 (LocusID 65975), mouse SCUBE1 (LocusID 64706), and mouse SCUBE2 (LocusID 56788).
We thank Alex Schier and members of our lab for helpful discussions and comments on the manuscript, and Nipam Patel for supplying the 4D9 antibody. This work was supported by National Institutes of Health grant R01 RR12349 (WST) and a predoctoral fellowship from the Howard Hughes Medical Institute (IGW).
Competing interests. The authors have declared that no competing interests exist.
Author contributions. IGW and WST conceived and designed the experiments. IGW performed the experiments. IGW and WST analyzed the data and wrote the paper.
Citation: Woods IG, Talbot WS (2005) The you gene encodes an EGF-CUB protein essential for hedgehog signaling in zebrafish. PLoS Biol 3(3): e66.
Abbreviations
BACbacterial artificial chromosome
con
chameleon
hpfhours post fertilization
MHBmidbrain-hindbrain boundary
MOmorpholino oligonucleotide
SSLPsimple sequence length polymorphism
==== Refs
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Christensen EI Birn H Megalin and cubilin: Multifunctional endocytic receptors Nat Rev Mol Cell Biol 2002 3 256 266 11994745
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Blaess S Graus-Porta D Belvindrah R Radakovits R Pons S Beta1-integrins are critical for cerebellar granule cell precursor proliferation J Neurosci 2004 24 3402 3412 15056720
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Brand M Heisenberg CP Warga RM Pelegri F Karlstrom RO Mutations affecting development of the midline and general body shape during zebrafish embryogenesis Development 1996 123 129 142 9007235
Karlstrom RO Trowe T Klostermann S Baier H Brand M Zebrafish mutations affecting retinotectal axon pathfinding Development 1996 123 427 438 9007260
van Eeden FJ Granato M Schach U Brand M Furutani-Seiki M Genetic analysis of fin formation in the zebrafish, Danio rerio
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| 15660164 | PMC544551 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2005 Mar 11; 3(3):e66 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030066 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030075SynopsisCell BiologyDevelopmentGenetics/Genomics/Gene TherapyDanio (Zebrafish)Fish Need You Synopsis3 2005 11 1 2005 11 1 2005 3 3 e75Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
The you Gene Encodes an EGF-CUB Protein Essential for Hedgehog Signaling in Zebrafish
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There is little in biology that compares in beauty and limpidity to the development of a zebrafish embryo as viewed through a light microscope. The transparent eggshell and embryo tissues expose the minutest details of cell migrations and organ assembly to the curious viewer. Within a day, distinct vertebrate features emerge: a distinct head with the outlines of two large eyes, a quickly pumping heart, a notochord, and a growing array of somites—the bone and muscle precursors—stretching from trunk into tapering tail.
The transparent zebrafish embryo has allowed geneticists to discover a large number of mutants with anomalies in the development of external and internal organs. Seven mutations, collectively known as “You-class,” turn the pointed, chevron-like somites into shallow, rounded arcs (“You” stands for “U-shaped”). Ian Woods and William Talbot now show that the You mutation disrupts a new modulator of Hedgehog signaling.
Hedgehog is an extracellular signaling protein that can impose various fates on target cells at close proximity or over longer distances. Much research is focused on understanding the factors that promote or limit Hedgehog's activity and range. Woods and Talbot propose that the You protein acts in the extracellular environment to promote Hedgehog signaling.
Hedgehog was originally named for mutations that cause excess brush-like denticles to grow on the surface of fruitfly embryos, but it is now known to direct countless developmental decisions in invertebrates and vertebrates alike. In addition, several cancers are known to result from inappropriate Hedgehog signaling. In fish, Hedgehog's best-documented role is in muscle development. In the absence of Hedgehog signaling, cells destined to become slow muscle fibers fail to differentiate properly. A subset of these slow muscle cells—the muscle pioneers—congregate near the dorso-ventral midline of the embryo, where the dorsal and ventral halves of somites converge. When these specialized cells are absent, abnormal somite assembly leads to the U-shaped phenotype.
The authors found that You mutants showed many telltale signs of reduced Hedgehog signaling. Proteins that are normally expressed at certain times during the development of slow muscle cells were not activated in You mutants, indicating that these cells did not form. Mutant embryos also displayed reduced expression of the Hedgehog receptor Patched, a universal reporter of Hedgehog signaling activity. In addition, You mutants had specific ventral spinal chord defects that are shared by known Hedgehog pathway mutants. Yet You mutants expressed Hedgehog normally. Moreover, Hedgehog targets could still be activated in You mutants in response to excess Hedgehog signaling, suggesting that the signaling cascade is left intact. The authors concluded that the You protein was a facilitator rather than a crucial transmitter in Hedgehog signaling, likely acting at a step upstream of a cell's response to Hedgehog. Normal muscle pioneers could form in chimeric embryos (embryos made of wild-type and You mutant cells) regardless of which cells—the Hedgehog-producing cells or Hedgehog-responding muscle precursors—expressed You. This made it most likely that the You protein acted outside the cells, perhaps as a cell matrix component.
The authors mapped the You mutation and found that it disrupted the coding region of a gene encoding a putative secreted protein. The predicted You protein is closely related to members of the mouse SCUBE family, a group of proteins that are defined by characteristic extracellular motifs (although these proteins have not yet been linked to Hedgehog signaling). This observation strengthens the hypothesis that the You protein has extracellular functions, and the researchers' experimental evidence supports a role for You in transport or stabilization of Hedgehog. At later stages You could also participate in other signaling pathways, as its expression does not always coincide with that of Hedgehog during zebrafish development.
| 0 | PMC544552 | CC BY | 2021-01-05 08:21:19 | no | PLoS Biol. 2005 Mar 11; 3(3):e75 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030075 | oa_comm |
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PLoS MedPLoS MedpbioplosbiolPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1574041110.1371/journal.pmed.0020025Policy ForumImmunologyInfectious DiseasesMicrobiologyScience PolicyAllergy/ImmunologyEpidemiology/Public HealthHIV/AIDSSexual HealthHIV Infection/AIDSImmunology and allergyGlobal healthMedicine in Developing CountriesThe Global HIV/AIDS Vaccine Enterprise: Scientific Strategic Plan Policy ForumCoordinating Committee of the Global HIV/AIDS Vaccine Enterprise Members of the Coordinating Committee: M. K. Bhan (Department of Biotechnology, New Delhi, India), S. Berkley (International AIDS Vaccine Initiative, New York, United States of America), M. DeWilde (Aventis Pasteur, Swiftwater, United States of America), J. Esparza
* (Bill & Melinda Gates Foundation, Seattle, United States of America; Interim Secretariat), A. S. Fauci (National Institutes of Health, Bethesda, United States of America), H. Gayle (Bill & Melinda Gates Foundation, Seattle, United States of America), M. I. Johnston (National Institutes of Health, Bethesda, United States of America), P. Kaleebu (Uganda Virus Research Institute, Entebbe, Uganda), M. D. Kazatchkine (Agence Nationale de Recherches sur le SIDA, Paris, France), R. D. Klausner (Bill & Melinda Gates Foundation, Seattle, United States of America), E. S. Lander (Massachusetts Institute of Technology, Cambridge, United States of America), M. W. Makgoba (University of KwaZulu-Natal, Durban, South Africa), P. Mocumbi (European and Developing Countries Clinical Trials Partnership, the Hague, the Netherlands), P. Piot (United Nations Programme on HIV/AIDS, Geneva, Switzerland), O. Quintana-Trias (European Commission, Brussels, Belgium), W. Snow (AIDS Vaccines Advocacy Coalition, New York, United States of America), M. J. Walport (The Wellcome Trust, London, United Kingdom), and H. Wigzell (Karolinska Institute, Stockholm, Sweden).
Competing Interests: The authors have declared that no competing interest exist. Most of the authors hold directive or managerial positions in agencies and organizations conducting or supporting HIV vaccine research and development.
*To whom correspondence should be addressed. E-mail: [email protected] 2005 18 1 2005 2 2 e2517 11 2004 08 12 2004 This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose2005This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
A Shot in the Arm for AIDS Vaccine Research
A Strategy for Developing an HIV Vaccine
The development of an HIV vaccine remains one of the most difficult challenges confronting biomedical research today. A new international collaboration shares its plan to address the challenge
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Introduction
In June 2003, an international group of scientists proposed the creation of a Global HIV Vaccine Enterprise [1]. The authors invited discussion of this proposal, and challenged scientists to identify new strategies and mechanisms to accelerate the global effort to develop a safe and effective HIV vaccine. This paper describes the processes that led to agreement on the major roadblocks in HIV vaccine development, summarizes current scientific priorities, and describes an initial strategic approach to address those priorities. Specific research is not prescribed. Rather, the intent is to stimulate both researchers and funders to explore new, more collaborative, cooperative, and transparent approaches to address the major obstacles in HIV vaccine development identified in the plan, in addition to continuing the productive, high-quality programs already underway.
The major difficulties encountered in the development of an HIV vaccine are scientific, not organizational.
The motivation behind the proposal for a Global HIV/AIDS Vaccine Enterprise was the recognition that development of an HIV vaccine remains one of the most difficult challenges confronting biomedical research today [2,3]. Fortunately, scientific progress has created new opportunities that could be harnessed more effectively through global coordination and collaboration. These new opportunities include an expanded HIV vaccine candidate pipeline, improvements in animal models, a growing database from clinical trials, and the availability of new quantitative laboratory tools that make comparisons among vaccine studies feasible. Confronting major roadblocks and harnessing these new opportunities requires an effort of a magnitude, intensity, and design without precedent in biomedical research, with the Human Genome Project as a potentially useful model [4]. More specifically, the critical scientific insights generated by the creativity of individual investigators, as well as small groups and individual networks, could be significantly augmented by a properly organized, managed, and systematized international effort targeted on the design and clinical evaluation of novel HIV immunogens. An international collaborative effort that addresses a shared scientific plan, provides information exchange among groups, links clinical trials with standardized laboratory assays and evaluation in animal models, applies new knowledge to improvements in vaccine design in an iterative manner, and supports a transparent process for decision making in all aspects of vaccine discovery, design, development, and clinical testing will prove critical to success.
The Global HIV/AIDS Vaccine Enterprise represents a novel paradigm to seek and identify international agreement on the critical roadblocks for developing an HIV vaccine and on creating a shared scientific plan that addresses those roadblocks (see Box 1). The Enterprise proposes to coordinate efforts at a global level, facilitate use of common tools and technologies, and help ensure access to optimized resources. Furthermore, the Enterprise approach is a way of behaving as a global community of problem-solvers, more openly sharing information, ensuring that the shared scientific plan is implemented, and basing decisions on evidence rather than advocacy.
Box 1. Key Points in the Scientific Strategic Plan
• More new HIV infections and AIDS deaths occurred in 2004 than in any prior year (Figures 1–3). A vaccine is critical for the control of the pandemic.
• Development of an HIV vaccine is one of the world's most difficult and important biomedical challenges.
• Harnessing new scientific opportunities for HIV vaccine development will require an effort of a magnitude, intensity, and design without precedent in biomedical research.
• The Global HIV Vaccine Enterprise is an alliance of independent organizations committed to accelerating the development of a preventive HIV/AIDS vaccine based on a shared scientific plan.
• The scientific strategic plan was developed with the collaboration of over 140 scientists and other participants from 17 countries and several international organizations.
• The plan identifies critical unanswered scientific questions along the critical path for vaccine discovery, from antigen design to the conduct of clinical trials.
• Novel vaccine candidates need to be designed to induce high levels of broadly reactive and persistent immune responses against HIV strains circulating in different parts of the world.
• Standardization and validation of high-throughput laboratory assays conducted under GLP will allow comparison of results from different vaccines, which is a linchpin of rational decision making in vaccine development.
• The Enterprise will encourage decision makers to establish clear and transparent processes to identify and prioritize the most promising vaccine candidates.
• The Enterprise will seek to engage the best researchers who are willing to work in a highly collaborative manner and to dedicate the majority of their efforts to solve the fundamental roadblocks in HIV vaccine development.
• To mount an accelerated global search for a safe and effective HIV/AIDS vaccine, annual funding for such research should double—to US$1.2 billion per year.
• Several founding partners of the Enterprise have already committed, or are planning to commit, new funding to support the proposed Enterprise activities, and to create a culture of mutual accountability for the effective implementation of the scientific strategic plan.
• Enterprise activities are guided by an international Coordinating Committee, supported by different technical expert groups, including representatives from funders and implementers of HIV vaccine R&D.
It must be emphasized, however, that the major difficulties encountered in the development of an HIV vaccine are scientific, not organizational, and arise directly from the complexities of HIV and AIDS. “Small science” should not be replaced with “big science.” Both approaches must be undertaken. Creation of research environments that support the creativity both of individual investigators and of larger, collaborative efforts will accelerate the scientific breakthroughs needed to successfully develop a safe and effective HIV vaccine.
Scientific Priorities
Prioritization process
In August 2003, the authors of the Enterprise proposal invited a group of leading scientists, public health experts, and policy makers to meet at the Airlie House in Warrenton, Virginia, United States, to refine the vision for the Enterprise. The Airlie group agreed that the Global HIV/AIDS Vaccine Enterprise should be developed as an alliance of independent organizations committed to accelerating the development of a preventive vaccine for HIV/AIDS through implementation of a shared scientific strategic plan, mobilization of additional resources, and greater collaboration among HIV vaccine researchers worldwide [5].
The subsequent initial planning phase of the Enterprise involved leading government research agencies, private industry, non-governmental organizations, and funders involved in HIV vaccine research and development (R&D) activities, including the Bill & Melinda Gates Foundation (BMGF), the International AIDS Vaccine Initiative (IAVI), the National Agency for Research on AIDS of France (ANRS), the United States National Institutes of Health (NIH), the United Nations Joint Programme on HIV/AIDS (UNAIDS), the World Health Organization (WHO), and the Wellcome Trust. The Enterprise is expected to grow with time and include additional organizations and research groups willing to contribute to the implementation of its scientific strategic plan. A Steering Committee composed of representatives from several of the founding organizations provided guidance and coordination, with the BMGF serving as interim Secretariat.
Six Working Groups involving more than 120 participants from 15 countries, the WHO, and UNAIDS were formed to develop the scientific plan of the Enterprise. These Working Groups met from January to April 2004, identified critical unanswered questions, and proposed actions to address them. In May 2004, the Steering Committee of the Enterprise analyzed the recommendations from the Working Groups and identified the scientific priorities for initial action.
Several common themes emerged from the Working Groups. There was clear agreement on the key scientific challenges, as well as strong consensus that the HIV vaccine field has progressed to a point where it should be possible to answer some of the persistent questions more definitively. To meet these challenges, the Working Groups called for enhanced access to reagents and technologies, adequate resources, and strengthened human capacity in several key areas, especially in developing countries, where clinical trials need to be conducted. There was also agreement that the present way of doing business, which centers primarily on individually led research groups or networks, needs to be supplemented by establishing focused, collaborative structures and providing access to common standards and technologies, which would enable comparison of data and candidate vaccines. This would, in turn, support a rational process for decision making to advance candidate vaccines through the different phases of evaluation.
Vaccine discovery
One immediate goal is to design HIV candidate vaccines that consistently induce potent, broadly reactive, persistent neutralizing antibodies, as well as memory T cells that suppress viral replication and prevent escape of virus from immune control [6,7]. Additional research is also needed to identify how mucosal [8] and innate [9,10] immunity could be harnessed to develop effective HIV vaccines. The ability to develop effective vaccines would be greatly enhanced by an understanding of what specific immune response or responses correlate with vaccine-induced protection [11].
The current state of the art suggests a two-pronged strategy to accelerate the development of a safe and effective HIV vaccine. One component should center on candidate vaccines already in the pipeline, nearly all of which are designed primarily to induce T cell responses. In some animal models these T-cell-inducing candidate vaccines suppress post-infection viremia and prevent or delay HIV disease, rather than prevent infection [12,13]. In studies of individuals infected with HIV, viral load correlates with efficiency of transmission [14], suggesting that a vaccine capable of suppressing viral load might reduce HIV transmission.
The second component should address critical gaps in scientific knowledge through carefully designed, focused, coordinated, and well-supported approaches. The fruits of this work will be a clearer understanding of what properties are needed for a successful vaccine and how to design candidates that incorporate those properties.
Scientific areas in which a more collaborative and organized Enterprise approach will be beneficial include the following: vaccine design based on the characteristics of recently transmitted viruses, evaluation of immune correlates of protection in animal models, and design of novel candidates vaccines that induce neutralizing antibodies and T cell immune responses.
Identifying which T cell candidate vaccine is most promising has become an urgent priority.
Vaccine design
Strategically, vaccines that are designed based on recently transmitted viruses hold the best hope of inducing relevant immune responses against currently circulating strains. Recent data suggest that the subset of viral strains that are sexually transmitted has unique genetic and antigenic properties, including greater susceptibility to neutralization than the bulk of circulating virus [15]. While such observations require confirmation, newly transmitted viruses are nonetheless the crucial targets of vaccine-induced immunity. Therefore, virological and immunological characterization of acute/early HIV infection should inform the design of vaccines and also guide the design of trials capable of determining whether immunization impacts virus levels and the course of HIV infection.
To address these issues, a representative number of virus strains derived from recently infected individuals representing those populations who will participate in vaccine efficacy trials, including populations in developing countries, should be obtained. These virus isolates should be subjected to a comprehensive genetic and biologic characterization, together with an analysis of host immune responses and the genetic background of those populations participating in the clinical trials.
Figure 1 Adults and Children Estimated to Be Living With HIV as of the End of 2004 (Total: 39.4 [35.9–44.3] million)
(Map: UNAIDS/WHO)
This continuous and ongoing effort will require a multidisciplinary global approach, linking investigators who are conducting epidemiological and cohort studies (to allow for detection of acute/early infections), laboratory scientists working on the virology and immunology of acute/early infection and on the genetic characterization of affected human populations, vaccine designers and manufacturers, and clinical trialists. In addition, systems for data management and analysis that will facilitate the rapid translation of new information into improved vaccine designs need to be developed.
Immune correlates
Nonhuman primate models of AIDS offer opportunities to evaluate potential correlates of immune protection. While a particular immunization strategy that works in animal models may or may not predict protection in humans, important insights into potential immunologic mediators of protection would result from such studies. Several experimental vaccines induce varying degrees of protection against simian immunodeficiency virus (SIV) or chimeric simian/human immunodeficiency virus in rhesus macaques. In particular, studies using models in which a very high level of protection from acquisition of infection was achieved are needed, i.e., immunization with live attenuated SIV and attenuation of SIV infection by short-term antiretroviral treatment administered immediately after SIV inoculation [16,17].
To facilitate this process, assays for many different immune responses to SIV and chimeric simian/human immunodeficiency virus need to be standardized, validated, and made available to different research groups. Likewise, agreements need to be reached on those monkey challenge models that most closely resemble HIV transmission and infection in humans. Large numbers of animals will be needed to achieve statistical significance for experimental findings [18], which in turn will require expanded primate breeding and housing capability. A multidisciplinary approach that links virologists, immunologists, vaccine developers, primatologists, data and project managers, and others will be needed.
Neutralizing antibodies
There is increasing agreement that a successful vaccine needs to induce both humoral and cell-mediated immunity. Development of immunogens capable of inducing antibodies that neutralize primary HIV isolates from all genetic subtypes and regions of the world remains the most difficult challenge in the field of HIV vaccinology [19,20]. Success will likely require a deeper understanding of the structural motifs of the HIV envelope protein that interact with cellular receptors and/or that are recognized by broadly neutralizing antibodies [19]. This strategy will require numerous well-characterized, broadly neutralizing monoclonal antibodies, the application of peptide and carbohydrate chemistry, structural biology, and genetic engineering approaches to immunogen design, and the use of iterative approaches guided by the immunogenicity of new designs.
Given the importance of these endeavors and the uncertainty as to what path will lead to success, multiple intersecting approaches need to be explored, including, for example, the design, production, and evaluation of (1) envelope proteins that stably reveal neutralization epitopes that may be only transiently exposed during viral entry into target cells, (2) immunogens that contain rigid, stable epitopes that mimic the portion or portions of envelope recognized by broadly neutralizing monoclonal antibodies, (3) modified envelope proteins that better expose existing relevant epitopes, and (4) molecules that resemble a stabilized version of the mature envelope trimer on the virion surface. These are examples of current approaches being explored, some or all of which may prove ineffective. Additional novel ideas need to be proposed and explored.
To achieve the above objectives, new tools and technologies such as those able to detect rare, broadly neutralizing monoclonal antibodies through large-scale screening of human sera will have to be developed. In addition, the very limited existing capacity to translate structural information into stable immunogen products needs to be expanded.
T cell vaccines
Nearly all current vaccine candidates in the clinical pipeline are T-cell-inducing vaccines, e.g., poxvirus recombinant vectors, adenoviral vectors, DNA constructs with or without adjuvants, and lipopeptides. The ongoing effort to evaluate these products and to develop new ones is considerable [21]. Identifying which T cell candidate vaccine or vaccines are most promising has become an urgent priority. However, these evaluations are being conducted within separate preclinical research groups and, to a lesser extent, separate clinical trial networks, with the result that candidate vaccines may not be optimally compared preclinically or clinically. This approach may result in delays in identifying the most promising candidates, and it risks devoting time and resources to inferior products, although it is recognized that the specific immune responses needed for a successful vaccine remain unknown.
The identification and optimization of promising candidates will require (1) defining clear, transparent processes for decision making, (2) establishing agreement on vaccine characteristics upon which decisions should be based, (3) developing and using validated assays to assess those parameters, to allow for preclinical and clinical comparison among candidates, and (4) establishing closer coordination and data-sharing among product developers, which will accelerate the availability of critical information needed to identify and further develop the most promising candidates.
Development of an HIV vaccine remains one of the most difficult challenges confronting biomedical research today.
Research is also needed to develop improved novel T-cell-inducing candidate vaccines, especially those that avoid or otherwise circumvent anti-vector immune responses [22], and those that induce persisting high levels of immunity, especially mucosal immunity. In addition, a thorough, systematic exploration of adjuvants that markedly enhance the quantity, quality, and durability of immune responses to HIV vaccines is needed.
Laboratory standardization
Comparison of results from preclinical and clinical studies is the linchpin of rational decision making regarding further development of vaccine candidates. Therefore, the initiation of approaches that will permit valid comparisons is crucial.
Progress to standardize and validate a limited number of T cell assays has been made within the laboratories of vaccine developers and within some partnering research networks. This approach now needs to be more broadly applied and extended to the analysis of neutralizing antibody responses. A robust infrastructure that develops, expands, and ensures broad access to quality assay technologies will allow valid comparison of data across trials and networks worldwide.
In order to achieve this goal, the following are required: (1) a decision-making process to select a set of robust assays, standardized and validated across laboratories, for measuring vaccine-induced immune responses in humans and animals; (2) wide availability of common reagents (such as peptides, control sera, and virus panels); (3) capacity for developing novel assays and reagents of potential value and for their translation to preclinical and clinical settings; (4) “core” laboratories that run selected assays and serve as a reference laboratory for satellite laboratories (clinical and preclinical work would take place in separate facilities, and clinical studies would require Good Laboratory Practices [GLP] conditions); (5) satellite laboratories located at or very near clinical trial sites to carry out a range of activities such as processing blood, storing and shipping specimens, and conducting basic immunological evaluation, and to participate in other Enterprise-organized activities such as acute/early infection studies; (6) an ongoing global quality assurance function encompassing all participating core and satellite laboratories and covering both routine safety as well as immunologic and virologic assessments; and (7) transfer of research assays and, when and where feasible, validated endpoint assays to satellite labs, including the necessary training activities.
In addition, new assay development has failed to keep pace with current understanding of the biology of the immune system and recent advances in technology. A more active program of applied research and assay development is needed to explore new concepts that would advance technical abilities and provide a better understanding of the immune responses generated by HIV vaccines.
Cellular immunity
Two assays are currently used for the primary evaluation and enumeration of antigen-specific T cells: Interferon-γ ELISPOT and multiparameter flow cytometry. The ELISPOT assay was initially developed to measure CD8+ T cell responses. Several observations in both mice and humans have indicated that protective immune responses will likely require stimulation of both CD4+ and CD8+ T cell effector and memory functions; it is unlikely that induction of Interferon-γ-secreting T cells alone correlates with protective immunity [11]. Therefore, additional laboratory assays measuring multiple HIV-specific cell types as well as functional capabilities will be needed to thoroughly evaluate vaccine-induced immune responses. These assays should also permit rapid assessment of the magnitude and breadth of immune responses, and enumerate the specific epitopes that are recognized.
Figure 2 Estimated Number of Adults and Children Newly Infected with HIV during 2004 (Total: 4.9 [4.3–6.4] million)
(Map: UNAIDS/WHO)
Humoral immunity
Different laboratories use different assays to measure antibodies that neutralize HIV and related viruses, SIV and chimeric simian/human immunodeficiency virus. These assays vary technically, but the most widely accepted assays measure reduction in virus infectivity in cells that express the receptors necessary for virus entry. Assays that offer the greatest value are those that are validated, amenable to high throughput, low in cost, readily transferable, and that can be performed according to GLP guidelines.
The ability to measure the magnitude and breadth of neutralization against diverse HIV strains is essential to evaluating responses generated by candidate HIV vaccines. Only with multiple strains of virus can neutralization breadth be ascertained in a meaningful way. Standard panels of HIV strains are in early stages of development. Expansion or extension of current standardization and validation activities, production and provision of necessary reagents, and access to quality assurance programs are needed to ensure worldwide comparability of assay results [23]. The strains of virus incorporated into a worldwide panel need to be carefully selected to reflect the current epidemic and should include early isolates from individuals at potential vaccine trial sites [24]. Molecular epidemiological studies and elucidation of the role of genetic factors and immune responses of the host in the transmission of HIV at the population level will also help guide vaccine design and evaluation [25,26]. Another specific priority is an assessment of the neutralizing antibody response generated in the recently completed Phase III trials of HIV envelope glycoprotein 120 candidate vaccines using a global virus panel. The results would establish a baseline level of neutralization potency and breadth that is non-protective, which would be extremely valuable in reaching informed decisions about advancing future antibody-based candidate vaccines.
As more HIV candidate vaccines enter the pipeline, current capacity will be rapidly exhausted.
A major obstacle to designing a suitable global virus panel is the paucity of information on neutralization serotypes. There is general agreement that if a reasonably small number of neutralization serotypes exist, their identification would guide the creation of an optimal panel of isolates for neutralizing antibody assays and the design of polyvalent immunogens. Although there is some controversy as to whether HIV-1 neutralization serotypes exist, the magnitude of benefit that would result if serotypes were identified warrants establishment of a neutralization serotype discovery program that employs the latest technologies.
Product development and manufacturing
Manufacture of vaccine candidates for large clinical trials and to meet eventual worldwide demand requires the development of processes for producing consistent, active vaccine batches on a large scale. Development of these bioprocesses must be integrated with analytical work (e.g., toxicity and stability testing), incorporate validated assays, and be applicable to the manufacture of sufficient vaccine to meet global needs after licensure. These processes are typically individually developed as a candidate vaccine advances from early clinical testing to late-stage evaluation and licensure. Worldwide expertise and capacity for this bioprocess development work is already limiting and exists almost exclusively in the private sector. As more HIV candidate vaccines enter the pipeline, current capacity will be rapidly exhausted.
The initial priority is to identify or establish one or more dedicated HIV vaccine bioprocess and analytical development groups that bring together the skill set and capacity to manufacture different promising candidates for clinical trials. The bioprocess development groups would also help train people and transfer manufacturing skills in whole or in part to manufacturing sites around the world. This training program would address the acute shortage of bioprocess experts.
At a later stage, building, acquiring, or contracting facilities to carry out bioprocess and analytical work and to produce several different types of candidate vaccines should be considered. Such facilities would further assist in transferring manufacturing technology to other production facilities, preferably in one or more developing countries. Decisions about which candidates a facility undertakes would be made through a well-defined, comprehensive evaluation process. The facilities could eventually be expanded to provide production capacity to launch a vaccine for public health use, should no manufacturer be available to produce the vaccine quickly upon licensure.
Clinical trials capacity
As a growing number of HIV candidate vaccines begin to move through the clinical trials pipeline, the gap between existing global capacity and future requirements for conducting large efficacy trials has grown in magnitude and urgency, especially in developing countries. This gap in developing countries must be addressed through (1) increasing the quantity and quality of research staff, (2) establishing sustainable research facilities to support trials, and (3) expanding access to large, well-defined populations of uninfected people at high risk of HIV infection.
The acute shortage of qualified personnel is a major bottleneck to the conduct of clinical trials in developing countries.
The recommended solutions take a long-term view and are aimed at building site capacity rather than preparing for specific trials. Sites should not be confined to conducting HIV vaccine trials but should be positioned to contribute to other research of public health importance to the community and the country, including, for example, other areas of HIV research (e.g., microbicides and treatment) and/or other diseases. Additional field trial sites must be developed to be able to conduct planned and anticipated efficacy trials. Sites should be selected in a strategic, data-driven manner, and should demonstrate the ability to recruit and retain large numbers of HIV-negative volunteers from populations with substantial HIV incidence. New efficacy trial sites should be developed in regions with emerging epidemics rather than only in areas with already-established disease. “Early-warning systems” must be available to identify these newly emerging sub-epidemics. Defining optimal methods for collection of HIV incidence data from populations at potential efficacy trial sites is essential. Whenever possible, efficacy trial sites should be linked to (1) academic medical centers to enhance research capacity and help train clinical researchers, (2) accredited local and regional laboratory facilities to provide infection endpoint and safety assessments, and (3) centers that can provide appropriate care and treatment to trial participants.
Figure 3 Estimated Adult and Child Deaths from AIDS during 2004 (Total: 3.1 [2.8–3.5] million)
(Map: UNAIDS/WHO)
The acute shortage of qualified personnel is a major bottleneck to the conduct of clinical trials in developing countries with severe or rapidly emerging HIV epidemics. Development of intellectual capacity at these sites should emphasize (1) expanding research training opportunities for personnel in the broad range of topics required to conduct high-quality clinical research, (2) establishing and adequately supporting long-term career paths for such individuals, and (3) fostering political and social environments locally and nationally that support the conduct of clinical research. Building HIV scientific and operational expertise at clinical trial sites should be linked to other HIV/AIDS research activities (e.g., identifying and characterizing incident/early HIV infections, collecting newly transmitted strains, and measuring incidence in high-risk populations).
Site development must include strategies to develop or enhance existing capacity to deliver health care, including HIV prevention, care, and treatment, to the local community participating in clinical trials. Provision of, or referral to, basic clinical services such as voluntary counseling and testing and diagnosis and treatment of sexually transmitted infections will be essential.
In addition, site development should include building skills that are ancillary but critical to the actual conduct of clinical trials, such as educating communities, building community partnerships, managing site finances, and piloting applications through regulatory decision-making processes.
Regulatory considerations
The Enterprise must address a number of problems that currently impact the review of HIV vaccine trial protocols and that could delay future decisions regarding product licensure in developing countries. Most regulatory challenges arise from the fact that regulatory approvals are granted at the national level, but many developing countries lack the expertise, well-defined processes, clear delineation of authority, and/or other system components needed to make regulatory decisions expeditiously. As a result, new products are often licensed in these regions based on prior approval in the US or Europe and/or endorsement by the WHO. Under these circumstances, data specific to developing country populations (e.g., disease burden or childhood vaccination schedules) often do not enter into the decision making. The absence of defined pathways to approve products targeting a country's needs when a product is not also submitted to regulators in the US or Europe remains another obstacle. The Enterprise process has identified these action-item priorities: (1) harmonize and exchange information needed by regulatory bodies within the differing legal frameworks of different countries, (2) facilitate regulatory decision making, possibly using regional approaches for conducting reviews and making recommendations, (3) build regulatory capacity, (4) perform risk/benefit evaluations in the context of differing epidemic dynamics and country needs and resources, (5) identify and remove potential scientific impediments to rapid regulatory decision making, and (6) address ethical issues that interface with regulatory decision making, such as ensuring informed consent and defining the degree to which trial participants should receive a standard of care that is higher than others in their community.
Intellectual property issues
Given the Enterprise focus on stronger collaboration, data sharing, and use of common materials and reagents, an intellectual property (IP) framework that facilitates this “enabling environment” is crucial for success. While IP issues may arise throughout the vaccine development process, at present the top priority is to stimulate early stage research and vaccine design by increasing scientific freedom to operate and sharing of data and biological materials.
Specific areas for further consideration include: (1) minimizing restrictions on freedom of operation, perhaps by early stage covenants not to litigate and followed by later stage agreements based on true valuations of IP; (2) sharing of information (including clinical trial data), materials, expertise, trade secrets, and platform technologies in a protected and secure manner while also remaining in compliance with national laws devised to prevent monopolies and insider trading; (3) recognizing the contribution of different countries to HIV vaccine development through approaches that assure affordable access to successful vaccines; and (4) maximizing access to essential technologies and inventions.
Scientific Plan
Scientific activities
On October 21, 2004, a group of participants from 16 countries, the European Commission, UNAIDS, and the WHO met to finalize the scientific plan and to discuss how to formulate specific actions.
Participants noted that the structure of an activity should depend on several factors, including, for example, the degree to which the activity can be predefined, the degree to which the creativity of academic researchers needs to be harnessed, and the mechanisms available to the funding organization.
A number of options were discussed, with consensus as to those that would fit various scientific priorities.
First, networks of focused consortia and real or virtual centers are well suited to systematically address many of the major scientific roadblocks identified in this plan. These consortia or centers would link to each other to ensure a comprehensive, systematic approach, sharing information so that each can be as productive as possible, and also to share reagents and procedures so that data among groups can be compared and, where possible, merged for analysis (Figure 4). The specific scientific areas that could be supported by consortia or centers include (1) addressing fundamental scientific problems, such as the definition of correlates of immune protection in selected animal models and the characterization of acute/early infection in potential vaccine trial sites; (2) designing and evaluating novel vaccines, such as immunogens that neutralize primary isolates, and improved T cell vaccines that avoid immunological escape and/or that induce persisting mucosal or persisting systemic responses; and (3) providing for a systematic evaluation of potential adjuvants. The success of consortia or virtual centers will depend on engaging the best researchers, getting them to work collaboratively and dedicate the majority of their effort to HIV vaccine research, resolving IP issues, obtaining support for researchers from their institutions, and keeping the group focused on specific, well-defined questions. More than one consortium may be needed for systematic coverage of vaccine design research (e.g., monoclonal-antibody-identified epitopes, native envelope, and modified envelope).
Figure 4 A Possible Model to Address Key Scientific Questions through an Appropriate Organizational Infrastructure
(Courtesy of John Mascola; illustration: Giovanni Maki)
Second, a global system of central laboratories linked to satellite laboratories that work together (using GLP) would provide a range of standardized functions, help ensure the quality of clinical research, and enable comparison of data from different trials (Figure 5). Together this system could (1) conduct preclinical or clinical assays, particularly critical endpoint assays that require standardization and/or validation; (2) develop, optimize, and validate new assays and platforms; (3) transfer assays from central labs to satellite labs; (4) develop and implement a global quality control/quality assurance program and proficiency testing for assays performed at central and satellite laboratories; (5) implement vaccine-related research that requires validated assays and close cooperation and collaboration among labs globally, such as a Virus Neutralization Serotype Discovery Program, and the characterization of recently transmitted HIV isolates; and (6) contribute to the development of technological infrastructure in developing countries.
Figure 5 A Possible Model for a Comprehensive Global Laboratory Network for the Standardized Assessment of Humoral Immune Responses
(Courtesy of David Montefiori; illustration: Giovanni Maki)
Third, a number of contract laboratories capable of developing, acquiring, storing, and distributing common reagents will prove critical to the success of collaborative research and development projects, and to ensuring reagent quality. These reagents could include (1) peptides, antisera/antibodies, and viral isolates for immune assays, including a standard panel of virus strains and sera representative of the global genetic and immunologic variability of HIV, and (2) additional broadly neutralizing monoclonal antibodies, especially from non-clade B viruses, to facilitate elucidation of the motif or motifs they recognize. These contract laboratories would be expected to work very closely with and enable the work of Enterprise consortia, centers, immune assessment laboratories, and clinical sites.
Fourth, a network of Clinical Research Training Centers in developing countries could work collaboratively to ensure development of quality trial sites. These centers would (1) conduct or facilitate training of trial site personnel in activities that are generic to the conduct of clinical trials, as well as those specific for HIV vaccine trials, for example, an HIV vaccine fellowship program for developing country scientists; (2) coordinate and work together with other Enterprise consortia or centers, such as those established to characterize acute/early infection in developing country settings or to prepare a standard panel of HIV strains representative of currently circulating viruses; and (3) share standard operating procedures, vaccine development plans, and strategies for engaging and ensuring community and political support.
Fifth, a network of individuals and companies with manufacturing experience, particularly process development expertise, could link to consortia, centers, and others involved in vaccine development to provide development and manufacturing expertise to facilitate the advancement of improved HIV vaccine candidates.
The above structures are proposed to address the initial Enterprise scientific priorities. Additional consultative groups, reference and centralized facilities, and other mechanisms may be needed to facilitate collaborative work and strengthen the global capacity for the conduct of HIV vaccine research and development as the field progresses.
Different implementing and funding agencies will need to work in close collaboration to ensure harmonious implementation of the scientific plan. Initial actions should focus on the areas of vaccine discovery and standardization of laboratory assays, which are considered critical for the success of the Enterprise and the eventual development of a safe and effective HIV vaccine. Activities to address recommendations in the areas of product development and manufacturing, clinical trials capacity, regulatory considerations, and IP issues should be launched after these initial components of the plan are under way.
Regardless of timing, each scientific endeavor needs to outline specific strategies to ensure information exchange and capacity building among the collaborating partners and institutions. The funding mechanisms employed (i.e., contracts, grants, interagency agreements, etc.) will depend on the task to be accomplished and the needs and capabilities of each funding organization. In the spirit of coordination, collaboration, and transparency promoted by the Enterprise, two or more partners may jointly support one or more activities, taking care to avoid duplication in the use of their respective resources. When a research area is jointly funded, all communication regarding goals, research plans, progress, obstacles, etc., should be openly and transparently shared among all stakeholders—funders, project managers, and researchers.
Guiding principles
As an alliance of independent entities, the Global HIV/AIDS Vaccine Enterprise will be challenged to carry out three essential functions. One is to continue regular scientific assessments. The scientific priorities outlined in this paper will need to be monitored, re-evaluated, and updated. An evolving scientific plan must reflect lessons learned, new opportunities, and the influence of new scientific findings and new technologies. Revised versions of the scientific plan must be made fully and publicly available. The second essential function is to establish global processes. To optimize progress across a large and complex set of activities at the global level, standards, performance criteria, and processes for data sharing, communication, and convening must be established. The Enterprise will convene fora to address policy issues such IP, clinical trials, site development, and regulatory hurdles. And the third essential function is shared accountability. The partners in this alliance will need to create a culture of mutual accountability for the effective implementation of the scientific strategic plan. Since the Enterprise is not a single organization, a shared “way of doing business” is one of its most important defining traits. Articulating an explicit set of “working principles” is therefore crucial to the identity and smooth functioning of the Enterprise.
For the Enterprise as a whole the following conditions apply: (1) the central task is to develop and implement an ambitious scientific plan with the necessary scale, balance and sequence of activities, and structure to carry it out; (2) the plan must focus on critical roadblocks that would benefit substantially from global collaboration while fostering continued R&D by individuals, small groups, and individual networks; (3) the incentives holding the alliance together will include collaborative arrangements and structures that give people the resources, necessary critical mass, centralized facilities, common reagents, assays and technologies, and data they need to effectively remove critical roadblocks; (4) all activities will reflect the commitment to create an environment that maximizes the ability of participants to share data and biological materials, e.g., through the use of common standards for measurements and appropriate IP arrangements; and (5) the Enterprise also commits to working for rapid global access to a successful vaccine.
For participating investigators and organizations, key principles include (1) the willingness and desire to work in an open, collaborative fashion, sharing data and reagents in a collegial fashion, with the appropriate balance between productive competition and effective collaboration, and (2) the willingness and ability to devote the majority of their time to tackling these problems within a focused environment, completely committing to solve the problems at hand.
Organizational structure of the Enterprise
The implementation of the scientific plan of the Enterprise will be overseen and supported by the organizational structure described in Figure 6.
Figure 6 Proposed Organizational Structure of the Global HIV/AIDS Vaccine Enterprise (Illustration: Giovanni Maki)
The Coordinating Committee will facilitate all aspects of the Enterprise's activities. This committee consists of representatives of the Enterprise founders as well as additional scientific leaders selected from inside and outside the field of HIV vaccine research and development. The committee will develop procedures for term rotation and inclusion of new members, to ensure appropriate representation of all relevant partners, and will engage external stakeholders for advice, expertise, and assistance, appointing technical expert groups as needed. A Secretariat will provide logistical and administrative support to the Coordinating Committee and Enterprise partners. The BMGF will serve as Interim Secretariat until a permanent Secretariat is established.
The road to success will be a bumpy one.
The Funders Forum will be an open forum of sovereign, independent funding organizations, starting with a nucleus of those who already embrace the principles of the Enterprise and who are actively supporting or intend to support and fund HIV vaccine research and development. Members of the Funders Forum will be high-level decision makers within the ranks of funding organizations and governments, as close as possible to the source of resources. Since the Enterprise is not a discrete organization with a pool of money, funders will support specific areas using their own mechanisms, according to their own practices and policies, and following Enterprise principles. The scientific plan will provide guidance that may help funders better align existing resources but, more importantly, will facilitate the efficient and focused application of new resources as they become available. Multiple funders who wish to support a single Enterprise-defined project could form collaborative agreements, memoranda of understanding, or other forms of written agreement among themselves to outline their respective roles and responsibilities; address IP, program management, oversight, and other issues; and establish mechanisms for communication and conflict resolution. The funders with greatest flexibility could provide incentives for sharing reagents and data, and linking projects together, e.g., by supporting the additional work that nationally or regionally funded laboratories would need to undertake in order to participate in a global network, or by supporting a program to develop and share reagents.
In some cases, funders may wish to support an implementing organization that will take responsibility for managing the project and reporting back to the funder and other stakeholders. In other cases, funders may have the capability and capacity to play a substantial role in facilitating the project. In still other cases, funders may have the capability to assume a leadership role in overseeing the conduct of the activity, particularly in cases where the activity is well defined in advance.
In addition, an Annual Stakeholders Forum will be organized to bring together the broader community of scientists, policy makers, public health officials, and community representatives involved in the search for an HIV/AIDS vaccine. This meeting will serve as a forum to (1) update the broader community on Enterprise activities and progress, and (2) provide the community with a mechanism for feedback and dialog.
Funding issues
Global expenditures on HIV vaccine research and development in 2002 were tentatively estimated to be on the order of US$624–670 million, the large majority (67.3%) provided by the public sector, followed by the philanthropic sector (17.4%) and industry (15.3%). An analysis of how those funds have been invested revealed that the large majority (43.1%) is being used in preclinical research activities, followed by clinical trials (28.2%), basic research (20.7%), cohort development and clinical trial infrastructure (6.5%), and vaccine education, advocacy, and policy development (1.4%) [27].
The largest funder of HIV vaccine research and development activities has been the NIH, with almost US$350 million in 2002. The NIH budget for HIV vaccine research has grown from less than US$50 million in 1996, to an estimated US$514.6 million for 2005, corresponding to 17.6% of the NIH total HIV-related research budget for 2005.
The Enterprise Coordinating Committee will analyze the additional financial requirements to fully implement the scientific plan of the Enterprise, and the Enterprise Secretariat will explore options to leverage these funds from the public and private sector. Initial estimates by Enterprise partners suggest that US$1.2 billion per year, or double the current expenditures on HIV vaccine research and development, will be needed. Although this amount may appear unrealistic at present, it would represent only a fraction of the total global expenditures in response to the AIDS pandemic and a very reasonable investment in view of the enormous social, political, and economic consequences of the pandemic. However, it is essential that the proposed increase in funding for HIV vaccine R&D be additional to existing AIDS expenditures, and not at the expense of current prevention, treatment, and care efforts.
The founding partners of the Enterprise, including the NIH, the BMGF, and the Wellcome Trust have already committed, or are considering committing, resources towards new initiatives that will begin to enact portions of the Enterprise scientific plan over the next six to nine months. Each funder will utilize their own funding processes and will align the design, scope, and scale of programs to those laid out in this plan. For example, the NIH National Institute of Allergy and Infectious Diseases will establish the Center for HIV Vaccine Immunology, which will target several scientific priorities identified here.
Political support
As a sign of global recognition of the importance of better, more strategic coordination in the search for an HIV vaccine, the “Group of Eight” leading industrialized nations in June 2004 endorsed the goals of the Enterprise and agreed to review progress in implementation at its 2005 summit meeting in the United Kingdom [28]. Likewise, on October 19, 2004, Ministers of Health from seven European countries (France, Germany, Italy, the Netherlands, Spain, Sweden, and the United Kingdom) adopted a statement of intent to coordinate efforts to accelerate research for an HIV vaccine within the context of the global effort.
Next Steps
With almost 5 million new HIV infections and 3 million AIDS deaths occurring every year worldwide, the development of a safe, effective, and accessible HIV vaccine represents one of the most urgent global public health needs. This global emergency led to the proposal to harness the power of science to find a definitive solution to one of the most catastrophic health problems of our time. The Global HIV/AIDS Vaccine Enterprise has evolved over the past 18 months from a concept proposed in a scientific journal by a cadre of researchers to a global consensus concerning the major scientific roadblocks facing HIV vaccine development, a strategic approach to address those roadblocks, and guiding principles for the plan's implementation in a manner and degree commensurate with the challenges at hand. Several organizations have already embraced the Enterprise concept and are moving to tackle portions of the scientific plan. Still, much more remains to be done. The road to success will be a bumpy one requiring the energy, commitment, and action of a wide number of government and non-governmental organizations globally. Recognizing the enormity of the roadblocks as well as the potential benefits of a safe and effective HIV vaccine, it is essential that many more organizations and agencies contribute additional expertise and resources and work together as a global community in a cooperative, collaborative, and transparent manner to fully implement the Enterprise scientific plan.
The scientific strategic plan of the Global HIV/AIDS Vaccine Enterprise was developed though a complex process of consultation that involved more than 140 participants from 17 countries, the European Commission, the WHO, and UNAIDS. Special thanks are given to the Co-Chairs of the different Working Groups of the Enterprise that provided invaluable insights and recommendations for the development of this document (L. Corey, G. Douglas, E. Emini, N. Ketter, A. McMichael, G. Monroy, D. Montefiori, G. Nabel, G. Pantaleo, H. Rees, G. Sadoff, and J. Wasserheit). Thanks are also given to C. Hankins and J. Whitworth for their valuable comments and suggestions.
Citation: Coordinating Committee of the Global HIV/AIDS Vaccine Enterprise (2005) The Global HIV/AIDS Vaccine Enterprise: Scientific strategic plan. PLoS Med 2(2): e25.
Abbreviations
BMGFBill & Melinda Gates Foundation
GLPGood Laboratory Practices
IPintellectual property
NIHUnited States National Institutes of Health
R&Dresearch and development
SIVsimian immunodeficiency virus
UNAIDSJoint United Nations Programme on HIV/AIDS
WHOWorld Health Organization
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| 15740411 | PMC544553 | CC0 | 2021-01-05 10:39:29 | no | PLoS Med. 2005 Feb 18; 2(2):e25 | utf-8 | PLoS Med | 2,005 | 10.1371/journal.pmed.0020025 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1569621310.1371/journal.pmed.0020035EditorialImmunologyInfectious DiseasesMicrobiologyScience PolicyAllergy/ImmunologyEpidemiology/Public HealthHealth PolicyHIV/AIDSSexual HealthHIV Infection/AIDSImmunology and allergyGlobal healthMedicine in Developing CountriesA Strategy for Developing an HIV Vaccine EditorialThe PLoS Medicine Editors 1 2005 18 1 2005 2 1 e35Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
The Global HIV/AIDS Vaccine Enterprise: Scientific Strategic Plan
A Shot in the Arm for AIDS Vaccine Research
The newly published strategic plan for developing an HIV vaccine is crucially important, say the PLoS Medicine editors, but it must be followed by clear milestones and a process for monitoring progress
The new global plan is exciting, but now needs clear milestones
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In 1997, United States President Bill Clinton announced the challenge to develop an AIDS vaccine by 2007. Since 1997, the AIDS Vaccine Advocacy Coalition (AVAC) has published annual reports on the global status of the effort to meet Clinton's deadline. Last year's report, entitled “AIDS Vaccine Trials—Getting the Global House in Order,” officially ends the countdown. Saying that “we are on a long term mission,” AVAC concludes that there will not be a safe and efficient vaccine in 2007, and that we need to “focus on the long haul and set an agenda for sustained and sustainable action that stretches well beyond 2007.” It is not that there are no vaccine candidates in clinical trials, but there is little hope that any of the current candidates will turn out to be a cheap and safe vaccine that affords long-term protection.
Among notable developments over the past 12 months, the AVAC report highlights the Global HIV/AIDS Vaccine Enterprise as an effort to improve coordination within the AIDS vaccine field. The Enterprise was announced in June 2003 and now shares its scientific strategic plan with everyone affected by the AIDS pandemic—that is, all of us—by publishing it in PLoS Medicine (DOI: 10.1371/journal.pmed.0020025).
In its plan the Enterprise presents itself as a global endeavor and emphasizes the need for integration and capacity building around the world. It is not “a discrete organization with a pool of money” but a “coordinating group of individual funding agencies that will support specific areas of research using their own mechanisms, according to their own practices and policies, and following the Enterprise's principles.” These principles include collaboration, standardization, and coordination among international researchers and agencies. The plan focuses on specific scientific roadblocks that need to be overcome, but also looks ahead and mentions the need to build capacity for product manufacturing and clinical trials, and to address regulatory issues.
These are noble goals, and the fact that they are stipulated jointly by many of the leaders in the field will generate excitement and expectations, even though much of what is said has been said before. The plan stresses collaboration and coordination; there are clear benefits from a concerted effort. But might a level of competition, rather than collaboration, be healthy, and, if so, what level of competition would work best? The Enterprise members seem to have wrestled with that question. The plan mentions an “appropriate balance between productive competition and effective collaboration,” and suggests that certain incentives could be provided by “the funders with greatest flexibility.” As long as it remains unclear where scientific breakthroughs will come from, diversity and flexibility should be encouraged and not stifled. David Ho, in his Perspective on the plan (DOI: 10.1371/journal.pmed.0020036), mentions the danger of “group think,” and the Enterprise must not fall into that trap.
Notably absent from this initial plan is any mention of a timeline or milestones. The remit of the plan's authors was not to prescribe specific research but “to stimulate both researchers and funders to explore new, more collaborative, cooperative, and transparent approaches…in addition to continuing the productive, high-quality approaches already underway.” However, without a timeline, the plan fails to convey a sense of urgency. This is problematic, as any delays in developing a vaccine will increase the burden from HIV/AIDS in the parts of the world that can least afford it. To accelerate vaccine development, the plan urgently needs to be supplemented with a list of specific tasks, responsible individuals, necessary resources, and allocated time.
The next document from the Enterprise must provide specifics on project management, although one problem with putting a time frame on HIV vaccine development is a fundamental one: we do not know whether it is actually possible to develop a safe and effective vaccine. (One assumes the Enterprise members agree, though there is no explicit acknowledgement of this uncertainty in the plan.) Moreover, provided it can be done, it is impossible to predict when the necessary scientific advances will happen. That said, without a list of specific projects, project leaders, and a time frame for achieving or at least evaluating specific goals, it will be impossible to define success and failure, review progress, and assure internal and external accountability.
There is another reason why a best-guess timeline is essential: realistic expectations about an AIDS vaccine would stress the urgency of combating the AIDS pandemic over the next decade—and maybe longer—in the absence of an effective vaccine. The potential benefits of a vaccine cannot be overestimated, and its development has to be one top priority for the global scientific community. But its success cannot be taken for granted and will come too late for millions. Therefore, parallel efforts to prevent or reduce transmission and to treat infected individuals need to be accelerated now.
The Enterprise's plan should be hailed as a crucially important outline for vaccine development, but the goodwill surrounding it won't last unless it is quickly followed up with a set of milestones, and a transparent process by which progress will be measured and course corrections implemented.
| 15696213 | PMC544554 | CC0 | 2021-01-05 10:40:25 | no | PLoS Med. 2005 Jan 18; 2(1):e35 | utf-8 | PLoS Med | 2,005 | 10.1371/journal.pmed.0020035 | oa_comm |
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PLoS MedPLoS MedpbioplosbiolPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1574041210.1371/journal.pmed.0020036PerspectivesOtherScience PolicyVirologyAllergy/ImmunologyHealth PolicyHIV/AIDSSexual HealthHIV Infection/AIDSMedicine in Developing CountriesImmunology and allergyInternational healthA Shot in the Arm for AIDS Vaccine Research PerspectiveHo David D David D. Ho is the Irene Diamond Professor and Scientific Director of the Aaron Diamond AIDS Research Center, The Rockefeller University, New York, New York, United States of America. E-mail: [email protected]
Competing Interests: The author declares that he has no competing interests.
2 2005 18 1 2005 2 2 e36Copyright: © 2005 David D. Ho.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
The Global HIV/AIDS Vaccine Enterprise: Scientific Strategic Plan
A Strategy for Developing an HIV Vaccine
Why haven't we developed an HIV vaccine yet? And will the new roadmap from the Global HIV/AIDS Vaccine Enterprise help our efforts towards vaccine development? Ho addresses these crucial questions
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The scientific strategic plan of the Global HIV/AIDS Vaccine Enterprise, published in this month's PLoS Medicine, is a clear and cogent document describing how major funders and stakeholders in HIV vaccine development should move forward in a collaborative fashion [1]. There is no doubt that this roadmap will be regarded as a useful instrument to bring greater cohesion and coordination to the field. The individuals who championed this effort should be commended for providing a great service to the scientific community. It is an excellent start to a continuing dialogue of utmost importance.
The Challenge
Why is it that we still do not have a protective vaccine against HIV 22 years after its initial identification? Many possible explanations come to mind.
In the natural course of HIV infection, the virus wins 99% of the time, showing that specific immunity in an infected person is unable to completely clear the virus. We have also known for over a decade that primary HIV isolates are relatively resistant to antibody neutralization, probably because of a “protective shield” on the viral envelope glycoproteins, consisting of variable loop sequences and extensive N-linked glycosylations. Another explanation is the extreme plasticity of HIV that allows new viral variants to evade immune recognition in the same way that they escape from drugs. Moreover, superinfection by a second viral strain has been documented in a number of individuals who have already mounted immune responses to the initial HIV infection. Yet another problem is that the AIDS research community has yet to uncover the correlates of immune protection in vivo. Lastly, proven vaccine approaches from the past have either failed (whole killed virus and subunit vaccines) or faced seemingly insurmountable regulatory hurdles (live attenuated virus vaccine).
Given these daunting obstacles, why have so many continued in the long struggle to develop an HIV vaccine? The answer must lie, in part, in the noble cause at hand. Yet there are also some encouraging clinical and experimental observations (Figure 1). Rare patients do control HIV infection spontaneously. Certain people remain virus-negative despite repeated exposures. That superinfection is not more commonly found supports the notion of immune control. Vaccine-mediated protection against simian immunodeficiency virus is indeed possible using live viruses attenuated by specific mutations or by pharmacological interventions. Finally, and perhaps most importantly, HIV transmission by sex in the natural setting is typically inefficient (and thus easier to block), unlike most experimental challenge systems employed in monkey studies to date. Collectively, these findings provide a ray of hope to push on.
Figure 1 Rays of Hope: Clinical and Experimental Observations Suggesting That an HIV Vaccine Is Feasible
(Illustration: Giovanni Maki)
The Enterprise
The scientific strategic plan of the Enterprise is spot-on in identifying the major roadblocks in HIV vaccine development, as well as in establishing the key scientific priorities as we see them today [1]. It rightly recommends the formation of a growing alliance of organizations to foster a better collaborative spirit that could lead to, among other things, stronger political support and increased funding. The proposed greater coordination and management, sharing of information, technologies, and reagents, and harmonization of standards, assays, and approaches could only add to our overall efforts.
One might ask, however, whether there are potential downsides to the plan. In the name of continuing this important dialogue, I would like to offer one general concern. Arguably, the reason for the lack of an effective HIV vaccine today is rooted in the basic problems posed by the virus itself. What we need foremost are new scientific solutions, although a prim and proper “process and structure” in our approach will be helpful. The needed breakthroughs to develop a vaccine will likely emerge from the creativity of scientists doing fundamental research that is free of preconceived biases. It is my contention that great new ideas are as likely to come from curiosity-driven basic studies as from the mission-oriented approach that is represented by the new proposal. Therefore, the leadership of the Enterprise must safeguard against the kind of “group think” that is so pervasive in large collaborative endeavors of this nature. The views of a small number of researchers, no matter how smart or accomplished, must not supersede the collective wisdom of the scientific community at large.
No doubt important contributions will be made by scientists working outside of the Enterprise. Measures should be taken to ensure that their views and approaches, even if deemed unconventional, are not stifled by the newly established system. Likewise, their research support should not be compromised because the creation of the Enterprise concentrates the funding into the hands of a relatively small number of designated scientists. To me this is a serious risk given the current “flat funding” at the National Institutes of Health.
The Future
The authors of the “The Global HIV/AIDS Vaccine Enterprise: Scientific Strategic Plan” have laid out a timely and insightful plan to address perhaps the greatest public-health need of the millennium. This document and its later revisions will serve as useful guideposts for the AIDS vaccine development effort for years to come. To be successful in this mission, our research community will ultimately need a specific “scientific blueprint” for making an HIV vaccine. That day will come only after we get another shot in the arm, infusing us with new knowledge and know-how. Is there any doubt that we need to redouble our investment in basic research on the challenges posed by HIV?
Citation: Ho DD (2005) A shot in the arm for AIDS vaccine research. PLoS Med 2(2): e36.
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Reference
Coordinating Committee of the Global HIV/ AIDS Vaccine Enterprise The Global HIV/AIDS Vaccine Enterprise: Scientific strategic plan PLoS Med 2005 2 e25 15740411
| 15740412 | PMC544555 | CC BY | 2021-01-05 10:39:35 | no | PLoS Med. 2005 Feb 18; 2(2):e36 | utf-8 | PLoS Med | 2,005 | 10.1371/journal.pmed.0020036 | oa_comm |
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BMC Musculoskelet DisordBMC Musculoskeletal Disorders1471-2474BioMed Central London 1471-2474-5-441554832810.1186/1471-2474-5-44Research ArticleThe effect of short-duration sub-maximal cycling on balance in single-limb stance in patients with anterior cruciate ligament injury: a cross-sectional study Ageberg Eva [email protected] David [email protected]öm Eva [email protected]én Thomas [email protected] Department of Rehabilitation, Lund University Hospital, Lasarettsgatan 13, SE-221 85 Lund, Sweden2 Department of Orthopedics, Lund University Hospital, SE-221 85 Lund, Sweden3 Department of Physical Therapy, Lund University, Lasarettsgatan 7, SE-221 85 Lund, Sweden2004 17 11 2004 5 44 44 6 4 2004 17 11 2004 Copyright © 2004 Ageberg et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
It has previously been shown that an anterior cruciate ligament (ACL) injury may lead to impaired postural control, and that the ability to maintain postural control is decreased by fatigue in healthy subjects. To our knowledge, no studies have reported the effect of fatigue on postural control in subjects with ACL injury. This study was aimed at examining the effect of fatigue on balance in single-limb stance in subjects with ACL injury, and to compare the effects, and the ability to maintain balance, with that of a control group of uninjured subjects.
Methods
Thirty-six patients with unilateral, non-operated, non-acute ACL injury, and 24 uninjured subjects were examined with stabilometry before (pre-exercise) and immediately after (post-exercise) short-duration, sub-maximal cycling. In addition, the post-exercise measurements were compared, to evaluate the instantaneous ability to maintain balance and any possible recovery. The amplitude and average speed of center of pressure movements were registered in the frontal and sagittal planes. The paired t-test was used for the intra-group comparisons, and the independent t-test for the inter-group comparisons, with Bonferroni correction for multiple comparisons.
Results
No differences were found in the effects of exercise between the patients and the controls. Analysis of the post-exercise measurements revealed greater effects or a tendency towards greater effects on the injured leg than in the control group. The average speed was lower among the patients than in the control group.
Conclusions
The results of the present study showed no differences in the effects of exercise between the patients and the controls. However, the patients seemed to react differently regarding ability to maintain balance in single-limb stance directly after exercise than the control group. The lower average speed among the patients may be an expression of different neuromuscular adaptive strategies than in uninjured subjects.
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Background
The anterior cruciate ligament (ACL) is the most commonly injured ligament in the knee. The risk of future joint problems, in the form of functional limitations, secondary lesions, and arthrosis, is increased following such an injury. Secondary effects commonly seen after an ACL injury include defective neuromuscular function with reduced strength and functional performance, a different movement and activation pattern, defective proprioception and impaired postural control [1]. Impaired postural control has been reported after acute [2], and chronic ACL injury [3-5], as well as after ACL reconstruction [6-8]. Higher amplitude values [2-5] and longer reaction time when subjected to perturbations [4,6,7] have been observed among patients compared to controls. Studies have also shown that patients with better subjective function have lower amplitude values [[8], Ageberg E, Roberts D, Holmström E, Fridén T: Balance in single-limb stance in individuals with anterior cruciate ligament injury – relation to knee laxity, proprioception, muscle strength, and subjective function. Manuscript submitted]. The present study was initiated by the clinical knowledge that although patients with ACL injury have had extensive neuromuscular training and function well during daily life and (modified) physical activities, they experience a decreased ability to maintain balance during weight-bearing on the injured leg in demanding situations while fatigued. This may be related to an increased risk of further injuries.
Fatigue is caused by a combination of different physiological mechanisms occurring at both the central and peripheral levels [9], affecting afferent neuromuscular pathways, observed as proprioceptive deficiency [10-12], and efferent neuromuscular pathways, seen, for example, as a delay in muscle response [13,14]. Thus, muscular fatigue leads to a decline in work performance, which may also include effects on postural control. A decreased ability to maintain balance in bilateral stance [15-17], and single-limb stance [15,18-20] after fatiguing exercise (i.e., higher values after exercise) has been reported in uninjured subjects, and it has been suggested that individuals are therefore at increased risk of injury when fatigued [15,19,20]. Studies of balance in single-limb stance are of importance and of interest since these movement patterns resemble the stance phase, and since many knee injuries occur during weight-bearing on one leg [21].
To our knowledge, no studies evaluating the effect of fatigue on postural control in subjects with ACL injury have been reported. The main purposes of this study were: 1) to examine the effect of short-duration, sub-maximal exercise performed on a cycle ergometer, on postural control, measured by stabilometry in single-limb stance on a force platform, in individuals with ACL injury in comparison with that of a control group of uninjured subjects, and 2) to explore the patients' instantaneous ability to maintain balance in single-limb stance after exercise, in comparison with that of the control group. Furthermore, the patients were compared to the control group in order to verify previous findings that postural control is affected in both legs by a unilateral ACL injury [2-5]. No comparisons were, therefore, made between the injured and uninjured legs. Our hypothesis was that the patients with ACL injury would be more affected by exercise than the uninjured subjects, since fatigue has been shown to reduce postural control in healthy subjects, and since postural control may already be impaired due to the injury.
Methods
Patients
Thirty-six patients (18 men and 18 women) were included in the study. Inclusion criteria were: 1) age between 15 and 35 years, 2) unilateral, non-operated, non-acute ACL deficiency with or without associated lesions of other structures of the knee, 3) an uninjured contralateral extremity, back and neck, and 4) no history of neurological disease, vestibular or visual disturbance. Their mean age was 26 years (SD 5 years), mean height 174 cm (SD 9 cm), and mean body mass 72 kg (SD 13 kg). Their median activity level before injury was 6.5 (range 3 to 9) and on the test occasion 4 (range 1 to 9) according to the Tegner activity level scale [22]. The mean time elapsed from injury to the test occasion was 3.8 years (SD 3, range 0.5 to 11 years). The patients had undergone an extensive neuromuscular training program [23] after the injury under the supervision of physical therapists, with a mean duration of 7 months (SD 5 months). A visual analog scale graded from 0 to 100 mm was used for subjective evaluation of extremity function, where 0 was "as if the knee had been recently injured" and 100 was "perfect" [24]. The patients' mean value and median value on this scale were 68 mm and 59 mm (range 12–95 mm), respectively.
Control group
The measurements of twenty-four uninjured volunteers (11 men and 13 women) from a previous study [18], with no history of neurological disease, major orthopedic lesion, vestibular or visual disturbance, constituted control values. Their mean age was 24 years (SD 3 years), mean height 176 cm (SD 8 cm), and mean body mass 71 kg (SD 13 kg). Their median activity level was 5 (range 2 to 9) according to the Tegner activity level scale [22]. The subjects in the control group were chosen in order to have the same distribution in age, sex, and physical activity as the patients [25]. No significant difference was found between the groups in age, height, body mass or activity level. The Research Ethics Committee at Lund University approved the study. All subjects gave their written informed consent to participate in the study.
Assessment
Stabilometry
Balance in single-limb stance was tested by means of a strain gauge force plate (33 × 38 cm) with the subject barefoot in a standardized position [5,26,27] (Figure 1). This measurement was performed before (pre-exercise) and immediately after fatiguing exercise (post-exercise). The foot was placed pointing straight forward in relation to reference lines in the frontal and sagittal planes (origin of coordinates). The other leg was flexed 90° at the hip and knee joints with both arms hanging relaxed at the sides. The subjects were instructed to stand as motionless as possible, looking straight ahead at a point on the wall 65 cm away; they were allowed to practice maintaining this position for about 20 s before three measurements on each leg were made, with the subjects standing alternately on their right and left leg. The test order between legs was randomized regarding injured/uninjured leg in the patient group (injured leg n = 20, uninjured n = 16), and regarding right/left leg in the control group (right leg n = 13, left n = 11). No differences were observed in the stabilometric measurements between these randomization groups. Accordingly, the assessment included three measurements made on each leg, giving a total of six measurements pre- and post-exercise, respectively. These six measurements lasted for approximately 3 1/2 minutes, with about 10 seconds between each measure. The median value of the three measurements on each leg was used to compare pre- and post-exercise values. Decreasing values in the three measurements have been observed in a previous study, indicating a learning effect [26]. Some degree of recovery may, therefore, occur during the three post-exercise measurements. For this reason, the first and third of the three post-exercise measurements on each leg were used, to evaluate the instantaneous value of the ability to maintain postural control (first measurement) and the possible recovery (third measurement). Movements of the center of pressure (CP) in the frontal plane (FP) and sagittal plane (SP) were recorded for 25 s at a sampling frequency of 20 Hz. A computer program (Viewdac 2.1, Keithley Instruments, Inc., Cleveland, Ohio, USA), was used to analyze the following variables: 1) average speed of CP movements in mm·s-1; and 2) number of movements exceeding 10 mm from the mean value of CP (DEV 10), giving a total of four variables (two variables in each plane). The mean value of CP is the average distance (mm) of the CP from the reference lines, and DEV 10 is the number of movements exceeding 10 mm from the mean value of CP. DEV 10 (n) reflects the deviation of CP (i.e., displacement of CP), and the average speed (mm·s-1) reflects the amplitude and frequency of CP movements. Figure 2 shows raw data from a stabilometry test. Average speed and DEV 10 were used in the present study, since our previous studies have shown that these variables are reliable [18,26], and sensitive in detecting differences between patients and uninjured subjects [2], and sensitive in detecting the effects of exercise [18]. We expected to find higher values after exercise [18].
Short-duration sub-maximal exercise
Short-duration, sub-maximal exercise was performed on a cycle ergometer. The subjects' heart rate was continuously recorded during the entire test. Borg's scale for Rating of Perceived Exertion (RPE scale) was used to assess the subjective effort level during exercise [28]. On this scale, numbers ranging from 6 to 20 are matched with descriptors (e.g., 6 = No exertion at all, 13 = Somewhat hard, 15 = Hard, 17 = Very hard, 19 = Extremely hard, and 20 = Maximal exertion). The RPE scale was designed to increase linearly with exercise intensity and heart rate for work on a bicycle ergometer, and correlates closely with several physiological variables, including heart rate and blood lactate concentration [28]. A linear relationship exists between heart rate and oxygen consumption with increasing rate of work. A given percentage of the maximum oxygen consumption (VO2max) results in a higher percentage of the maximum heart rate (HRmax); e.g., 75% of VO2max represents an intensity of 86% of HRmax [29]. The maximum heart rate can be estimated from the following equation: maximum heart rate (beats/min) = 220 – age (years) [29]. Effects of fatigue are likely to occur after a few minutes of sub-maximal exercise [9].
The rate of pedaling was kept constant at 60 revs/min. The level of exercise was calculated so as to be similar to that perceived during a general exercise session. The workload (W) was set individually, depending on the sex and physical condition of each subject, with the aim of reaching a heart rate above 60% of the predicted HRmax [30] in all subjects. Cycling was stopped when the subjects had reached a heart rate exceeding 60% of the predicted HRmax, perceived the exercise as hard or very hard (values 14–17 of the RPE scale), and had reached steady-state heart rate, i.e., after approximately 5 min.
Statistical analysis
The average of the right and left legs; i.e., (right+left)/2, was used for statistical analysis in the control group, since there were no clinically or statistically significant differences between the legs. The use of the mean value of both legs when performing parametric statistics can be questioned, since this may affect the data variability. It cannot, however, be excluded that a dominance of one or the other side exists, which is difficult to define [25], and therefore it is hard to determine which leg to use in comparison with the patients. For this reason we used the average of the right and left legs. However, the results were confirmed using the right and left legs separately as the control leg. The median value of the three measurements was used to compare pre- and post-exercise values. In addition, the first and third of the three post-exercise measurements were compared, to evaluate the instantaneous ability to maintain postural control in single-limb stance (first measurement) and the possible recovery (third measurement). We used the paired t-test for the intra-group comparisons, and the independent t-test for the inter-group comparisons, with Bonferroni correction for multiple comparisons. The present study is of exploratory character, and the level of correction for multiple comparisons was chosen with regard to this. For each stabilometric variable, five separate t-tests were performed in comparisons between pre- and post-exercise values for the injured leg and the control group: 1) injured leg pre-exercise vs. post-exercise, 2) control group pre-exercise vs. post-exercise, 3) injured leg vs. control group pre-exercise, 4) injured leg vs. control group post-exercise, and 5) effects of exercise (post-exercise minus pre-exercise) injured leg vs. control group. These five t-test were also performed in the analysis of possible differences between pre- and post-exercise values for the uninjured leg and the control group. Since five comparisons were made in the above-mentioned analyses, the alpha level was set at 0.05/5 = 0.01. For each stabilometric variable, three separate t-tests were performed in comparisons between post-exercise measurements 1 and 3 for the injured leg and the control: 1) injured leg measurement 1 vs. measurement 3, 2) control group measurement 1 vs. measurement 3, and 3) effects of exercise (measurement 3 minus measurement 1) injured leg vs. control group. These three t-tests were also performed in the analysis of possible differences between post-exercise measurements 1 and 3 for the uninjured leg and the control group. Since three comparisons were made in the above-mentioned analyses, the alpha level was set at 0.05/3 = 0.02. The statistical analyses were performed using the program package SPSS 11.0 (SPSS Inc., Chicago, Illinois, USA).
Results
Fatiguing exercise
All subjects exceeded the 60% value of the predicted HRmax; the mean level being 82% (SD 6%, range 66 to 92%) among the patients and 81% (SD 7%, range 68 to 99%) among the controls. The median power output produced by the patients and the control group at the end of fatiguing exercise was 125 W (range 75 to 200 W) and 150 W (range 100 to 200 W), respectively, and the mean value of perceived exertion, rated according to the RPE scale, was 15.8 (SD 1.1) and 15.4 (SD 0.9), respectively. The final heart rate attained among the patients and the control group was 159 beats/min (SD 11 beats/min) and 159 beats/min (14 beats/min), respectively, and the heart rate after the stabilometric assessment, approximately 3 1/2 minutes after exercise, was 112 beats/min (SD 14 beats/min) and 117 beats/min (SD 16 beats/min), respectively. No significant differences were found between the patients and controls with regard to the above-mentioned variables.
Average speed of CP movements
Higher values were noted post- than pre-exercise in the FP and SP in the injured and uninjured legs, but only in the FP in the control group (Table 1). No differences were noted between the groups regarding the effects of exercise (mean difference of post-exercise minus pre-exercise values) (Table 2). Figures 3 and 4 show the pre-and post-exercise values for the injured leg and the control group.
A lower value was observed in the third than in the first of the post-exercise measurements on the injured leg in the FP, but no differences were noted on the uninjured leg or in the control group (Table 3). The injured leg of the patients was more affected by exercise directly after cycling than the legs of the control group in the FP (Table 4). Figures 7 and 8 show the first and third of the post-exercise measurements on the injured leg and in the control group.
Lower values were observed pre-exercise in the SP in the injured and uninjured legs of the patients than in the control group (Table 5).
Number of movements exceeding 10 mm from the mean value of CP
A higher value was found post- than pre-exercise in the uninjured leg in the FP, and the post-exercise value tended to be higher in the injured leg and in the control group (Table 1). No differences were found between pre- and post-exercise values in the SP (Table 1), or between the groups regarding the effects of exercise (mean difference of post-exercise minus pre-exercise values) (Table 2). Figures 5 and 6 show the pre-and post-exercise values for the injured leg and the control group.
The third of the post-exercise measurements was lower than the first in the injured leg in both planes, but no differences were found for the uninjured leg or in the control group (Table 3). No differences were noted between the groups regarding the effects of exercise directly after cycling (Table 4). Figures 9 and 10 show the first and third of the post-exercise measurements on the injured leg and in the control group.
No differences were found between the injured leg and the control group, or between the uninjured leg and the control group (Table 5).
Discussion
Short-duration, sub-maximal exercise on a cycle ergometer resulted in increased average speed in both planes, and in the amplitude of CP movements (DEV 10) in the FP during balance in single-limb stance among the patients with ACL injury. In the intra-group comparisons, three of four variables showed higher values post- than pre-exercise in the uninjured leg, and two of four variables were higher post-exercise in the injured leg. In the control group, one of four variables was higher post- than pre-exercise (Table 1). However, no differences in the effects of fatigue (mean difference of post-exercise minus pre-exercise values) were found in the inter-group comparisons (Table 2, and Figures 3, 4, 5, 6). The variables were more sensitive in detecting the effects of exercise in the FP than in the SP. The primary motions of the knee joint occur in the SP, and the joint has limited capacity to make postural adjustments in the FP due to anatomical constraints, whereas the hip joint and ankle are involved in postural corrections in both the FP and SP during weight-bearing [31]. Since many injuries to the knee occur during weight-bearing on one leg [21]; i.e., in a closed kinetic chain including the hip joint and ankle, it is of interest to examine postural control in both the FP and SP in individuals with ACL injury. The results of a previous study [18] and the present one indicate that measurements in the FP may be more sensitive and revealing in detecting effects of exercise than measurements in the SP.
It has been demonstrated that afferent information has an effect on the neuromuscular function of both the ipsilateral and contralateral limb muscles [32], which may explain why more variables were higher post- than pre-exercise not only in the injured leg, but also in the uninjured one, than in the control group. Several studies have reported bilateral defects in postural control after an ACL injury or reconstruction [2-7], which may be due to central nervous system modifications following the loss of knee mechanoreceptors after the injury [33,34]. Another explanation may be that the patients had inherently poor balance, which might have contributed to the original injury. This has been reported by Tropp et al. [35], where soccer players with abnormal stabilometric values (defined as a value exceeding 2 SD of the mean value in a control group), ran a higher risk of sustaining an ankle injury than players with normal values.
In a previous study [26], we observed decreasing values in the three measurements, indicating a learning effect. In another study [36], fatigue was shown to interfere with this learning process, which is in agreement with the results that we found on the uninjured leg and in the control group. However, the injured leg reacted differently from the uninjured one, and the control group when the first and third of the post-exercise measurements were compared. It was assumed that the first measurement could provide us with the instantaneous value of the ability to maintain postural control in single-limb stance. The results showed that the third measurement was lower, or tended to be lower, than the first in the injured leg, regarding average speed and DEV 10 in both planes. No such effect was, however, found in the uninjured leg or in the control group (Table 3). The inter-group comparisons for these post-exercise measurements showed greater effects in the injured leg than in the control group in average speed in the FP, and a tendency towards greater effects in the other three variables (Table 4 and Figures 7, 8, 9, 10). This finding indicates that balance standing on one leg may be improved during the recovery period, and that a learning process may be needed in the injured leg after exercise. A different strategy in the injured leg than in the uninjured one has been reported in individuals with ACL injury [37]. In that study, Di Fabio et al. [37], found that postural responses, measured with external perturbations while standing on a force platform, could be unilaterally restructured and preprogrammed to compensate for the injury.
Mechanoreceptors in the ACL contribute to the neuromuscular control of the muscle tonus around the knee joint via the reflex arc (i.e., reflex from joint afferents to the muscle spindles via the gamma motoneurons), and therefore to the stabilization of the knee joint [32]. Decreased proprioception [11,12], increased joint laxity in the knee joint [14,38], and a delay in muscle response in leg muscles [13,14] have been described after fatiguing exercise. In these studies, uninjured subjects were tested. The activity of joint receptors, muscle spindles and Golgi tendon organs may be reduced by fatigue, resulting in proprioceptive deficiency in muscle receptors and loss of muscular reflexes responsible for joint stability [10]. Since this afferent information is important for the maintenance of postural control [32], this may lead to decreased muscle response and poorer ability to maintain balance. The increase in joint laxity following fatigue has been suggested to be due to reduced muscle tone [38], viscoelastic changes in the collagenous tissues of the knee and fatigued muscle stabilizers [14], and results in inadequate ligament mechanoreceptor feedback, which is required to elicit the muscular reflexes responsible for joint stability [10]. It has been suggested that muscle receptors are the primary determinant of joint position sense, and capsular receptors may have a secondary role [12,32]. Therefore, the decreased proprioceptive ability following fatigue has been proposed to be due to the decrease in muscle receptor activity [11,12]. Since defects in proprioception [39], impaired postural control [2-5], increased joint laxity [32], and a delay in muscle reaction time [4,40,41] are present already in an unfatigued state in individuals with ACL injury, they may, at least theoretically, be more affected by fatigue than uninjured subjects. Although we found effects of exercise after a short period of cycling above 60% of the predicted HRmax, it is possible that greater effects of exercise on balance in single-limb stance may be seen after longer durations of exercise than in the present study. It is also possible that larger effects of exercise may be reflected in more challenging measures of postural control, such as dynamic balance tests. Since, to our knowledge, this is the first study on the effects of fatigue on postural control in patients with ACL injury, the clinical relevance of our results remains unclear. More research is needed to further study whether this may be related to an increased risk of further injuries.
The lower average speed and lack of difference in DEV 10 in the patients compared to the control group, indicate sway movements at a lower speed with retained amplitudes to be neuromuscular adaptive strategies, rather than more rapid, smaller adjustments (Table 5). These strategies may be the result of decreased proprioception [39], and a delay in muscle reaction time [4,40,41], which has been reported after an ACL injury, and thus, these strategies may be needed to generate sufficient afferent impulses to obtain dynamic stabilization of the knee joint. Another possible explanation may be that the patients had all undergone neuromuscular training, which may have affected the strategies of maintaining balance in single-limb stance compared with the control group who had not undergone such training. The clinical relevance of the fact that the patients' post-exercise values approached those of the control group, remains, however, unclear. More research is needed to elucidate this further.
Conclusions
The results of the present study showed no differences in the effects of exercise between the patients and the controls. However, the injured leg was more affected or tended to be more affected directly after exercise than the control group, which indicates that patients with ACL injury react differently regarding their ability to maintain balance in single-limb stance after short-duration, sub-maximal cycling, than a control group of uninjured subjects. The patients used sway movements at a lower speed with retained amplitudes, which may be an expression of neuromuscular adaptive strategies.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
EA participated in the design of the study, participated in collecting the data, performed the statistical analysis, and drafted the manuscript. DR participated in collecting the data. EH participated in the progress and revision of the manuscript. TF participated in the design of the study, and in the progress and revision of the manuscript. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We would like to thank all the subjects who volunteered for this study, Per-Erik Isberg at the Department of Statistics, Lund University for statistical advice, the Swedish Foundation for Health Care Sciences and Allergy Research, the Swedish Rheumatism Association, "Vårdrådet" at Lund University Hospital, "Centrum för Idrottsforskning", and the Faculty of Medicine, Lund University.
Figures and Tables
Figure 1 Stabilometry in single-limb stance, tested by means of a strain gauge force plate. A standardized position was used. The subject is a model who did not participate in the study. Reprinted from Ageberg E, Zätterström R, Moritz U, Fridén T. Influence of supervised and nonsupervised training on postural control after an acute anterior cruciate ligament rupture: A 3-year longitudinal prospective study. Journal of Orthopaedic & Sports Physical Therapy. 2001:31: 632–644, with permission of the Orthopaedic and Sports Sections of the American Physical Therapy Association.
Figure 2 Raw data from a stabilometry trial. A measurement in the frontal plane for the right leg in a subject who did not participate in the study. The reference line, the mean value of center of pressure (CP), and movements of the CP are shown in the Figure. In this trial, the average speed was 20 mm·s-1 and the number of DEV 10 was 5.
Figure 3 Pre- and post-exercise values. Average speed (mm·s-1) in the frontal plane (FP) pre- and post-exercise, for the injured leg of the patients and the uninjured legs of the control group. The circles denote outliers (i.e., cases with values between 1.5 and 3 box lengths from the upper or lower edge of the box).
Figure 4 Pre- and post-exercise values. Average speed (mm·s-1) in the sagittal plane (SP) pre- and post-exercise, for the injured leg of the patients and the uninjured legs of the control group. The asterisks denote extreme values (i.e., cases with values more than 3 box lengths from the upper or lower edge of the box).
Figure 5 Pre- and post-exercise values. DEV 10 (n) in the frontal plane (FP) pre- and post-exercise, for the injured leg of the patients and the uninjured legs of the control group. The circles denote outliers (i.e., cases with values between 1.5 and 3 box lengths from the upper or lower edge of the box), and the asterisks denote extreme values (i.e., cases with values more than 3 box lengths from the upper or lower edge of the box).
Figure 6 Pre- and post-exercise values. DEV 10 (n) in the sagittal plane (SP) pre- and post-exercise, for the injured leg of the patients and the uninjured legs of the control group. The circles denote outliers (i.e., cases with values between 1.5 and 3 box lengths from the upper or lower edge of the box), and the asterisks denote extreme values (i.e., cases with values more than 3 box lengths from the upper or lower edge of the box).
Figure 7 Post-exercise measurements 1 and 3. Average speed (mm·s-1) in the frontal plane (FP) post-exercise measurements 1 and 3, for the injured leg of the patients and the uninjured legs of the control group. The circles denote outliers (i.e., cases with values between 1.5 and 3 box lengths from the upper or lower edge of the box).
Figure 8 Post-exercise measurements 1 and 3. Average speed (mm·s-1) in the sagittal plane (SP) post-exercise measurements 1 and 3, for the injured leg of the patients and the uninjured legs of the control group. The asterisks denote extreme values (i.e., cases with values more than 3 box lengths from the upper or lower edge of the box).
Figure 9 Post-exercise measurements 1 and 3. DEV 10 (n) in the frontal plane (FP) post-exercise measurements 1 and 3, for the injured leg of the patients and the uninjured legs of the control group. The circles denote outliers (i.e., cases with values between 1.5 and 3 box lengths from the upper or lower edge of the box), and the asterisks denote extreme values (i.e., cases with values more than 3 box lengths from the upper or lower edge of the box).
Figure 10 Post-exercise measurements 1 and 3. DEV 10 (n) in the sagittal plane (SP) post-exercise measurements 1 and 3, for the injured leg of the patients and the uninjured legs of the control group. The circles denote outliers (i.e., cases with values between 1.5 and 3 box lengths from the upper or lower edge of the box), and the asterisks denote extreme values (i.e., cases with values more than 3 box lengths from the upper or lower edge of the box).
Table 1 Pre- and post-exercise values for the stabilometric variables.
Patients Control group
Injured leg Uninjured leg
Pre-exercise Mean (SD) Post-exercise Mean (SD) Mean diff.
(99% CI) P-value Pre-exercise Mean (SD) Post-exercise Mean (SD) Mean diff.
(99% CI) P-value Pre-exercise Mean (SD) Post-exercise Mean (SD) Mean diff.
(99% CI) P-value
Frontal plane
Average speed (mm·s-1) 20.8 (5.3) 22.6 (5.7) 1.8 (0.6–3.1) 0.0003 21.4 (5.8) 23.2 (5.6) 1.8 (0.7–2.9) 0.0001 23.2 (5.2) 24.4 (5.2) 1.2 (-0.0–2.5) 0.01
DEV 10 (n) 3.5 (2.6) 4.4 (2.7) 0.9 (-0.3–2.0) 0.048 3.4 (2.1) 4.7 (2.6) 1.3 (0.6–2.1) 0.00002 3.5 (1.8) 4.4 (2.3) 0.9 (-0.2–2.0) 0.03
Sagittal plane
Average speed (mm·s-1) 20.3 (5.4) 22.8 (6.6) 2.5 (1.1–3.8) 0.00002 20.1 (4.4) 22.6 (4.9) 2.5 (1.1–3.8) 0.00002 24.0 (5.0) 25.2 (6.2) 1.2 (-0.7–3.0) 0.09
DEV 10 (n) 6.0 (3.0) 6.0 (2.8) 0.0 (-1.0–1.1) 0.94 5.4 (2.1) 5.7 (2.2) 0.3 (-0.4–1.1) 0.23 6.5 (2.5) 6.6 (2.5) 0.1 (-0.7–0.9) 0.78
Mean (SD), mean difference (99% CI) (post-exercise minus pre-exercise), and P-value for stabilometric variables in the injured and uninjured legs, and in the control group before (pre-exercise) and after (post-exercise) short-duration sub-maximal cycling. A level of P < 0.01 indicates statistical significance.
Table 2 Effects of exercise (post-exercise minus pre-exercise) patients vs. controls.
Injured leg vs. control group Uninjured leg vs. control group
Mean diff. (99% CI) P-value Mean diff. (99% CI) P-value
Frontal plane
Average speed (mm·s-1) 0.6 (-1.2–2.4) 0.37 0.6 (-1.0–2.2) 0.33
DEV 10 (n) -0.03 (-1.6–1.6) 0.95 0.4 (-0.8–1.6) 0.34
Sagittal plane
Average speed (mm·s-1) 1.3 (-0.9–3.4) 0.12 1.3 (-0.9–3.5) 0.12
DEV 10 (n) -0.06 (-1.5–1.4) 0.92 0.2 (-0.8–1.3) 0.54
Mean difference (99% CI) (post-exercise minus pre-exercise), and P-value for stabilometric variables for the injured leg vs. control group, and the uninjured leg vs. control group. A level of P < 0.01 indicates statistical significanc
Table 3 First and third post-exercise measurements for the stabilometric variables.
Patients Control group
Injured leg Uninjured leg
Meas. 1 Mean (SD) Meas. 3 Mean (SD) Mean diff. (98% CI) P-value Meas. 1 Mean (SD) Meas. 3 Mean (SD) Mean diff. (98% CI) P-value Meas. 1 Mean (SD) Meas. 3 Mean (SD) Mean diff. (98% CI) P-value
Frontal plane
Average speed (mm·s-1) 24.6 (7.6) 21.7 (5.5) -2.9 (-5.0–
-0.9) 0.001 24.8 (6.2) 23.8 (6.6) -1.0 (-2.7–0.7) 0.15 24.8 (6.2) 24.3 (5.5) -0.5 (-1.8–0.7) 0.25
DEV 10 (n) 5.7 (3.8) 4.0 (2.5) -1.7 (-3.2–-0.4) 0.004 5.7 (3.7) 4.5 (3.1) -1.2 (-2.6–0.3) 0.06 4.8 (2.6) 4.5 (2.7) -0.3 (-1.2–0.4) 0.24
Sagittal plane
Average speed (mm·s-1) 23.8 (6.9) 22.3 (6.5) -1.5 (-2.9–-0.1) 0.03 23.1 (5.4) 22.9 (5.8) -0.2 (-1.7–1.3) 0.72 25.3 (6.4) 25.0 (6.2) -0.3 (-1.3–0.8) 0.52
DEV 10 (n) 6.6 (2.8) 5.6 (3.8) -1.0 (-2.0–-0.0) 0.02 5.7 (2.3) 6.1 (3.1) 0.4 (-0.9–1.7) 0.47 6.8 (2.7) 6.9 (3.2) 0.1 (-1.1–1.5) 0.75
Mean (SD), mean difference (98% CI) (measurement 3 minus measurement 1), and P-value for the first and third of the post-exercise measurements for the stabilometric variables in the injured and uninjured legs and in the control group. A level of P < 0.02 indicates statistical significance.
Table 4 Effects of exercise (measurement 3 minus measurement 1) patients vs. controls.
Injured leg vs. control group Uninjured leg vs. control group
Mean diff. (98% CI) P-value Mean diff. (98% CI) P-value
Frontal plane
Average speed (mm·s-1) -2.4 (-4.7–-0.0) 0.02 -0.5 (-2.4–1.6) 0.62
DEV 10 (n) -1.4 (-3.0–0.2) 0.04 -0.8 (-2.4–0.8) 0.25
Sagittal plane
Average speed (mm·s-1) -1.2 (-2.9–0.6) 0.13 0.05 (-1.8–1.8) 0.95
DEV 10 (n) -1.1 (-2.7–0.4) 0.08 0.2 (-1.6–2.1) 0.78
Mean difference (98% CI) (measurement 3 minus measurement 1), and P-value for stabilometric variables the injured leg vs. control group, and the uninjured leg vs. control group. A level of P < 0.02 indicates statistical significance.
Table 5 Patients vs. control group.
Pre-exercise Post-exercise
Injured leg Control group Uninjured leg Injured leg Control group Uninjured leg
Mean
(SD) P-value
(inj vs.
control) Mean
(SD) P-value
(uninj vs.
control) Mean
(SD) Mean
(SD) P-value
(inj vs.
control) Mean
(SD) P-value
(uninj vs.
control) Mean
(SD)
Frontal plane
Average speed (mm·s-1) 20.8
(5.3) 0.09 23.2
(5.2) 0.23 21.4
(5.8) 22.6
(5.7) 0.21 24.4
(5.2) 0.41 23.2
(5.6)
DEV 10 (n) 3.5
(2.6) 0.97 3.5
(1.8) 0.87 3.4
(2.1) 4.4
(2.7) 0.98 4.4
(2.3) 0.60 4.7
(2.6)
Sagittal plane
Average speed (mm·s-1) 20.3
(5.4) 0.009 24.0
(5.0) 0.002 20.1
(4.4) 22.8
(6.6) 0.16 25.2
(6.2) 0.07 22.6
(4.9)
DEV 10 (n) 6.0
(3.0) 0.51 6.5
(2.5) 0.06 5.4
(2.1) 6.0
(2.8) 0.44 6.6
(2.5) 0.15 5.7
(2.2)
Mean (SD) pre-exercise and post-exercise for stabilometric variables in the injured (inj) and uninjured (uninj) legs, and in the control group, and P-value for the injured leg vs. control group, and the uninjured leg vs. control group. A level of P < 0.01 indicates statistical significance.
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| 15548328 | PMC544556 | CC BY | 2021-01-04 16:03:42 | no | BMC Musculoskelet Disord. 2004 Nov 17; 5:44 | utf-8 | BMC Musculoskelet Disord | 2,004 | 10.1186/1471-2474-5-44 | oa_comm |
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BMC BiochemBMC Biochemistry1471-2091BioMed Central London 1471-2091-5-181560359210.1186/1471-2091-5-18Research ArticleIdentification of α-type subunits of the Xenopus 20S proteasome and analysis of their changes during the meiotic cell cycle Wakata Yuka [email protected] Mika [email protected] Ryo [email protected] Katsutoshi [email protected] Yoshitaka [email protected] Toshinobu [email protected] Department of Biology and Geosciences, Faculty of Science, National University Corporation Shizuoka University, Shizuoka 422-8529, Japan2 CREST Research Project, Japan Science and Technology Corporation, Japan3 Laboratory of Reproductive Biology, National Institute for Basic Biology, Okazaki 444-8585, Japan2004 17 12 2004 5 18 18 21 9 2004 17 12 2004 Copyright © 2004 Wakata et al; licensee BioMed Central Ltd.2004Wakata et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The 26S proteasome is the proteolytic machinery of the ubiquitin-dependent proteolytic system responsible for most of the regulated intracellular protein degradation in eukaryotic cells. Previously, we demonstrated meiotic cell cycle dependent phosphorylation of α4 subunit of the 26S proteasome. In this study, we analyzed the changes in the spotting pattern separated by 2-D gel electrophoresis of α subunits during Xenopus oocyte maturation.
Results
We identified cDNA for three α-type subunits (α1, α5 and α6) of Xenopus, then prepared antibodies specific for five subunits (α1, α3, α5, α6, and α7). With these antibodies and previously described monoclonal antibodies for subunits α2 and α4, modifications to all α-type subunits of the 26S proteasome during Xenopus meiotic maturation were examined by 2D-PAGE. More than one spot for all subunits except α7 was identified. Immunoblot analysis of 26S proteasomes purified from immature and mature oocytes showed a difference in the blots of α2 and α4, with an additional spot detected in the 26S proteasome from immature oocytes (in G2-phase).
Conclusions
Six of α-type subunits of the Xenopus 26S proteasome are modified in Xenopus immature oocytes and two subunits (α2 and α4) are modified meiotic cell cycle-dependently.
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Background
Eukaryotic cells, from yeast to human, contain large nonlysosomal proteases called proteasomes [1]. The 26S proteasome is part of the ubiquitin-dependent proteolytic system, which regulates proteins through a mechanism of selective degradation [2-4]. The 26S proteasome is composed of a 20S proteasome as a catalytic core and regulatory particles at either end. The subunits of the 20S proteasome subunits can be classified into two families, α and β. In eukaryotes, the 20S proteasome contains seven α-type subunits and seven β-type subunits. The fourteen kinds of subunits are arranged in four rings of seven subunits and form an α7β7β7α7 structure [5].
Fully grown frog oocytes arrest in the late G2 phase of meiosis. Maturation-inducing hormone (MIH) acts on the oocytes, inducing final maturation and triggering germinal vesicle breakdown (GVBD), and the oocytes arrest again at the second meiotic metaphase until fertilization. The proteasomes are thought to be involved in regulating the maturation and fertilization of oocytes [6,7]. Previously we identified the proteasomal subunit modified during oocyte maturation in Xenopus and goldfish as α 4 [8,9]. In the present study, we cloned three unidentified α-type subunits of Xenopus and prepared antibodies for a total of five subunits. Using a set of specific antibodies, we analyzed changes in all α subunits composing the 26S proteasome during the meiotic cell cycle. We demonstrated that 6 of the subunits exist as a heterogeneous population in frog oocytes and identified another subunit in addition to α4 which was modified meiotic cell cycle dependently.
Results and discussion
Isolation and characterization of cDNA clones
A BLAST search of the Xenopus EST database was conducted using known proteasomal subunit α cDNAs. From the data for each subunit, full-length ORFs were obtained by PCR. The amplified cDNAs were 741, 726 and 786 bp long. The clones encode proteins of 246, 241 and 261 amino acid residues with a predicted molecular mass of 27463, 26402 and 29327 daltons, respectively (Fig. 1). Comparison of the amino acid sequence revealed that these molecules are highly homologous to the α1, α5 and α6 subunits in humans (overall identity 91.5–95.4%) [10,11], Drosophila (53.2–69.1%) [12,13] and yeast (53.2–61.7%) [14-16] (Fig. 2). Thus, we concluded that the cDNAs isolated in this study encode the α1, α5 and α6 subunits of the Xenopus 20S proteasome. We named these clones α1_xl, α5_xl and α6_xl (α1, α5 and α6 subunits of Xenopus laevis) according to a systematic nomenclature [5]. Figure 2 represents a comparison of amino acid sequences predicted from cDNA sequences of α-type subunits of the Xenopus 20S proteasome. Overall identity between the subunits was 25.1–38.4 %. A consensus sequence for α-type proteasomal subunits was conserved. Interestingly, a conserved sequence for β-type proteasomal subunits was found in the α3 subunit [17].
Figure 1 Amino acid sequence comparison of the Xenopus, human, Drosophila, and Yeast α1, α5 and α6 proteasome subunits. Amino acid sequence comparisons of α1 (A), α5 (B) and α6 (C) proteasome subunits are indicated. Matched sequences are boxed. Consensus sequences for calcium/calmodulin-dependent kinase II (CaMKII), cAMP/cGMP-dependent kinase (cAMP/cGMP), casein kinase II (CKII) and Ca2+-dependent kinase (PKC) are indicated. The numbers refer to the amino acid position at the end of each line.
Figure 2 Amino acid sequence comparison of the Xenopus proteasomal α subunits. Matched sequences are boxed. The proteasomal α-type and β-type signatures were detemined by using the 'PROSITE' database [17] and are boxed. The numbers refer to the amino acid position at the end of each line.
Comparison of proteasomes purified from immature and mature oocytes
Polyclonal antibodies specific for five subunits (α1, α3, α5, α6, and α7) were raised against purified recombinant proteins. The specificity of the antibodies was examined by immunoblotting with the cytosol fraction and the purified 26S proteasome (Fig. 3). Each antibody preparation displayed a specific reaction for different polypeptides in both samples. Recombinant proteins from the cDNAs clearly cross-reacted with each antibody (data not shown). Thus, specific antibodies for each subunit were prepared. With these antibodies and previously described monoclonal antibodies for subunits α2 and α4 [18], changes to all α-type subunits during Xenopus meiotic maturation were analyzed. The modifications were demonstrated by 2D-PAGE (Fig. 4). The α7 subunit antibodies gave a single spot but all of the other antisera produced more than one spot, suggesting that the α1–α6 subunits undergo some type of modification in oocytes of Xenopus as demonstrated in other species [19,20]. A difference in the spots between the 26S proteasome from immature and mature oocytes was detected in the blots of subunits α2 and α4. In blots of α2 and α4, only a major spot was detected in the 26S proteasome from mature oocytes (in M-phase). It is suggested that the α4 subunit is phosphorylated in immature oocytes and dephosphorylated in mature oocytes [8]. Likewise, it is speculated that part of the α2 subunit is phosphorylated in interphase and dephosphorylated in metaphase. These results suggest that the subunits of 26S proteasomes are changed by meiotic cell cycle-dependent modifications. It can be speculated that these modifications are involved in the regulation of the meiotic cell cycle.
Figure 3 Immunoblotting of the cytosol fraction and purified 26S proteasome. The cytosol fraction and purified 26S proteasome were electrophoresed under denaturing conditions (10.0% gel) and stained with Coomassie Brilliant Blue (CBBR), or immunostained with antibodies for α subunits of the 20S proteasome. Lanes cyt and 26S indicate the cytosol fraction and the 26S proteasome from immature oocytes, respectively. Molecular masses of standard proteins are indicated at the left. Protein bands of each subunit are indicated by arrows.
Figure 4 2D-PAGE analysis of 26S proteasomes from immature and mature oocytes. The 26S proteasomes from immature (I) and mature (M) oocytes were subjected to 2D-PAGE followed by immunostaining with polyclonal antibodies against each of the Xenopus 20S proteasome subunits as indicated. The spots detected by each antibody are represented at high magnification and indicated by arrows. The spots differing between immature and mature oocytes are indicated by asterisks.
The modification of proteasomal subunits and factors interacting with proteasomes may be involved in the regulation of proteasome function [21]. By two-dimensional polyacrylamide gel electrophoresis, up to 20 different polypeptides were separated from the 20S proteasome which was shown to be composed of 14 gene products [22]. Furthermore, changes in proteasomal subunit composition under different physiological conditions and the likely existence of a different subpopulation of proteasomes have been reported [12,23]. All these results suggest that the subunit composition of proteasomes, and likely their activity, is under complex control in vivo. Some of these changes may be due to post-translational modifications of the proteasomal subunits. Regarding protein modification, there have been several reports about the phosphorylation of proteasomal subunits. Phosphorylated proteasomal subunits were detected in crude preparations from cultured Drosophila cells [22]. Several subunits of the 20S proteasome could be phosphorylated in vitro by a cyclic AMP-dependent protein kinase copurifying with the bovine pituitary 20S proteasome [24]. Castaño et al. [25] (1996) identified the CKII phosphorylating subunit and its phosphorylation sites as the C8 component (α7 subunit) and serine-243 and serine-250, respectively. CKII was also reported to phosphorylate the C2 component (α6 subunit) in rice [26]. The phosphorylation of subunits in the 26S proteasome in vivo was investigated using cultured human cells. Mason et al. [27] (1996) showed the phosphorylated subunits to be the C8 (α7 subunit) and C9 (α3 subunit) components in the 20S core, and the S4 (Rpt2p) subunit and several other components in regulatory particles [28]. Recent approaches have revealed post-translational modifications to many of the subunits. In the yeast 20S proteasome, the α2- and α4-subunits are phosphorylated at either a serine or threonine residue, and the α7-subunit is phosphorylated at tyrosine residue(s) [20]. In the human 20S proteasome, more than two spots were identified in all α-type subunits except α5 and phosphorylation of the α7-subunit at serine-250 was revealed [19]. However the sites and kinases responsible for the phosphorylation of the α2 and α4 subunits of the 20S proteasome have yet to be demonstrated. The modification of these proteins is one possible mechanism regulating the functions of the 26S proteasome during the meiotic cell cycle. Consensus sequences for phosphorylation sites are conserved in these subunits [8,29]. Cyclic-AMP dependent protein kinase is responsible for the G2/M and metaphase/anaphase transitions [30]. Calcium/calmodulin-dependent protein kinase II is shown to be involved in the exit from metaphase II arrest at fertilization in Xenopus [31]. It can be hypothesized that these kinases are involved in the regulation of 26S proteasome activity. The identification of kinases and the phosphorylation sites of the α2 and α4 subunits may reveal how the modification of proteasomal subunits is involved in controlling the cell cycle. Currently, we have identified one of the protein kinase for α4 subunit as Casein KinaseIα [32]. Possible regulation of 26S proteasome activity by this kinase is under investigation.
Recently, alternative subunits of proteasomes have been identified. In Drosophila where alternative α-type, β-type and 19S cap subunits are expressed from separate genes during spermatogenesis [33] and in Arabidopsis and rice where alternative isoforms of most proteasome subunits are differentially expressed from separate genes during development [34,35]. There are also examples of alternative β-type subunits in mammals (e.g., γ-interferon inducible "immunoproteasome" subunits β1i, β2i and β5i) [36]. Alternative subunits have yet to be identified in Xenopus, there is a possibility that the changes in the spots identified in this study may derive from differential expression of alternative subunits from paralogous genes.
Conclusions
(1) cDNAs for three α-type proteasome subunits (α1_xl, α5_xl and α6_xl) of X. laevis were identified.
(2) Six subunits but not α7_XL are modified in immature oocytes in X. laevis.
(2) α2, α4_XLs are modified during the meiotic cell cycle in X. laevis.
Methods
Purification of proteasomes
Frogs (Xenopus laevis) were purchased from Jo-hoku Seibutsu Kyozai (Shizuoka, Japan) and maintained till used. 26S proteasomes were purified from immature oocytes and ovulated oocytes as described [37].
Electrophoresis and immunoblotting
SDS-PAGE was carried out according to the method of Laemmli [38] (1970). 2D-PAGE (first dimension, NEPHGE; second dimension, SDS-PAGE) was carried out as described by O'Farrell et al. [39] (1977) using a precast polyacrylamide gel for NEPHGE (Immobiline Dry Strip pH3-10NL and pH6-11L for α4 subunit, Amersham biosciences) as reported [8]. Electroblotting and detection using antibodies were conducted as described [18].
cDNA cloning and sequencing
Identification and sequence analysis of cDNAs. A BLAST search of the Xenopus EST database was conducted using known proteasomal subunit α cDNAs. From the data obtained for each subunit, independent sequences were linked and the full-length ORF sequences were eliminated(α1: BG347128 and CB558360, α5: BQ398972 and BJ043946, α6: BJ072624 and BJ091555). The specific primers for amplification of the full-length ORF were α1: 5'-GGAATTCCATATGTCTCGGGGATCTAGCGCG-3' and 5'-CCGCTCGAGGTCACGCTCAGCTAGTGCAAC-3', α5: 5'-GGAATTCCATATGTTCCTAACCCGCTCCGAG-3' and 5'-CCGCTCGAGGATGTCCTTAATAACTTCCTC-3', and α6: 5'-GGAATTCCATATGTTTCGCAATCAGTATG-3' and 5'-CCGCTCGAGGTGCTCCATAGGCTCCTCCTGC-3'), in which EcoRI (5'end) and XhoI (3'end) recognition sequence was added for cloning to the vector pET21a (Novagen). PCR was carried out using KOD DNA polymerase (TOYOBO) or LA taq DNA polymerase (TaKaRa), with Xenopus ovarian cDNA as a template, and the product was cloned to pET21a. The DNA sequencing was performed using a 377A DNA sequencer (Applied Biosystems). The sequences that include the full-length ORF identified here were deposited into GenBank (accession nos. AB164677, AB164678 and AB164679 for α1_xl, α5_xl and α6_xl, respectively). Pairwise comparisons of sequence homology were conducted using the Genetyx-Mac ver.12 computer program (Software Development, Tokyo, Japan).
Production of recombinant proteins and preparation of antibodies
The recombinant proteins were produced in E. coli BL21 (LysE) and purified by SDS-PAGE as described [6]. Polyclonal antibodies specific for each subunit were raised against purified recombinant proteins according to a procedure described before using guinea pigs [40]. Anti serums, which recognize the bands of each subunit, were obtained.
Abbreviations
bp, base pair; BLAST, basic local alignment search tool; cDNA, DNA complementary to RNA; EST, expressed sequence tags; kDa, kilodalton; NEPHGE, non-equilibrium pH gradient gel electrophoresis; PCR, polymerase chain reaction; SDS-PAGE SDS-polyacrylamide gel electrophoresis; 2D-PAGE, two-dimensional-PAGE.
Authors' contributions
YW carried out cDNA cloning, expression of recombinant proteins, antibody production and 2D-PAGE analysis. MT and RH participated in cDNA cloning, expression of recombinant proteins. YN and KI participated in coordination of the study. TT carried out the protein purification and also participated in the design of the study and drafted the manuscript.
Acknowledgements
We thank M. Matsuda for nucleotide sequence analysis. This work was supported by the CREST Research Project of the Japan Science and Technology Corporation to Y.N. and Grants-in-Aid for Scientific Research on Priority Areas from the Ministry of Education, Culture, Sports, Science and Technology of Japan. Part of this study was carried out under the National Institute for Basic Biology Cooperative Research Program (3–107 and 4–110 to T.T). M.T. is a Research Fellow of the Japan Science and Technology Corporation.
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-2041560692010.1186/1471-2105-5-204Research ArticleAn SVD-based comparison of nine whole eukaryotic genomes supports a coelomate rather than ecdysozoan lineage Stuart Gary W [email protected] Michael W [email protected] Department of Life Sciences, Indiana State University, Terre Haute, IN 47809, USA2 Visiting Scientist, Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47405, USA3 Department of Computer Science, University of Tennessee, Knoxville TN 37996-3450, USA2004 17 12 2004 5 204 204 22 7 2004 17 12 2004 Copyright © 2004 Stuart and Berry; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Eukaryotic whole genome sequences are accumulating at an impressive rate. Effective methods for comparing multiple whole eukaryotic genomes on a large scale are needed. Most attempted solutions involve the production of large scale alignments, and many of these require a high stringency pre-screen for putative orthologs in order to reduce the effective size of the dataset and provide a reasonably high but unknown fraction of correctly aligned homologous sites for comparison. As an alternative, highly efficient methods that do not require the pre-alignment of operationally defined orthologs are also being explored.
Results
A non-alignment method based on the Singular Value Decomposition (SVD) was used to compare the predicted protein complement of nine whole eukaryotic genomes ranging from yeast to man. This analysis resulted in the simultaneous identification and definition of a large number of well conserved motifs and gene families, and produced a species tree supporting one of two conflicting hypotheses of metazoan relationships.
Conclusions
Our SVD-based analysis of the entire protein complement of nine whole eukaryotic genomes suggests that highly conserved motifs and gene families can be identified and effectively compared in a single coherent definition space for the easy extraction of gene and species trees. While this occurs without the explicit definition of orthologs or homologous sites, the analysis can provide a basis for these definitions.
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Background
Several methods have been developed for the detailed global comparison of multiple whole genomes and the production of global phylogenies. Most of these methods require the prior identification and selection of a reasonably small subset of putative orthologs within which individual homologous sites are identified with some degree of confidence using alignment [1-7]. Frequently, detailed alignment information is subdivided and compressed into a smaller number of complex characters (such as gene content or gene order), which are then used for quantitative comparison [[4,5]; see [6] for review], but the more or less direct use of large scale sequence alignments have also been attempted [7].
Though generally less developed, many non-alignment methods, considered initially by Blaisdell [8], are currently being explored for a similar purpose [[9-15]; see [17] for review]. Rarely do such methods simultaneously provide 1) detailed and unbiased comparisons of a high fraction of biomolecular sequences within full genome datasets, and 2) globally consistent gene and species trees based on this exhaustive comparison. We have recently developed an SVD-based phylogenetic method that provides accurate comparisons of a high fraction of sequences within whole genomes without the prior identification of orthologs or homologous sites [13]. This method has been successfully applied to a number of diverse genome datasets, including mitochondrial genomes, bacterial genomes, and viral genomes [13-15]. Here we apply this method to a diverse set of nine complete eukaryotic nuclear genomes, resulting in the production of a species tree based on the automatic identification and simultaneous comparison of over 400 conserved amino acid motifs and gene families.
Results and discussion
Proteome data sets and sequence conversion
The nine eukaryotic genomes compared in this analysis are listed in Table 1. The protein sets obtained from NCBI for the malaria parasite (Pfal) and the budding yeast (Scer) each contributed only 3% of the 175,559 total proteins in the dataset, while the proteins for Frub provided nearly 21% of the total. Only the Frub proteins were obtained from the Ensemble Genome Browser [16], since protein predictions for this organism were not available from NCBI. Differences in methods used to predict proteins by these two organizations might be responsible for the large difference in the number of proteins predicted for comparable vertebrate genomes (>37,000 for Frub, but only 21–25,000 for all other vertebrates). These differences could, in principle, drastically effect the gene and species trees derived from a global comparison of all proteins. However, the position of Frub in the final species tree suggests that these effects were relatively minor (see below). We have noted previously that even drastic genome size differences can be accommodated by our method [14].
SVD-derived vector definitions for motifs and gene families
All the proteins in the dataset were recoded as overlapping tetrapeptide frequency vectors and the resulting data matrix was decomposed by the SVD. A total of 437 singular triplets were obtained as output. The "protein" vectors provided in the "right" factor matrix are known to provide reduced dimensional definitions for all proteins in the dataset as linear combinations of the orthogonal "right" singular vectors (rsv's). Conversely, the right singular vectors themselves frequently represent "ideal" versions of proteins defining a given gene family [13,14] Protein vectors having the strongest projections on a given rsv are therefore likely to represent members of a given gene family. In this analysis, the proteins with the five strongest projections (referred to as the "top 5") for each rsv were used to identify and summarize a number of gene families. The total number of proteins from each species that appear in the "top 5" for all 437 right singular vectors are listed in Table 1. Although the fraction of "top 5" proteins identified by the SVD roughly parallels the fraction of total proteins from each species, the mammalian proteins tend to dominate the analysis.
Each right singular vector can potentially define two distinct gene families. In this case, the highest positive elemental values within a vector identify proteins associated with one protein family, while the highest negative values identify proteins associated with an anti-correlated family (i.e. proteins that rarely share the same tetrapeptides). Frequently, however, strong family definitions are provided for just one protein family. In this case, the anti-correlated proteins are seen to be derived from a mixture of two or more families. Since the choice of sign is arbitrary, strong family definitions are equally likely to be provided by either the positive or the negative values within a vector. Family definitions provided by positive vector values are denoted below using the simple vector index (e.g. 277 = the 277th singular vector). Those provided by negative vector values are followed by an "a" (e.g. 277a). Its worth noting at this point that protein family definitions provided by the SVD necessarily account for not only "what is there" (tetrapeptides that form the motifs that define the family), but also "what is not there" (tetrapeptides excluded by that family of proteins, but likely to form anti-correlated motifs within other families of proteins).
Protein family definitions provided by right singular vectors
An abbreviated list of 58 protein families identified within the 437 SVD-derived singular triplets are provided in Table 2. For each listed singular triplet, the gi# of an example protein chosen from among the "top 5" values within the right singular vector is provided, along with its corresponding Name and a Protein Description provided within the NCBI annotation for that protein. In general, proteins described by the more dominant singular triplets were selected for presentation from the complete list of 437 triplets. However, some were chosen due to their historical utility for evolutionary comparisons (ribosomal proteins) and/or their tendency to be accompanied by strongly correlated peptide motifs (last column of Table 2). Relatively few families appear in the table due to the fact that some vectors strongly describe only one family rather than two, some vectors describe only families from species that lack annotation or are poorly annotated at NCBI (i.e. Frub proteins, Agam proteins, etc.), some vectors describe protein families listed by NCBI merely as "unknown" or "conserved unknown", some vectors describe proteins with weakly conserved motifs, and some vectors describe distinct subfamilies of proteins. In the latter case, multiple right singular vectors are apparently required in combination to describe some of the more diverse families of proteins. Included in Table 2 is the number of singular vectors that include the chosen example protein within its "top 5". When multiple vectors are involved in defining multiple related subfamilies, the most "dominant" vector (the one on which the example protein casts its strongest projection) is listed in the first column. Thus, some proteins are seen to have multiple subfamily affiliations. The multiple vectors observed per family effectively subdivide the 58 families into 179 distinct subfamilies. For instance, Table 2 includes a set of 18 ribosomal protein families described by a total of 65 singular vectors (highlighted in bold). Ribosomal proteins are frequently well conserved, effectively aligned, and commonly used for estimating evolutionary relationships. Their presence within our list of dominant singular vectors suggests their utility for establishing evolutionary relationships even in the absence of explicit alignments and explicit a priori assignments of orthology.
The diverse families of ras proteins present within the eukaryotic data set provide good examples of the ability of SVD-derived singular triplets to identify and describe both superfamilies and subfamilies of proteins. The ras proteins are well described by at least 13 vectors, including the 6 dominant vectors highlighted in italics in Table 2. All the "top 5" members of the protein families identified by these 6 vectors are listed in Table 3. Vector 197a summarizes the brain-associated ras11 subfamily (Rab11), vector 236a summarizes the Aplysia-related ras subfamily (ApRas), vectors 277 and 277a summarize the brain-associated ras 5 subfamily (Rab5) and the complex Ha/K/Nras subfamily (HaRas) respectively, vector 350a summarizes the ras-related C3 botulinum toxin substrate 1 subfamily (Rac1), and vector 387a summarizes the brain-associated ras1B subfamily (Rab1B). The most dominant ras vector, 389a, appears to describe a more generalized version of the Rab1 subfamily, since this vector includes both Rab1A and Rab1B proteins within the "top five". In addition, as explained below, this vector also summarizes a high fraction of the entire set of 34 ras sequences within all subfamilies.
For comparison, KOG and Homologen memberships are also listed, when available, for each of the "top 5" proteins listed in Table 3. Table 4 provides a similar comparison for a set of four arbitrarily selected protein families unrelated to ras or to each other (potassium channel, enolase, solute carrier protein, and ADP-ribosylation factor). Since most of the genomes used in our study have not yet been included within the KOG classification scheme, only fly and human proteins have official KOG affiliations. However, we expect with high likelihood that most if not all of the top 5 proteins listed in Tables 3 and 4 would also be members of the particular KOG family listed for each vector. Given this, there would be a good correspondence in Tables 3 and 4 between KOG family members and the proteins identified by singular vectors. In contrast, the Homologen resource appears to provide a more selective classification method, dividing the KOG protein families into two or more subfamilies within which members are more likely to represent specific orthologs.
Conserved motif definitions provided by left singular vectors
Members of any particular ras subfamily represented by a given right singular vector share a uniquely conserved set of correlated tetrapeptides we have previously referred to as a "copep motif". These motifs are explicitly described by the corresponding "left" singular vectors (lsv's) comprising a given singular triplet. The lsv's describe these copep motifs as linear combinations of the 160,000 possible tetrapeptides. Those with high positive values identify peptides found with high probability in the conserved motif of a given subfamily, while those with a high negative value identify peptides excluded with high probability. Therefore, like the rsv's, the lsv's frequently describe two distinct anti-correlated entities (in this case motifs rather than protein families) using either positive or negative values within the vector. Using essentially the same procedure described above for any given rsv, the tetrapeptides having the largest positive or largest negative projections on any given lsv were identified in order to provide a focused summary of the motifs described by that vector. For motif extraction, however, an arbitrary cut-off value (absolute value > 0.025) was used to identify dominant tetrapeptides.
In most cases, it is possible to cluster the resulting short list of dominant tetrapeptides into several uninterrupted copep strings formed by tetrapeptides that overlap in 3 of 4 consecutive amino acid positions. Using this procedure, one long copep string was identified for each of the singular triplets listed in Table 2. The length of the identified long copep string and its corresponding E-value (resulting from pairwise BLAST) are provided as a summary in the last column. The precise amino acid sequences of the long copep strings identified for all listed vectors are provided in a supplementary table [see Additional file 1]. The E-values listed provide a measure of the specificity with which each corresponding protein is identified by the copep string extracted from a given lsv. Its important to note that the long copep string provides only an approximate summary of the lsv from which it is extracted, yet the small E-values clearly indicate that the vast majority of the proteins identified in Table 2 are very specifically recognized by their corresponding copep string.
Figure 1 provides a more detailed demonstration of how correlated peptide motifs and their associated gene families are simultaneously identified and described by SVD-derived singular vectors. In order to allow a clear comparison of SVD-derived motifs with alignment-derived motifs, the dominant tetrapeptides were superimposed over matching regions of a standard ClustalX alignment of the 34 ras proteins identified in the "top five" of the corresponding right singular vectors listed in Table 3. In this example, the dominant tetrapeptides extracted from the six selected left singular vectors are demarcated within (shaded/colored) boxes. Many of the dominant tetrapeptides are seen to form extended strings of overlapping peptides that correspond well to conserved contiguous regions within particular subsets of the ras proteins. For example, vectors 350a and 236a identify and provide distinct descriptions for motifs within the Ras-related botulinum toxin C3 substrate proteins (RasC3) and the Aplysia-related ras proteins (ApRas), respectively. The two most dominant left singular vectors of Figure 1 (389a and 387a) describe motifs within overlapping subsets of the nine Rab1 proteins. In addition, the most dominant left singular vector (389a) appears to describe a highly conserved motif within the entire set of 34 ras proteins reasonably well (solid clear boxes). This vector conspicuously identifies dominant tetrapeptides that span the two regions of the alignment in which unbroken strings of two or more invariant amino acids (asterisks) are present. These two regions are known to be required for ras GTPase activity [18]. It is notable that although these 34 ras proteins have only one stretch with more than two globally conserved consecutive amino acids (DTAGQE), vector 389a is capable of describing large regions of all 34 proteins by recognizing the latent similarity of multiple equivalent tetrapeptides. For example, this single vector recognizes KSAL, KSCL, and KTCL (residues 18–21 of the alignment) as dominant tetrapeptides that occupy equivalent positions within four of the six subtypes of ras proteins (Figure 1). Vector 389a also provides a reasonably strong summary of the large number of other ras proteins present within the genomes of these organisms, but not included in Figure 1 (not shown). In general, the most dominant singular vectors appear to identify highly conserved peptides present in a high fraction of individual members of a protein family or superfamily, while the less dominant vectors appear to describe conserved tetrapeptides present within a restricted set of proteins comprising a subfamily.
Instead of simply providing restricted motif summaries using the most dominant elements of the left singular vectors, we have also attempted to examine entire vectors in order to gain a better understanding of the motifs (and associated protein families) they describe. A reasonably efficient method for depicting left singular vectors is presented in Figure 2, using vectors 389 and 277 as examples. Both vectors are shown as frequency distributions (purple bars) that summarize the approximate magnitudes of the projections provided by all 160,000 tetrapeptides on the vector in question. These distributions are compared to a normal distribution having the same standard deviation (blue bars). In both examples, a significant fraction of tetrapeptides have high or low values in considerable excess of that expected from a normal distribution. Many of these also exceed the arbitrary cut-off value of 0.025 (dashed lines) used to extract the dominant tetrapeptides that serve to summarize the corresponding motifs. Parts of the Rab5 and HaRas motifs extracted from vector 277 are shown in Figure 2A as overlapping dominant tetrapeptides with associated projection values. Similar motifs extracted from vector 389 are shown in Figure 2B. In the latter case, a motif from the large subunit ribosomal protein rpL29 represents the "anti-motif"of the Ras/Rab proteins described by the extreme vector elements of opposite sign.
Species vectors for the production of species phylogenies
The detailed comparative information contained within the hundreds of singular vectors and their corresponding motifs and gene families was subsequently used to build a species phylogeny by summing all the SVD-derived right protein vectors separately for each organism and then comparing the relative orientation of the resulting species vectors [13]. Figure 3A shows the SVD-based topology obtained for the nine eukaryotes compared in this study. This tree supports a coelomate rather than ecdysozoan lineage. Two distinct re-sampling methods were used to estimate branch statistics for this tree. The top value of each pair of support values for each branch shown in Figure 3A was generated using a traditional bootstrap procedure [19]. In this case, 100 random sets of 437 re-sampled singular vectors were made and used to construct 100 species trees. Alternatively, a novel "successive, delete one" jackknife procedure [14] was used to generate the bottom value shown for each branch. In this case, the least dominant singular vector was removed successively (down to 10 vectors) to generate 427 ordered sets of singular vectors, and a new tree was estimated following each removal. Although bootstrap support values for the branches grouping arthropods with vertebrates (37%) and worms with other metazoa (49%) are relatively weak, support values for these branches are strong (100%) using the modified jackknife procedure. All other branches are strongly supported by both procedures. The branch separating Cele from the coelomates is of special interest, since the weak bootstrap support observed (37%) might suggest a significant affinity between Cele and the arthropods consistent with the "ecdysozoan" model (Figure 3A – alternative branching pattern shown in red). Bootstrap support for the alternative ecdysozoan cluster, however, was only 24%.
Use of the "successive, delete-one" jackknife procedure as a species tree branch statistic is justified by the fact that SVD provides singular triplets in order of their "dominance" in explaining the data set [20]. Mathematical dominance provides an objective measure of importance that can be utilized to weight characters. Since the modified jackknife procedure used here deletes the least dominant singular vectors one at a time in order, the more dominant singular vectors (i.e. conserved motifs/families) are automatically weighted more heavily within the consensus tree. Hence, one can argue that our novel jackknife procedure provides stronger support for the derived phylogeny because the most dominant singular vectors generally contain stronger information about gene and species relationships.
Poorly described proteins and species tree quality
While our SVD-based analysis technically considers all proteins present within all nine genomes of the data set, it is likely that accurate vector definitions are provided for only a small fraction of these proteins. Theoretically, the 437 singular triplets could effectively describe as many as 2 × 437 = 874 protein families. However, many of these vectors appear to best describe particular subfamilies of larger groups of closely related proteins. Thus, the 58 protein families listed in Table 2 are each represented by anywhere from 1 to 8 triplets. Although, as mentioned earlier, some protein families lacking clear functional annotation were omitted from this table, it still serves to provide a conservative lower estimate of the number of well-described protein families provided by the SVD. Assuming the number of identifiable protein families in our nine genome data set significantly exceeds the 58 to 179 protein families unambiguously demarcated and subdivided in our analysis, then hundreds or perhaps thousand of the poorly described proteins included in our species vector sums might be contributing a high fraction of "noise" to the definition of species.
In an attempt to increase the fraction of well described proteins used to define species, proteins having poor projections on all 437 right singular vectors were ignored during the summation process. Arbitrary vector magnitude cut-off values of 0.005 or 0.05 were applied to reduce the number of poorly described proteins used to build species trees. Even though the highest and most stringent cut-off value removed the majority of proteins during summation, both new species trees had identical topologies to that of the tree shown in Figure 3A in which all proteins were included. Bootstrap and modified jackknife branch support values for these tree are shown in Figure 3B along with those derived from the inclusive analysis. The removal of only a small fraction of poorly described proteins (cut-off = 0.005, about 103 proteins removed) resulted in 22% bootstrap and 100% modified jackknife support for the coelomate lineage, but 0% support for the ecdysozoan lineage. Removal of a much higher fraction of poorly described proteins (cut-off = .05, about 105 proteins removed) produced an equivalent result. Hence, poorly described proteins contribute little to the support that our analysis provides for the coelomate model.
Conclusions
As demonstrated above, an SVD-based analysis of multiple genomes automatically interprets proteins from input genomes as potential members of a limited list of hierarchically defined protein families and subfamilies. Each subfamily is defined in detail by one or more singular vectors as linear combinations of a large number of peptides (160,000 tetrapeptides, in this case). Potentially, a large number of proteomes lacking annotation can be directly interpreted using this method, assuming a sufficient number of annotated proteomes are included in the analysis. Although most of the genomes used in the present analysis were already accompanied by detailed protein annotations, formal annotations of the Frub and Agam proteins were not readily available. Nevertheless, our SVD-based analysis was able to provide precise protein motif descriptions and subfamily affiliations, not only for the six Frub or Agam proteins shown in Figure 1, but also for any of the hundreds of other Frub or Agam proteins exhibiting strong vector projections on any of the 437 derived singular vectors (see "SVD top five" of Table 1).
Our method bears partial resemblance to a recently described graph-theoretic method for rapidly clustering massive datasets of whole genome protein sequence [22]. In this case, the protein definitions generated were not used to derive gene or species trees, but to provide for a comprehensive clustering of all proteins into families having one or more members. The nodes of their graphs, like the vectors from the right matrix in our analysis, represent proteins, while the edges between nodes in their graphs, like the angles between vectors in our analysis, contain the distance information used to compare proteins. However, the distance information in their analysis was obtained ultimately from exhaustive pairwise BLAST alignments. In contrast, our distance information was derived without alignment, by reference to the 437 most dominant SVD-derived orthonormal left singular vectors. These vectors provide "motif models" expressed as particular linear combinations of the 160,000 possible tetrapeptides. The projections of these motif models on a given protein vector serve to quantitatively define the protein. Since no more than 874 motif models would be provided by our truncated SVD, our method would be less effective than other methods for providing comprehensive family designations for all proteins in a dataset [22,23]. However, a high fraction of these protein families are found to contain only one or a few members [22]. Singletons and small families would generally provide unimportant contributions to relative species definitions, since the majority of species would lack a homolog for comparison. Hence small or poorly conserved protein families, presumably represented by the weaker singular triplets in a complete SVD, are profitably ignored in our analysis.
Although our descriptive analysis of singular triplets (e.g. Table 2, Figure 1) suggests that the protein vectors in our high dimensional definition space can be effectively clustered, we have not applied any specific clustering algorithm. Hence no explicit clustering of proteins, equivalent to the identification of orthologs or homologs, is required. Nevertheless, the application of a clustering algorithm to our vector based symmetric protein distances is clearly feasible and results in accurate clustering for a high fraction of proteins. In fact, the accuracy with which proteins are clustered into known families via Neighbor Joining was used previously to establish optimal dimensionality for a well characterized data set [13]. In addition, unlike other methods, our method provides a straightforward vector addition mechanism for converting relative protein definitions into relative species definitions for the production of species phylogenies.
Alternative non-alignment methods exist for comparing sequences [reviewed in [17]]. Some of these methods may prove to be scalable and adaptable to the problem of whole genome phylogeny. For example, a comprehensive bacterial phylogeny was recently derived using species vectors that include a set of background corrected pentapeptide or hexapeptide (K-tuple) frequency values [12]. Although apparently effective for producing global species phylogenies, this method fails to provide quantitatively comparable protein definitions or interpretable predictions for conserved motifs. While many phylogenetically informative pentapeptides and hexapeptides are likely derived from homologs or orthologs, no mechanism exists for extracting, summarizing, and interpreting this information in terms of motif and gene family definitions. This high stringency method provides a low false positive rate (strong connections between probable orthologous peptides), but comes at the expense of a high false negative rate (little or no recognition of other homologous regions within proteins). For organisms exhibiting a significant level of horizontal gene transfer [24-26], models for motifs and protein families may be crucial tools for identifying "borrowed" genes and assessing their impact on phylogenetic hypotheses.
Our SVD-based species tree supports the traditional "coelomate" model of animal phylogeny. Other large-scale, genome level analyses also tend to support this model [27,28]. The alternative "ecdysozoan" model is supported by comparative analyses of rRNA and analyses that include morphological characters [28,29]. Although genome-scale analyses should perhaps carry considerable weight due to the higher fraction of "total information" used as input, the separation of "signal" from "noise" represents a serious hurdle for these methods. Our method represents a uniquely independent solution that provides a noise-reduced simultaneous global comparison of all proteins within multiple genomes without the need for alignments and without the prior application of operational definitions of orthology. As such, it provides a global perspective on gene and species relationships that is based on a much larger subset of information than that normally used. Since it is a non-alignment method, it provides a fundamentally different kind of analysis, and to the extent that the resulting species phylogenies agree with those provided by other analyses that depend upon highly filtered subsets of aligned orthologs or close homologs, we may derive an additional degree of confidence in these relationships. However, the balanced comparison of a large number of additional whole genome sequences from a variety of animals will likely be required in order to produce an unambiguous and universally accepted animal phylogeny.
Methods
Datasets
Complete reference protein sequences for nine whole eukaryotic genomes ranging from yeast to man were compiled into a single dataset (Figure 1a). Curated protein sequence files were obtained from NCBI dated as follows: human (Hsap) 10/10/03, mouse (Mmus) 10/31/03, rat (Rnov) 9/23/03, mosquito (Agam) 10/24/03, fly (Dmel) 10/24/03, worm (Cele) 11/12/03, malaria (Pfal) 10/17/02, and yeast (Scer) 11/12/03. Pufferfish (Frub) protein sequences dated 8/26/02 were obtained from the DOE Joint Genome Institute.
Peptide frequencies and SVD
Each protein sequence in the dataset was recoded as a high dimensional vector containing raw frequencies for each of the 160,000 possible tetrapeptides. Previous work has established that although tripeptides work well for estimating similarities between highly divergent proteins contained within small sets of viral genomes [15], tetrapeptides work better for larger data sets derived from vertebrate mitochondrial genomes or whole bacterial genomes [13,14]. Although pentapeptides also worked well with the mitochondrial datasets (unpublished), our computational capacity precluded the use of pentapeptides (3.2 million patterns) and larger data sets, like the one used here. Following a log-entropy transformation [21], the singular value decomposition of the resulting data matrix was computed. The log-entropy transformation tends to down-weight evenly distributed high frequency peptides that are likely sources of homoplasy. After 1500 Lanczos iterations (residual errors less than 10-6), three output matrices were obtained, consisting of 437 singular triplets (left and right singular vectors and their corresponding singular value). Each left singular vector produced by the SVD defines one or two conserved motifs within the dataset as particular linear combinations of tetrapeptides [13,14]. Similarly, each of the right singular vectors defines one or two conserved gene families (or subfamilies) as particular linear combinations of proteins. Each gene family identified by a given right singular vector contains motifs described by the corresponding left singular vector. Two distinct motif/families are frequently identified per triplet, since each triplet describes both a correlated motif/family (positive values) and an anti-correlated motif/family (negative values).
Vector based motif and protein family models
"Dominant" vector elements (absolute values in excess of 0.025) were extracted from the left singular vectors and summarized using the C++ program "Copepx" [14]. These values were associated with the most "dominant" (i.e. highly conserved) tetrapeptides found within the motifs described by a given left vector. In addition, the "top five" positive and "top five" negative elements were extracted from the right singular vectors and summarized using the C++ program "Coprotx". These values represent the most dominant members of the gene families described by a given right vector.
Species trees and branch support
Distance matrices were derived by summing all the SVD-derived right protein vectors for a given organism and then comparing the relative orientation of the resulting species vectors using the program Cosdist [13,14]. Species trees were subsequently derived from distance matrices using Phylip-Neighbor [30]. Two distinct resampling methods were used to provide branch support: a traditional bootstrap procedure [19], and a modified jackknife procedure. For the bootstrap, 100 random sets of 437 resampled singular vectors were made and used to construct 100 species trees. For the "successive, delete one" jackknife procedure [14], the least dominant singular vector was removed successively (down to 10 vectors) to generate 427 ordered sets of singular vectors, and a new tree was estimated following each removal.
List of abbreviations used
Homo Sapiens (Hsap), Mus musculus (Mmus), Rattus Norvegicus (Rnov), Anopheles gambiae (Agam), Drosophila melanogaster (Dmel), Caenorhabditis elegans (Cele), Plasmodium falciparum (Pfal), and Saccharomyces cerevisiae (Scer), Fugu rubripes (Frub), correlated peptide (copep), correlated protein (coprot), right singular vector (rsv), left singular vector (lsv).
Authors' contributions
GS conceived the study, gathered the input data, provided primary interpretation of the output, and drafted the manuscript. MB wrote and adapted software, performed computational analysis on the input data, and provided manuscript modifications. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Copep Motifs. Long copep strings identified within the left singular vectors of a given s-triplet.
Click here for file
Acknowledgements
This work was supported in part by a Computer and Information Technology Resources challenge grant from the University of Tennessee, Knoxville.
Figures and Tables
Figure 1 Ras families and sub-families defined by singular vectors (labeled at right). For comparison, dominant peptide strings identified by SVD (boxes) are shown within a Clustal-X alignment. The aligned region corresponds to the first 181aa's of the 192aa Human Rac3 protein. Protein sequences are labeled by gi# (or ensemble# for Frup). Asterisks (*) indicate globally conserved residues. Subfamily motifs associated with negative vector values are denoted with an "a" suffix (e.g. 350a).
Figure 2 Left singular vectors depicted as tetrapeptide projection value frequency distributions. Distributions for singular vectors 277 (A) and 389 (B) are shown in purple, normal distributions having the same standard deviation are shown in blue. For both distributions, the vast majority of values fall between 0.015 and -0.015. Dashed lines mark the cut-off values used to extract dominant tetrapeptides summarizing correlated peptide (copep) motifs. Selected strings of overlapping tetrapeptides describing parts of these motifs are shown boxed above the approximate regions in the distribution in which they appear.
Figure 3 SVD-based proteome phylogeny (A) of nine eukaryotes with percentage branch support: top – bootstrap; bottom – novel jackknife. An unsupported alternative phylogeny containing the "ecdysozoan" lineage is indicated by the dashed red branches. Percentage branch support values for the various clades of the tree are also provided to the left (B) for trees built using all proteins, as well as trees built after poorly described proteins are removed using either of two alternative vector magnitude inclusion values (>0.005, >0.05).
Table 1 Genes and Genomes Compared
Organism SVD "top 5" Genome Total
Hsap 996 (23%) 25,319 (14%)
Mmus 881 (20%) 25,371 (14%)
Rnov 670 (15%) 21,204 (12%)
Frub 536 (12%) 37,439 (21%)
Agam 573 (13%) 16,091 (9%)
Dmel 443 (10%) 18,107 (10%)
Cele 135 (3%) 21,124 (12%)
Scer 113 (3%) 5,855 (3%)
Pfal 23 (1%) 5,049 (3%)
4370 (100%) 175559 (100%)
Table 2 A selected list of protein family/motifs identified by SVD-derived singular triplets (st's). In this summary table, unique example proteins (rsv-gi#) were chosen from the 5 to 40 "top five" proteins identified as members of a given family by as many as 8 distinct right singular vectors. As examples, six individual ras proteins representing six broad categories of ras (highlighted in italics) are defined by a total of 13 right singular vectors, and 18 ribosomal proteins (highlighted in bold) are defined by a total of 65 right singular vectors. The lengths of continuous copep strings identified from the corresponding left singular vectors and their specificities (E-values) as revealed by pairwise BLAST are also provided.
triplet # rsv-gi# Name Protein Description lsv copep string (E-value)
421a 1 11415030 HIST1H4J H4 histone family, member E 62 aa's (1e-54)
417a 2 21166389 HIST1H2BC H2B histone family, member L 75 aa's (4e-67)
413a 1 31560385 Rpl21 ribosomal protein L21 60 aa's (2e-55)
408 1 4501885 ACTB beta actin; beta cytoskeletal actin 42 aa's (9e-38)
405 1 4506661 Rpl7a ribosomal protein 7a 79 aa's (3e-62)
392a 1 5174735 TUBB2 tubulin, beta, 2 45 aa's (7e-41)
389a 2 13569962 RAB1B RAB1B, RAS oncogene family; small GTP-binding 14 aa's (2e-11)
389 3 6677781 Rpl29 ribosomal protein L29 77 aa's (3e-60)
387 3 31981690 Hspa8 heat shock 70kD protein 8 40 aa's (2e-35)
385a 1 11024714 UBB ubiquitin B precursor; polyubiquitin B 77 aa's (2e-68)
378a 5 26051216 CAMK2B calmodulin-dependent protein kinase IIB isoform 7 14 aa's (2e-10)
373a 2 4502201 ARF1 ADP-ribosylation factor 1 86 aa's (1e-41)
371a 3 6679439 Ppia peptidylprolyl isomerase A; cyclophilin A 55 aa's (2e-48)
368a 5 25150942 Tcb-1 transposable element tcb1 transposase (1O615) 88 aa's (7e-74)
363 3 33149310 UBE2D3 ubiquitin-conjugating enzyme E2D 3 isoform 1 138 aa's (7e-91)
354 3 4502549 CALM2 calmodulin 2; phosphorylase kinase delta 40 aa's (1e-19)
352a 4 17105394 RPL23A ribosomal protein L23a 44 aa's (3e-33)
350a 4 9845511 RAC1 ras-related C3 botox sub 1 isoform Rac1, rho 15 aa's (2e-12)
347a 3 51873060 Eef1a1 eukaryotic translation elongation factor 1 alpha 1 24 aa's (4e-19)
345 2 27679110 Rpl17 ribosomal protein L17 (L23) 92 aa's (2e-89)
341a 5 31980772 Ppp1cc protein phosphatase 1, catalytic, gamma isoform 20 aa's (5e-17)
337 5 24648716 mod(mdg4) modifier of mdg4 32 aa's (2e-29)
334 5 24653107 Galpha49B G protein alpha49B 19 aa's (9e-18)
333a 3 4506633 RPL31 ribosomal protein L31 78 aa's (8e-74)
329a 2 34878793 Pcdha13 protocadherin alpha 13 17 aa's (8e-14)
327 3 32307119 PPP2R2B Serine/threonine protein phosphatase 2A, neuronal 23 aa's (7e-20)
324 1 31982919 ZNF430 zinc finger protein 430 18 aa's (3e-11)
322a 3 34871376 LOC287293 similar to high mobility group 1 protein 15 aa's (9e-13)
321a 3 4504445 HNRPA1 heterogeneous nuclear ribonucleoprotein A1 23 aa's (2e-18)
320a 2 25141298 kin-1 cyclic AMP-dependent catalytic subunit (kin-1) 66 aa's (4e-62)
316a 5 22094075 Slc25a5 solute carrier family 25; adenine nucleotide 27 aa's (7e-22)
308a 3 9845502 LAMR1 laminin receptor 1 (67kD, ribosomal protein SA) 68 aa's (1e-60)
304 3 6978809 Eno1 enolase 1, alpha 32 aa's (3e-27)
301 4 27676004 LOC365206 similar to ribosomal protein L9 139 aa's (1e-13)
295 2 31083250 PPP2R5C Ser/threo protein phosphatase 2A, 56 kD regulator, 16 aa's (6e-12)
292 4 31560517 Rpl27a ribosomal protein L27a 58 aa's (7e-56)
291 2 15011936 RPS26 ribosomal protein S26 77 aa's (7e-64)
288 1 22129671 Olfr493 olfactory receptor MOR204–35 12 aa's (3e-08)
287 2 38076430 LOC193565 similar to T-cell receptor alpha chain 16 aa's (2e-12)
285a 3 6754140 H2-Q7 histocompatibility 2, Q region locus 7 19 aa's (5e-16)
280a 5 16418339 Rpl10 ribosomal protein 10 27 aa's (4e-23)
277a 1 15718763 KRAS2 cellular c-Ki-ras2 proto-oncogene 9 aa's (2e-06)
277 2 27689505 Rab5c similar to Rab5c protein 17 aa's (4e-13)
276 4 24580529 M(2)21AB Minute (2) 21AB CG2674-PA 25 aa's (5e-20)
272 1 25742772 Kcna2 potassium voltage-gated channel, shaker-related, 12 aa's (1e-09)
270 4 33186863 Rpl13 ribosomal protein L13 11 aa's (3e-09)
266 4 4506697 RPS20 ribosomal protein S20 54 aa's (2e-49)
256 3 4506597 RPL12 ribosomal protein L12 34 aa's (8e-30)
253a 6 15809016 MRLC2 myosin regulatory light chain MRCL2 19 aa's (7e-16)
247 3 31981515 Rpl7 ribosomal protein L7 10 aa's (4e-08)
240a 5 24639734 Dlc dynein light chain ATPase 22 aa's (4e-21)
237a 4 34865959 gpdh similar to glyceraldehyde-3-phosphate 16 aa's (7e-13)
236a 2 10835049 ARHA Aplysia ras-related homolog 12; oncogene RHO 9 aa's (9e-07)
230 6 15431293 RPL15 ribosomal protein L15 11 aa's (6e-09)
224 5 13592069 Rps10 ribosomal protein S10 81 aa's (1e-78)
197a 2 14249144 Rab11b RAB11B, member RAS oncogene family 15 aa's (4e-12)
190a 6 4506621 RPL26 ribosomal protein L26 16 aa's (8e-14)
183a 5 14277700 RPS12 ribosomal protein S12 13 aa's (1e-10)
Table 3 Comparison of seven ras family clusters provided by right singular vectors with KOG and Homologen clusters. Only proteins having one of the five strongest projections ("top five") for a given singular vector are used in the comparison. Few genomes have KOG members specifically identified by NCBI, however, most or all of the "top 5" proteins for a given rsv would likely be identified as members of the same KOG family. For 197a (Rab11), the KOG # provided in parentheses is that of the closely related human protein.
rsv# gi# projection organism GeneName kog# hg#
197a 6679583 0.06900 Mmus Rab11b (0087) 3109
(Rab11) 14249144 0.06892 Rnov Rab11b na 3109
31209781 0.06827 Agam na na 3109
31209783 0.06827 Agam na na 3109
31209785 0.06826 Agam na na 3109
236a 31542143 0.05883 Mmus Arha na 1257
(ApRas) 16923986 0.05883 Rnov Arha2 na 1257
10835049 0.05873 Hsap RHOA 0393 1257
28395033 0.05610 Hsap ARHC 0393 22408
en131312 0.05412 Frub na na na
277 27689505 0.07229 Rnov Rab5c na 20961
(Rab5) 4759020 0.07214 Hsap RAB5C 0092 20961
31225537 0.07022 Agam na na 20961
31225545 0.07022 Agam na na 20961
31225553 0.07022 Agam na na 20961
277a 15718763 0.04278 Hsap KRAS2 0395 2159
(HaRas) 4885425 0.04243 Hsap HRAS 0395 3907
34861217 0.04243 Rnov Hras1 na 3907
4505451 0.04176 Hsap NRAS 0395 20564
34859609 0.04165 Rnov Nras na 20564
350a 9845511 0.07403 Hsap RAC1 0393 23126
(RasC3) 38081613 0.07403 Mmus Rac1 na 23126
9845509 0.06942 Hsap RAC1b 0393 23126
4826962 0.06820 Hsap RAC3 0393 3705
18875380 0.06820 Mmus Rac3 na 3705
387a 34861437 0.03486 Rnov Rab1B na 23689
(Rab1) 21313162 0.03413 Mmus Rab1B na 23689
13569962 0.03400 Hsap RAB1B 0084 23689
27709432 0.03400 Rnov Rab1B-like na 27733
en156199 0.03396 Frub na na na
389a 4758988 0.04851 Hsap RAB1A 0084 3067
(Rab/Ras) 6679587 0.04851 Mmus Rab1A na 3067
13569962 0.04840 Hsap RAB1B 0084 23689
en160503 0.04824 Frub na na na
13592035 0.04811 Rnov Rab1A na 3067
Table 4 Comparison of four unrelated protein clusters provided by right singular vectors with KOG and Homologen clusters. Descriptions for each of these clusters are provided in Table 2. Only proteins having one of the five strongest projections ("top five") for a given singular vector are used in the comparison.
rsv# gi# projection organism GeneName kog# hg#
272a en165011 0.06928 Frub na na na
(Kcna) 25742772 0.06865 Rnov Kcna2 na 21034
4826782 0.06834 Hsap Kcna2 1545 21034
31543024 0.06821 Mmus Kcna2 na 21034
27465523 0.06632 Rnov Kcna1 na 183
304 12963491 0.101507 Mmus Eno1 na 1093
(Eno) 6978809 0.101252 Rnov Eno1 na 1093
4503571 0.097337 Hsap Eno1 2670 1093
51770896 0.092899 Mmus Eno1 na 1093
en150208 0.091209 Frub na na na
316a 32189350 0.11376 Rnov Slc25a5 na 37448
(Slc25) 22094075 0.11343 Mmus Slc25a5 na 37448
4502099 0.11202 Hsap Slc25a5 0749 37448
en159404 0.1034 Frub na na na
20863388 0.10117 Mmus Slc25a4 na 36058
373a 4502201 0.12887 Hsap Arf1 0070 1253
(Arf) 6680716 0.12887 Mmus Arf1 na 1253
11968098 0.12887 Rnov Arf1 na 1253
24668762 0.12856 Dmel Arf79F 0070 1253
24668773 0.12856 Dmel Arf79F 0070 1253
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==== Front
BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-921560146910.1186/1471-2407-4-92Research ArticleRole of the p53/p21 system in the response of human colon carcinoma cells to Doxorubicin Ravizza Raffaella [email protected] Marzia B [email protected] Laura [email protected] Elena [email protected] Dept. of Structural and Functional Biology, Section of Pharmacology, University of Insubria, Via A. da Giussano 10, 21052 Busto Arsizio (VA), Italy2004 15 12 2004 4 92 92 15 9 2004 15 12 2004 Copyright © 2004 Ravizza et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Colon adenocarcinomas are refractory to a number of widely used anticancer agents. Multifactorial mechanisms have been implicated in this intrinsically resistant phenotype, including deregulation of cell death pathways. In this regard, the p53 protein has a well established role in the control of tumor cell response to DNA damaging agents; however, the relationship between p53-driven genes and drug sensitivity remains controversial. The present study investigates the role of the p53/p21 system in the response of human colon carcinoma cells to treatment with the cytotoxic agent doxorubicin (DOX) and the possibility to modify the therapeutic index of DOX by modulation of p53 and/or p21 protein levels.
Methods
The relationship between p53 and p21 protein levels and the cytotoxic effect of DOX was investigated, by MTT assay and western blot analysis, in HCT116 (p53-positive) and HT29 (p53-negative) colon cancer cells. We then assessed the effects of DOX in two isogenic cell lines derived from HCT116 by abrogating the expression and/or function of p53 and p21 (HCT116-E6 and HCT116 p21-/-, respectively). Finally, we evaluated the effect of pre-treatment with the piperidine nitroxide Tempol (TPL), an agent that was reported to induce p21 expression irrespective of p53 status, on the cytotoxicity of DOX in the four cell lines. Comparisons of IC50 values and apoptotic cell percentages were performed by ANOVA and Bonferroni's test for independent samples. C.I. calculations were performed by the combination Index method.
Results
Our results indicate that, in the colon carcinoma cell lines tested, sensitivity to DOX is associated with p21 upregulation upon drug exposure, and DOX cytotoxicity is potentiated by pre-treatment with TPL, but only in those cell lines in which p21 can be upregulated.
Conclusions
p21 induction may significantly contribute to the response of colon adenocarcinomas cells to DOX treatment; and small molecules that can exploit p53-independent pathways for p21 induction, such as TPL, may find a place in chemotherapeutic protocols for the clinical management of colorectal cancer, where p53 function is often lost, due to genetic or epigenetic defects or to post-transcriptional inactivating mechanisms.
==== Body
Background
Colorectal cancer is the second most common cause of cancer-related mortality in Western countries, with about 1 million new cases every year diagnosed world-wide and 500,000 patients dying from the disease [1]. Of the patients, 30% have advanced disease at presentation, either locally or at distant sites; in this setting, chemotherapy remains the only viable therapeutic option. However, even this option is severely hindered by the inherent resistance of metastatic colon cancer to many currently used anticancer agents. A variety of mechanisms by which cancer cells resist chemotherapy have been described, including enhanced export of drugs from cancer cells and alterations in drug metabolism and/or in drug-target interactions [2]. In addition, the response of cancer cells to genotoxic therapies may be critically impaired by defects in the response mechanisms to DNA damage [3] or in cell cycle regulatory pathways [4].
Over the past decade, induction of apoptosis has emerged as a major event in tumor cell response to cytotoxic agents (for a recent review see [5]). This view, although recently challenged by some Authors [6], has attracted considerable attention on deregulation of cell death pathways as a key determinant of drug resistance.
Two separable, although extensively cross-talking, pathways leading to apoptosis have been characterized [7,8]. The extrinsic pathway is initiated by ligation of transmembrane receptors to activate membrane proximal "activator" caspases, which in turn cleave and activate downstream "effector" caspases. The intrinsic pathway requires disruption of the mitochondrial membrane and the release of mitochondrial proteins, two events that are regulated by the opposing actions of pro- and antiapoptotic Bcl-2 family members. "Intrinsic stresses", such as those produced by DNA-damaging agents, activate the intrinsic apoptotic pathway; the multifunctional transcription factor p53 is thought to be part of a "fast track" connection between nuclear DNA damage and the intrinsic pathway machinery [9]. p53 regulates multiple responses to genotoxic stress by transcriptional activation or repression of a number of genes encoding proteins involved in cell cycle control (p21WAF1/Cip1), DNA repair (gadd45), and apoptosis (e.g. Bax, Bcl2 and survivin) [10]. Mutations in p53 and in the p53 pathway can produce multidrug resistance in vitro and in vivo, and reintroduction of wildtype p53 into p53 null tumor cells can re-establish chemosensitivity [11]. p53 status is not a universal predictor of treatment response, in part because not all drugs absolutely require p53 for their apoptotic function [12] and in some settings, p53 loss can enhance drug-induced apoptotic cell death [13]. Still, loss of p53 function correlates with multidrug resistance in many tumor types [11] and the observation that this is a common defect in human tumors has spurred an active search for strategies aimed at directly activating cell death pathways downstream of p53. In this scenario, the role played by the cyclin-dependent kinase inhibitor p21 is particularly intriguing, as this protein can be activated by both p53-dependent and p53-independent mechanisms and can assume pro- or anti-apoptotic functions, depending on the cellular context (for a review see [14]).
The present research focuses on the role of p21 in tumor cell response to treatment with cytotoxic agents, and on the possibility to improve the therapeutic index of such agents by modulating p21 status by p53-dependent and independent pathways. The following issues have been addressed: (a) analysis of the relationship between p21 status and sensitivity to treatment with the cytotoxic anticancer agent doxorubicin (DOX) in p53-positive and -negative colon cancer cell lines; (b) design of treatment strategies based on the use of small molecules able to modulate p21 status; for the present study we have used a low molecular weight, stable nitroxide radical, 4-hydroxy-2,2,6,6,tetramethylpiperidne-N-oxyl (also known as Tempol, TPL; figure 1) that was shown to exert an antiproliferative effect against different cancer cell lines [15] and to increase p21 levels in a p53-null human leukemic cell line [16]. Our data indicate that p21 modulation may significantly affect cell response to DOX treatment in the colon cancer cell lines tested.
Methods
Reagents
Standard chemicals, including 4-hydroxy-2,2,6,6-tetramethylipiperidine-N-oxyl (Tempol, TPL) and cell culture reagents were purchased from Sigma-Aldrich srl. (Milan, Italy), unless otherwise indicated; doxorubicin (DOX) was kindly provided by Dr. A Suarato (Pfizer-Pharmacia, Milan, Italy).
Cell lines
The human colon carcinoma cell lines HCT116, HT29 (obtained from ATCC, Rockville, MD) and HCT116 p21-negative cells (HCT116 p21-/-), kindly provided by Dr B. Vogelstein (Johns Hopkins University, Baltimore, MD, USA), were maintained in DMEM medium supplemented with 10% FBS (Mascia Brunelli); the HCT116-E6 cell line, obtained from HCT116 cells by transfection with pCMVneo-E6 plasmid (provided by Dr B. Vogelstein) containing the HPV16-E6 human gene, was maintained in ISCOVE medium supplemented with 10% FBS and geneticin (500 μg/ml). All the cell lines were cultured at 37°C, in an atmosphere of 5% CO2 and 95% humidity.
Cytotoxicity assays
The effects of DOX and/or TPL on cell growth were assessed by the MTT assay [17]. Briefly, cells were seeded onto 96-well plates and allowed to grow for 24 h prior to treatment. Three different treatment schedules were used: a) 24 h medium, 1 h DOX (0.05 – 10 μM) followed by 72 h incubation in drug-free medium; b) 24 h TPL (0.05 – 10 mM) followed by 72 h incubation in drug-free medium; c) 24 h TPL followed by 1 h DOX and by 72 h incubation in drug-free medium. For combination experiments, the whole range of DOX concentrations (0.05 – 10 μM) was tested following pretreatment with fixed TPL concentrations, corresponding to the IC25 or IC50values obtained for each cell line according to schedule (b). At the end of the treatment period, 50 μl of MTT (2 mg/ml in PBS) were added to each well at 37°C for 3 h and the reduction of MTT by viable cells was measured colorimetrically at 570 nm, using a Universal Microplate Reader EL800 (Bio-Tek Instruments). IC25 and IC50 values (i.e. the concentrations yielding 75% and 50% cell survival fractions, respectively) were calculated according to the median effect equation and analysis of the interaction between DOX and TPL was performed as described by Chou & Talalay [18].
Transfection of HCT116 cells
Transfection of HCT116 cells with the pCMVneo-E6 plasmid was performed by electroporation as described by Yanez and Porter [19], using a Bio-Rad Gene Pulser unit at the following conditions: 280 V, 960 μF. 48 h post-transfection the cells were selected by adding 500 μg/ml of geneticin to the culture medium. The efficiency and stability of transfection were checked by Western blot analysis of whole cell lysates. Control cells were mock-transfected with the pCMVneo plasmid.
Preparation of cell extracts and immunoblotting
The expression of p53 and p21, before and after 1 h exposure to DOX (1.0 and 10 μM) followed by 23 h incubation in drug-free medium, or after 24 h exposure to TPL (1.0 and 2.5 mM), was evaluated by Western blot analysis of total protein extracts (lysis buffer: NP40 1%, leupeptin 10 μg/ml and aprotinin 10 μg/ml in TBS). Protein concentration in the cellular lysates was determined by the BCA assay (Pierce, Rockford, IL, USA). 15 μg of protein extract/lane were loaded onto 11% polyacrylamide gels and separated under denaturing conditions. Protein samples were then transferred onto nitrocellulose membrane and Western blot analysis was performed by standard techniques. using a mouse anti-p53 monoclonal antibody (DO-1; Santa Cruz Biotechnology, Inc., Santa Cruz, CA, USA) and a rabbit anti-p21 polyclonal antibody (C-19; Santa Cruz Biotechnology). Proteins were visualized using peroxidase-conjugated anti-mouse and anti-rabbit secondary antibodies (Amersham Pharmacia Biotech) and the ECL Plus Western Blotting Detection Reagents (Amersham Pharmacia Biotech).
Densitometric analysis was performed using the Scion Image software (Scion Corporation, Frederick, MD).
Flow cytometric analysis of apoptotic cells
The presence of apoptotic cells in HCT116, HT29, HCT116-E6 and HCT116 p21 -/-, before and after 1 h exposure to DOX (1.0 and 10 μM) followed by 23 h incubation in drug-free medium, was evaluated by flow cytometric analysis, using a Becton Dickinson FACScalibur flow cytometer. Cells were detached by trypsinization, washed in phosphate-buffered saline (PBS) and fixed in ice-cold 70% ethanol for 20 min at -20°C. After an additional wash in PBS, DNA was stained with 50 mg/ml propidium iodide in PBS in the presence of RNAse A (30 U/ml) at 37°C for 30 minutes. 5 × 105 cell samples were analyzed and data were processed using the CellQuest software (Becton Dickinson). The percentage of apoptotic cells in each sample was determined based on the sub-G1 peaks detected in monoparametric histograms.
Statistical analysis
Comparisons of IC50 values and apoptotic cell percentages in the four different cell lines were performed by ANOVA and Bonferroni's test for independent samples. C.I. calculations an relative statistical analysis were performed as described by Chou and Talalay [18]. According to this method, a combination index (C.I.) can be calculated from dose-response curves obtained following exposure to DOX and/or TPL as single agents and in combination. C.I. values approximating 1.0 indicate additive interactions between the two agents; C.I. < 1.0 indicate synergy and, conversely, C.I. > 1.0 indicate antagonism.
Results
Cytotoxicity assays
Figure 2 shows the dose-response curves for HCT116, HT29, HCT116-E6 and HCT116 p21-/- cells following 24 h incubation in drug-free medium, 1 h exposure to DOX and 72 h incubation in drug-free medium. IC50 values calculated from these data are reported in table 1; the resistance index (R.I.) is calculated as the ratio between the IC50 value obtained for each cell line and that obtained for HCT116 cells. The HT29 cell line, which carries a mutant form of the p53 gene, is significantly more resistant to the cytotoxic action of DOX than the HCT116 cell line, carrying a wild-type p53 gene (IC50 values: 2.197 ± 0.11 μM vs. 0.38 ± 0.03 μM, respectively; mean ± s.e.m. of 4–6 experiments, p < 0.05). HCT116-E6 cells are 2-fold more resistant to DOX than HCT116 cells (IC50: 0.770 ± 0.06 μM; mean ± s.e.m. of 4–6 experiments, p < 0.05 vs. HCT116), while HCT116 p21-/- cells are 14-fold more resistant to DOX than HCT116 cell line (IC50: 5.457 ± 0.163 μM; mean ± s.e.m. of 4–6 experiments, p < 0.05 vs. HCT116), in spite of the presence of a wild type form of p53.
Immune detection of p53 and p21
Figure 3 shows the expression of p53 (A) and p21 (C) proteins, before and after exposure to DOX (1 and 10 M for 1 h followed by 23 h in drug-free medium), in the four cell lines tested; panels B and D report the densitometric analysis of p53 and p21 immune reactive bands, respectively. In the absence of drug treatment, it is possible to observe that HT29 cells show higher p53 but lower p21 protein levels, as compared with the HCT116 cell line. DOX treatment induces a dose-dependent increase in both p53 and p21 levels in HCT116 cells, whereas in HT29 cells p53 protein levels are not significantly modified by the treatment and p21 protein levels are only detectable when 25 μg of protein extract/lane are loaded onto the gel (instead of the 15 μg loaded for the other cell lines), and even then only at the highest DOX concentration used. As expected, HCT116 E6 cells do not show detectable p53 levels, both under baseline conditions and following DOX treatment; in contrast in HCT116-E6 cells p21 levels are increased by DOX treatment in a dose-dependent fashion, although to a lesser extent than in HCT116 cells. HCT116 p21-/- cells show higher baseline expression levels of p53 compared with HCT116 cells and DOX treatment in this cell line enhances p53 expression to a an even greater extent than in HCT116 cells line. As expected, p21 is undetectable in HCT116 p21-/- cells, and DOX treatment does not modify the intracellular levels of this protein.
Evaluation of apoptotic cells by flow cytometric analysis
Figure 4 shows the percentage of apoptotic cells following treatment of the four colon cell lines with DOX (1.0 and 10 μM) for 1 h followed by 23 h in drug-free medium. No significant differences in the percentage of apoptotic cells were observed in untreated HCT116, HT29, HCT116-E6 and HCT116 p21-/- cells. Exposure to DOX induces concentration-dependent increases in apoptotic cells in all the cell lines tested; HCT116-E6 cells were the least susceptible apoptosis induction by DOX.
Effects of TPL on cell survival and p53/p21 levels
Table 2 reports the IC25 and IC50 values obtained for the four cell lines after 24 h of continuous TPL exposure followed by 72 h in drug-free medium. TPL can be observed to inhibit cell growth in all four cell lines; although no significant differences can be detected among IC50 values, HCT116 p21-/- cells appear to be less responsive than the other three cell lines. Figure 5 shows that 24 h exposure to TPL (1 and 2.5 mM) induces a dose-dependent increase in both p53 and in p21 levels in HCT116 cells, whereas in HT29 cell line TPL treatment only induces a dose-dependent increase in p21 expression. Exposure of HCT116-E6 cells to TPL (1 and 2.5 mM) for 24 h does not induce any variations in p53 expression, while a dose-dependent increase in p21 expression can be observed following treatment with the nitroxide. In HCT116 p21-/- cells TPL induce a slight increase in p53 protein levels but, as expected, p21 levels were unaffected by TPL treatment.
Effects of TPL pretreatment on DOX-induced cytotoxicity
Figure 6 shows the effect of 24 h pretreatment with TPL, at fixed concentrations corresponding to the IC25 and IC50 values obtained for each cell line, on DOX cytotoxicity. The cells' response to DOX is expressed as the IC50 values derived from dose/response curves obtained after 1 h exposure to DOX with or without pretreatment with TPL (24 h), followed by 72 h in drug-free medium. Analysis of cytotoxicity data shows a synergistic interaction (C.I.<1) between DOX and TPL for both TPL concentrations in HCT116 cells and in HCT116-E6 and HT29 cells at the lower concentration; only additive effects (C.I. ≈ 1) can be observed in HCT116 p21 -/- cells. IC50 values for DOX and TPL according to the three different schedules are reported in table 3.
Discussion
Resistance of colorectal cancer to established treatment regimens remains a major concern in oncology; thus attempts at improving the survival of patients affected by this disease depend largely on strategies targeting tumor cell resistance, which cannot be rationally planned without a detailed knowledge of the mechanisms underlying this phenomenon. A current paradigm regarding cancer chemotherapy indicates disabling of the intrinsic apoptotic pathway as a key factor in the response of tumor cells to anticancer drugs [3,5,12]. Therefore, strategies aiming at re-establishing the cell's capability to activate a cell death program are an area of active research. The present study was performed in order to define the role of the p53/p21 pathway in the response of colorectal carcinoma cells to DOX, a cytotoxic agent that is typically devoid of effects in this tumor type. The results obtained in our cytotoxicity studies indicate that in the cell lines examined p53 status is not unequivocally related to the response to DOX: in fact, while p53-deficient cells (HT-29, HCT116-E6) are indeed less responsive than the p53/wt parental HCT116 cell line, the highest resistance index was obtained for HCT116 p21-/- cells, harboring two wildtype p53 alleles. As expected, treatment with DOX leads to p53 upregulation in the cell lines expressing wildtype p53; this effect has been thoroughly documented in colon cancer cells as well as in tumor cell lines derived from other tissues, and has been attributed to phosphorylation and subsequent stabilization of p53, possibly through activation of DNA-dependent protein kinase or ATM (ataxia-teleangectasia mutated) kinase (see e.g. [20-22]). In HCT116 cells, p21 expression parallels p53 activation; however, data obtained in HT29 and HCT116-E6 cells clearly indicate the existence of p53-independent pathways for p21 induction, that have been extensively characterized (for a review see [23]) and can be activated to variable extents (HCT116 E6 > HT29) upon exposure to DOX. Interestingly, the extent of the cytotoxic effects observed in the small panel of colon cell lines tested rather seems to parallel the cells' ability to upregulate p21 (HCT116 > HCT116-E6 > HT29 > HCT116 p21-/-). This result is somewhat unexpected: in fact, whereas the function of p21 in cell growth arrest following DNA damage has been established for a long time [24], the role played by this protein in the ultimate fate of tumor cells exposed to cytotoxic agents is far from clear-cut [14,25]. In a number of studies, p21 has actually been reported to protect tumor cells against cell death induced by enforced p53 expression [26] or by low doses of cytotoxic agents [13,27-30]. However, in other experimental settings, p53-dependent or -independent induction of p21 expression seems to be a prerequisite for apoptosis [31-34] and to sensitize tumor cells to the action of different agents [35-37]. The putative mechanisms by which p21 might actually induce apoptosis have recently been reviewed [38], but still await full elucidation.
Interestingly, the situation outlined by our results does not seem to conform to either view: in fact, while induction of p21 in p53-proficient and -deficient cell lines is associated with increased response to drug treatment, this was not accompanied by a parallel increase in apoptotic cells, as no significant differences in apoptosis were observed between HCT116 cells and the 14-fold resistant HCT-116 p21-/- cell line (figure 4). This suggests that modes of cell death other than apoptosis may operate in tumor cells following exposure to DOX, or, more generally, to DNA-damaging agents, a concept that is beginning to be proposed by a number of Authors [39,40]; of note, recent experimental evidence indicates p21 as one of the major determinants of terminal growth arrest induced by cytotoxic agents [41-43]. Therefore, although issues related to terminal growth arrest and senescence have not been specifically addressed in the present study, the possibility that these phenomena might play a role linking cell death to the observed increases in p21 levels should not be disregarded.
The hypothesis that the cytotoxic response of the tumor cell lines tested in the present study may depend on p21 induction is further corroborated by data obtained following pre-treatment with the piperidine nitroxide TPL. The choice of this compound was dictated by previous findings indicating that TPL induces cell death in a number of tumor cell lines irrespective of their p53 status [15], and that it increases p21 levels in p53-null cells [16]. The results of the present study show that TPL affects the four colon cell lines to similar extents, thus confirming that its growth inhibitory effect is independent of p53 function. HCT-116 p21-/- cells are actually slightly less responsive than the other cell lines [even though the difference does not attain statistical significance), which suggests the possibility that the effects of TPL are due in part to its ability to increase p21 levels. Interestingly, the nitroxide also induces p21 expression even in p53-deficient cell lines; this observation suggests that TPL can activate p53-independent pathways for p21 induction, as already noted following exposure of HT29 and HCT116 E6 cells to DOX. Moreover, activation of such pathways by TPL appears to sensitize tumor cells to the action of DOX: in fact, synergistic potentiation of DOX cytotoxicity is achieved by TPL in those cell lines where p21 expression can be induced, but only additive effects between TPL and DOX are observed in HCT116 p21-/-, where p21 expression is constitutively absent.
Conclusions
In summary, the results of the present study strongly suggest that 1) p21 induction may significantly contribute to the response of colon adenocarcinoma cells to DOX treatment; and 2) small molecules that can exploit p53-independent pathways for p21 induction, such as TPL, may find a place in chemotherapeutic protocols for the clinical management of colorectal cancer, where p53 function is often lost, due to genetic or epigenetic defects or to post-transcriptional inactivating mechanisms.
Competing interests
The author(s) declare that they have no competing interests
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
Supported by a grant from the Italian Ministry for Education, University and Research (MIUR), Projects with Relevant National Impact (PRIN), 2003 (to E.M.).
Figures and Tables
Figure 1 Structure of 4-hydroxy-2,2,6,6,tetramethylpiperidne-N-oxyl (also known as Tempol, TPL)
Figure 2 Dose-response curves of HCT116 (■), HT29 (▲), HCT116E6 (▼), and HCT116 p21-/- (●) cells after 1 hour exposure to DOX followed by 72 h incubation in drug free medium (mean ± s.e.m. of 4–6 experiments).
Figure 3 p53 (A) and p21 (C) protein levels in HCT116, HT29, HCT116 E6 and HCT116 p21-/- cells following 1 h exposure to DOX and 23 h incubation in drug-free medium. Panels B and D: densitometric analysis of p53 and p21-immune reactive bands, respectively (white bars: untreated; light grey bars: DOX 1 μM; dark grey bars: DOX 10 μM).
Figure 4 Percentage of apoptotic cells in HCT116, HT29, HCT116 E6 and HCT116 p21-/- cells before and after 1 h exposure to DOX followed by 23 h incubation in drug-free medium (white bars: untreated; light grey bars: DOX 1 μM; dark grey bars: DOX 10 μM). Mean ± s.e.m. of 3 independent experiments.
Figure 5 p53 (A) and p21 (C) protein levels in HCT116, HT29, HCT116 E6 and HCT116 p21-/- cells following 24 h exposure to TPL. Panels B and D: densitometric analysis of p53 and p21-immune reactive bands, respectively (white bars: untreated; light grey bars: TPL 1.0 mM; dark grey bars: TPL 2.5 mM).
Figure 6 Effect of 24 h pre-treatment with TPL on DOX IC50 values obtained in HCT116, HT29, HCT116 E6 and HCT116 p21-/- cells following 1 h exposure to DOX and 72 h in drug-free medium. For each cell line, two TPL concentrations corresponding to IC25 and IC50 (see table 2), were used. Mean ± s.e.m. of 4–6 experiments.
Table 1 IC50values obtained after 1 h exposure to DOX followed by 72 h incubation in drug free medium (mean ± s.e.m. of 4–6 independent experiments).
cell lines IC50 (μM) R.I.a
HCT116 0.38 ± 0.03 1.0
HT29 2.37 ± 0.13 * 6.24
HCT116 E6 0.732 ± 0.06 * 1.93
HCT116 p21-/- 4.98 ± 0.32 * 13.10
*p < 0.05 vs HCT116
aThe resistance index (R.I.) was calculated as the ratio between the IC50 values obtained for each cell line and that of HCT116 cells
Table 2 IC50values obtained after 24 h exposure to TPL followed by 72 h incubation in drug free medium. (mean ± s.e.m. of 4–6 independent experiments).
Cell lines IC25 (μM) IC50 (μM) R.I.a
HCT116 280.00 ± 39.21 560.00 ± 77.78 1.0
HT29 320.40 ± 50.03 665.63 ± 102.91 1.19
HCT116 E6 400.06 ± 60.00 837.3 ± 103.99 1.49
HCT116 p21-/- 700.00 ± 99.14 1114.55 ± 182.2 1.99
a The resistance index (R.I.) was calculated as the ratio between the IC50 values obtained for each cell line and that of HCT116 cells
Table 3 IC50values obtained after 24 h exposure to TPL followed by 1 h exposure to DOX and 72 h incubation in drug free medium (mean ± s.e.m. of 4–6 independent experiments).
DOX + TPL (IC25) + TPL (IC50) C.I.a
HCT116 0.38 ± 0.03 0.102 ± 0.04 0.053 ± 0.003 0.4–0.6
HT29 2.20 ± 0.11 1.06 ± 0.12 0.49 ± 0.015 0.6–0.8
HCT116 E6 0.77 ± 0.06 0.54 ± 0.6 0.24 ± 0.07 0.6–0.8
HCT116 p21-/- 5.457 ± 0.16 4.98 ± 0.15 2.549 ± 0.21 0.8–1.1
aCombination index (C.I.) ≈ 1.0: additivity; C.I.<1.0: synergy; C.I.>1.0: antagonism
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| 15601469 | PMC544559 | CC BY | 2021-01-04 16:03:02 | no | BMC Cancer. 2004 Dec 15; 4:92 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-92 | oa_comm |
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BMC Dev BiolBMC Developmental Biology1471-213XBioMed Central London 1471-213X-4-151559601610.1186/1471-213X-4-15Research ArticleThe product of the split ends gene is required for the maintenance of positional information during Drosophila development Mace Kimberly [email protected] Antonio [email protected] Surgical Research Lab; University of California, San Francisco, Box 1302, San Francisco, CA 94143-1302, USA2 Almirall Prodesfarma SA, Cardener 68, 08024 Barcelona, SPAIN3 Department of Biology, 0349. University of California, San Diego. 9500 Gilman Dr., La Jolla, CA 92093, USA2004 13 12 2004 4 15 15 27 7 2004 13 12 2004 Copyright © 2004 Mace and Tugores; licensee BioMed Central Ltd.2004Mace and Tugores; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The Drosophila split ends (spen) gene encodes a large nuclear protein containing three RNP-type RNA binding motifs, and a conserved transcriptional co-repressor-interacting domain at the C-terminus. Genetic analyses indicate that spen interacts with pathways that regulate the function of Hox proteins, the response to various signaling cascades and cell cycle control. Although spen mutants affect only a small subset of morphological structures in embryos, it has been difficult to find a common theme in spen mutant structural alterations, or in the interactions of spen with known signaling pathways.
Results
By generating clones of spen mutant cells in wing imaginal discs, we show that spen function is required for the correct formation and positioning of veins and mechanosensory bristles both on the anterior wing margin and on the notum, and for the maintenance of planar polarity. Wing vein phenotypic alterations are enhanced by mutations in the crinkled (ck) gene, encoding a non-conventional myosin, and correlate with an abnormal spatial expression of Delta, an early marker of vein formation in third instar wing imaginal discs. Positioning defects were also evident in the organization of the embryonic peripheral nervous system, accompanied by abnormal E-Cadherin expression in the epidermis.
Conclusions
The data presented indicate that the role of spen is necessary to maintain the correct positioning of cells within a pre-specified domain throughout development. Its requirement for epithelial planar polarity, its interaction with ck, and the abnormal E-Cadherin expression associated with spen mutations suggest that spen exerts its function by interacting with basic cellular mechanisms required to maintain multicellular organization in metazoans. This role for spen may explain why mutations in this gene interact with the outcome of multiple signaling pathways.
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Background
The morphological complexity of metazoans is achieved through the regulation of multiple genes in an orchestrated spatial and temporal manner. One of these genes, split ends (spen), was initially identified in a screen for mutations affecting axonal outgrowth in the nervous system in Drosophila [1]. Additional mutations in spen were isolated in a screen for genetic modifiers of Deformed (Dfd) function. Dfd encodes a Hox transcription factor that specifies maxillary segment identity during development [2]. spen was subsequently found to enhance embryonic thoracic defects resulting from loss of function mutations in the Hox gene Antennapedia [3]. Other studies have found mutations in Drosophila spen as modifiers of mutations in components of Ras/MAP kinase pathways, including Raf kinase [4], kinase suppressor of Ras [5], loss of function mutations in the gene encoding the protein tyrosine phosphatase Corkscrew [6], and in the ETS family transcription factor, Aop/Yan [7,8]. Mutations in the spen gene have also been identified as enhancers of gain of function phenotypes caused by overexpression of E2F or Cyclin E in eye cells [9,10], both of which are required for progression through the S phase of the cell cycle, as well as Dacapo, a cyclin dependent kinase inhibitor [9]. Overexpression of Spen may interfere with Notch signaling during the development of adult external sensory organs [11], and spen function is required for the maternal expression of the Notch pathway transcription factor encoded by Suppressor of Hairless (Su(H)) [12]. Recent evidence also suggests that spen may participate in the transduction of the Wingless (Wg) signal within a subset of cells in the wing imaginal disc [13].
The Spen protein is ubiquitously expressed throughout embryogenesis. Differential splicing of spen results in isoforms encoding at least two proteins of ~5500 amino acids containing three tandem RNP-type RNA binding domains and a SPOC (Spen Paralogous and Orthologous C terminal) domain at the carboxy terminus [3]. These domains are highly conserved in both the mouse and human orthologs, called Msx-2 Interacting Nuclear Target (MINT) and SMRT/HDAC1 Associated Repressor Protein (SHARP), respectively. There is increasing evidence indicating that Spen-related polypeptides play a role in transcriptional repression. MINT may participate in bone development by binding to the osteocalcin promoter, via its RNP motifs, and repressing transcription in a binding complex with the homeodomain protein Msx-2 [14]. The interaction between SHARP and Silencing Mediator for Retinoid and Thyroid-hormone receptors (SMRT) can lead to the recruitment of histone deacetylase complexes through the conserved SPOC domain [15,16]. Both SHARP and MINT have also been proposed as negative regulators of the Notch signaling pathway in mammals. SHARP has been shown to bind directly to RBP-Jκ and repress the HES-1 promoter in an HDAC-dependent manner [17]. Although deletion of MINT coding sequences in mice results in embryonic lethality around E 14.5 due to multiple abnormalities, the analysis of hematopoiesis derived from MINT-/- precursors reveals a defect in B cell development that could be attributed to defects in Notch signaling [18].
Despite the sum of genetic and biochemical evidence, a selective role for Spen-like proteins in a particular pathway in mammals or Drosophila remains unclear. Because wing development is a well characterized system for the study of primary pattern formation, diverse signaling pathways, and cell cycle control [19,20], we have used mitotic recombination in the wing disc to analyze spen mutant mosaics. An additional advantage is that, because wings are not essential for adult viability, the study of a large number of specimens is possible. In this study, we show that the function of spen is necessary for the maintenance of planar polarity, and for the correct formation and positioning of veins and mechanosensory bristles on the anterior wing margin and the notum. Alterations in vein formation in spen clones correlate with abnormal spatial expression of Delta, an early marker of vein formation in third instar wing imaginal discs. All wing phenotypes are enhanced in a crinkled (ck) mutant background, a gene encoding the non-canonical myosin VIIa. The abnormal position of sensory organs is also observed during embryonic PNS development. In contrast with previous reports, we show that spen is not essential for the determination of specific cell fates nor for cell survival, nor is it directly required for the outcome of the Ras, Notch, and Wingless signaling pathways. Based on our observations, we propose that spen is required for cells to maintain positional identity and tissue cohesiveness during the organized growth of wing and notum epithelia.
Results
spen function is essential throughout development
spen has been shown to be involved in many processes during embryonic development. Mutant modifier and gain of function screens indicate that spen participates in a variety of developmental processes in Drosophila, including Hox gene function [2,3], cell cycle control [9,10] and the modulation of signal transduction pathways such as Ras [4-8], Notch [11,12], and Wingless [13]. Given these varied interactions, it is unclear how spen functions through a common mechanism of action. To better understand a role for spen, we have generated genetic mosaics in adult tissues by using Flp1-mediated mitotic recombination [21].
The spenpoc361 and spenpoc231 mutant alleles have been previously described [3]. Although not molecularly characterized, there is evidence indicating that spenpoc361 is a null allele. First, in maternal and zygotic spenpoc361 mutant embryos, Spen protein cannot be detected with a polyclonal antibody raised against the region encoding amino acids 3203–3714 [[3], and not shown], suggesting that this mutant is either a protein null or that it is truncated before this region. Second, the spenpoc361 allele displays nearly the same strength as the TE21A deficiency, which deletes the entire locus [3].
Both spen alleles were recombined onto chromosomes containing an FRT sequence at 40A [3], and were subjected to heat shock driven Flp1-mediated mitotic recombination with a M(2)36F FRT40A chromosome. Heat shocks delivered at different times during larval and pupal development rendered few escapers (<1%), which presented with little or no mosaicism as revealed by the absence of the M dominant marker (not shown), indicating that the function of spen is essential during all stages of development.
spen mutant mosaics affect vein morphology and planar polarity in adult wings
Because the studies described above did not provide any information about the nature of this lethality, mosaics were generated through expression of the Flp1 recombinase in wing imaginal discs, where spen mRNA is expressed ubiquitously as revealed by RNA in situ hybridization (not shown). Using the MS1096-GAL4 driver [[22], Figure 1A], the expression of a UAS-Flp1 transgene [23] induced mitotic recombination in wing discs between the FRT chromosome bearing a spen mutant allele and an FRT chromosome containing a ubiquitously expressed GFP transgene on 2L. The homozygous viable 2piM FRT40A chromosome [21] was used as a control in all crosses. Initially, to avoid non-specific interference of additional mutations in the analysis (see below), clones were not phenotypically marked in adults, although their formation could be followed in discs by looking at the expression of the GFP transgene. In order to prevent the evaluation of non-specific abnormalities caused by the MS1096-GAL4 insertion on the X chromosome of hemizygous males, only heterozygous females were analyzed.
Figure 1 Generation of spen mutant clones in wing imaginal discs. (A) Expression of a UAS-GFP transgene driven by MS1096 GAL4 in third instar wing imaginal discs. As described previously [22], the transgene is expressed mainly in the dorsal wing pouch, with weaker expression in the ventral side and in the prospective notum. (B, C) Virgin females with the genotype {MS1096 GAL4; 40 2piM FRT40A}, or {MS1096 GAL4; spenpoc361 FRT40A/ ln (2LR) CyO}, were crossed to {w*; GFP FRT40A; UAS-Flp1/TM6b} males. Shown are wing imaginal discs isolated from the resulting third instar larvae with the genotypes {MS1096 GAL4/+; 40 2 piM FRT40A/GFP FRT40A; UAS Flp1/ +} (B), and {MS1096 GAL4/+; spenpoc361 FRT40A/GFP FRT40A; UAS-Flp1/ +}(C). Green fluorescence reveals either compound heterozygous cells (not subjected to mitotic recombination) or GFP homozygotes (brightest), while dark spots indicate 2 piM (B) or spenpoc361 (C) homozygous clones. Dorsal is up and anterior is to the left. To know the relative area covered by either 2 piM or spenpoc361 homozygous clones versus wt clones, regions of equal intensity within the images were artificially colored in Adobe Photoshop using the Paint Bucket tool (D, E). Colors corresponding to mutant (red) or wt (blue) areas were extruded independently, and the total number of pixels contained within the regions of interest were calculated using the Kodak 1D Image Analysis Software (Eastman Kodak Company, Rochester, NY). The results are represented as the fraction covered by each genotype for each cross in a total of three discs for the crosses involving 2 piM, and 5 discs for spenpoc361 (F).
Using this approach, clones were generated on the dorsal side of the wing pouch, haltere imaginal discs, and to a minor extent on the ventral side of the wing pouch and on the prospective scutellum (Figure 1A-C). In third instar imaginal discs, the relative area covered by spen mutant cells was comparable in size to that obtained in the control crosses (Figure 1F), indicating that loss of function of the spen gene did not appear to autonomously affect cell viability during growth of the disc.
The generation of spen mutant mosaics during wing development caused phenotypic abnormalities that included both the formation of ectopic patches of vein material and the loss of vein material (mostly distally), as well as subtle mis-localization of both longitudinal and cross veins, frequently accompanied by thickening of the veins (Figure 2). The ectopic vein material was always observed around veins, and was never detected in the middle of intervein regions. Additional abnormalities included the disruption of cell polarity, as evidenced by the abnormal orientation of wing blade trichomes (Figure 3) and the mis-placement of bristles at the wing margin (see below on Figure 4), whereas their morphology appeared normal. No disruptions in any of the main axes of the wing (A/P, D/V and P/D) were observed in wings containing spen mutant clones.
Figure 2 The presence of spen mutant clones affects wing vein morphology. Virgin females with the genotype {MS1096 GAL4; 40 2piM FRT40A}, {MS1096 GAL4; spenpoc231 FRT40A/ ln (2LR) CyO}, or {MS1096 GAL4; spenpoc361 FRT40A/ ln (2LR) CyO}, were crossed to {w*; GFP FRT40A; UAS-Flp1/TM6b} males, and adult wings were isolated from progeny females with the following genotypes: {MS1096 GAL4/+; 40 2 piM FRT40A/GFP FRT40A; UAS Flp1/ +} (A), {MS1096 GAL4/+; spenpoc361 FRT40A/ GFP FRT 40A; TM6b/ +} (B), {MS1096 GAL4/+; spenpoc231 FRT40A/ GFP FRT40A; UAS-Flp1/ +} (C, D), and {MS1096 GAL4/+; spenpoc361 FRT40A/ GFP FRT40A; UAS-Flp1/ +} (E, F). Arrows indicate gain (C, E, F), loss (D, F), or misplacement (E) of vein material.
Figure 3 spen mutations affect wing hair polarity. Crosses were performed as described in Figure 1. Shown are details (see inset in A) of adult wings isolated from progeny females with the genotypes: {MS1096 GAL4/ +; 40 2piM FRT40A/ GFP FRT40A; UAS-Flp1/ +} (A), {MS1096 GAL4/ +; spenpoc361 FRT40A/ GFP FRT40A; TM6b/ +} (B), {MS1096 GAL4/ +; spenpoc231 FRT40A/GFP FRT40A; UAS-Flp1/ +} (C), and {MS1096 GAL4/+; spenpoc361 FRT40A/ GFP FRT40A; UAS-Flp1/ +} (D). Arrows indicate the direction of the bristles.
Figure 4 Incorrect positioning of wing elements in mosaic spen mutant wings is enhanced by mutations in the crinkled (ck) gene. Virgin females with the genotype {MS1096 GAL4; ck FRT40A/ ln (2LR) CyO }, or {MS1096 GAL4; spenpoc361ck FRT40A/ ln (2LR) CyO }, were crossed to {w*; GFP FRT40A; UAS-Flp1/ TM6b} males, and adult wings were isolated from progeny females with the following genotypes: {MS1096 GAL4/+; ck FRT40A/ GFP FRT40A; UAS-Flp1/ +} (A, F), {MS1096 GAL4/+; spenpoc361 ck FRT40A/ GFP FRT40A; TM6b/ +} (B-E, G). Red lines separate either wt or compound heterozygous cells (indicated as wt) from spen mutant cells.
All phenotypes were more penetrant when the spenpoc361 allele was used instead of the spenpoc231, confirming previous results indicating that spenpoc361 represents a stronger allele [2,3]. None of the phenotypes described above were observed when a control chromosome was subjected to mitotic recombination (Figures 2A, and 3A), or when either the driver (MS1096-GAL4), or the UAS-Flp1 transgene, were independently present in a spen heterozygous background (Figures 2B, 3B, and not shown).
Morphological alterations in mosaic spen mutant wings are enhanced by mutations in crinkled (ck)
To determine whether the phenotypes observed in spen mosaic mutant wings were cell autonomous, spen mutant chromosomes were marked with crinkled (ck), a commonly used recessive marker on 2L. While the presence of ck homozygous clones had no effect on wing vein patterning and morphology (Figure 4A), the presence of ck on the spen mutant chromosome markedly enhanced the severity of the phenotypes previously observed with spen mutants alone (Figure 4B). Likewise, we observed an excess (Figure 4C) or absence (Figure 4D) of vein material, and misplacement of both longitudinal and cross veins (Figure 4E). In most cases, these abnormalities correlated with the presence of the ck marker phenotype. However, there were cases in which clones on the dorsal side affected vein morphology on the ventral side and vice versa. Therefore, the phenotypic effects caused by spen mutations are not exclusively cell autonomous, although it appears autonomous in cells within a given cell layer.
A "misplacement" effect was also observed at the wing margin, most frequently affecting the dorsal bristles. In a normal wing, the bristles are evenly spaced in a row along the dorsal side of the anterior wing margin (Figure 4). In the presence of spen mutant clones, the spacing between these bristles was altered (Figure 4G). Additionally, a mis-alignment and occasional tufting of the thick trichomes that are found along the wing margin was also observed. Again, these phenotypes were not exclusively cell autonomous, as was the case with aberrations in wing vein morphology. Alternatively, it is plausible that spen mutations alter the phenotypic manifestation of ck mutants, therefore leading to an incorrect conclusion about the autonomy of the spen mutations.
spen mutant clones disrupt the expression of Delta and Cut in wing imaginal discs
Veins are generated in specific domains within the wing field and require the action of early patterning genes that establish basic positional values composing the main axes (D/V, A/P). This process is followed by the initiation of vein formation, and finally, vein differentiation [19]. The phenotypes observed upon generation of spen mutant clones in wing imaginal discs are consistent with a role for spen at later stages when vein formation takes place, as the establishment of compartment boundaries appears unaffected (Figure 2). One gene product that is involved in early vein patterning and differentiation is Delta (Dl), which participates in delimiting vein boundaries along prospective vein forming domains through lateral inhibition [19]. In third instar wing imaginal discs, expression of Dl correlates with the prospective L3, L4, and L5 veins (see Figure 5B).
Figure 5 Delta protein expression is abnormally distributed in the presence of spen mutant clones. Crosses were performed as described in Figure 1, and third instar imaginal disks were isolated from progeny female larvae with the following genotypes: {MS1096 GAL4/+; 40 2piM FRT40A/ GFP FRT40A; UAS-Flp1/ +} (A-C), and {MS1096 GAL4/ +; spenpoc361 FRT40A/ GFP FRT40A; UAS-Flp1/ +} (D-I). Delta expression (in red) was detected in by using a mouse anti-Delta MAb followed by a Cy3-conjugated anti mouse antibody. The area indicated by the arrows in E and F is shown at higher magnification in G to I. Dorsal is up and anterior is to the left.
Dl protein expression was normal in third instar wing discs containing homozygous clones for both the 2 piM, and the GFP FRT marker chromosomes, which confer nuclear GFP expression (Figure 5A-C). In the presence of spen mutant clones, Dl expression was inconsistently abnormal: In some cases we observed ectopic expression of Dl within spen mutant clones, although this was not the norm. Similarly, Dl expression could be absent from normal regions adjacent to spen mutant cells, suggesting a non-autonomous effect (Figure 5D-I). Most frequently, however, the abnormal expression of Dl within a spen clone was consistent with a shift or misplacement of Dl-expressing cells. As shown in Figure 5 (Panels E, F and H, I), Dl-positive cells are located away from the position where they are expected, at the intersection of the prospective L4 vein at the wing margin.
One of the genes whose expression delineates the D/V boundary at the wing margin beginning at mid to late third instar is the homeodomain transcription factor Cut (Ct) [24,25]. The analysis of Ct expression is of particular interest for our study because both the N and wg signaling pathways cooperate to maintain its expression at the margin [26,27], and both signaling pathways have been reported to be affected by spen mutations [11-13]. As shown (Figure 6), the expression of Ct was not subjected to major alterations at the wing margin in the presence of spen mutant clones. Occasionally, Ct expression was broader, or detected in cells a few cell diameters away from the margin (Figure 6G-L), coinciding with the presence of spen mutant cells. This observation is consistent with our previous results showing abnormal spatial expression of Dl, which is in turn required to restrict Ct expression to the margin [27].
Figure 6 Analysis of Cut protein expression in the presence of spen mutant clones. Crosses were performed as described in Figure 1, and third instar imaginal disks were isolated from progeny female larvae with the following genotypes: {MS1096 GAL4/+; 40 2piM FRT40A/ GFP FRT40A; UAS-Flp1/ +} (A-C), and {MS1096 GAL4/ +; spenpoc361 FRT40A/ GFP FRT40A; UAS-Flp1/ +} (D-L). Cut expression (in red) was detected with an anti-Cut MAb as described in Figure 4 for Dl, and the absence of GFP (in green) defines mutant cells as explained on Figure 4. G to H shows a magnification of the spot indicated by an arrow on E and F. Note that the Cut protein is present in spen mutant cells. Panels J to L show the margin of another disc not shown in the figure. The arrow indicates a group of heterozygous cells that are away from the margin, surrounded by a group on spen mutant cells. Dorsal is up and anterior is to the left.
The organization of the PNS is abnormal in both adult spen mutant clones and maternal and zygotic spen mutant embryos
It was unclear whether the effect of spen mutant clones on the spacing of dorsal sensory bristles at the anterior wing margin was due to abnormal positioning or to a defect in the correct specification of sensory organ precursors. To determine if this effect could be generalized to other adult structures, expression of the Flp1 recombinase was directed to the anterior compartment of the wing disc with a dppDISC-GAL4 driver [28]. Although in this case the clones were not marked in adults, analysis of GFP expression in third instar imaginal discs showed that spen mutant clones were generated throughout the anterior compartment of the wing disc, including most of the prospective notum (data not shown).
The generation of large spen mutant clones in the notum severely affected the final adult pattern of both the macro and microchaetae (Figure 7). This patterning defect was easily observed in the alignment of microchaetae along the dorsal midline, and the overall phenotype was more penetrant in males than in females. The number and position of macrochaetae were also affected, and the observed defects included their loss (Figure 7C), gain (Figure 7B,C,E, and 7F), and abnormal positioning (Figure 7B,C,E,F). In each case, these abnormalities were never associated with the appearance of double trichogens (bristles) or thormogens (sockets), which would indicate a defect in the specification of particular cell fates during the formation of the external sensory organs.
Figure 7 Sensory bristle number and position is abnormal in notums containing spen mutant clones. Virgin females with the genotype {spenpoc231 FRT40A/ ln (2LR) CyO; dppDISK GAL4/ TM6b} or {spenpoc361 FRT40A/ ln (2LR) CyO; dppDISK GAL4/ TM6b}, were crossed to {w*; GFP FRT40A; UAS-Flp1/ TM6b} males, and adult notums were isolated from progeny females (A-C), or males (D-F) with the following genotypes: {spenpoc361 FRT40A/ GFP FRT40A; dppDISK GAL4/ TM6b} (A, D), {spenpoc231 FRT40A/ GFP FRT40A; dppDISK GAL4/ UAS-Flp1} (B, E), and {spenpoc361 FRT40A/ GFP FRT40A; dppDISK GAL4/ UAS-Flp1} (C, F). Lines delineate the bristles at the dorsal midline. Empty circles indicate loss of bristles (in C), and arrows indicate either gain, or abnormal location of macrochaetae (B, C, E, and F).
Previous reports have linked spen to the organization of the embryonic peripheral and central nervous systems [1,8,12]. In agreement with these studies, and with our findings in adults, we observed an abnormal distribution of neurons in spen maternal and zygotic embryos, as evidenced by immunodetection of the pan neural marker Elav (Figure 8). Similar to the previously described phenotypes, they had variable penetrance with a change in the number of Elav positive cells (either more of fewer) not consistently observed.
Figure 8 Abnormal positioning of PNS neurons in spen maternal and zygotic mutant embryos. Maternal and zygotic spen mutant embryos were obtained as described in Materials and Methods, and at stage 14–15 were stained for the neuron specific marker Elav, together with an anti β-galactosidase antiserum to reveal the presence of the CyO, wg-lacZ balancer in heterozygotes (not shown), followed by biotinylated secondary antibodies, and streptavidin conjugated horse radish peroxidase. Brown staining reveals the nuclei of PNS neurons in wt (A, B), maternal and zygotic spen231 (C, D), or spen361 (E, F) embryos.
Abnormal PNS distribution correlates with altered epidermal expression of E-cadherin in spen mutant embryos
Peripheral neurogenesis starts at the epidermis, where sensory organ precursors are specified, and give rise to sensory organ cells through carefully polarized cell divisions. Aside from these divisions, additional cell types might be recruited from the epidermis to take part in the formation of internal sensory organs [reviewed in [29]]. Increased epithelial activity at the sites of sensory organ formation may reveal defects in cell adhesion, such as those caused by mutations in the E-Cadherin mutant shotgun (shg), whose loss of function results in holes in the epithelium. These holes, which later appear in the cuticle, presumably arise from a failure to re-establish a status quo at sites of high morphogenetic activity [30].
The fact that spen mutant embryos die at the end of embryogenesis with sclerotic patches and holes in their epidermis in the ventrolateral thorax and lateral abdomen [[3]; K. Mace, J. Pearson, W. McGinnis, submitted], together with the evidence that the embryonic PNS is disorganized in these embryos, may suggest a defect in cell adhesion at these sites. As shown in Figure 9, embryos lacking spen function display abnormal PNS neuron positioning and morphology compared to wild type as revealed by 22C10 staining (compare Figures 9A and 9D). The surrounding epidermal cells show a dramatic upregulation of E-cadherin (compare Figures 9B and 9E). The placement of these abnormal neurons is precisely within this field of epidermal cells (see merge, Figure 9F) that have been shown to be undergoing a wound response due to a failure of epidermal epithelial integrity [K Mace, J Pearson, W McGinnis, submitted].
Figure 9 Increased epidermal expression of E-cadherin correlates with abnormal positioning of embryonic PNS neurons. Stage 16 wild type embryos (A-C) or maternal and zygotic spen mutant embryos (D-F) were generated as described in Materials and Methods. The expression of the PNS neuronal marker 22C10 (green) and E-cadherin (red), were detected with specific MAbs followed by fluorescein conjugated anti-mouse antiserum and a Cy3 conjugated anti rat as described earlier in Figure 4, and in the Material and Methods section.
Because E-cadherin is an important intercellular adhesion molecule, we wanted to test whether the defects seen in the developing adult wing were also associated with changes in its expression. Additionally, we assayed the expression of Crumbs, another component of the adherens junction, and β-tubulin, a marker of cell polarity. None of these proteins showed any detectable changes in expression or localization within spen mutant clones in the developing wing disc (not shown). It is possible that, because the wing disc epithelium is not subject to the same level of stress as the embryonic epidermis at the sites of internal sensory organ formation, upregulation of E-Cadherin expression is specific to the embryonic phenotype, and does not occur in wing discs. Therefore, it remains unclear how spen could affect, if at all, cell adhesion in this tissue.
Discussion
spen mutant cells are viable
The generation of large spen mutant clones in adults, using heat-shock driven expression of FLP recombinase in a Minute background indicated that spen function was essential during all stages of development. This result is consistent with the pleiotropic effects previously described for spen. However, when clones were generated specifically in wing imaginal discs, we observed that mutations in the spen gene did not affect the viability or the size of mutant cells during growth of the disc, as evidenced by normal cell size and number within the adult clones. These observations are intriguing given previous reports linking the function of spen to cell cycle progression [9,10]. In these studies, spen mutations were shown to enhance morphological abnormalities upon overexpression of wild type E2F [9] and Cyclin E [10], leading to the conclusion that spen has a negative regulatory role on these cell cycle regulators. If spen had a negative role in cell cycle progression in the wing imaginal disc, we would expect that spen mutant clones would be larger than their twin spots after mitotic recombination. However, other authors have observed that spen mutant clones are indeed smaller than their wild type counterparts [13]. If that were the case, we should expect that the spen mutant cells would increase their size, in order to offset a decrease in cell division rates, as it is the case for E2F mutants [20]. However, as stated above, both cell size and total area coverage were similar between spen mutant and wild type cells. This observation is in agreement with our observations in spen maternal and zygotic mutant embryos, which do not show differences in cell cycle progression versus wt embryos as assessed by 8-Bromo deoxy-Uridine incorporation, string transcript expression, or Proliferating Cell Nuclear Antigen (PCNA) protein expression (data not shown). Alternatively, there is no evidence of increased cell death in spen mutant cells as revealed by Acridine Orange staining, or reaper mRNA expression (data not shown). Therefore, we conclude that the function of the spen gene per se is not essential for cellular viability and normal progression through the cell cycle.
spen and Ras signaling
Recently, there has been increasing experimental evidence suggesting that the product of the spen gene might be an integral component of the Ras signaling cascade. Such an interaction has been found in the search for genes interacting with a viable allele of corkscrew [6], and in gain of function screens utilizing the overexpression of components of the Ras pathway during eye development. These include activated Raf [4], kinase supressor of ras (ksr) [5], and a constitutive repressor form of Anterior Open (Aop) /Yan [7]. Wing vein formation in Drosophila provides an amenable system to analyze mutations affecting the MAPK pathway, as it depends on the function of the EGF receptor (DER), as well as other genes encoding components of the pathway, such as Star (S), rhomboid (rho), and the DER ligand, vein (vn) [19,31]. While loss of rho and S function result in non-autonomous loss of vein phenotypes, gain of function of DER and rho have the opposite effect [19,31,32].
If the product of the spen gene were an integral component of the Ras pathway, we would expect that the loss of function of spen would generate similar phenotypes as those obtained with other genes acting in the DER pathway. However, this was not the case: spen mutant clones generated during wing development showed both indiscriminate loss and gain of vein material, resulting in a vein phenotype that could not be directly correlated with any mutant in the Ras pathway known to date. Similarly, the phenotypes observed in spen mutant embryos do not clearly correlate with mutations in components of the Ras signaling pathway. For instance, the expression of orthodenticle (otd) mRNA at the ventral midline, which is dependent on DER function, and is abnormal in mutants that are defective in this signaling pathway [33], was indistinguishable between spen maternal and zygotic mutant embryos and wild type embryos (data not shown). Thus, our data do not support a direct correlation between the loss of spen function and a specific defect in DER/Ras-dependent signaling during embryogenesis or during imaginal disc development.
spen and Notch signaling
A relationship between spen and Notch (N) function has been previously suggested, where the former appears to be necessary to maintain the embryonic expression of Suppressor of Hairless (Su(H)) [12] a downstream effector of N [34]. The observation that the gain of function of spen may also interact with N signaling [11] further strengthens the relationship between N and spen function. Additionally, there is supporting biochemical evidence indicating that both the human and murine orthologs of Spen, interact with RBP-Jκ/CBF-1, a mammalian ortholog of Su(H) [17,18]. The interaction of both SHARP and MINT with RBP-Jκ/CBF-1 prevents the interaction of the latter with the intracellular fragment of activated N, thus suggesting that both SHARP and MINT are negative regulators of N signaling in mammals. On the other hand, inactivation of the murine MINT gene does not clearly reflect a defect in N signaling. Loss of function of MINT in hematopoietic precursors, revealed that splenic B cells differentiated more efficiently toward the marginal zone than to the follicular type. This phenotype, attributed by the authors to a defect in N signaling, is in conflict with the fact that the election of T versus B cell fates in the lymphoid lineage, also dependent on N signaling, appears unaffected [18]. Indeed, variations in the numbers of follicular and marginal zone B cells, as reported for the MINT-deficient B cells, may also be attributed to migration defects leading to their abnormal distribution within the spleen [35]. Thus, it seems that a specific role for MINT in the mammalian N pathway has not been clearly defined yet.
The displacement of veins observed in adult wings in the presence of spen mutant clones, frequently accompanied by widening of the veins, is a phenotype consistent with defects in the N signaling pathway [19]. Furthermore, this phenotype correlates with abnormal (diffuse and/or ectopic) expression of Delta (Dl) in third instar imaginal discs both in spen mutant clones, and in cells adjacent to these clones. However, the role of the N signaling pathway has been well established as crucial for the determination of cell types during the development of external sensory organs. Defects in the N pathway alter sense organ cellular composition by affecting alternative cell fate decisions [36,37]. In our experimental system, the generation of spen mutant clones did not interfere with bristle formation per se, but with their spatial distribution. Both micro and macrochaetae were incorrectly positioned throughout the notum and, in some cases, chaetae were absent, but ectopic supernumerary bristles were also seen. Nevertheless, all external sensory organs appeared to have normal morphology, suggesting that there were no mis-specifications of cell fates, as it would be expected for N signaling defects. Additional evidence that supports this is that the expression of Cut at the wing margin, which is dependent upon both Su(H) and N function [26,27], was detected within spen mutant clones. Therefore, we conclude that the function of spen is not essential for N signaling in wing imaginal discs.
spen and wg signaling
Recent evidence suggests that spen is required to transduce some aspects of the Wingless (Wg) signal in the wing imaginal disc, showing a requirement for spen for the expression of senseless, a downstream target of the Wg pathway [13]. The loss of Senseless in spen mutant clones would be predicted to lead to the absence of external sensory organs in adult wings [38], which is a phenotype that we did not observe with the alleles tested. Furthermore, we could not consistently correlate any of the abnormalities observed in adult wings containing spen mutant clones to known defects in the Wg signaling pathway. Clonal analysis in third instar imaginal discs did not reveal specific defects in the Wg pathway either: Wg signaling is required for Dl expression at the wing margin [27], and Dl expression was topologically affected but not absent in spen mutant clones. Taken together, it appears that spen function is required for one Wg signaling target (Senseless) [13], but not for others (Dl). These results, that could be explained by the differential penetrance of the different spen alleles used, do not seem to support a principal role for Spen in Wg signaling during wing imaginal disc development.
A general role for Spen?
Based on previous reports and the data presented herein, the available evidence does not support a specific role for spen in any signaling pathway in particular. We would like to propose that the common theme that could best define spen function at the morphological level is that it appears necessary for the correct spatial organization of individual cells within a specific group during growth and development.
How could Spen instruct cells to maintain a specific position, without affecting their fate directly? A plausible explanation is that it could affect cell adhesion. In fact, in spen mutant embryos, the expression of E-cadherin was up-regulated at sites of high epithelial morphogenetic activity, generating a phenotype similar to the E-cadherin mutant embryos, as it has been observed in other studies [39]. It is plausible that the increase in E-cadherin expression is the result of a wound response to a defect in epithelial integrity, caused by spen mutations [K. Mace, J. Pearson, and W. McGinnis, submitted]. A defect in cell adhesion and/or cytoskeletal rearrangements could also explain specific aspects of the spen embryonic phenotype. The holes that result in abnormal cuticle deposition in the embryonic epidermis are due to a failure of epidermal epithelial integrity. These cells subsequently undergo a wound response at the end of embryogenesis. Additionally, some of the phenotypes resulting from the loss of spen are indeed similar to those seen in mutants for the gene encoding Daschous, a cadherin involved in cell adhesion [40]. However, blistering of the wings, a phenotype that is often found in cell adhesion mutants, was never observed in any of the spen mutant clones.
A role for Spen in cell adhesion and/or cytoskeletal rearrangements could also be inferred through its genetic interaction with crinkled (Myosin VIIA), and the planar cell polarity phenotype observed in mutant cells for spen in the wing blade. Myosin VIIA is associated with the cadherin-catenins complex and participates in the creation of a tension force between the actin cytoskeleton and adherens junctions, which is predicted to strengthen cell-cell adhesion [41]. Furthermore, ck acts downstream of Drosophila Rho-associated kinase (Drok), which links Frizzled-mediated planar cell polarity signaling to the actin cytoskeleton [42]. Myosin VIIA mutations have been described in vertebrates, including those causing the Usher syndrome in humans [43], the shaker-1 mutation in mice [44], and the mariner mutation in zebrafish [45]. Interestingly, these mutations, among other symptoms, cause splaying and abnormal distribution of sensory hair cells in the inner ear, leading to deafness in mice and humans, and mechanosensory defects in zebrafish. It seems plausible that spen may regulate the expression or function of components affecting the outcome of pathways involved in cytoskeletal rearrangements and epithelial planar polarity and, hence, affect cell positioning. However, a direct requirement for spen function in the Ck or Drok pathways is unlikely, since mutations in these genes result in different phenotypes than those observed in spen mutants.
An influential role for spen in mechanisms of intercellular adhesion and/or cytoskeletal rearrangements may also be relevant to understanding its suggestive role in human cancer. The search of public human sequence resources [46,47] reveals one spen ortholog (SHARP), and three putative Short Spen-like Protein (SSLP) orthologs in the human genome (Figure 10). At least one of these genes (OTT/RBM15) is involved in a recurrent translocation detected in acute megakaryocitic leukemia [48,49], and a potentially aberrant transcript for another human SSLP ortholog at 3p21 has been identified in cDNA isolated from human cancer cells (Figure 10). Despite the presence of common domains, the functional relationship between large and small Spen-related polypeptides is still unknown. It is plausible that in Drosophila, SSLP might rescue some of the functions of spen during early embryonic development, as evidenced by the incomplete penetrance of phenotypes seen in spen maternal and zygotic mutant embryos. Complementation at this level has been suggested by others to explain the incomplete penetrance of spen mutations in wing discs [13], although it should be noted that the region required for Spen to interact with transcription factors such as Msx-2 or nuclear receptors, is apparently missing in SSLP proteins. Therefore, the potential redundancy of Spen and SSLP will have to be determined.
Figure 10 Human spen-related genes. The figure shows all Drosophila and human related sequences putatively encoding polypeptides with three RNP-type RNA binding motifs at the N terminus (purple boxes), and the SPOC domain at the C terminus (yellow boxes). The gray box on Hs SHARP indicates the region that contains motifs required for the interaction of SHARP/MINT with Msx-2 (residues 2070 to 2394 [14]), nuclear receptors (residues 2201 to 2707 [15]), and RBP-Jκ (residues 2803 to 2817 [17], and 2638 to 2777 [18]). The expected sizes for each peptide is shown on the right, while names and chromosomal localization in humans is on the left. Asterisks indicate that the peptides have been predicted from the genomic sequence, either because there are no known full length ESTs corresponding to the genomic regions analyzed (case for Hs SSLP at 3p21), or because there are no reported ESTs at all (Hs SSLP at 5q23). In the case of the SSLP at 5q23, there are also stop codons in frame with the putative ORF, so it is likely that this sequence represents a pseudogene. Accession numbers for the sequences likely to represent full length cDNAs are: AAF13218 (Dm SPEN), NP_055816 (Hs SHARP), AAF59160 (Dm SSLP), NP_073605 (OTT/RBM15), and CAC38829 (OTT-MAL fusion). The putative full length ORF for the Hs SSLP at 3p21 was predicted using GENESCAN [54] on the genomic sequence AC092037. The truncated cDNA arising from this gene is found under AAA72367 or NP_037418. The Hs SSLP at 5q23 was predicted with GENESCAN from the genomic segment AC005915, and the assembly was completed with ENSP322787, a predicted peptide from the Ensembl Database [42].
Conclusions
We have shown that the function of the spen gene is essential for all stages of development. The experimental evidence indicates that Spen participates in processes that regulate planar cell polarity and may influence cytoskeletal organization, and its loss results in specific phenotypes that can not be solely explained by defects in a specific signaling pathway. In order to unify our observations with those previously reported by others, we would like to propose that the function of Spen is necessary for the maintenance of correct cell positioning during growth, ensuring that structures that are determined early during development are correctly positioned in the adult (Figure 11). As cells are determined early during development to become part of a specific structure, their position has to be maintained during growth according to a pre-established pattern. If cells were unable to maintain their position, we would expect phenotypes similar to those obtained in spen mutant clones. Structures would be misplaced, and in some cases would be absent if the cells that were predetermined to adopt a specific fate fall within "forbidden" positions (Figure 11). This mechanistic model could explain why spen interacts genetically with signaling pathways that require and/or specify precise spatial organization during metazoan development.
Figure 11 A mechanistic model for Spen function. The cartoon illustrates our conclusions to explain the defects seen in the presence of spen mutant clones. Wild type or heterozygote cells are depicted with green nuclei, and spen mutant cells with gray nuclei. The appearance of spen mutant cells in fields that will give rise to specific structures such as bristles or veins would imply the lack of an instructive signal to remain in place during growth of the disc. This will finally result in a progressive mis-localization of cells, ultimately leading to the abnormal positioning of structures after development is completed. Such a model would explain that, in some cases as in the proneural clusters, a change of fate is generated because negative instructive signals that depend on cell to cell contact are lost, resulting in the formation of two sensory organ precursor (SOP) cells within the same pro neural cluster (shown as red cells in B and C). The same situation may occur in the formation of wing veins, where similar Notch dependent regulatory mechanisms take place (E). Loss of or veins (or bristles) may occur when vein-forming cells move into intervein regions after their commitment has taken place, therefore leaving a hole where they should have been, which has been filled with cells that are unable to form vein at that spot.
Methods
Drosophila stocks
All flies were grown at 21°C in standard medium, and were obtained from the Bloomington Stock Center, unless indicated otherwise. The stocks {y, w; spenpoc361, ck, FRT40A/In(2LR)O, Cy}, {MS1096-GAL4; spenpoc361, ck, FRT40A/In(2LR)O, Cy, wg-lacZ}, {MS1096-GAL4; spenpoc231, FRT 40A/In(2LR)O, Cy, wg-lacZ}, {MS1096-GAL4; ck, FRT40A/In(2LR)O, Cy, wg-lacZ}, {MS1096-GAL4; 2 piM FRT40A/ In(2LR)O, Cy, wg-lacZ}, {ck FRT40A/ In(2LR)O, Cy; dppDISC-GAL4/TM6B}, {spenpoc361, ck FRT 40A/In(2LR)O, Cy; dppDISC-GAL4/TM6B}, {spenpoc361, FRT40A/In(2LR)O, Cy; dppDISC-GAL4/TM6B}, {spenpoc231, FRT40A/In(2LR)O, Cy; dppDISC-GAL4/TM6B}, and {y, w; GFP(2L), FRT40A; UAS-Flp1/TM6B} were generated with the following lines: {spenpoc231/In(2LR)O, Cy, wg-lacZ}, {spenpoc361/In(2LR)O, Cy, wg-lacZ} [3], {ck, FRT40A /In(2LR)O, Cy} (obtained through recombination from the line {SX155, ck, FRT 40A/In(2LR)O, Cy} [31], {w*; P{w+mC = UAS-Flp1.D}JD2/TM3, Sb1}, {w1118; P{w+mC = Ubi-GFP(S65T)nls}2L P{ry+t7.2 = neoFRT40A}/In(2LR)O, Cy}, {w1118; P{w+mC = piM}21C P{w+mC = piM}36F P{ry+t7.2 = neoFRT40A}, {w1118; +; dppDISC-GAL4/In(3LR)TM6B} [28], and {MS1096-GAL4X} [22]. The line {hsFlp1; M(2)36F, FRT40A} was generated with {hsFlp1; nocSco/In(2LR)O, Cy}, {y, w; FRT40A}, and {M(2)36F/SM5}. Other stocks used were {ck14/In(2LR)O, Cy}, {ck16/ In(2LR)O, Cy}, and y, w*; P{w+mC = UAS-GFP::lacZ.nls}15:3.
The ovoD technique [50] was used to generate maternal germline clones as previously described [3]. These females were crossed to {Df (2L)TE21A/In(2LR)O, Cy, wg-lacZ} males, that carry a deletion spanning the spen locus, to obtain maternal and zygotic mutant embryos that were collected at 25°C.
Immunodetection
To preserve GFP fluorescence, third instar imaginal discs were collected in cold PBS and fixed for 10–20 minutes on ice with methanol free 10% formaldehyde (Polysciences, Inc, Warrington, PA). After washing thoroughly with PBT (PBS with 0.1% Tween 20), and preincubating in the same buffer containing 10% Bovine Serum Albumin (BSA), the fixed discs were incubated with antibodies in PBT with 1% BSA. The mouse anti Drosophila Delta 594-9B monoclonal antibody (MAb) [51] was used purified at a concentration of 1:1000. The 22C10 [52], and anti Cut 2B10 [53] MAbs were obtained from the Developmental Studies Hybridoma Bank (DSHB, University of Iowa Department of Biological Sciences, Iowa City, IA), and used as recommended. The E-Cadherin rat MAb [54] was used at a 1:20 dilution. For fluorescent detection, FITC or Cy3-conjugated donkey anti mouse, or goat anti rat (Jackson Immunoresearch, West Grove, PA) were used. Discs were whole-mounted in mounting medium (Vector Laboratories, Burlingame, CA). Fluorescent images were captured with a Spot Digital Camera (Diagnostic Instruments, Inc, Sterling Heights, MI) using a Nikon Microphot-FXA microscope. Confocal images were acquired on a Leica TCS SP2 confocal microscope (Mannheim, Germany).
The embryonic expression of the neuron-specific marker Elav was immunodetected as described [55] with the mAb 9F8A9 [56] obtained from DSHB, and was used as a culture supernatant at 1:100, followed by incubation with a biotinylated goat anti-mouse (Jackson Immunoresearch, West Grove, PA), and a streptavidin-horse radish peroxidase (HRP) conjugate (DAKO, Carpinteria, CA). Similarly, β-galactosidase (LacZ) was detected with a rabbit antiserum (Cappel-ICN Biomedicals, Irvine, CA), at a 1:100 dilution, followed by a biotinylated goat anti-rabbit as above. Peroxidase activity was detected with the Immunopure Metal Enhanced DAB Substrate Kit (Pierce Biotechnology, Rockford, IL).
Adult structures
Adult flies were collected in 70% ethanol, and stored in isopropanol. Wings were detached from the dehydrated adults and mounted with DPX (Fluka, Buchs, Switzerland). Notums were dissected, embedded in Lactic Acid: Hoyers (1:2) [57], and photographed in the same medium after clearing (usually 24 hours) using the equipment described above.
Computer aided sequence analysis
Human genomic, and human and Drosophila cDNA sequences were retrieved from the Ensembl Genome Server [46], and from databases at the National Center for Biotechnology Information [47]. Sequence searches were performed using BLAST [58]. Composite multiple alignments were performed with MACAW [59] and Clustal X [60]. Genomic DNA sequences coding for putative spen related cDNAs in the human genome were analyzed by using GENESCAN [61].
Abbreviations used
DAB: Di amino benzidine
GFP: Green Fluorescent Protein
HRP: Horseradish peroxidase
LacZ: β-galactosidase
MAb: Monoclonal antibody
SSLP: Short Spen-like protein
Authors' contributions
KM performed the experiments shown in Figure 9, contributed to the generation and analysis of maternal and zygotic spen mutant embryos, and examined the expression of cell adhesion and polarity markers in spen mutant clones in wing discs, most of which are results not shown. She also actively participated in the writing and the elaboration of the conclusions of this work. AT did the rest.
Acknowledgements
This work was carried out in the laboratory of Bill McGinnis, and we are very grateful for his support and advice. We thank Ethan Bier, Jim Posakony, and Matt Ronshaugen for helpful comments and discussions, and John Newport and Dave Kosman for help with the confocal microscope. Katherine Harding, Elizabeth Wiellette, Ethan Bier, Annabel Guichard, Tadashi Uemura, and Spyros Artavanis-Tsakonas kindly provided materials used in this study. We also acknowledge the Bloomington Stock center for providing fly stocks, and the Developmental Studies Hybridoma Bank for antibodies. KM was supported by a shared NIH Cancer Training Grant and a U.S. Dept of Education GAANN fellowship and AT was supported by Bill McGinnis.
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| 15596016 | PMC544560 | CC BY | 2021-01-04 16:30:29 | no | BMC Dev Biol. 2004 Dec 13; 4:15 | utf-8 | BMC Dev Biol | 2,004 | 10.1186/1471-213X-4-15 | oa_comm |
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BMC GeriatrBMC Geriatrics1471-2318BioMed Central London 1471-2318-4-111558505810.1186/1471-2318-4-11Research ArticleDuloxetine for the long-term treatment of Major Depressive Disorder in patients aged 65 and older: an open-label study Wohlreich Madelaine M [email protected] Craig H [email protected] John G [email protected] Donald P [email protected] Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN 46285, USA2004 7 12 2004 4 11 11 12 4 2004 7 12 2004 Copyright © 2004 Wohlreich et al; licensee BioMed Central Ltd.2004Wohlreich et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Late-life depression is a common, chronic and recurring disorder for which guidelines recommend long-term therapy. The safety and efficacy of duloxetine for the treatment of major depressive disorder (MDD) were evaluated using data from elderly patients (age ≥ 65 years; n = 101) who participated in a large, multinational, open-label study.
Methods
Patients meeting DSM-IV criteria for MDD received duloxetine 80 mg/d (40 mg twice daily (BID)) to 120 mg/d (60 mg BID) for up to 52 weeks. Efficacy measures included the Clinical Global Impression of Severity (CGI-S) scale, the 17-item Hamilton Rating Scale for Depression (HAMD17), the Beck Depression Inventory-II (BDI-II), the Patient Global Impression of Improvement (PGI-I) scale, and the Sheehan Disability Scale (SDS). Safety and tolerability were evaluated using discontinuation rates, spontaneously reported adverse events, and changes in vital signs, ECG, and laboratory analytes.
Results
Mean changes in HAMD17 total score at Weeks 6, 28, and 52 were -13.0, -17.4 and -17.5 (all p-values <.001). Significant improvement (p < .001) in both clinician- (CGI-S) and patient-rated (PGI-I) measures of improvement were observed at Week 1 and sustained throughout the study. Observed case response rates at Weeks 6, 28, and 52 were 62.9%, 84.9%, and 89.4%, respectively, while the corresponding rates of remission were 41.4%, 69.8%, and 72.3%. Adverse events led to discontinuation in 27 (26.7%) patients. Treatment-emergent adverse events reported by >10% of patients included dizziness, nausea, constipation, somnolence, insomnia, dry mouth, and diarrhea. Most events occurred early in the study. Mean changes at endpoint in blood pressure and body weight were less than 2.0 mm Hg, and -0.1 kg, respectively.
Conclusions
In this open-label study, duloxetine was effective, safe, and well tolerated in the long-term treatment of MDD in patients aged 65 and older.
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Background
Late-life depression is a common and disabling condition which represents a substantial public health concern [1]. The prevalence of major depressive disorder (MDD) in the community-dwelling elderly population is estimated at 1–3%, with depressive symptoms being present in approximately 15% [2]. The rate of occurrence of MDD is even higher among institutionalized older patients. In long-term care patients the incidence has been estimated to be 12% to 25%, with subsyndromal depressive symptoms present in an additional 18% to 30% [3].
Despite advances in available antidepressant treatments, limitations still exist in both efficacy and safety. Tricyclic antidepressants (TCAs) generally provide robust efficacy, but a number of side effects associated with this class of medications are of particular concern in older patients (e.g. anticholinergic adverse events, orthostatic hypotension, and sedation). Selective serotonin reuptake inhibitors (SSRIs) have provided an improved tolerability profile compared to the TCAs through lower rates of adverse events, and substantially lower toxicity in overdose [4]. Furthermore, SSRIs do not appear to exhibit age-related increases in occurrence of adverse events [5]. However, these newer selective antidepressants appear, in general, to achieve equivalent or lower remission rates compared with the older tricyclics [6].
Duloxetine is a potent dual reuptake inhibitor of serotonin (5-HT) and norepinephrine (NE) [7]. The efficacy of duloxetine in the acute treatment of MDD has been established in randomized, double-blind, placebo-controlled studies in patients aged 18 and older [8-11]. A subsequent post-hoc analysis of efficacy data from these studies, focusing upon those patients aged 55 and older receiving once-daily duloxetine (60 mg), supported the findings in the general patient population [12].
The safety and tolerability of duloxetine have also been demonstrated under double-blind conditions. In placebo-controlled trials of duloxetine in patients aged 18 and older (doses from 40 – 120 mg/d) the most frequently reported adverse events were nausea, headache, dry mouth, fatigue, insomnia, and dizziness, while the overall safety profile of duloxetine was comparable to that of available SSRI medications [11]. A comparable safety and tolerability profile was observed following a post-hoc analysis of data from those patients aged 55 and older, including a low incidence of cardiovascular adverse events and minimal effects upon blood pressure and heart rate [12].
However, these acute placebo-controlled trials of duloxetine were of 9 weeks duration or less. An NIH consensus panel has recommended that geriatric patients be given continuing antidepressant treatment for at least 6 months for a first episode and for at least 1 year for recurrent episodes [13], while some investigators suggest that maintenance treatment in the elderly be extended to 2 years [14]. In order to evaluate the long-term tolerability, safety, and efficacy of duloxetine, a one-year open-label trial in depressed patients was undertaken. This report examines the subset of patients aged 65 and older who participated in the study. While patients in this study received doses of 80 mg/d or 120 mg/d, it should be noted that the approved dose range for duloxetine for the treatment of MDD is 40–60 mg/d.
Methods
Study design
This was a 52-week, open-label, single-arm study of outpatients (aged ≥ 18 years) meeting Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) [15] criteria for MDD. The study included a total of 1279 patients at 52 investigative sites in Argentina, Brazil, Canada, Columbia, Mexico, the United States, and Venezuela. The primary objective of the study was to evaluate the safety of duloxetine (80 or 120 mg/d given as two equal doses per day, i.e. 40 to 60 mg BID) for up to 52 weeks. During the first week of therapy, all patients received duloxetine 40 mg BID. Patients unable to tolerate 40 mg BID could have their dose decreased to 20 mg BID, but were required to increase the dose to 40 mg BID at Week 2. Patients unable to tolerate 40 mg BID were discontinued from the study. During the remainder of the study, the patient's dose could be adjusted up to 60 mg BID or down to 40 mg BID, based upon the physician's clinical evaluation of tolerability and efficacy.
This report focuses upon data taken from the subset of patients aged 65 years and older (n = 101) within the larger study described above.
Patients
The study protocol was approved by the ethics committee at each site in accordance with the principles of the Declaration of Helsinki. All patients provided written informed consent prior to the administration of any study procedures or study drug. All patients were required to have a Clinical Global Impression of Severity (CGI-S) score ≥ 3 at the screening and baseline study visits.
Patients were excluded for the following reasons: a previous or current diagnosis of schizophrenia, schizophreniform disorder, schizoaffective disorder, or bipolar disorder; presence of an Axis II disorder that would interfere with protocol compliance; serious medical illness; taking benzodiazepines on a daily basis for ≥ 2 weeks prior to enrollment; a history of substance dependence within the last year; or a positive urine drug screen. Subjects judged to be at risk for suicide were also excluded.
Concomitant medications
Patients were not permitted to receive other antidepressant, antimanic, or antipsychotic agents during the study. Episodic use (≤ 3 consecutive days, and no more than 100 total days) of benzodiazepines was permitted. The use of benadryl, chloral hydrate, cough and cold medications, and narcotics, was allowed on an episodic basis only. Subjects were permitted to take antihypertensives, antiarrhythmics, antibiotics, and multivitamins among other medications while in the study.
Efficacy measures
Efficacy was assessed using the CGI-S scale [16] (a priori specified as the primary outcome), the HAMD17 total score [17], HAMD17 subscales (core – Items 1, 2, 3, 7, and 8; Maier – Items 1, 2, 7, 8, 9, and 10; anxiety/somatization – Items 10, 11, 12, 13, 15, and 17; retardation – Items 1, 7, 8, and 14; sleep – Items 4, 5, and 6), the Beck Depression Inventory-II (BDI-II) [18], and the Patient Global Impression of Improvement (PGI-I) scale [16]. Patient-rated quality of life was evaluated using the Sheehan Disability Scale (SDS) [19], which is a composite of 3 self-rated 10-point Likert response subscales (0 = no disability, 1–3 = mild, 4–6 = moderate, 7–9 = marked, 10 = extreme) to assess work, family, and social functioning during the past month. All outcomes were assessed at Weeks 6, 28, and 52, or upon early discontinuation, except for PGI-I and CGI-S scales which were collected at all visits. Patients were defined as responders if they had a decrease from baseline of at least 50% in HAMD17 total score. Patients were defined as remitters if they had a HAMD17 total score ≤ 7.
Safety measures
Safety measures included spontaneously reported adverse events, serious adverse events (events that led to outcome of death, inpatient hospitalization, cancer, severe or permanent disability, congenital abnormality, or life-threatening condition), vital signs, electrocardiograms (ECGs), and laboratory analyses. Adverse events and vital signs were collected at each visit.
Lilly reference ranges were used to define limits for abnormal laboratory values [20], and potentially clinically significant (PCS) changes in selected laboratory analytes [21]. PCS changes in blood pressure were defined as follows:
(i) Low supine (or standing) systolic BP: ≤ 90 mm Hg and a decrease from baseline of ≥ 20 mm Hg;
(ii) High supine (or standing) systolic BP: ≥ 180 mm Hg and an increase from baseline of ≥ 20 mm Hg
(iii) Low supine (or standing) diastolic BP: ≤ 50 mm Hg and a decrease from baseline of ≥ 15 mm Hg;
(iv) High supine (or standing) diastolic BP: ≥ 105 mm Hg and an increase from baseline of ≥ 15 mm Hg.
Patients were considered hypertensive at baseline if they had a historical diagnosis, secondary condition, or adverse event at the baseline visit consistent with a clinical diagnosis of hypertension or high blood pressure.
ECGs were collected at baseline and Weeks 4, 28, 52 or at early discontinuation. Patients at 2 sites in Mexico and 1 site in Columbia also had ECGs over-read by a cardiologist at a central location. For these ECGs, QT intervals were corrected (QTc) using Fridericia's correction (QTcF). All other patients had ECGs read by the site for classification as either normal or abnormal. Limits for PCS QTc values were an increase in QTcF of ≥ 30 msec and any postbaseline value ≥ 450 msec for males or ≥ 470 msec for females [22].
Statistical analyses
Mean changes from baseline to last observation in laboratory analytes, vital signs, and ECG intervals were assessed using ANOVA with models that included investigator. Longitudinal mean changes and categorical changes (temporal patterns) were assessed via a likelihood-based repeated measures approach. Models for mean changes included investigator, visit, baseline value, and baseline-by-visit interaction.
Mean change in CGI-S score was compared between younger (age <65) and elderly (age ≥ 65) patients using the repeated measures analysis as previously described, with age group and age group-by-visit interaction added to the model. Differences between young and elderly patients in rates of treatment emergent adverse events were assessed using Fisher's exact test.
Results
Patient disposition
This report was based on data from 101 patients aged 65 and older. The oldest patient was 87 years of age, while the median age was 70. Patient characteristics at baseline are summarized in Table 1.
Table 1 Summary of patient demographics and psychiatric historya
Duloxetine, 80–120 mg/d† (n = 101)
Gender, n (%)
Female 72 (71.3)
Male 29 (28.7)
Age, y 71.9 (5.4)
Age range, y 65 – 87
Weight, kg 66.5 (14.5)
Ethnicity, n (%)
Caucasian 43 (42.6)
Hispanic 55 (54.5)
Other 3 (3.0)
Age at onset, y 63.5 (13.3)
Current duration, wks 86.0 (161.0)
Number of previous episodes 1.1 (2.1)
Duration of last episode, wks 57.6 (110.2)
a. Listed as mean (SD) unless otherwise stated.
† Administered as 40 mg BID or 60 mg BID
Efficacy
Mean changes from baseline for all efficacy outcomes were highly significant (p < .001, t-test for mean change) at all assessment times (Table 2). In the case of CGI-S and PGI-I scales, significant improvements were observed at Week 1 and at all subsequent visits (p < .001, t-test for mean). Observed case response rates at Weeks 6, 28, and 52 were 62.9% (44/70), 84.9% (45/53), and 89.4% (42/47), respectively, while the corresponding rates of remission were 41.4% (29/70), 69.8% (37/53), and 72.3% (34/47), respectively.
Table 2 Efficacy outcome measures
Outcome measure Mean baseline score Mean change (SE)
Week 6 Week 28 Week 52
CGI-Severity 4.51 -2.08 (0.11)** -2.93 (0.12)** -3.15 (0.12)**
PGI-Improvement N/A 2.33 (0.14)** 1.83 (0.16)** 1.84 (0.16)**
HAMD17 Total Score 21.8 -13.0 (0.7)** -17.4 (0.8)** -17.5 (0.8)**
Anxiety subscale 6.70 -3.46 (0.30)** -4.89 (0.33)** -4.90 (0.34)**
Core subscale 8.83 -5.65 (0.33)** -7.50 (0.36)** -7.61 (0.38)**
Maier subscale 10.7 -6.64 (0.38)** -8.90 (0.42)** -9.06 (0.44)**
Retardation subscale 7.84 -4.49 (0.27)** -6.58 (0.30)** -6.49 (0.31)**
Sleep subscale 3.68 -2.34 (0.21)** -2.84 (0.23)** -2.83 (0.24)**
HAMD17 Item 1 2.64 -1.73 (0.11)** -2.30 (0.13)** -2.30 (0.13)**
HAMD17 Item 3 0.74 -0.58 (0.06)** -0.59 (0.06)** -0.61 (0.06)**
BDI-II Total Score 29.5 -15.8 (1.0)** -22.3 (1.1)** -22.0 (1.1)**
Sheehan Disability Scale
Work item 6.91 -3.01 (0.32)** -4.60 (0.37)** -4.27 (0.39)**
Family item 6.82 -3.63 (0.32)** -4.88 (0.35)** -4.95 (0.37)**
Social item 7.27 -3.45 (0.34)** -4.57 (0.38)** -4.85 (0.40)**
** p < .001 from t-test for mean change
CGI-Severity = Clinical Global Impression of Severity; PGI-Improvement = Patient Global Impression of Improvement; HAMD17 = 17-item Hamilton Rating Scale for Depression; BDI-II = Beck Depression Inventory-II
A comparison of visitwise mean changes in CGI-S score between elderly patients (age ≥ 65, n = 101) and those patients in the study aged <65 years (n = 1178; Figure 1) revealed a somewhat more rapid onset of efficacy in younger patients, with differences between age groups being statistically significant at Weeks 2, 3 and 4. At subsequent visits the differences between age groups became progressively smaller, and mean changes were essentially equal at the study endpoint.
Figure 1 Comparison of mean change in CGI-Severity score for duloxetine-treated patients aged ≥ 65 years (n = 98) and age 18–64 years (n = 1121). * p ≤ .05 for between-group comparison.
Treatment discontinuation
The most common reasons for study discontinuation were adverse event (26.7%), personal conflict/other reasons (9.9%), and noncompliance (5.0%). The adverse events leading to discontinuation in >1.0% of enrolled patients at a duloxetine dose of 80–120 mg/d were somnolence (4.0%), dizziness (3.0%), diarrhea (2.0%), hypertension (2.0%), and vomiting (2.0%). Two-thirds of the discontinuations due to adverse events (18/27) occurred within 2 weeks of initiation of therapy.
Serious adverse events
A total of 9 enrolled patients reported serious adverse events during the study. Most of these events were considered by the investigator to be unrelated to duloxetine exposure. The serious adverse events reported by more than 1 patient were hip fracture (2), and confusion (2), while there were single reports of agitation, angina pectoris, cerebrovascular disorder, coronary artery atherosclerosis, dementia, dizziness, hypomania, and myocardial ischemia. Individual occurrences were few, thus no clear temporal pattern of incidence of each event could be determined.
Treatment-emergent adverse events
Treatment-emergent adverse events occurring in >5% of patients during the open-label therapy phase (Weeks 1 through 52) are summarized in Table 3. The incidence for these events during Weeks 1 to 8 and Weeks 9 to 52 are also listed in Table 3. During Weeks 1 through 52, adverse events reported by more than 10% of patients were dizziness, nausea, constipation, somnolence, insomnia, dry mouth, diarrhea, headache, and increased sweating. Over 75% of occurrences of these events were rated as mild or moderate in severity. The incidence of treatment-emergent adverse events was lower during the latter 44 weeks of the study (Weeks 9 to 52) than during the first 8 weeks. Each event with an incidence of at least 5% during Weeks 9 to 52 was also present at the same or higher rate during the first 8 weeks.
Table 3 Treatment-emergent adverse events†
Event Weeks 1–8, n (%) Weeks 9–52, n (%) Weeks 1–52, n (%)
Nausea 29 (28.7) 0 (0.0) 29 (28.7)
Dizziness 27 (26.7) 5 (5.0) 31 (30.7)
Somnolence 22 (21.8) 1 (1.0) 23 (22.8)
Constipation 20 (19.8) 5 (5.0) 23 (22.8)
Dry mouth 16 (15.8) 4 (4.0) 18 (17.8)
Insomnia 15 (14.9) 8 (7.9) 22 (21.8)
Headache 11 (10.9) 6 (5.9) 16 (15.8)
Increased sweating 11 (10.9) 4 (4.0) 15 (14.9)
Diarrhea 11 (10.9) 6 (5.9) 17 (16.8)
Tremor 7 (6.9) 2 (2.0) 9 (8.9)
Anxiety NEC 7 (6.9) 3 (3.0) 10 (9.9)
Fatigue 7 (6.9) 4 (4.0) 9 (8.9)
Decreased appetite 7 (6.9) 1 (1.0) 7 (6.9)
Vomiting 7 (6.9) 3 (3.0) 10 (9.9)
Anorexia 6 (5.9) 3 (3.0) 8 (7.9)
Back pain 5 (5.0) 2 (2.0) 6 (5.9)
Abdominal pain upper 4 (4.0) 2 (2.0) 6 (5.9)
† Events with an occurrence > 5% in Weeks 1–52.
Rates of occurrence of other adverse events of importance in an elderly population were low: 2 patients experienced a fall, while there were single reports of syncope and postural hypotension. When analyzed by age group, patients aged 65 and older were found to report a significantly lower incidence of insomnia and headache than those patients aged <65 (Table 4). No other significant differences were observed between age groups.
Table 4 Treatment-emergent adverse events by age group†
N (%)
Event Age 18 – 64 (n = 1178) Age ≥ 65 (n = 101) p-Value
Nausea 406 (34.5) 29 (28.7) .274
Insomnia 378 (32.1) 22 (21.8) .034
Headache 373 (31.7) 16 (15.8) <.001
Somnolence 358 (30.4) 23 (22.8) .114
Dry mouth 282 (23.9) 18 (17.8) .180
Dizziness 267 (22. 7) 31 (30.7) .085
Constipation 250 (21.2) 23 (22.8) .705
Increased sweating 177 (15.0) 15 (14.9) 1.00
Anxiety 176 (14.9) 10 (9.9) .188
Diarrhea 157 (13.3) 17 (16.8) .363
Fatigue 125 (10.6) 9 (8.9) .735
† Events with an occurrence > 10% in Weeks 1–52.
Cardiovascular profile
Mean changes from baseline to last observation for standing and supine systolic and diastolic blood pressures were less than 2 mm Hg and not significantly different from zero: supine systolic BP -1.5 mm Hg (p = .364), supine diastolic BP -1.8 mm Hg (p = .141), standing systolic BP -1.9 mm Hg (p = .269), standing diastolic BP -0.1 mm Hg (p = .907). Using repeated measures analysis, mean changes in blood pressure were <4 mm Hg at every visit from baseline to endpoint.
A mean change analysis was utilized to compare blood pressure in patients who were hypertensive (n = 40) versus non-hypertensive (n = 58) at baseline. Baseline hypertensive patients exhibited small mean decreases (<4 mm Hg) in both standing and supine systolic and diastolic blood pressures from baseline to endpoint, while patients who were not hypertensive at baseline demonstrated mean changes in these same measures of 0.3 to -1.1 mm Hg (Figure 2).
Figure 2 Mean change from baseline to endpoint in blood pressure (mm Hg) for baseline hypertensive and non-hypertensive patients aged ≥ 65 years receiving duloxetine (80–120 mg/d). p > .10 for all between-group comparisons.
Mean baseline-to-endpoint increases were observed for supine pulse (mean change = 1.6 bpm, p = .105) and standing pulse (mean change = 1.1 bpm, p = .338) but these values did not differ significantly from zero.
Rates of occurrence of potentially clinically significant (PCS) values for systolic and diastolic blood pressures were generally low. The incidence of PCS low standing systolic blood pressure was 5/96 (5.2%), while all other assessed blood pressure and pulse readings had incidences of PCS values <2.5%.
There were no significant changes in cardiac intervals detected by ECG. Mean changes from baseline to last observation were: PR -3.3 msec (p = .363), QRS -2.5 msec (p = .420), QT 5.0 msec (p = .730), and QTcF 6.2 msec (p = .553). No patient experienced a PCS QTcF value during the course of the study.
Body weight
After 52 weeks of treatment, the mean change in weight from baseline to last observation was -0.1 kg (p = .741), while a mean weight change of +0.3 kg was determined using MMRM analysis (p = .386 for t-test for mean change at endpoint; Figure 3). Mean changes in weight at early visits were negative (weight loss), mean changes at intermediate visits were near zero, while mean changes at later visits were positive (weight gain). A total of 3/98 patients (3.1%) experienced PCS weight loss while 6/98 (6.1%) reported a PCS weight gain (PCS weight change is defined as a change of ≥ 10% of baseline body weight). The 3 patients displaying PCS weight loss had baseline body mass indices (BMI) of 24.9, 28.5 and 32.1, while those experiencing weight gain had BMIs at baseline ranging from 19.9 to 26.7.
Figure 3 Mean change in weight (kg) for duloxetine-treated patients aged ≥ 65 years (dose 80–120 mg/d, n = 98). *p ≤ .05 from t-test for mean change.
Laboratory analytes
Statistically significant mean changes were observed in some laboratory analytes. Despite the statistical significance, the magnitudes of the mean changes were generally small and not considered clinically relevant in light of the low incidence of potentially clinically significant (PCS) values.
Discontinuation-emergent adverse events
All patients who proceeded past Week 52 received no study drug for 2 weeks until Week 54 via abrupt discontinuation (no taper). Discontinuation-emergent adverse events occurring in ≥ 5% of patients were dizziness (8.9%), anxiety (7.9%), headache (5.0%), and insomnia (5.0%).
Discussion
The current analysis focused upon 101 depressed patients aged 65 years and older who received long-term, open-label treatment with duloxetine (80 mg/d or 120 mg/d). Efficacy was demonstrated on all assessed outcome measures, both clinician- and patient-rated. Highly significant improvements were seen in both patient- and clinician-rated depression and health outcome scales (CGI-S, HAMD17, BDI-II, PGI-I, SDS) at all visits. By way of comparison, significantly greater improvements for duloxetine compared with placebo were observed in HAMD17 total score, HAMD17 subscales and CGI-S score in two 9-week, placebo-controlled studies of duloxetine (60 mg once daily (QD)) in patients aged 55 years and older [12].
Onset of efficacy is an important consideration in antidepressant trials, but in the absence of a placebo arm it is especially difficult to define and assess [23]. However, the significant improvements from baseline in CGI-S and PGI-I scales at Weeks 1 and 2 are consistent with results from double-blind, placebo-controlled trials in which duloxetine demonstrated significant superiority over placebo as early as Week 1 on core emotional symptoms of depression (HAMD17 Maier subscale), and global improvement (CGI-S scale) [8]. It has also been suggested that treatment response may be slower and/or less robust in an elderly population compared with a younger cohort [24]. Indeed, in the present study a more rapid onset of efficacy was observed in duloxetine-treated patients aged 18–64 when compared with those patients aged ≥ 65. However, the magnitude of treatment differences between age groups progressively diminished and was not significant at any visit after Week 4. This result may have substantial clinical relevance for long-term treatment. It suggests that, although those patients aged 65 and older may exhibit a somewhat less rapid onset of antidepressant action than a younger cohort, elderly patients are able to reach and sustain a level of depressive symptom improvement equal to that observed in younger patients.
In this study, observed case response and remission rates following 6 weeks of open-label duloxetine therapy (62.9% and 41.4%, respectively) were comparable to the response and remission rates (52.8% and 44.1%, respectively) observed in older patients in two 9-week double-blind, placebo controlled trials of duloxetine (60 mg QD) [12]. Furthermore, remission rates at 52 weeks in the present study were only slightly less than response rates (72.3% and 89.4%, respectively), implying that those patients who responded had a high probability of achieving complete symptom resolution. A growing body of evidence suggests that remission, rather than response, should be the goal of antidepressant treatment [25]. Responders who do not remit may have appreciable residual symptomatology, and patients with residual symptoms have been found to be at higher risk for relapse or recurrence [26]. Given the high rates of relapse and recurrence observed among elderly patients, achievement of remission assumes an added degree of importance.
In light of the recommendation that elderly patients receive at least 12–18 months of antidepressant therapy [27], the long-term safety and tolerability of these medications are of considerable importance. Duloxetine was safely administered and well-tolerated in this long-term study. While the discontinuation rate due to adverse events (26.7%) was somewhat higher than that observed in older patients (aged ≥ 55) in two 9-week, placebo-controlled trials of duloxetine 60 mg QD (21.0%), the difference in these rates suggests that few patients stopped taking medication during the periods associated with continuation and maintenance treatment. The discontinuation rate is also comparable to that observed in a 54-week study of fluoxetine in elderly patients [28], and is only slightly higher than that obtained from a meta-analysis of acute-phase (≤ 8 week) trials of SSRIs in elderly patients (14.3%–22.8%) [29]. Given the one-year duration of this study, and the administration of duloxetine at the upper end of its studied dose range (80–120 mg/d) throughout the trial, the long-term tolerability of duloxetine in elderly patients appears to be comparable to that of SSRIs.
The incidence and pattern of treatment-emergent adverse events during Weeks 1 to 8 of this study were generally similar to those observed in acute-phase, placebo-controlled trials in older patients [12]. The most frequently reported adverse events were nausea, dizziness, somnolence, constipation, and dry mouth. Most of the events were either mild or moderate in severity and transient in nature. During the last 44 weeks of the study, no adverse event occurred in more than 8% of the patient population and the incidence of each specific event was generally lower in the entire period from Weeks 9–52 than in the initial 8 weeks of the study. Thus, patients who tolerated duloxetine during the early period of the trial were likely to tolerate long-term dosing.
Administration of medication to elderly patients necessitates consideration of the physiological changes which accompany aging. Such changes can result in substantial differences in adverse event profiles between older and younger patient populations [30]. In this study, comparisons between age groups (18–64 years vs. ≥ 65 years) of the most commonly reported treatment emergent adverse events revealed significant differences only in the rates of insomnia and headache. Furthermore, in each of these cases the higher rates were observed in the younger age group. In the absence of a placebo control arm these results must be viewed with an appropriate degree of caution, but they provide an indication that the adverse event profile for duloxetine in the elderly may be similar to that observed in younger patients.
Antidepressants with benign cardiovascular profiles may be particularly suitable for the treatment of an elderly population, in which heart disease is more prevalent than in younger patients [31]. In this study, duloxetine-treated patients exhibited small (less than 2 mm Hg) mean changes in blood pressure from baseline to endpoint and low rates of PCS blood pressure values. Furthermore, those patients with baseline hypertension demonstrated a mean decrease in blood pressure compared with normotensive patients. Consistent with the profile of duloxetine as a NE reuptake inhibitor, small mean increases (less than 2 bpm) were observed in heart rate. Mean changes in corrected QT interval were small and not significantly different from zero, suggesting duloxetine did not prolong QT intervals. Collectively, these data indicate that duloxetine exhibits a favorable cardiovascular profile in elderly patients.
Weight change is an important consideration in older patients being treated with antidepressants [32], especially during long-term treatment. Following 52 weeks of open-label duloxetine treatment, mean change in weight from baseline to last observation was -0.1 kg. Repeated measures analysis was used to derive a longitudinal profile of weight change. This revealed a small (<1 kg) decrease in weight at early visits, consistent with the weight change of -0.2 kg observed in older patients in two 9-week acute trials of duloxetine [12]. However, mean changes at intermediate visits approached zero, while mean changes at the last 2 visits were positive (weight gain). A total of 3/98 patients (3.1%) reported a PCS weight loss while 6/98 (6.1%) reported a PCS weight gain. By way of comparison, a recent study of weight change among depressed nursing facility residents aged >65 who received ≥ 6 months of antidepressant treatment found rates of clinically important weight loss and weight gain (defined as ≥ 10% change in body weight or Minimum Data Set-Plus weight loss or weight gain marker) of 14.7% and 14.4%, respectively [33].
It is important to consider all of the safety findings described here in light of the dosing and design requirements of the study. The doses used in this open-label study were up to 2-fold greater than the once-daily 60 mg duloxetine dose which has been shown to provide robust efficacy in older patients in placebo-controlled trials [12]. The dosing and other design features of the study (e.g. the intensive visit schedule) were specifically included to maximize the probability of uncovering adverse reactions to duloxetine. Furthermore, no special dosing guidelines were implemented for these elderly patients. While lower doses of many antidepressants are recommended in the elderly [34], especially due to concerns of adverse events among the TCAs, this can lead to the use of subtherapeutic doses and corresponding reductions in efficacy [35]. In this study, however, the comparable adverse event profiles observed for elderly and younger age groups suggest that a duloxetine dose which has been shown to provide robust efficacy may be safely administered in depressed patients regardless of age. Only in particularly sensitive elderly patients may dosing adjustments be required.
Conclusions
Results from this open-label study of depressed patients aged 65 and older suggest that duloxetine is safe and well tolerated in long-term use. Statistically significant and clinically relevant improvements in all assessed efficacy measures were observed at each patient visit. Furthermore, the efficacy and adverse event profile of duloxetine appears to be comparable in older (age ≥ 65) and younger patients (age 18–64). These results, together with those obtained from acute phase, double-blind, placebo-controlled trials, support the efficacy of duloxetine in the treatment of major depression in older patients.
Competing interests
Drs. Wohlreich, Mallinckrodt, and Watkin are employees of Eli Lilly and Company. Dr. Hay was employed by Eli Lilly and Company at the time of the study.
Authors' contributions
MMW, CHM, JGW, and DPH participated in interpretation of data and drafting of the manuscript. CHM carried out the statistical analyses. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This work was sponsored by Eli Lilly and Company. All of the authors accept full responsibility for the conduct of this trial. The authors had full access to all data from the trial and participated in the decision to publish the data.
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| 15585058 | PMC544561 | CC BY | 2021-01-04 16:30:12 | no | BMC Geriatr. 2004 Dec 7; 4:11 | utf-8 | BMC Geriatr | 2,004 | 10.1186/1471-2318-4-11 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-491557920610.1186/1471-2202-5-49Research ArticleHeritability of Stroop and flanker performance in 12-year old children Stins John F [email protected] Baal G Caroline M [email protected] Tinca JC [email protected] Frank C [email protected] Dorret I [email protected] Department of Biological Psychology, Free University of Amsterdam, Van der Boechorststraat 1, 1081 BT Amsterdam, the Netherlands2 Department of Child and Adolescent Psychiatry, ErasmusMC-Sophia Children's Hospital, PO Box 2060, 3000 CB Rotterdam, The Netherlands2004 3 12 2004 5 49 49 1 9 2004 3 12 2004 Copyright © 2004 Stins et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
There is great interest in appropriate phenotypes that serve as indicator of genetically transmitted frontal (dys)function, such as ADHD. Here we investigate the ability to deal with response conflict, and we ask to what extent performance variation on response interference tasks is caused by genetic variation. We tested a large sample of 12-year old monozygotic and dizygotic twins on two well-known and closely related response interference tasks; the color Stroop task and the Eriksen flanker task. Using structural equation modelling we assessed the heritability of several performance indices derived from those tasks.
Results
In the Stroop task we found high heritabilities of overall reaction time and – more important – Stroop interference (h2 = nearly 50 %). In contrast, we found little evidence of heritability on flanker performance. For both tasks no effects of sex on performance variation were found.
Conclusions
These results suggest that normal variation in Stroop performance is influenced by underlying genetic variation. Given that Stroop performance is often hampered not only in people suffering from frontal dysfunction, but also in their unaffected relatives, we conclude that this variable may constitute a suitable endophenotype for future genetic studies. We discuss several reasons for the absence of genetic effects on the flanker task.
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Background
The Stroop test [1] is arguably the best-known neuropsychological test to tap attentional (dys)function. In the color words version of this test the instruction is to attend to the color of the ink in which a word is printed and name this color aloud. At the same time, the printed words may also read certain color names that are different from the color of the ink in which it is printed. As has been observed on numerous occasions, there is a strong tendency to respond to the content of the word, and not to the ink color. This is evidenced by an increase in response time and a decrease in accuracy relative to a neutral control condition.
The Stroop test has been used both to tap fundamentals of human information processing (e.g. [2]), and as a clinical aid to assess attentional dysfunction, e.g., due to a frontal or fronto/parietal deficit. Brain imaging and neurological studies consistently point to the prefrontal cortex (PFC) as the site involved in resolving the response conflict. As a consequence, people suffering from attentional impairments, caused by prefrontal abnormalities (developmental or acquired), tend to suffer more from Stroop interference than controls. For example, the test succesfully differentiates unaffected controls from people suffering from schizophrenia (e.g., [3]). In a similar vein, people suffering from attention-deficit/hyperactivity disorder (ADHD) suffer from Stroop interference ([4]; see also [5]), although a recent meta-analysis cast some doubt about the usefulness of the Stroop task in differentiating people with ADHD from controls [6].
There now exist numerous versions of the Stroop test. For example, instead of using color words, researchers have adopted more ecologically relevant items, such as emotion words, pictures of food items or of threatening objects, etc. In addition, it is now also common to use computerized versions of the Stroop task, permitting a trial-by-trial analysis of performance. But what all these different Stroop versions have in common is that the subject is always presented with a stimulus that simultaneously activates two conflicting response channels; one response is activated by the instructions, whereas the other response is activated by elements in the array that strongly invite an alternative – yet incorrect – response. In order to resolve this response conflict the subject has to direct attention to task relevant information and ignore information from the task irrelevant channel. The time needed to resolve this conflict is derived using subtractive logic, and can be used as an index of the efficiency of the attentional system under investigation.
A task that is less widely used in clinical circles, but that also indexes the efficiency of the frontal network is the Eriksen flanker task. In the arrow version of this task, subjects have to respond to the direction of a left or right pointing arrow, and ignore flanking arrows that point in the opposite direction as the target arrow [7]. Similar to the Stroop task, there is a tendency to respond to the distracting flanker elements, and subjects have to resolve this response conflict prior to emitting the designated response. It is consistently found that response times are elevated due to the target-flanker incongruity, relative to a neutral control condition where target and flankers are congruent (that is, they all point in the same direction). There is evidence that the Stroop task and the flanker task are supported by the same cognitive system. For example, using functional magnetic resonance imaging (fMRI) it was found that both tasks activated largely overlapping brain regions, viz. the anterior cingulate cortex (ACC) and the left prefrontal cortex [8]. In addition, and similar to the Stroop task, subjects with ADHD spend more time resolving the conflict between the competing responses than controls (e.g., [9]). Also, adult subjects with ADHD showed consistent underactivation in the ACC (cognitive division) during a counting version of the Stroop task, compared to controls [10].
The Stroop task (and, to a lesser extent, the flanker task) has thus acquired a strong neuropsychological validation, and is nowadays widely used in clinical settings. However, studies adopting an individual differences paradigm have revealed that the time needed to resolve the response conflict in the Stroop task does not predict the time needed to resolve the response conflict in the flanker task. In an earlier study we [11] found that the interference scores between the tasks were uncorrelated. A similar finding was reported by [12] using slightly different task versions.
Further insight into the nature of these interference tasks might be gained by adopting a genetic perspective on normal and abnormal frontal functioning. A wealth of studies has now shown that many frontal psychopathologies are influenced by genes. For example, the heritability of ADHD is estimated to be around 80% (e.g.[13]). In a similar vein, the heritability of attention problems as established by questionnaires is estimated to be around 70 – 90% (e.g. [14]). However, genetic studies are often hampered by the fact that psychopathologies are multifacetted and complex. A recent line of inquiry has started to use a 'bottom-up' approach, trying to decompose the complex phenotype (behavior) into a set of variables that are thought to represent more basic processes or traits. In this so-called endophenotypic approach the search is for neuro-behavioral vulnerability markers that are somewhere intermediate the genes and the disease [15,16]. Endophenotypic measures gathered in children can be used to assess genetic vulnerability to adult psychiatric disorders [17]. In this paper we will try to assess whether, and to what extent Stroop and/or flanker performance can qualify as genetic indicators for frontal abnormalities. The usesfulness of measures of Stroop performance in a genetically informative design has recently been demonstrated in a study [18] that compared Stroop performance among children suffering from ADHD, their unaffected sibs, and a group of controls. It was found that not only the children with ADHD, but also their unaffected sibs suffered more from Stroop interference than the controls. In a similar vein, it was found that not only euthymic bipolar and schizophrenic patients, but also their unaffected first-degree relatives suffered from increased Stroop interference, relative to a healthy control group [19,20]. However, another study [21] failed to find deteriorated Stroop performance in unaffected ADHD sibs.
The usefulness of measures of flanker performance in a genetically informative design was demonstrated by a series of studies conducted by Fan and co-workers. Using a sample of healthy monozygotic (MZ) and dizygotic (DZ) twins, it was tested whether genetic variation contributed to variations in performance on basic attentional tasks [22]. These tasks were designed to tap distinct attentional brain networks (see also [23]). Of interest is performance on the flanker task, which was supposed to index the efficiency of the dopamine rich frontal executive network. Performance on this task indeed showed evidence of heritability. In a follow-up study, 200 subjects were genotyped, and were tested on a range of attention tasks. Modest associations were then found between genetic polymorphisms of several genes implicated in frontal (dys)function, such as drd4 and dat1, and the efficiency of the frontal executive attention network [24]. Using the same twin methodology, it was also found [25] that performance on the flanker task was heritable. In addition, there was a correlation between flanker performance and IQ, and this correlation was completely mediated by a common set of genes.
In this paper we ask whether variation in normal Stroop and flanker performance is caused by genetic variation. By using monozygotic twins, who share all their genetic material, and dizygotic twins, who share on average half of their segregating genes, the influence of genetic factors and environmental factors can be teased apart. If genetic effects are important, then members of monozygotic twin pairs will be more similar than members of dizygotic twin pairs in test performance. Conversely, if MZ twins show the same degree of resemblance as DZ twins, influences of environmental factors that are shared by both twins (e.g., the school or family environment) will be important. The contributions of additive genetic factors, shared environmental factors and unique environmental factors for explaining the variance observed for these measures can be explored using this twin design. If Stroop and/or flanker performance is found to be heritable, we will have a further genetic (in addition to neuropsychological) validation of these tasks with respect to frontal dysfunction. Furthermore, a high heritability of performance measures may ultimately help to unravel the genetic pathways of complex psychiatric traits.
This paper is a follow-up to a previous paper, where we reported behavioral data on Stroop and flanker performance [11]. The current paper extends the previous one by investigating genetic effects on variation in performance.
Results
For the Stroop task, the data from 5 subjects (3 first born twins and 2 second born twins) could not be analyzed because they failed to comply with the instructions. Visual inspection of the data revealed that a few subjects had an extremely high Stroop interference score. Four subjects whose interference score was larger than 120 s were excluded from the analysis.
Due to technical problems the data of 24 subjects for the flanker task were not stored or collected. Furthermore, there were 2 subjects who had an extremely high error score (> 20 errors out of 80 trials). These subjects were excluded from the analyses.
Descriptives
Table 1 shows the time to complete each of the three cards, separate for the first-born twins and the second born twins. The table reveals a clear increase in performance time from Card 1 to Card 2 to Card 3. The analysis of variance (ANOVA) for the first-born twins showed that there was a significant effect of card type, F(2, 278) = 1351.7, p < .001. The main effect of sex was not significant (p > .1), nor its interaction with card type. For the second born twins a near-identical pattern of results was found: a main effect of card type, F(2, 276) = 1236.2, p < .001, and no effects involving sex. Thus, we obtained a robust Stroop effect, and this was not affected by the sex of the subject. Accuracy data can be found in Table 2.
For the flanker task we found the following effects: For the first born twins the main effect of stimulus type was significant, F(1, 128) = 463.00, p < .001. Congruent stimuli yielded faster RTs than incongruent ones (556 vs. 662 ms). The main effect of sex was not significant (p > .1), nor its interaction with stimulus type. For the second born twin the main effect of stimulus type was significant, F(1, 132) = 556.02, p < .001. Again, congruent stimuli yielded faster RTs than incongruent ones (551 vs. 653 ms). Also, the main effect of sex was significant, F(1, 132) = 5.38, p < .05. Boys were somewhat faster than girls (587 vs. 618 ms).
The same analyses done on the error rates yielded a comparable pattern of results. For the first born twins the main effect of stimulus type was significant, F(1, 128) = 84.86, p < .001. Congruent stimuli yielded more correct responses than incongruent ones (98.1% vs. 94.9%). In addition, the main effect of sex was significant, F (1, 128) = 4.57, p < .05, as was its interaction with stimulus type, F (1, 128) = 5.03, p < .05. These effects indicate that boys tend to respond somewhat less accurate than girls on incongruent trials. For the second born twins also the main effect of stimulus type was significant, F(1, 132) = 115.18, p < .001. Again, congruent stimuli yielded more correct responses than incongruent ones (98.8% vs. 93.9%). The main effect of sex was not significant, but the interaction was significant, F(1, 132) = 4.05, p < .05. Again, boys tended to respond somewhat less accurate than girls on incongruent trials.
In order to test whether for the flanker task there was a trade-off between response speed and accuracy, we simply correlated RTs with accuracy, separately for the first born and second born twins. A possible speed-accuracy trade-off would manifest itself as a significant negative correlation between mean reaction time and percentage of errors. For the first born twins we found a significant positive correlation (r = .37, p < .001). But this correlation appeared to be due to a handful of subjects who were both quite slow and error prone. For the second born twins the correlation was small (r < .1), and not significant. Thus, we conclude that in our sample there was no evidence of a speed-accuracy trade-off.
A similar analysis was done for the Stroop task. We correlated the average completion time of Card 3 with the number of errors commited with Card 3. We did not correlate speed with the number of corrections, because these measures are not independent. For both the first born twins and the second born twins we found a significant positive correlation (r = .31, and r = .21, respectively. p's < .05). Thus, subjects who were slow also tended to be inaccurate. However, the distribution of the number of errors was rather skewed (most subjects made 0 or 1 errors), which makes it difficult to interpret these correlations. So, similar to the flanker task, we conclude that there was no evidence of a speed-accuracy trade-off.
Genetic analyses
Table 3 shows twin correlations of times to complete Card 1, 2 and 3, and of the interference effect (Stroop effect; difference between Card 3 and Card 2). For the 3 cards, a very consistent pattern is seen: MZ correlations are high, around .7, and DZ correlations are approximately half, implying the existence of genetic influences and unique influences, with a heritability of around 70%. The twin correlations of the interference effect are somewhat lower, probably because difference scores tend to have a lower reliability[11]. MZ correlations were around .5 and DZ correlations were lower, pointing to a heritability of about 50%.
Table 4 shows the twin correlations for the overall RT and the flanker interference effect (RT [incongruent] minus RT [congruent]). The pattern of twin correlations is hardly indicative of genetic effects on performance. Even though the MZ twin correlations on response speed were higher than the DZ correlations, the highest twin correlation was obtained with the DOS zygosity group. Furthermore, the highest twin correlation for the flanker effect was obtained with the DZF group. In addition, the twin correlations within the monozygotic groups were low. We therefore conclude that there were no genetic effects of flanker performance.
The twin correlations obtained with the Stroop task thus appeared strongly indicative of genetic effects. Using structural equation modelling, these effects were formally tested. But prior to testing we had to establish whether there were significant differences in variances across sex and zygosity, since one of the assumptions underlying structural equation modelling is the assumption of homogeneity of variances. We conducted the Levene test on all variables, separately for the first-born and the second born twins. For none of the variables the Levene test yielded a significant effect, with the possible exception of the completion time of Card 1 for the second born twins, F(3, 136) = 2.664, p = 0.05. So, we felt it was legitimate to use structural equation modelling to test for genetic effects.
Additional file 1 shows the results. The full ACE model, which allowed for sex differences in parameter estimates fitted well to the data (χ2 ranged from 7.732 to 8.883, df = 9, p's ranged from .448 to .561), with the possible exception of Card 1 (χ2 = 15.339, df = 9, p = 0.082), although the most parsimonious model for Card 1 fitted slightly better (χ2 = 17.240, df = 13, p = 0.189). Sex differences in parameter estimates could be discarded from the models, although they were almost significant for Card 3 (χ2 = 7.652, df = 3, p = 0.054). Common environmental influences were not necessary to describe the data, but additive genetic influences explained a significant part of the variance in all 4 variables. Heritabilities for the 3 cards were 75%, 70% and 74% respectively, with confidence intervals indicating that these were well above half the variance. Heritability of the interference effect was 49%, with a 95% confidence interval between 29 and 64%.
Discussion
In this study we assessed the heritability of performance on two well-known response interference tasks: the color word Stroop task and the Eriksen flanker task, using a large sample of 12-year old twins. The aim was to test whether Stroop performance and/or flanker performance could qualify as a suitable endophenotype for genetic frontal abnormalities, such as ADHD. First, we found that the time to complete each of the three cards was highly heritable. This may represent a general factor related to processing speed and/or rapid naming speed. Of greater importance was the finding that the interference score (the difference between completion times of Card 2 and 3) was also heritable: nearly 50% of the variation in performance was due to genetic variation. Thus, the efficiency of the network that deals with response conflict is -in part- under genetic influence.
For the theoretically similar flanker task, however, there was little evidence of genetic influences on performance. Even though the MZ twin correlations on response speed were higher than the DZ correlations (as in [25]), the highest correlation was observed for the DOS twins, for which we have no explanation. In addition, there was no evidence of genetic influences on variations in the size of the flanker effect. Variation in performance thus simply appeared to be due to noise. This latter finding is at odds with a previous study where it was found that variation in the size of the flanker interference effect was 89% due to variations in genes [22]. This discrepancy could of course be due to minor differences between task versions. For example, the flanker test adopted by [22] was embedded in a visual orienting paradigm. But it could also be the case that genetic effects on flanker performance are somehow age specific. Our age group was 12 years old, whereas Fan et al.'s [22] age group was between 14 and 42 years of age. It is well known that heritabilities of different traits vary with age. For example, the heritability of IQ is known to steadily increase with increasing age (e.g. [26]), and it could be the case that genetic effects on flanker performance only emerge at a later age. Finally, it could be that Fan et al. [22] have obtained a false positive result, due to their somewhat modest sample size (26 MZ pairs and 26 DZ pairs).
The question now is whether Stroop and flanker performance can qualify as a suitable endophenotype of frontal pathologies. Recently a list of 5 criteria was compiled that are ideally possessed by endophenotypes [15]. Criteria 3 to 5 deal with the relationship between phenotype and endophenotype. In brief, there should be a high correlation between the phenotype and endophenotype, this correlation should be based in genetics, and the correlation should be theoretically meaningful. In our Introduction we have briefly touched upon the relationship between performance on response interference tasks and high-level phenotypes, such as the efficiency of the frontal executive network. Our selective review of the literature indicated that there was a clear genetic link between Stroop performance and frontal pathologies, whereas evidence for a the genetic link between flanker performance and frontal pathologies was less conclusive.
Criteria 1 and 2 of [15] state that the endophenotype should be reliable and heritable. With respect to heritability, we have demonstrated that – at least for this age group – there is strong evidence for genetic influences on Stroop performance, but not on flanker performance. With respect to reliability, we have no test-retest data but we can assess reliability by examining the MZ correlations, because these correlations provide a lower limit to reliability [27]. Inspection of Table 2 reveals that the Stroop performance measures are characterized by high MZ correlations, which implies high reliabilities. The flanker performance measures reported in Table 3, however, revealed quite low MZ correlations. This finding, in combination with a low split-half reliability (reported in [11]) leads us to conclude that Stroop performance provides a more reliable measure than flanker performance.
Conclusions
We have found evidence for the existence of strong genetic effects on conflict resolution, although the effects are task dependent. We conclude that performance on the Stroop test yields a better endophenotype for frontal (dys)function than performance on the flanker task. So, despite the overlapping regions of brain activation in the Stroop and flanker tasks, and despite their face-value similarity, we believe that these interference tasks differ in important, yet unknown ways.
Methods
Subjects
The subject group consisted of a group of 290 12-year old twins. There were 33 monozygotic male pairs (MZM), 24 dizygotic male pairs (DZM), 45 monozygotic female pairs (MZF), 16 dizygotic female pairs (DZF), and 27 opposite sex pairs (DOS). The twins participate in a longitudinal study of attention and attention problems. The twins are registered in the Netherlands Twin Registry (NTR), which is hosted by the Vrije Universiteit of Amsterdam [28]. The twins were randomly selected from the NTR subject pool. None of the children suffered from severe mental or physical impairments.
Twin pairs were first asked in writing whether they were willing to participate in the study. Permission was also asked of the parents or caretakers. If permission was granted, the families received further information on the study, and were invited to come to the campus site to do the tests. The study was approved by the local Ethics Committee, and on the day of testing the children and their parents / legal representatives signed an informed consent form.
Procedure
Twins were tested on the same day. The children performed a range of neuropsychological tests that lasted approximately 4 hours per child. All tests were performed in the same order. Here we focus on the Stroop Color and Word Test [1] and the Eriksen flanker test. In the Stroop test, subjects complete 3 cards, each with 10 columns of 10 items. Subjects have to name aloud the items on each card, from the top-left corner to the bottom-right corner. Card 1 involves naming the words 'red', 'green', 'yellow' and 'blue' printed in black ink. Card 2 involves naming the colors of squares that are printed in different colors. Card 3 involves naming the ink color that the words 'red', 'green', 'yellow' and 'blue' are printed in. In Card 3 word content and ink color never match, i.e., all color words are incongruent. Speed and accuracy are stressed in the instructions. Each card is scored as the time (in seconds) to complete the card. Time is recorded by the experimenter using a stopwatch. The experimenter also recorded the number of errors (the wrong item is named, or an item is skipped), and the number of corrections (the wrong item is named, but the subject immediately corrects himself afterwards). Note that in this task we do not have trial-by-trial information on speed and accuracy; we only have summary scores across the entire Stroop card.
In the Eriksen flanker task subjects were presented with a horizontal array of 5 arrows. Subjects were instructed to attend to the direction of the center arrow, and ignore the 4 flanking ones. Subjects had to press the left key to a left facing center arrow, and the right key to a right facing center arrow. The flanking arrows could either all point in the same direction as the target arrow (e.g., < < < < <; congruent condition), or they all pointed in the opposite direction (e.g., < < > < <; incongruent condition). Subjects received 40 congruent and 40 incongruent trials in a random order. For each trial, the computer stored the RT and whether the correct key was pressed. The number of valid cases in the flanker task is lower than in the Stroop task. This was due either to practical problems (some data were not collected or not stored on the computer), or because some subjects made an extremely high number of errors (less than 75 % correct). Further details of the Stroop task and the flanker task can be found in [11].
Data analysis: test of means
In order to test for the effects of card type and sex on Stroop performance we performed an analysis of variance on the completion times with card type (1, 2, and 3) as within-subjects factor, and sex (males and females) as between subjects factor. For the flanker task, we performed an ANOVA on the mean correct response times and on the mean percentages correct, with stimulus type (congruent vs. incongruent) as within-subjects factor, and sex (males and females) as between subjects factor. The same analysis was done for the error rates. These analyses were done separately for the first-born twins and the second born twins, because twins within a family do not yield independent data. We adopted an alpha-level of .05.
From the completion times we also calculated the size of the Stroop effect (i.e., the interference score), which is simply defined as Completion time Card 3 minus Completion time Card 2. There exists another method to determine the size of Stroop effect, which also takes into account the completion times of Card 1 [6,29]. But a preliminary analysis revealed that this method and the method used by us yielded virtually identical results, so we present no data based on the method proposed by [29].
Data analysis: genetic analysis
Data from monozygotic (MZ) and dizygotic (DZ) twins were used to decompose the variance in performance on the both tasks into a contribution of the additive effects of genes, environmental influences that are shared by twins living in the same family, and environmental influences that are not shared by twins. Resemblance between MZ twins is an effect of both their common genetic constitution and their shared environment. Because DZ twins share on average half of their segregating genes, the shared environment contributes fully, but genetic factors only partly to their resemblance. Therefore, if the degree of MZ resemblance on some measure is higher than the degree of DZ resemblance we have strong evidence for the influence of genetic effects.
Pearson correlations were calculated for the different measures between first born and second born twins for all zygosity groups. A first indication of the heritability can be derived by doubling the difference between correlations for MZ twins and those for DZ twins [h2 = 2(rMZ-rDZ)] [27].
A structural equation modeling approach as implemented in Mx [30] was used for genetic data analysis. The dependent variables were analyzed using a model including three latent independent factors – additive genetic factors (A), shared or common environmental factors (C) and non shared or unique environmental factors (E) – that influence variation in a particular phenotypic measure of attention (P). A path diagram of an ACE model is presented in Figure 1. Because these latent factors are standardized to have a variance of 1.0, the double-headed arrow connecting them represents the correlation among them. The correlation between genetic effects in twin 1 and twin 2 is 1.0 for MZ twins and 0.5 for DZ twins. These between-twin correlations are represented as fixed parameters in the Mx model, as is the correlation between the common environmental factors (shared by both twins of a twin pair), which is fixed to unity for both twin groups. Parameters a, c and e represent the influence of genes, common environment and unique environment on the phenotypes (P) of twin 1 and twin 2. The total variance of the phenotype (P) = a2 + c2 + e2. The heritability (h2) is calculated as a2/VP.
To test if parameter estimates are equal for boys and girls the fit of a model with constrained parameter estimates for a, c and e to be equal across sexes was compared to one in which they were allowed to vary. After this, the significance of c and a was investigated by dropping them one by one from the model and comparing the fit of a full model to that of a reduced model. The chi-squared statistic is computed as twice the difference between the likelihood for the full model (-LL0) and that for a reduced or constrained model (-LL1) (χ2 = 2 × (LL0-LL1)) and is tested against the difference in degrees of freedom between the two models.
Authors' contributions
JFS drafted the manuscript, and performed the descriptive analyses. GCMvB performed the genetic analyses. TJCP collected the data. FCV and DIB conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Model fitting and parameter estimates Model fitting and parameter estimates of heritability (h2), common environmentability (c2), and unique environmentability (e2), for completion times of the three cards, and of the Stroop interference score. Best fitting models are shown in bold. Parameter estimates are given in percentages (95% confidence interval in brackets).
Click here for file
Acknowledgements
This research was financially supported by The Netherlands Organization for Scientific Research NWO (904-57-94).
Figures and Tables
Figure 1 A path diagram of a univariate ACE model A path diagram of a univariate ACE model (A = Additive genetic factors, C = shared of common environmental factors, E = nonshared environmental effects) in which the three latent independent variables influence variation (indicated by single headed arrows) in a particular behavior or phenotype (P; P1 for twin 1 and P2 for twin 2). MZ = monozygotic; DZ = dizygotic. Partial regression coefficients (letters a, c and e) reflect the degree of relationship between the latent variables and the phenotype. Double headed arrows indicate the correlations among variables.
Table 1 Completion time for each of the three Stroop cards in seconds (standard deviations in brackets), separate for first and second born twins (sample size is given in brackets).
Card type first born twins (141) second born twins (140)
Card 1 52.0 (8.2) 51.0 (7.7)
Card 2 71.0 (11.8) 69.1 (11.3)
Card 3 117.5 (22.9) 117.2 (24.6)
Interference score (3 minus 2) 46.5 (16.6) 48.2 (17.3)
Table 2 Accuracy data for each of the three Stroop cards, separate for first and second born twins (sample size is given in brackets).
Card type Number of errors, first born twins (136) Number of errors, second born twins (135) Number of corrections, first born twins (136) Number of corrections, second born twins (135)
Card 1 .19 .19 .24 .27
Card 2 .61 .51 1.42 1.74
Card 3 1.32 1.44 3.15 3.36
Shown are the mean number of errors (the wrong item is named, or an item is skipped), and the number of corrections (the wrong item is named, but the subject immediately corrects himself afterwards). Note: the sample size is somewhat smaller than for the completion times because accuracy was not recorded with some subjects.
Table 3 Twin correlations for the completion times of each of the three cards and the interference score (95% confidence interval in brackets).
Zygosity N Twin1 N Twin2 Card 1 Card 2 Card 3 Interference score (3 minus 2)
MZM 32 33 0.68 (0.45 – 0.83) 0.78 (0.60 – 0.88) 0.75 (0.55 – 0.87) 0.44 (0.12 – 0.68)
DZM 23 22 0.41 (0.02 – 0.69) 0.47 (0.08 – 0.74) 0.37 (-0.03 – 0.67) 0.11 (-0.30 – 0.49)
MZF 43 43 0.70 (0.51 – 0.82) 0.60 (0.36 – 0.76) 0.70 (0.51 – 0.83) 0.55 (0.29 – 0.73)
DZF 16 16 0.25 (-0.24 – 0.64) 0.38 (-0.10 – 0.72) 0.68 (0.32 – 0.87) 0.32 (-0.17 – 0.68)
DOS 27 26 0.29 (-0.08 – 0.60) 0.31 (-0.07 – 0.61) 0.19 (-0.18 – 0.52) 0.10 (-0.28 – 0.46)
Note: MZM = monozygotic males; DZM = dizygotic males; MFZ = monozygotic females; DZF = dizygotic females; DOS = opposite sex pairs. N Twin 1 is the number of first born twins per zygosity group; N Twin 2 is the number of second born twins.
Table 4 Twin correlations for the RTs on the Eriksen flanker task, and the flanker interference score (flanker effect).
Zygosity N Twin1 N Twin2 Overall RT Flanker effect
MZM 29 31 0.38 0.12
DZM 20 19 -0.01 -0.07
MZF 41 41 0.35 0.09
DZF 14 15 0.18 0.52
DOS 25 26 0.55 0.04
Note: MZM = monozygotic males; DZM = dizygotic males; MFZ = monozygotic females; DZF = dizygotic females; DOS = opposite sex pairs. N Twin 1 is the number of first born twins per zygosity group; N Twin 2 is the number of second born twins.
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| 15579206 | PMC544562 | CC BY | 2021-01-04 16:03:45 | no | BMC Neurosci. 2004 Dec 3; 5:49 | utf-8 | BMC Neurosci | 2,004 | 10.1186/1471-2202-5-49 | oa_comm |
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Environ HealthEnvironmental Health1476-069XBioMed Central London 1476-069X-3-131558506510.1186/1476-069X-3-13ResearchLung function, asthma symptoms, and quality of life for children in public housing in Boston: a case-series analysis Levy Jonathan I [email protected] LK [email protected] Jane E [email protected] Robin E [email protected] Suzanne [email protected] HP [email protected] Harvard School of Public Health, Department of Environmental Health, Landmark Center Room 404K, P.O. Box 15677, Boston, MA, 02215, USA2 School of Nursing, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555-1029, USA3 Department of Pediatrics, Boston Medical Center, 715 Albany St., Boston, MA, 02118, USA4 Boston University School of Public Health, Department of Environmental Health, 715 Albany St., Boston, MA, 02118, USA2004 7 12 2004 3 13 13 14 9 2004 7 12 2004 Copyright © 2004 Levy et al; licensee BioMed Central Ltd.2004Levy et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Children in urban public housing are at high risk for asthma, given elevated environmental and social exposures and suboptimal medical care. For a multifactorial disease like asthma, design of intervention studies can be influenced by the relative prevalence of key risk factors. To better understand risk factors for asthma morbidity in the context of an environmental intervention study, we conducted a detailed baseline evaluation of 78 children (aged 4–17 years) from three public housing developments in Boston.
Methods
Asthmatic children and their caregivers were recruited between April 2002 and January 2003. We conducted intake interviews that captured a detailed family and medical history, including questions regarding asthma symptom severity, access to health care, medication usage, and psychological stress. Quality of life was evaluated for both the child and caregiver with an asthma-specific scale. Pulmonary function was measured with a portable spirometer, and allergy testing for common indoor and outdoor allergens was conducted with skin testing using the prick puncture method. Exploratory linear and logistic regression models evaluating predictors of respiratory symptoms, quality of life, and pulmonary function were conducted using SAS.
Results
We found high rates of obesity (56%) and allergies to indoor contaminants such as cockroaches (59%) and dust mites (59%). Only 36% of children with persistent asthma reported being prescribed any daily controller medication, and most did not have an asthma action plan or a peak flow meter. One-time lung function measures were poorly correlated with respiratory symptoms or quality of life, which were significantly correlated with each other. In multivariate regression models, household size, body mass index, and environmental tobacco smoke exposure were positively associated with respiratory symptom severity (p < 0.10). Symptom severity was negatively associated with asthma-related quality of life for the child and the caregiver, with caregiver (but not child) quality of life significantly influenced by caregiver stress and whether the child was in the intensive care unit at birth.
Conclusion
Given the elevated prevalence of multiple risk factors, coordinated improvements in the social environment, the built environment, and in medical management would likely yield the greatest health benefits in this high-risk population.
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Background
Asthma morbidity and mortality have been increasing in recent years, with a disproportionate impact on urban minority children [1-4]. Hospitalization and morbidity rates have been shown to be elevated for nonwhites versus whites [3] and in inner-city settings with low-income populations [4]. Multiple recent studies have attempted to explain these disparities by evaluating environmental exposures and housing conditions, racial/ethnic variations, poverty, and social or psychological factors, with no definitive conclusions regarding the dominant factors [1,2,5-9].
Regardless of the relative contributions of these and other factors, children in urban public housing are important to consider, because they likely have elevated exposures across numerous domains, some of which could be addressed through development-wide interventions. However, there has been only limited evaluation to date of asthma in this high-risk subpopulation [10,11].
The Healthy Public Housing Initiative (HPHI) is a collaborative effort that includes the Boston Housing Authority (BHA), West Broadway and Franklin Hill Tenant Task Forces, Committee for Boston Public Housing, Boston Public Health Commission, Boston University and Harvard University Schools of Public Health, and Tufts University School of Medicine. A primary goal of HPHI is to evaluate the effectiveness of interventions in reducing known asthma triggers and improving the health of pediatric asthmatics in public housing in Boston.
The effectiveness of environmental interventions in this context will clearly depend on the prevalence of environmentally-linked risk factors within this cohort (i.e., allergy status), as well as the prevalence of other risk factors for asthma morbidity. In addition, when evaluating the efficacy of environmental interventions, numerous health endpoints may be valuable to consider. Health care utilization will inform cost-effectiveness analyses, typically driven by infrequent but severe events such as hospitalizations [12,13]. On the other hand, self-rated quality of life can capture a broad array of activity-based and psychosocial outcomes, and pulmonary function measures or respiratory symptoms provide more objective and sensitive markers of health improvements.
Past studies have demonstrated varied relationships among these parameters. For example, percent of predicted forced expiratory volume in one second (FEV1%) has been correlated with asthma attacks [14] and symptom score but not with symptom days [15]. Asthma-related quality of life was correlated with FEV1% in a low-income adult population [16] but not in a general population sample [17]. FEV1% was correlated with asthma-related quality of life in mild asthmatics, but was a weaker predictor than symptom intensity and was not correlated significantly for more severe asthmatics [18]. Finally, pediatric asthma symptoms have been correlated with asthma-related quality of life but not FEV1% or measures of asthma control, with relationships that vary by age of the child [19]. However, none of these studies focused on low-income pediatric populations.
More broadly, past studies have generally not considered the full array of risk factors and health endpoints for inner-city asthmatics. The most comprehensive assessment to date has been the National Cooperative Inner-City Asthma Study (NCICAS), which evaluated many similar endpoints as our study in a longitudinal baseline assessment [20], although self-rated quality of life was not considered in this publication. In order to understand the characteristics of asthmatic children in our longitudinal intervention study and to determine the relationships among key health endpoints, we conducted an extensive baseline assessment for all children enrolled in our study.
Thus, the objective of our analysis is to characterize the baseline risk factors and health status of a cohort of asthmatic children enrolled in an intervention study based in public housing developments in Boston and to determine concordance between and risk factors for key health endpoints (e.g., pulmonary function, respiratory symptoms, and self-reported quality of life). We hypothesize that risk factors associated with housing quality and psychosocial stress will be elevated in our cohort when compared with reference groups, and that quality of life will be significantly influenced by asthma symptom severity and other caregiver characteristics.
Methods
We recruited asthmatic children from the Franklin Hill, West Broadway, and Washington Beech public housing developments in Boston (located in the neighborhoods of Dorchester, South Boston, and Roslindale, respectively). Recruitment was coordinated by Community Health Advocates, residents of the developments or surrounding neighborhoods who were involved in outreach and data collection, following training about asthma, its risk factors, and interviewing techniques. Recruitment methods included advertised enrollment open houses, community meetings, mailbox drops for flyer circulation, and door knocking. Any children between the ages of 4 and 17, who lived in the developments, had self-reported doctor-diagnosed asthma, and who were willing to enroll in a longitudinal intervention study, were eligible. Enrollment occurred between April 2002 and January 2003. Written informed consent was obtained from all caregivers, with assent forms completed by children above the age of 8, and the protocols were approved by the institutional review boards of all three participating universities.
In the intake interview, the caregiver (defined as the individual who knows most about asthma care for the child) was asked about family demographics, child and family asthma history, access to health care, exposure to smoking, and medication usage, with questions taken from NCICAS when possible to facilitate comparability [20]. Because psychological stress has been shown elsewhere to be a strong predictor of immune function [21] and airway inflammation and obstruction [22], the caregiver was given the Cohen four-item abbreviated Perceived Stress Scale [23]. In addition, she was asked about neighborhood social cohesion and exposure to violence [24], factors that have been related to respiratory symptoms and other measures of asthma morbidity [25]. Finally, she was surveyed about the influence her child's asthma had on her quality of life, using the Paediatric Asthma Caregiver's Quality of Life Questionnaire (PACQLQ) [26].
The Paediatric Asthma Quality of Life Questionnaire (PAQLQ) [27] was used to determine the influence of asthma on the child's quality of life, administered to the caregiver for children age 7 and younger and directly to children age 8 and older. In addition, we evaluated quality of life using the EuroQol EQ5D self-report questionnaire [28] combined with previously published formulas [29], providing a comparison between an asthma-specific scale and a general health status scale. The EQ5D also included a visual analogue scale (VAS).
Pulmonary function was assessed using the NDD EasyOne Diagnostic portable spirometer (NDD Medical Technologies, Andover, MA), an instrument which correlates well with office-based spirometry [30]. Although pulmonary function was measured longitudinally within the intervention study, we focus on the baseline assessment (conducted concurrently with the intake interview). To compare lung function across children, we determined the percent of predicted value for FEV1 and peak expiratory flow (PEF) using standard reference equations [31]. Spirometry results and questions regarding symptom severity and medication use were used to classify asthma severity following NHLBI guidelines [32]. In addition, given reported height and weight, we calculated body mass index (BMI) and used age-specific BMI distributions [33] to categorize children as overweight (above 95th percentile), at risk of overweight (85th to 95th percentile), or not at risk (below 85th percentile).
Allergy testing was conducted using similar methods as NCICAS [20], with skin testing using the prick puncture method. Valid tests had a negative control wheal at least 1 mm smaller than the positive histamine wheal, and tests were considered positive if the wheal for a given allergen exceeded the negative control wheal by at least 2 mm. Allergens evaluated included an 11-tree mix, a 7-grass mix, ragweed, dog, cat, mouse, cockroach, D. pteronyssinus, D. farinae, Alternaria, Aspergillus fumigatus, Cladosporium, and Penicillium.
For our exploratory regression models, we evaluated the relationship between our primary outcome measures (FEV1 % predicted, a respiratory symptom score, and caregiver and child asthma-related quality of life) and a subset of demographic variables, intrinsic risk factors, health care risk factors, physical risk factors, and social risk factors. Furthermore, we considered FEV1% as a potential predictor of the respiratory symptom score, and both FEV1% and the respiratory symptom score as hypothetical predictors of quality of life. We treated FEV1% in a logistic regression, using 80% of predicted FEV1 as the cutoff for low FEV1%, and evaluated other health outcomes in linear regressions. Given missing data and numerous covariates, we conducted an initial screen using univariate regressions (retaining variables for which p < 0.2), and then constructed a multivariate stepwise regression with p < 0.1 as the entry and exit criteria. Finally, for covariates with extensive missing data, we constructed multivariate regressions both with and without these terms to evaluate the sensitivity of our findings. All statistical analyses were conducted using SAS version 8.02, using PROC REG for linear regressions and PROC LOGISTIC for logistic regressions.
Results
Demographics and risk factors
In total, 78 children from 61 households were enrolled in the HPHI intervention study. As indicated in Table 1, 41 (53%) of these children were from Franklin Hill, with 27 (35%) from West Broadway and 10 (13%) from Washington Beech. The mean age at the time of enrollment was 8.7 years (median = 8.0), with a similar age distribution across the three developments. A majority of participants (64%) self-reported as Hispanic, with 33% self-reporting as black or African-American.
Table 1 Baseline demographic characteristics of asthmatic children in three public housing developments in Boston
Franklin Hill West Broadway Washington Beech Total
Number of children 41 27 10 78
Age (%)
< 6 27% 30% 40% 29%
6–9 32% 26% 20% 28%
10–12 22% 30% 20% 24%
>= 13 20% 15% 20% 18%
Race/Ethnicity (%) *
Hispanic 61% 67% 70% 64%
African-American 41% 22% 30% 33%
Caucasian 0% 11% 0% 4%
* Race/ethnicity was asked in an open-ended question, so respondents could indicate both Hispanic status and race. So, the total can be greater than 100%.
Considering prominent non-environmental risk factors associated with asthma, 16% of children were in an intensive care unit upon birth and 10% were on a respirator. Seventy percent of children had a parent or grandparent with asthma, while 43% had eczema or hay fever and 34% had a family history of eczema or hay fever. For the 75 children with recorded height and weight, 56% were categorized as overweight and 9% were categorized as at risk of overweight. Forty-two percent of children lived with a smoker, and 45% of children were around smokers at least several times per month.
For the subset of 46 children (59%) who underwent allergy testing, 78% were sensitized to at least one of the tested substances, with the most prevalent allergies including D. pteronyssinus (59%), cockroach (59%), D. farinae (50%), and tree pollen (30%) (Figure 1). Only 44% of these allergic children were reported to have allergies by their caregivers.
Figure 1 Prevalence of allergies among asthmatic children in HPHI and NCICAS [20].
Medical care
For the 70 children for whom we received information about medications, 87% were taking short-acting beta-agonists at the time of the survey. Thirty-one percent of these children on beta-agonists also reported the use of any long-term asthma control medication (corticosteroids, leukotriene modifiers, or mast cell stabilizers). All children who did not report the use of beta-agonists indicated that they were using long-term control medication. Seventeen percent of children indicated that they were currently taking allergy medication as part of their asthma control. Of note, for the remaining eight children, we could not ascertain whether non-responses indicated lack of any medication or lack of recall/medication availability.
There were additional factors and barriers that indicated potentially sub-optimal asthma care (Table 2). Although most (92%) children had current health insurance coverage that paid some portion of asthma-related medical expenses (with 97% covered by Medicaid/MassHealth during the past year), for 28% of children, the caregiver reported having no doctor to call other than the emergency room for asthma care. Only 37% of children had a written asthma action plan signed by their doctors. Fifty-four percent of children had a spacer to use with their inhalers. Furthermore, only 27% of children had a peak flow meter, with only 19% ever using it at home or school. Children with peak flow meters tended to be slightly older than those without peak flow meters, with a similar distribution of severity. As indicated in Table 2, the asthma management practices often differed significantly across developments.
Table 2 Access to medical care and asthma management practices for children in three public housing developments in Boston
Franklin Hill West Broadway Washington Beech Total p-value (Wilcoxon rank-sum test)
% with doctor to call other than emergency room 68%
(N = 41) 89%
(N = 27) 29%
(N = 7) 72%
(N = 75) 0.005
% with written asthma action plan 39%
(N = 41) 46%
(N = 24) 10%
(N = 10) 37%
(N = 75) 0.14
% with peak flow meter 28%
(N = 40) 33%
(N = 27) 10%
(N = 10) 27%
0.37
% of persistent asthmatics using long-term control medication 21%
(N = 19) 57%
(N = 21) 14%
(N = 7) 36%
(N = 47) 0.03
In addition, 71% of caregivers indicated that at least one of seven barriers impeded asthma management for their children during the last six months. The most frequently cited barrier was that the pharmacy did not have their asthma medication (38%), followed by the asthmatic child either not being home when it was time to take the medicine (29%) or refusing to take the medicine (26%). The cost of the medication was cited as a barrier by only 12% of caregivers, the lowest percentage among the seven questions, an indication that MassHealth/Medicaid coverage was largely viewed as adequate.
Social stressors
On the five-point social cohesion scale, there was a significant difference across developments, with a lower mean value at Franklin Hill than at West Broadway or Washington Beech (Figure 2). There was, however, much greater variability in perceived social cohesion within rather than across developments, consistent with previous findings [24].
Figure 2 Social cohesion and perceived stress for caregivers of asthmatic children in HPHI. Social cohesion scores range from 1–5 (5 = maximum, 1 = minimum), while Cohen perceived stress scale scores range from 0–16 (16 = maximum, 0 = minimum).
We also found significant differences across developments in exposure to violence. We asked caregivers whether they were afraid that they or their children would be hurt by violence in their neighborhood, whether they have had violence used against them or other household members in their neighborhood, and whether they fear letting their children play outside in their neighborhood because of community violence (Table 3). In all cases, the highest rates were reported at Franklin Hill. At Franklin Hill, but not at the other developments, the responses to these questions depended on the age of the asthmatic child. For example, 84% of caregivers of asthmatic children under 8 reported fear of violence, versus 29% of caregivers of asthmatic children 8 and older (with 54% of caregivers of children under 8 reporting having violence used against their household, versus 13% for caregivers of older children). In spite of these facts, there was no significant difference in the Cohen Perceived Stress Scale between developments, with more substantial within-development variability (Figure 2).
Table 3 Exposure to violence for caregivers of asthmatic children in three public housing developments in Boston
Franklin Hill West Broadway Washington Beech Total p-value (Wilcoxon rank-sum test)
% afraid of violence in neighborhood 63%
(N = 30) 20%
(N = 20) 43%
(N = 7) 46%
(N = 57) 0.01
% directly impacted by violence in neighborhood 41%
(N = 32) 14%
(N = 22) 0%
(N = 7) 26%
(N = 59) 0.02
% not let children play outside due to violence in neighborhood 60%
(N = 30) 23%
(N = 20) 14%
(N = 7) 41%
(N = 59) 0.009
Asthma severity and symptoms
As indicated in Table 4, a majority of children reported having wheezing or tightness in their chest, needing to slow down or stop their activities due to their asthma, or having nighttime asthma symptoms within the last two weeks, an indication of poorly controlled asthma. In addition, six percent of the children were reported to have a severe asthma attack (unable to say more than one or two words between breaths) in the last two weeks. Three of the children (4%) reported staying overnight in the hospital for asthma during the last two months. Although a small fraction of the cohort, this would correspond (if sustained) to an annual asthma hospitalization rate of 23% in this population.
Table 4 Frequency of reported asthma symptoms within two weeks prior to enrollment in intervention study
Never 1–2 times/week 3–6 times/week At least daily
Wheezing, tightness in the chest, or cough (N = 74) 20% 41% 24% 15%
Slow down/stop play or activities (N = 74) 34% 35% 19% 12%
Never 1–2 times 3–4 times At least 5 times
Wake up at night (N = 76) 32% 34% 25% 9%
Spirometry
For the subset of 49 children age six or older able to perform spirometry, the mean FEV1% was 88% (median of 88%, standard deviation of 15%). Twenty-nine percent of children had FEV1 less than 80% of predicted, though no values were less than 60% of predicted. The mean PEF% was 97% (median of 96%, standard deviation of 17%). Twelve percent of children had PEF less than 80% of predicted, with none having PEF less than 60% of predicted. As would be anticipated, these two measures were well correlated (Spearman correlation of 0.69), with all but one of the children with PEF below 80% of predicted also having FEV1 below 80% of predicted.
Of note, of the children under 6 tested, seven (30%) were able to record acceptable spirometry values. Only a child who had been recently hospitalized for asthma exacerbation had reduced FEV1 and PEF, both below 60% of predicted [31]. Given poor performance and issues in selecting appropriate reference equations for young children [34], spirometry for children under the age of 6 is not considered further in our analysis.
Based on the four questions addressing recent asthma symptoms (as summarized in Table 4), spirometry measures, and medications prescribed and used, we determined that a majority of the children in our cohort (56% of those with complete information) would be considered to have moderate persistent asthma, with 14% considered severe persistent, 10% mild persistent, and 20% mild intermittent. Of note, of the 47 children categorized as having persistent asthma who provided information on their medications, only 36% reported being prescribed any daily controller medication (63% of severe persistent, 33% of moderate persistent, and 17% of mild persistent children).
Quality of life
For children, using the PAQLQ, the median score on the seven-point respiratory symptoms subscale was 4.6, with medians of 4.2 for activity limitation, and 5.1 for emotional function, with a median overall quality of life score of 4.6 (where a score of 1 indicates maximal impairment and a score of 7 indicates no impairment). The range across children was substantial, with overall quality of life scores ranging from 1.4 to 7 (Figure 3). Similarly, caregivers reported median PACQLQ scores of 4.3 for activity limitation and 4.6 for emotional function, with an overall median score of 4.5 but a range from 1.4 to 7. Using the EQ5D, the median health-related quality of life was 0.81 (range from 0.28 to 1), while the visual analogue scale (VAS) yields a median quality of life score of 80 (range from 30 to 100). The wide ranges indicate some of the limitations in interpreting these values cross-sectionally rather than longitudinally.
Figure 3 Distribution of asthma-related quality of life scores for asthmatic children and their caregivers. Asthma-related quality of life scores range from 1–7 (7 = maximum, 1 = minimum).
The quality of life questionnaires provide insight regarding the perceived burdens of asthma beyond the aggregate quality of life scores. For example, in the EQ5D responses, 31% of children were reported to be moderately or extremely worried or depressed, with 50% of children having problems doing their usual activities and 53% of children in moderate or extreme pain or discomfort. In addition, 39% of caregivers reported that their child's asthma interfered with their job or work around the house at least some of the time, with 62% reporting sleepless nights related to their child's asthma at least some of the time.
Correlation and regression analyses
One key question is whether any of the primary measures of asthma severity (lung function, quality of life, and respiratory symptoms) are significantly correlated with one another in a cross-sectional baseline assessment. For lung function, we consider both FEV1% and PEF%. Quality of life outcomes include the aggregate PAQLQ score for the child, aggregate EQ5D and VAS scores for the child, and the aggregate PACQLQ scores for the caregiver. For respiratory symptoms, we develop a symptom score reflecting the responses to the questions in Table 4 as well as having a severe asthma attack in the last two weeks. The symptom score ranges from zero to eight, with a maximum of two points assigned for each question and a higher score reflecting more frequent symptoms. While this scale is simple and implicitly places equal weight on respiratory outcomes of differing severity, it is similar to scales developed elsewhere and reasonably captures the gradient in symptom frequency and severity.
Considering the Spearman correlations among these key covariates (Table 5), the lung function measures are strongly correlated with one another but are not significantly correlated with either the respiratory symptom score or quality of life. In contrast, the respiratory symptom score is significantly correlated with the VAS and both child and caregiver asthma-related quality of life, with lower quality of life given higher symptom frequency as anticipated. The various quality of life scales are generally significantly correlated with one another, with slightly weaker relationships for the EQ5D scale, which is not specific to asthma. Not surprisingly, the correlation between caregiver and child asthma-related quality of life is stronger for children age 7 and under, where the caregiver evaluates the child's quality of life (r = 0.75), than for children age 8 and older, where the child evaluates their own quality of life (r = 0.31).
Table 5 Spearman correlation coefficients between respiratory symptom score, quality of life measures, and lung function
Symptom score EQ5D VAS Child AQL Caregiver AQL FEV1%
EQ5D -0.07 - - - - -
VAS -0.29 * 0.20 - - - -
Child AQL -0.43 ** 0.44 ** 0.43 ** - - -
Caregiver AQL -0.46 ** 0.27 * 0.28 * 0.49 ** - -
FEV1% -0.12 0.14 -0.24 -0.08 -0.07 -
PEF% -0.03 0.05 -0.24 -0.09 0.08 0.65 **
*: p < 0.05, **: p < 0.01
Given our sample size, missing data for selected covariates (such as spirometry or allergy status), and the number of factors hypothesized to influence our outcome measures, we consider our regression analyses to be exploratory in nature. The goal is to better understand the above correlations and factors that might influence the relationships among our outcome measures. For each of the outcome measures, we evaluate a subset of demographic variables (age, race/ethnicity, gender, household size, housing development), intrinsic risk factors (BMI, being in the intensive care unit at birth, eczema), health care risk factors (having a doctor to call other than the emergency room), physical risk factors (allergies to roaches, dust mites, or any agents; or environmental tobacco smoke exposure), and social risk factors (social capital, perceived stress, fear of violence in the neighborhood, and not letting children play outside due to fear of violence in the neighborhood). It should be noted that some of these covariates may be direct causative agents, while others represent proxies or outcomes that could be influenced by asthma severity (such as caregiver stress).
The results from this analysis are summarized in Table 6, including significant terms from univariate and multivariate regressions. Although multiple risk factors were predictive of low FEV1% in univariate regressions (including age, BMI, cockroach allergy, environmental tobacco smoke exposure, and social capital), no terms were statistically significant in multivariate models. The respiratory symptom score was elevated for a variety of physical and social risk factors, with household size, BMI, and environmental tobacco smoke exposure remaining significant in multivariate models. Fewer factors were predictive of child asthma-related quality of life, but the respiratory symptom score was strongly and negatively associated with the PAQLQ score. Similarly, the respiratory symptom score strongly predicted caregiver asthma-related quality of life in both univariate and multivariate models, with a significant influence in multivariate models for the caregiver's perceived stress and for whether the child required NICU care at birth.
Table 6 Univariate/multivariate regressions of FEV1%, respiratory symptoms, and quality of life measures on selected risk factors
FEV% < 80% Respiratory symptom score Child asthma-related quality of life Caregiver asthma-related quality of life
Age 0.05 (+) NS NS NS
Hispanic NS NS NS NS
African-American NS NS NS NS
Gender NS NS NS NS
Household size NS 0.0007 (+)
0.009 (+) NS 0.04 (-)
Housing development NS NS NS NS
BMI 0.12 (+) 0.03 (+)
0.02 (+) NS NS
Born in NICU NS NS NS 0.09 (-)
0.03 (-)
Eczema NS 0.13 (+) NS NS
Doctor to call other than ER NS NS NS NS
Allergy to roaches 0.18 (+) NS NS 0.19 (+)
Allergy to dust mites NS NS NS NS
Any allergies NS NS NS NS
Environmental tobacco smoke exposure 0.19 (-) 0.03 (+)
0.08 (+) NS NS
Social capital 0.15 (+) NS NS NS
Perceived stress NS 0.03 (+) 0.04 (-) 0.001 (-)
0.004 (-)
Fear of violence in neighborhood NS 0.06 (+) NS 0.01 (-)
Not letting children play outside due to fear of violence in neighborhood NS 0.10 (+) NS NS
Low FEV% 0.15 (+) 0.13 (+)
0.04 (+) NS
Respiratory symptom score 0.0002 (-)
0.02 (-) < 0.0001 (-)
0.009 (-)
NS: No statistical significance in univariate regression (p > 0.2)
Value in italics: Statistically significant in univariate regression (p < 0.2), but not in multivariate regression. The value presented is the p-value for the univariate regression, and the +/- sign indicates the direction of the relationship
Values in bold: Statistically significant in multivariate regression (p < 0.1). The first value presented is the p-value for the univariate regression, and the second value presented is the p-value for the multivariate regression. The +/- sign indicates the direction of the relationship.
Discussion
To address our first hypothesis and interpret this baseline characterization of asthmatic children in public housing enrolled in an intervention study, it is instructive to compare the prevalence of selected risk factors with those reported in previous studies [20,25,35-39] (Table 7). Within our study, there appear to be a greater percentage of overweight children and children with a family history of asthma, as compared with NCICAS, other studies of low-income asthmatics, and general population studies. The fraction of children with cockroach or dust mite allergies is also high, although Alternaria allergy prevalence is quite low, indicating that the most effective interventions might differ between HPHI and NCICAS. In addition, exposure to violence and fear of violence is slightly higher than reported in NCICAS. Although the fraction of children with persistent asthma who are adequately medicated is quite low (36%) and indicative of poorly managed asthma, this figure is actually higher than the percentages reported in other studies, indicating that this is a pervasive problem in asthma management.
Table 7 Comparison of children in HPHI intervention study with children in other studies
HPHI NCICAS [20] Other low-income asthmatic populations Population-based studies
Family history of asthma 70% 57% - N/A
% overweight 56% 19% - 15% [39]
% of families with at least one smoker 39% 59% 46% [35] 41% [36]
% with cockroach allergy 59% 36% 52–78% [1] 22% [36]
% with European dust mite allergy 59% 31% - 27% [36]
% with Alternaria allergy 7% 38% - 16% [36]
% not let children play outside due to violence in neighborhood 41% 34% [25] - -
% of persistent asthmatics on long-term control medication 36% 24% 1 27% [37] 26% [38] 2
1 All asthmatics
2 Moderate/severe asthmatics only
More broadly, our findings regarding medication usage coupled with the prevalence of children with mild, moderate, or severe persistent asthma and the frequency of respiratory symptoms suggest that this population is being treated sub-optimally. NHLBI guidelines [32] indicate that all individuals with persistent asthma should be prescribed an inhaled steroid or other long-term control medication in addition to a fast-acting beta-agonist. In addition, for moderate or severe persistent asthmatics, it is recommended that long-acting bronchodilators be added to the medication regimen. Compounding the problem of inadequate medication is the relatively low usage of asthma action plans or peak flow meters, which are both part of recommended patient education and self-management activities. These shortfalls could be related to inadequate quality of care, limitations in access to and continuity of care, communication gulfs between caregivers and providers, or other factors, and further investigation is needed to determine the root causes of this management gap.
Although exploratory in nature, our correlation and regression analyses provide some useful insights regarding the factors associated with various measures of asthma severity in our cohort. FEV1% was not strongly correlated with other health measures, and was only weakly associated with a subset of risk factors in univariate regressions. As this measurement was taken at a single point in time at varying times of day and seasons, a weak relationship is unsurprising, especially when compared with symptoms over a two week period [18]. The lack of a relationship may also be related to a relatively small sample size and a small number of participants with low FEV1%. In addition, many asthmatics may be poor perceivers of symptoms of respiratory difficulty, potentially explaining the disconnect between pulmonary function measures and asthma symptoms or quality of life.
The respiratory symptom score was moderately associated with a number of risk factors in univariate regressions, with all coefficients in the anticipated directions (i.e., greater symptom frequency and severity with larger household sizes, higher BMI, reported eczema, higher environmental tobacco smoke exposure, higher psychosocial stress, higher fear of violence, and higher prevalence of low FEV1%).
The robust multivariate relationship with household size and presence of smokers could be indicative of the influence of indoor air quality or respiratory infection related to unit crowding, with the association between obesity and asthma symptom frequency and severity in agreement with past studies [40,41]. The extremely strong relationship between respiratory symptom score and asthma-related quality of life for the child is logical and provides some indication that both measures were reasonably constructed. The relationship between perceived stress and the caregiver's asthma-related quality of life, in addition to the child's respiratory symptoms, may indicate that the acute stresses associated with lack of control over one's surroundings could adversely affect quality of life. Since the NICU term is associated with caregiver's quality of life (but not the child's quality of life), it is less likely a surrogate of early respiratory injury and could be more broadly related to chronic stress about a child's health status. It is also interesting to note that variables such as race, ethnicity, gender, and housing development were not significant predictors in any models.
There are some clear limitations in interpreting the results of our investigation. First, given the numerous risk factors for asthma, both the univariate and multivariate regressions must be interpreted with caution. For example, fear of violence in the neighborhood was significantly associated with reductions in caregiver quality of life in univariate regressions (p = 0.01), but given a strong positive association between fear of violence and caregiver stress as well as the child's respiratory symptom score, this term did not enter into the final regression model. Thus, both the significant and insignificant terms from our regression models should be interpreted with caution, as they may not reflect causal relationships.
In addition, the participants in our study represented a convenience sample of individuals from three selected public housing developments in Boston, who were interested in enrolling in a longitudinal intervention study. It is unclear whether our participants are representative of asthmatic children across those developments, or more broadly, asthmatic children in public housing. Families who enrolled in our study may have been more desperate for help given current asthma conditions, or may have had greater confidence in the ability of a research project to improve their child's health. A comparison between the demographics of our study population and the demographics of the developments indicates that the age distribution and racial/ethnic composition are similar, but there is no way of knowing if our cohort represents typical characteristics of all asthmatic children across the BHA.
In addition, we have evaluated the prevalence of risk factors and correlations among key indicators of respiratory health in a cross-sectional survey without a defined control group. Thus, while we have found an elevated prevalence of obesity and allergies to cockroach and dust mites when compared with other populations, we cannot infer a causal relationship with asthma. However, this comparison illustrates the relative importance of various risk factors internal to our intervention study. In addition, the correlations among respiratory health measures will not necessarily be identical cross-sectionally and longitudinally. For an intervention study, the crucial question is whether observed changes in lung function would occur simultaneously with changes in quality of life or respiratory symptoms, and knowledge of the cross-sectional correlations is not necessarily informative. That being said, the correlations among health endpoints (Table 5) and the regression results (Table 6) do provide some indication that children with more frequent respiratory symptoms have lower quality of life at baseline, making these children candidates for improvements in both health endpoints.
In spite of these limitations, our study provides some important and unique information. To our knowledge, this is the most comprehensive evaluation focused on asthmatic children in public housing to date. Although many attributes of public housing are similar to low-income private housing, the viability of large-scale interventions in public housing based on common indoor environmental exposures and centralized management makes it important to characterize similarities and differences from other low-income populations. More broadly, our study demonstrates that a community-based participatory research paradigm, with members of the community conducting most of the primary data collection, is able to gather valid and meaningful data. Given that recruitment and retention of our study population would have been quite difficult without involvement from community members, it is clear that community-based participatory research is essential for detailed evaluations of asthma in public housing.
The findings from this cross-sectional baseline evaluation of asthmatic children in public housing in Boston have multiple policy implications. The inadequacy of medical care for a majority of asthmatic children indicates that medical interventions might yield substantial improvements in asthma status for poor children. The fact that significant differences existed in adequacy of medical care for asthma across housing developments indicates likely variability across providers (although the data indicate that this problem exists across community health centers and academic medical centers). In addition, the high prevalence of cockroach and dust mite allergies indicates that interventions aimed at reducing or eliminating these triggers are likely to provide health improvements. This reinforces the expectation that interventions in public housing, a setting with high asthma prevalence and high prevalence of allergic responses to indoor contaminants, are likely to be meaningful and effective. Finally, the responses to the psychosocial questions as well as the percentage of caregivers who feared neighborhood violence and modified their children's activities as a result indicates that violence and stress are substantial risk factors for both respiratory health and other outcomes. Our findings point to the need for coordinated improvements in the social environment, the built environment, and in medical management.
Future investigations should similarly evaluate asthma risk factors and severity in other public housing settings to determine whether our conclusions provide generalizable and relevant information for regional or national housing authorities in considering intervention strategies. In addition, a longitudinal comparison of correlations among measures of asthma severity would help determine whether conclusions drawn from a cross-sectional evaluation are robust.
Conclusions
We conclude that asthmatic children enrolled in a public housing-based intervention study would likely benefit from a coordinated intervention focused on reduction of indoor allergens (especially cockroach and dust mites), improved medical management, and increased social support.
List of Abbreviations
AQL – Asthma-related quality of life
BHA – Boston Housing Authority
BMI – Body mass index
FEV1 – Forced expiratory volume in one second
FEV1% – Percent of predicted forced expiratory volume in one second
HPHI – Healthy Public Housing Initiative
NCICAS – National Cooperative Inner-City Asthma Study
NHLBI – National Heart, Lung, and Blood Institute
NICU – Neonatal intensive care unit
PACQLQ – Paediatric Asthma Caregiver's Quality of Life Questionnaire
PAQLQ – Paediatric Asthma Quality of Life Questionnaire
PEF – Peak expiratory flow
PEF% – Percent of predicted peak expiratory flow
VAS – Visual analogue scale on EuroQol EQ5D quality of life questionnaire
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
JIL participated in study design and implementation, conducted statistical analyses, and drafted the manuscript. LKWH designed and administered survey instruments, provided support to participants as a nurse/case manager, and contributed to analyses related to adequacy of health care. JEC contributed to survey design and analyses for psychosocial risk factors. RED completed a literature review, drafted text, and conducted statistical analyses. SS provided asthma severity classifications and related analyses and conducted allergy testing. HPH collaborated in the conception and design of the study, and co-directed data collection and study coordination. All authors read and approved the final manuscript.
Acknowledgements
We wish to thank the Community Health Advocates for their efforts in data collection and outreach, including Linda Banks, Joyce Best, Katya Castillo, Damaris Cuello, Lucille Hairston, Linda Henson, Katie Jenner, Taina Palanco, Christina Potocki, Rosaira Perez, Mary VanGordon, and Maximo Vasquez. We thank Edna Rivera-Carrasco, Mary Russell, Theresa Kane, and Laura Bradeen for supervising the Community Health Advocates and contributing to survey design and implementation. In addition, we thank Mae Bradley, Attieno Davis, Doreen Lankhorst, Fernando Miranda, and Jose Vallarino for their invaluable contributions in working with the Community Health Advocates and contributing to data collection efforts, and we thank Steven Melly for his contributions to database development and management. Most importantly, we thank the families from the three public housing developments who agreed to participate in our study.
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| 15585065 | PMC544563 | CC BY | 2021-01-04 16:36:31 | no | Environ Health. 2004 Dec 7; 3:13 | utf-8 | Environ Health | 2,004 | 10.1186/1476-069X-3-13 | oa_comm |
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BMC Blood DisordBMC Blood Disorders1471-2326BioMed Central London 1471-2326-4-61558831510.1186/1471-2326-4-6Research ArticleA randomized trial of washed red blood cell and platelet transfusions in adult acute leukemia [ISRCTN76536440] Blumberg Neil [email protected] Joanna M [email protected] Jacob M [email protected] Department of Pathology & Laboratory Medicine (Transfusion Medicine Unit), University of Rochester Medical Center, Box 608, Rochester, NY 14642 USA2 Department of Medicine (Hematology-Oncology Division), University of Rochester Medical Center, Box 608, Rochester, NY 14642 USA3 Dept. of Hematology and BMT, Rambam Medical Center, Haifa 31096 Israel2004 10 12 2004 4 6 6 18 6 2004 10 12 2004 Copyright © 2004 Blumberg et al; licensee BioMed Central Ltd.2004Blumberg et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Platelet transfusion is universally employed in acute leukemia. Platelet concentrate supernatants contain high concentrations of biologic mediators that might impair immunity. We investigated whether washed platelet and red cell transfusions could improve clinical outcomes in adult patients with acute leukemia.
Methods
A pilot randomized trial of washed, leukoreduced ABO identical transfusions versus leukoreduced ABO identical transfusions was conducted in 43 adult patients with acute myeloid or lymphoid leukemia during 1991–94. Primary endpoints to be evaluated were platelet transfusion refractoriness, infectious and bleeding complications and overall survival.
Results
There were no significant differences in infectious or major bleeding complications and only one patient required HLA matched platelet transfusions. Minor bleeding was more frequent in the washed, leukoreduced arm of the study. Confirmed transfusion reactions were more frequent in the leukoreduced arm of the study. Overall survival was superior in the washed arm of the study (40% versus 22% at 5 years), but this difference was not statistically significant (p = 0.36). A planned subset analysis of those ≤50 years of age found that those in the washed, leukoreduced arm (n = 12) had a 75% survival at five years compared with 30% in the leukoreduced arm (n = 10) (p = 0.037)
Conclusion
This study provides the first evidence concerning the safety and efficacy of washed platelets, and also raises the possibility of improved survival. We speculate that transfusion of stored red cell and platelet supernatant may compromise treatment, particularly in younger patients with curable disease. Larger trials will be needed to assess this hypothesis.
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Background
In recent years, data have accumulated that platelet transfusion refractoriness and transfusion reactions in patients with hematologic malignancies can be reduced by use of leukoreduced [1-3] and/or ABO identical [4,5] platelet transfusions. Preliminary data also suggest that use of ABO identical [6] and leukoreduced transfusions [7] might potentially affect clinical outcomes such as survival and bacterial infection. Data also exist suggesting that alloimmunization to plasma antigens may play a role in platelet transfusion refractoriness, [8] and that removal of plasma supernatant can reduce the incidence of reactions to platelet transfusions [9]. One method for removing plasma supernatant from platelet concentrates is washing. However, washing involves loss of perhaps 20% of platelets. No clinical trial data exist comparing washed and unwashed platelet transfusions in terms of efficacy in preventing bleeding, and safety, in terms of unforeseen complications of transfusing platelets subjected to an additional manipulation.
Over the last two decades it has become apparent that allogeneic blood transfusions can modify host immunity and clinical outcomes [10]. Epidemiologic data, animal models, and, in some instances, randomized clinical trials demonstrate that transfusions reduce solid organ allograft rejection and repetitive spontaneous abortions, and increase the likelihood of post-operative bacterial infections [11]. Perhaps most controversial is the association between blood transfusion and cancer recurrence, which has been convincingly demonstrated in some animal models, [12] but for which randomized clinical trial evidence is lacking. We observed that patients with cancer had significantly greater recurrence rates if transfused with whole blood [13,14] rather than red cell concentrates, and this epidemiologic association has been confirmed by others [15].
Platelet transfusions are almost universally used in the supportive care of acute leukemia in adults. The original study design hypothesized a potential benefit from removing soluble immunomodulatory mediators in red cell and platelet concentrates derived from plasma and white cells. White cells and their secreted products are now largely removed prior to storage through filtration. However, there is now reason to be concerned about soluble platelet derived substances that would not be removed by pre-storage leukoreduction, as well as immunomodulatory mediators from plasma itself, such as IgG and soluble HLA antigens. More recent data document that stored platelet concentrate supernatants accumulate striking levels of biologic response modifiers during storage, including vascular endothelial growth factor (VEGF), soluble CD40L, histamine and transforming growth factor (TGF-β1) [16-18]. There is reason to believe that these molecules are infused at what may be clinically significant doses, and might alter recipient immune function. sCD40L has recently been demonstrated to be a growth promoting and apoptosis inhibiting factor for leukemic cells in vitro [19].
Platelet transfusions are given to patients undergoing myelotoxic chemotherapy for acute leukemia over a two to three week period when the peripheral blood immune system is regenerating. We speculated that platelet transfusions, in addition to causing platelet refractoriness and transfusion reactions, might impair anti-leukemic immunity because allogeneic transfusions have been shown to favor type 2 immunity (e.g., characterized by cytokines such as IL4 and IL10) and suppress type 1 cellular immunity (e.g., IL2, γ-interferon and TNF-α) [20,21]. There are data suggesting that host type 1 immunity may be important in the eradication of residual malignant cells after therapy [22].
To investigate the possible efficacy of washed platelet transfusions in preventing platelet refractoriness, transfusion reactions, bleeding and improving long term survival in adult acute leukemia, we performed a randomized trial in patients receiving either washed, leukoreduced, ABO identical platelet and red cell transfusions compared with our standard protocol of leukoreduced, ABO identical transfusions.
Methods
Patients
The diagnosis of acute leukemia was based upon laboratory results from our hematopathology laboratory. Only patients receiving chemotherapy with curative intent were included. Three patients died before receiving a full course of induction therapy but are included in the data. Data retrieval of clinical information was done in a blinded fashion. Patients and clinicians were not blinded as to study allocation due to the obvious difference in packaging of washed versus unwashed transfusions.
For acute myeloid or undifferentiated leukemia treatment invariably involved initial remission induction attempts with seven days of cytosine arabinoside and three days of an anthracycline. Patients with acute lymphoid leukemia received vincristine and prednisone, as well as other drugs. Patients with high risk features or those who failed to achieve remission received additional courses of induction therapy and/or additional agents depending on attending physician preference. Cytogenetic results, when available, were classified retrospectively according to a currently used scheme [23]. All stem cell transplants performed involved autologous bone marrow or that from an HLA identical sibling.
Patients entering remission also typically underwent consolidation therapy and bone marrow transplant depending on age, performance status, availability of a sibling HLA matched allogeneic donor and other factors. Our institutional review board for studies involving humans approved the study protocols and informed consent documents. Once placed on a particular transfusion protocol, as described below, a patient received only transfusions of that type throughout their treatment. This included consolidation, transplant and relapses, continuing until they were cured or died of their leukemia.
In 1991–94, 43 patients participated in a randomized trial of ABO identical, leukoreduced versus ABO identical, leukoreduced, plasma reduced (washed) transfusions. Study endpoints were platelet transfusion refractoriness, total platelet transfusion requirements during induction therapy, infections during induction therapy and overall survival. Randomization was arranged by our Department of Biostatistics employing computer generated random assignments and a blocked design with separate, sequential, sealed, opaque envelopes for patients with diagnoses of acute myeloid (AML) or acute lymphoid (ALL) leukemias. Leukoreduction for all patients was by post-storage, bedside filtration with Pall filters (RC50 and PL100), and plasma reduction of both red cell and platelet transfusions was by saline washing using the Cobe 2991 by a previously published method [24]. Neither transfusion service nor clinical staff was blinded to study assignment after opening of the envelope, but this was not considered essential as major clinical outcomes (platelet transfusion responsiveness and survival) were unlikely to be affected by staff knowledge of study assignment. Power calculations were not performed as the trial was considered primarily a feasibility trial. All adult patients with acute leukemia who were to receive full dose induction chemotherapy would be recruited during a three year period beginning in early 1991. A maximum of 60 patients were expected to be accrued.
Transfusion practice was consistent during the period of the study, employing almost exclusively whole blood derived random platelet concentrates, either ABO identical, leukoreduced, or washed, ABO identical, leukoreduced, according to protocol. Prophylactic transfusions were consistently given at morning platelet counts of <20 × 109/l and usually prior to invasive procedures at <50 × 109/l. Protocol violations in which patients received a transfusion of the incorrect type were <0.5% of all transfusions. All transfusions were treated with 2500 centigray gamma irradiation. We did not collect data on storage time of the red cells or platelets transfused in the study.
Patients were to be excluded from the analysis if they did not have acute leukemia or died prior to randomization. Since randomization occurred upon admission to the hospital with the diagnosis of suspected acute leukemia, some patients were found to have other diseases after randomization. These exclusions involved two patients who were randomized to the unwashed group but found not to have acute leukemia upon further investigation. Subgroup analysis was planned for patients ≤50 years of age because they are known to have substantially better prognosis, and for patients with acute myeloid leukemia, who may have a greater risk of HLA alloimmunization. Fifty years of age was also the maximal age for allogeneic bone marrow transplantation in patients with acute leukemia at the time of the study.
Data were collected by a blinded coauthor (JMH) from medical record review of the admission for initial induction, and follow-up data on survival obtained from the local tumor registry. The clinical data for blood component use and morbidity were collected only for the initial admission to achieve treatment time comparability. Bleeding was evaluated for the initial admission for remission induction, and defined as minor (1–2 days not requiring any therapeutic intervention such as wound site hematoma, guaiac positive stools, mild epistaxis not requiring transfusion nor packing, etc.) or major (requiring transfusion of red cells or otherwise mandating surgical or other therapeutic intervention). Refractoriness was evaluated for the entire duration of a patient's transfusion therapy until cessation of transfusions or death, and defined as the need for HLA matched platelet transfusions, as determined by the attending hematologist. Transfusion reactions were reported at the discretion of the nursing staff and evaluated by Transfusion Service residents who were not blinded as to the component received. A new fever or new rigors occurring during or shortly after a transfusion was considered a reaction whereas a fever or rigor that occurred shortly before the transfusion started was considered unrelated.
Statistical methods
Statview 5.0 (SAS Institute, Cary, NC) was employed to calculate survival curves by the Kaplan-Meier method and Cox proportional hazards regressions. For continuous variables, the Mann-Whitney test was used for bivariate comparisons, and for categorical variables, Fisher's exact test or Chi square with continuity correction were employed as appropriate. No corrections were made for multiple comparisons, because except for the major outcome variables of survival, platelet refractoriness, infectious complications and platelet utilization, the data comparisons are considered exploratory.
Results
The demographic pre-treatment and initial hospitalization clinical outcomes data for patients in the study are shown in Tables 1 and 2, and the long term survival results are shown in the Kaplan-Meier plot in figure 1. Except for cytogenetics there were few differences between the two cohorts in terms of pre-treatment variables. Of those who had cytogenetics successfully determined there were more patients with poor risk cytogenetics in the washed arm (11 of 18; 61%) than in the unwashed arm (2 of 9; 22%) of the study (p = 0.11 by Fisher's exact test). There was no statistically significant increased need for red cell or platelet transfusions, nor increased bleeding in the patients receiving washed transfusions.
Table 1 Demographic and pre-treatment clinical variables according to type of transfusions given.
ABO Matched, Leukoreduced ABO Matched, Leukoreduced, Washed P value by Mann Whitney or Fisher's Exact Test
N 18 25
Age 48 ± 23 47 ± 17 0.89
Male 9 (50%) 11 (44%) 0.76
ALL/AML 4/14 5/20 0.99
Antecedent Hematologic Disorder (MDS) 0 of 18 (0%) 3 of 25 (12%) 0.25
Favorable Risk Cytogenetics 1 (6%) 0 (0%) 0.09*
Standard Risk Cytogenetics 6 (33%) 7 (28%)
Poor Risk Cytogenetics 2 (11%) 11 (44%)
Unknown Cytogenetics 9 (50%) 7 (28%)
Admission Blast Count (×1000/μl) 25 ± 36 23 ± 41 0.59
All data are mean ± 1 SD. MDS = myelodysplastic syndrome
*For cytogenetics as a category overall
Table 2 Outcome variables according to type of transfusions received.
ABO Matched, Leukoreduced ABO Matched, Leukoreduced, Washed P value by Mann Whitney or Fisher's Exact Test
N 18 25
Red Cells (units) 16 ± 12 16 ± 6.2 0.21
Platelets (units) 84 ± 100 73 ± 49 0.64
Platelets/Day (units) 1.8 ± 1.2 1.9 ± 1.3 0.90
Red Cells/Day (units) 0.4 ± 0.1 0.4 ± 0.3 0.39
Platelets/Red Cell 4.6 ± 2.4 4.4 ± 2.5 0.74
Courses of Induction Chemotherapy 1.3 ± 0.6 1.3 ± 0.4 0.90
Length of stay (days) 43 ± 24 42 ± 17 0.47
Received HLA matched platelets 0 of 18 (0%) 1 of 25 (4%)
Days with fever >37 degrees Celsius 16 ± 11 15 ± 9.2 0.90
Days of antibiotics 36 ± 26 36 ± 18 0.25
Positive Microbial Cultures 1.0 ± 1.2 1.3 ± 1.4 0.53
Days with bleeding 0.62 ± 2.2 0.58 ± 1.2 0.19
Reported Transfusion Reactions per Patient 0.4 ± 0.8 0.4 ± 0.7 0.91
Complete remission at discharge 12 of 18 (67%) 17 of 25 (68%) 1.00
Received BMT 8 of 18 (44%) 14 of 25 (56%) 0.54
All continuous variables results are means ± 1 SD.
Figure 1 Results of the randomized trial of washed (n = 25 patients) versus unwashed (n = 18) platelet transfusions. There is no significant difference in survival by the logrank test (p = 0.36). Censored data points are patients remaining alive.
Clinically evident bleeding was uncommon (8 of 43 patients, 19%) and major bleeding occurred in only 2 of 43 patients (5%). Minor bleeding was more common in those receiving washed platelets (6 of 25, 24%) than unwashed (0 of 18, 0%) (p = 0.03 by Fisher's exact test) but major bleeding occurred in only one patient in each arm of the study (1 of 25 versus 1 of 18; not significantly different). Bleeding was in all instances related to specific anatomic lesions, such as minor epistaxis, hematomas at the site of trauma or invasive procedures, or guaiac positive stools. For six patients these mild bleeding episodes were only seen on one or two days during their admission, without generalized petechiae or purpura. One patient in the washed arm had melena for five days, which resolved. The only life threatening bleeding was non-fatal and occurred in one patient in the unwashed arm of the study who experienced eight days of hemoptysis and received 416 units of platelets and 52 units of red cells during induction therapy. Bleeding did not influence overall survival at last follow-up, which was 38% in those who had bleeding and 35% in those patients with no bleeding (p = 0.94 by logrank test). Patients with bleeding required more platelet (mean of 141 ± 118) (1 SD) and red cell transfusions (21 ± 13) than patients with no bleeding (61 ± 44 platelets and 15 ± 7 red cells), but this was almost exclusively due to the one patient in the unwashed arm of the study with eight days of hemoptysis. When this patient was removed from the analysis, the patients with bleeding received no more transfusions than those without bleeding.
Reported numbers of transfusion reactions were similar in both arms of the study (a mean of 0.4 per patient). In the washed arm of the study there were eight transfusion reactions reported due to intercurrent fever and two allergic reactions (rash and/or urticaria) to unwashed platelet transfusions given in violation of protocol due to clinical urgency. In the unwashed arm of the study there were three transfusions with intercurrent fever and four allergic reactions. Reactions were reported in 7 of 25 (28%) of patients in the washed study arm and 7 of 18 (39%) of the patients in the unwashed arm (p = 0.52). When evaluated by the transfusion medicine resident, seven of the reactions in the washed arm were considered to be pre-existing fever and the reported reaction likely unrelated to transfusion. When only "on protocol" transfusions judged causally related by a blood bank physician are considered, the patients in the washed arm were less likely to experience reactions (1 of 25; 4%) than patients in the unwashed arm (7 of 18; 39%) (p = 0.0058 by Fisher's exact test). During the course of the treatment, three of 18 patients randomized and analyzed in the unwashed arm had severe or repeated transfusion reactions and were placed on a washed protocol for future transfusions.
As determined by a Cox proportional hazards regression, survival was not associated significantly with type of leukemia (p = 0.37), cytogenetic results (p = 0.63), or receipt of washed blood transfusions (p = 0.62) but was significantly associated with age (p = 0.0002), with younger patients surviving longer.
Because long-term survival in acute leukemia is uncommon in those over the age of 50–60 years, we also performed a planned subset comparison of the 22 patients in the trial ≤50 years of age. We recognized that this subset would be small, rendering statistical analysis more difficult. These results are shown in figure 2, along with demographics on these patients in Tables 3 and 4.
Figure 2 Survival in those patients in the randomized trial of washed (n = 12) versus unwashed (n = 10) platelet transfusions ≤50 years of age is plotted by the Kaplan-Meier method. Those in the washed group had significantly better survival (p = 0.037 by logrank test). Two of the patients in the washed group were both alive and in remission at last recorded follow-up of 47 months. Two of the patients in the unwashed arm were both alive and in remission at last recorded follow-up of 116 months. At the minimum follow-up time of 41 months 9 of 12 patients in the washed arm were alive and in remission, as compared with 3 of 10 in the unwashed arm.
Table 3 Demographic and pre-treatment clinical variables according to type of transfusions given in those ≤50 years of age.
ABO Matched, Leukoreduced ABO Matched, Leukoreduced, Washed P value by Mann Whitney or Fisher's Exact Test
N 10 12
Age 30 ± 9.4 32 ± 9.5 0.77
Male 5 (50%) 6 (50%) 1.00
ALL/AML 1/9 4/8 0.32
Antecedent Hematologic Disorder (MDS) 0 of 10 (0%) 0 of 12 (0%) 1.00
Favorable Risk Cytogenetics 0 (0%) 0 (0%) 0.09
Standard Risk Cytogenetics 5 (33%) 2 (28%)
Poor Risk Cytogenetics 1 (11%) 6 (44%)
Unknown Cytogenetics 4 (50%) 4 (28%)
Admission Blast Count (×1000/μl) 29 ± 49 29 ± 49 0.56
All data are mean ± 1 SD. MDS = myelodysplastic syndrome
Table 4 Outcome variables according to type of transfusions received in those ≤50 years of age.
ABO Matched, Leukoreduced ABO Matched, Leukoreduced, Washed P value by Mann Whitney or Fisher's Exact Test
N 10 12
Red Cells (units) 20 ± 14 16 ± 6.2 0.93
Platelets (units) 127 ± 121 69 ± 52 0.23
Platelets/Day (units) 2.4 ± 1.1 1.4 ± 1.0 0.039
Red Cells/Day (units) 0.4 ± 0.06 0.4 ± 0.1 0.39
Platelets/Red Cell 5.9 ± 2.0 4.1 ± 3.0 0.069
Courses of Induction Chemotherapy 1.5 ± 0.8 1.2 ± 0.4 0.31
Length of stay (days) 50 ± 30 47 ± 18 0.56
Received HLA matched platelets 0 of 10 (0%) 0 of 12 (0%) 1.00
Days with fever >37 degrees Celsius 19 ± 12 14 ± 9.1 0.36
Days of antibiotics 44 ± 31 40 ± 21 0.74
Positive Microbial Cultures 1.1 ± 1.3 1.4 ± 1.5 0.76
Days with bleeding 1.0 ± 2.8 0.46 ± 0.67 0.36
Transfusion Reactions per Patient 0.4 ± 0.7 0.4 ± 0.5 0.73
Complete remission at discharge 9 of 10 (90%) 11 of 12 (92%) 1.00
Received BMT 8 of 10 (80%) 11 of 12 (92%) 0.57
All continuous variables results are means ± 1 SD.
There were more patients with ALL (p = 0.32) and more patients with poor risk cytogenetics in the washed arm of the study (p = 0.09). Otherwise there were few differences between the patients ≤50 years of age in the two cohorts. Patients in the washed arm had significantly better overall survival and required fewer platelets per day or per red cell transfused than patients in the unwashed arm. There were no significant differences in bleeding, use of growth factors or white cell transfusions between the two arms of the study, in either the entire or younger cohorts (data not shown but available from the authors on request).
Keeping in mind the extremely small number of patients involved, a Cox proportional hazards regression was performed on these younger patients in the randomized trial. Receipt of washed transfusions was a significant predictor of longer survival (p = 0.011), as was cytogenetics (patient's with unknown cytogenetics having poorer survival, p = 0.027) but age and type of leukemia were not statistically significant predictors of survival time.
The survival of all patients ≤50 restricted to those with AML is shown in Figure 3 confirming that the patients in the washed arm did not survive longer solely because of the larger number of patients with ALL in that arm of the study.
Figure 3 Survival in those patients in the randomized trial of washed (n = 8) versus unwashed (n = 9) platelet transfusions ≤50 years of age with AML is plotted by the Kaplan-Meier method. Those in the washed group experienced better survival but this was not statistically significant (p = 0.10 by logrank test). At a minimum follow-up of 41 months, 6 of 8 patients in the washed arm were in remission and alive compared with 3 of 9 in the unwashed arm (two of the patients in the unwashed arm were alive and in remission at 116 months).
In the subset of all patients ≤50 years of age with either type of leukemia who both achieved a complete remission and also received definitive post-remission therapy in the form of bone marrow transplantation, the survival in patients receiving washed transfusions (n = 12) was significantly better than those receiving unwashed transfusions (n = 8) (p = 0.031 by log rank test) (graph not shown).
Discussion and conclusions
To our knowledge, these data represent the first randomized trial of washed platelet transfusions in any setting. The issues of the efficacy and safety of washed platelet transfusions are of importance because (1) some patients require plasma-reduced transfusions to treat allergic or febrile reactions, (2) new methods of viral and bacterial pathogen-inactivation for transfusions may require washing prior to transfusion, and (3) a growing body of evidence suggests that immunologically important molecules are present in the stored supernatant of blood transfusions that might, speculatively, affect clinical outcomes. Our study design was less likely to detect a benefit of platelet washing because, unlike many centers, we routinely use ABO identical platelet transfusions for patients with leukemia. There is evidence from two small randomized trials in leukemia that this reduces refractoriness [4,5] and increases survival [6]. Preliminary results exist that ABO matching may also reduce morbidity and mortality in surgical patients [25].
There was no apparent benefit to washed transfusions in terms of reduced platelet transfusion refractoriness, reduced bacterial infections, reduction in reported febrile or allergic transfusion reactions or reduced length of stay. Confirmed transfusion reactions were less frequent in the washed arm of the study, and 15% of the patients in the leukoreduced arm of the study had severe or repeated reactions that led to their receiving washed transfusions, which abrogated those reactions. Our data do provide evidence, for the first time, that washed platelet transfusions are probably as safe and efficacious as standard leukoreduced transfusions. The mean number of days with bleeding was marginally but not significantly reduced in the patients in the washed group, as were the number of platelet transfusions needed per day in younger patients, and the ratio of platelet transfusions to red cell transfusions. However, there was more minor bleeding in the washed group raising the possibility that washed platelets are slightly less effective. However, the prevalence of major bleeding requiring treatment was the same in each group and very low (<5%). The only life threatening hemorrhage in these 43 patients occurred in a patient in the leukoreduced arm.
This study is also the first attempt to investigate whether transfusion practices during initial induction therapy are associated with changes in overall survival in acute leukemia in adults. The underlying rationale for studying this issue are observations demonstrating that allogeneic transfusions alter host T cell and natural killer cell immune function in surgical patients and experimental animals [11]. The major causes of death in adults with leukemia are failure to achieve complete remission, and relapse after achieving complete remission. There is some evidence from the allogeneic bone marrow transplant literature that host immune function may play a role in preventing relapse during the post-treatment period, but there are no data that demonstrate a role for the immune system in achieving complete initial remissions [26]. Our study found no difference in complete remission induction success rates with differing transfusion protocols, but does support the possibility of an association between type of red cell and platelet preparation transfused and long term survival.
Large numbers of red cell and platelet transfusions are given to patients with acute leukemia during the period of recovery from aplasia that is caused by cytotoxic chemotherapy given during remission induction. Additional transfusions are given during consolidation and bone marrow or peripheral blood stem cell transplantation. Many of these transfusions occur during the period when at least the bone marrow and peripheral blood compartments of the immune system are reconstituting and might be susceptible to the immunomodulatory effects of transfusions. Both white cell and platelet-derived mediators are present in the transfused red cells as well as platelets. Prestorage leukoreduction (not employed in this study) removes the vast majority of the white cells and platelets from stored red cells, and red cell concentrates would be expected to have much lesser concentrations of biologic mediators, even without washing. Platelet derived mediators would, of course, still be present in prestorage leukoreduced platelet concentrates.
Transfusions are known to cause suppression of type 1 cellular immunity and upregulation of type 2 humoral immunity [20,21]. Such immune deviation could, speculatively, impair host defenses provided by T cells, dendritic cells and natural killer cells that might be involved in the eradication or control of residual tumor. Potential mediators of down regulation of cellular immunity by transfusions include allogeneic white cells, red cells or platelets, ABO antigen-antibody complexes formed after repeated ABO non-identical platelet transfusions, and the stored supernatant of, in particular, platelet concentrates. Transfusion of stored platelet supernatant plasma might be hypothesized to mediate such effects by immunoregulatory and tumor growth promoting factors, such as sCD40L or angiogenic factors such as VEGF [16-18].
Leukoreduction filters remove white cells from the transfused blood components. In the case of pre-storage leukoreduction, filtration also removes some of the biologic response modifiers secreted by white cells during storage. Platelet concentrate supernatant contains large amounts of mediators such as soluble CD40L (CD154), VEGF, TGF-β1, histamine and other biologic response modifiers that might impair cellular immunity [16-18]. Washing immediately prior to transfusion removes most soluble materials from the transfused platelet and red cell transfusions, including those released during storage from the platelets, red cells and white cells. Our data provide initial support for the novel hypothesis that changing transfusion practices could play a role in long-term survival in acute leukemia, particularly in younger patients who can be potentially cured.
There are distinct limitations to what can be concluded from our data. The number of patients studied is very small and almost all of them had AML. The statistically significant improvement in survival in patients ≤50 years of age with AML who had unusually good survival represents a subset analysis, not a primary outcome group and thus may represent the play of chance. The survival of 75% of any cohort of adult patients with acute leukemia for 4–5 years is a distinctly unusual circumstance [27]. This could be due to the benefits of receiving washed transfusions, but could also be a chance occurrence in a small cohort of patients. Our overall survival in all patients of 40% in the larger cohort of patients of all ages and risk factors in the washed arm is equivalent to the best survival that has been reported in low risk, younger patients in other trials from the period of the early 1990s [27]. As shown in the figures, there was only one relapse and death that occurred at beyond five years in our cohorts, with a number of patients alive and in remission at 5–12 years after diagnosis. Thus it appears that if there was benefit from washing of transfusions, it probably involves an increased likelihood of durable remission, rather than purely delaying relapse.
These data, however promising, raise hypotheses for additional testing rather than proving a principle. If transfusion practice impacts anti-leukemic immunity, and/or survival as our data suggest, a moderate sized randomized clinical trial should be able to confirm this in relatively few years. This is a propitious time for such trials as the need for prophylactic platelet transfusion therapy is being revisited. The question is being raised as to whether platelet transfusions in non-bleeding patients are truly necessary or need be as frequent as currently employed in this disease [28]. One strategy for randomized trials would be to randomize patients to only therapeutic platelet transfusions (transfusion only for bleeding manifestations) versus current standard practice of prophylactic transfusions at a set threshold such as 10 × 109/μl.
Platelet washing is time consuming, delaying transfusion by about 2.5–3 hours, may present an additional opportunity for bacterial contamination, and involves some loss of platelets (about 20%). Platelet washing may not be feasible in clinically urgent situations. Direct costs are modest, at less than $40 per transfusion. Washing adds about $500–2000 to the total cost of caring for patients with acute leukemia in our center from diagnosis to cure or death. This is less than 0.5–1% of the total costs of treating AML or ALL with curative intent. There are few or no proven side effects of platelet washing other than reduced dose of platelets transfused. There are some additional potential clinical benefits of washing including a reduced likelihood of transfusion complications such as febrile non-hemolytic transfusion reactions, transfusion-related acute lung injury, and allergic reactions. These preliminary data, albeit from a very small number of patients, raise the possibility that some patients with acute leukemia might benefit from washed transfusions. Larger studies are indicated to explore this possibility.
Competing interests
Gambro BCT provided partial support for these studies. Dr. Blumberg has received lecture honoraria and a previous research grant from Pall Biomedical Corp. and lecture honoraria from Baxter.
Authors' contributions
NB and JMH had the original idea for the study. All authors contributed to design of the study, drafting of the manuscript and all revisions. NB supervised the laboratory preparation of blood components for the study. JMR provided overall clinical supervision of the study and care for many of the patients. JMH collected the data and JMH and NB analyzed the data.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank the technical staff of the Transfusion Service for their assistance in providing transfusion support and recording data for this study, in particular, Judith Cowles. We are deeply appreciative of the efforts of the nursing staff, housestaff, hematology fellows and attending physicians in caring for these patients and assisting in carrying out the studies described.
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| 15588315 | PMC544564 | CC BY | 2021-01-04 16:31:41 | no | BMC Blood Disord. 2004 Dec 10; 4:6 | utf-8 | BMC Blood Disord | 2,004 | 10.1186/1471-2326-4-6 | oa_comm |
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Ann Clin Microbiol AntimicrobAnnals of Clinical Microbiology and Antimicrobials1476-0711BioMed Central London 1476-0711-3-261560146810.1186/1476-0711-3-26ResearchOrganisms isolated from adults with Cystic Fibrosis McManus Terence E [email protected] Andrew [email protected] John E [email protected] Stuart J [email protected] Regional Adult Cystic Fibrosis Center, Belfast City Hospital, Belfast, Northern Ireland, BT9 7AB, UK2 Department of Bacteriology, Belfast City Hospital, Belfast, Northern Ireland, BT9 7AB, UK2004 15 12 2004 3 26 26 27 8 2004 15 12 2004 Copyright © 2004 McManus et al; licensee BioMed Central Ltd.2004McManus et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Patients with cystic fibrosis [CF] have frequent pulmonary exacerbations associated with the isolation of bacterial organisms from sputum samples. It is not clear however, if there are differences in the types of additional organisms isolated from patients who are infected with Burkholderia cepacia complex [BCC] or Pseudomonas aerugionsa [PA] in comparison to those who are not infected with either of these organisms [NI].
Methods
Adult patients attending the regional CF unit were followed over a two year period and patients were assigned to three groups depending on whether they were known to be chronically infected with BCC, PA or NI. We compared the numbers and types of organisms which were isolated in each of these groups.
Results
Information was available on a total of 79 patients; BCC 23, PA 30 and NI 26. Total numbers of organisms isolated, expressed as median and IQR for each group, [P = 0.045] and numbers of co-infecting organisms [P = 0.003] were significantly higher in the BCC group compared to PA, and in the PA group [P < 0.001, p = 0.007 respectively] compared to NI patients. The pattern of co-infecting organisms was similar in all three groups.
Conclusions
Total numbers of organisms isolated and numbers of co-infecting organisms were significantly higher in the BCC group compared to PA, and in the PA group compared to NI patients. Types of co-infecting organisms are similar in all groups of patients.
Cystic FibrosisBacterial InfectionAntibioticsBurkholderia cepacia complexPseudomonas aeruginosa
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Introduction
Patients with CF experience frequent exacerbations of their symptoms; contributing factors include infections which may be bacterial or viral in nature [1-3]. Exacerbations which are associated with the identification of an infecting organism are associated with a more rapid decline in lung function, admission to hospital and earlier acquisition of P. aeruginosa [PA] [4-6]. It is also known that those patients who are infected with B. cepacia complex [BCC] have a worse prognosis and often a more rapid decline in lung function with increased mortality [7]. Previous investigators examining pathogens infecting the CF lung have identified P. aeruginosa, S. aureus, H. influenzae, S. maltophilia, A. xylosoxidans and Aspergillus species as being important pathogens [1,8].
This study examines the bacterial organisms cultured from an adult CF population, subdividing them into three groups; BCC, PA and those who were not infected with either of these organisms [NI] and to correlate with several clinical parameters. We hypothesized that those patients with BCC have more co-infection with other bacteria and this contributes to the greater morbidity in these patients.
Materials and Methods
Study Design and Eligibility
Data were collected on all patients [>18 years old] attending the regional adult CF unit at the Belfast City Hospital for two consecutive years. Sputum specimens were obtained for each patient at regular clinic attendances [patients were reviewed at 3 monthly intervals] as well as twice during hospital admissions. BCC was cultured from sputum employing selective agar (Columbian Agar Base cat no: CM331, Oxoid Ltd., Hampshire + 5% Defibrinated Horse Blood E & O Laboratories, Bonnybridge, Scotland). All isolates were grouped into cultural phenotypes displaying similar visual characteristics and one colony from each phenotype identified by API. Sputum samples were well taken samples following physiotherapy.
Sputum culture results were retrieved from the Bacteriology database for all samples submitted over the study time period. Patients were then divided into three groups using the following criteria; BCC if there was ever any sputum culture positive for these organisms. PA, if this bacterium was identified on two or more occasions over a twelve month period, and NI for all other patients who were not chronically infected with either of these organisms. It was noted as to whether the positive culture consisted of one organism or if there were additional organisms identified. In the BCC and PA groups, when more than one organism was identified from a sputum culture the additional organism [s] where recorded as 'co-infecting', as where all organisms in cultures of two or more infecting organisms in the NI group. Co-infecting organisms were divided into groups; P. aeruginosa, S. aureus, H. influenza, S. pneumonia, S. maltophilia and all other organisms including fungi. The facility to detect respiratory viral infection was not available at the time of this study.
Spirometry [Vitalograph α ,Buckingham, UK] and oxygen saturation measurements were noted at the start and end of the study as well as the best measurement for each year. Weight was recorded at the beginning and end of the two year period. Other measurements included; smoking history, use of prophylactic and number of courses of intravenous antibiotics and a history of diabetes mellitus.
Statistical Analysis
Statistical analyses were performed using the SPSS version 11 package. The Chi-square test was used to analyse differences in the sex, smoking status and incidence of diabetes mellitus between groups. The Kruskal-Wallis and Mann Whitney test were used to compare antibiotic use and organisms isolated across groups. Lung Function and Oxygen saturation means were compared by one-way analysis of variance followed by the Student-Newman-Keuls method. A probability (P) value of less than 0.05 was considered statistically significant.
Results
Information was available on a total of 79 patients, mean [Standard Deviation] age 26 [7.9] years. Numbers in each group were as follows; BCC 23 [2 Burkholderia multivorans, 21 Burkholderia cenocepacia, all ET -12], PA 30 and NI 26. Those patients in the BCC group had a mean duration of infection of 67 months. There was no difference in the sex distribution between the groups, P = 0.18. There were a total of 10 [12.7%] patients with diabetes, but they had no relationship with the groupings [P = 0.19]. There were significantly more smokers in the BCC group [P = 0.005] in comparison to PA and NI. Prophylactic antibiotic use was similar throughout all groups, P = 0.14. Intravenous antibiotic use was similar in the BCC and PA patient groups but significantly higher compared to NI [P < 0.001], table 1. There was no difference in oral antibiotic use between the groups. Those patients in the BCC group had a significant reduction in their weight compared to the PA and NI groups (P < 0.05); there was an average weight loss of 1.1 Kg. Patients in the PA and NI groups gained weight, 0.69 ± 2.5 and 1.82 ± 2.7 kilograms respectively.
Table 1 Use of oral and intravenous antibiotics; median [Interquartile Range].
Antibiotic Courses Patient Group
BCC PA NI
No. of Patients 23 30 26
Oral 0 [0–2.0] 1 [0–2.0] 1 [0–2.3]
Intravenous 3 [1.0–11.0] 3 [2.0–5.0] 0 [0–2.0]
The changes between the best FEV1 measured each year, the change in FEV1 between the start and end were not significant between the groups. Oxygen saturation measured at the end of the study tended to be lower in the BCC and PA groups when compared to the NI group; however there were no significant differences for the best SaO2 values for each year.
Median and interquartile ranges of organism numbers [total numbers and numbers of co-infecting organisms] and specimen numbers in each group are shown in table 2 along with details of the types of co-infecting organisms. Total numbers of organisms isolated, that is the number of isolates of different organisms, [P = 0.045] and numbers of co-infecting organisms [P = 0.003] were significantly higher in the BCC group compared to PA and in the PA group [P < 0.001, P = 0.007] compared to NI. Total numbers of positive sputum cultures were greater in both the BCC and PA groups in comparison to NI [P < 0.0001]. The pattern of co-infecting organisms, Staphylococcus aureus, Haemophilus influenzae, Streptococcus pneumoniae, Stenotrophomonas maltophilia, was similar in all three groups except for P. aeruginosa being more frequently detected in the BCC group in comparison to NI [p < 0.0001].
Table 2 Median [Interquartile Range] of numbers of sputum specimens, total numbers of organisms and numbers of co-infecting organisms [per patient] in each group. Mean ± SD numbers of individual co-infecting organisms isolated per patient in each group.
Measurement Patient Group
BCC PA NI
No. of Patients 23 30 26
Total No. of Specimens / patient 24 [9.0–59.0] 19.5 [5.5–34.5] 0 [0–9.8]
Total No. of Organisms / patient † 32 [16.0–79.0] 24 [7.8–46.3] 0 [0–11.3]
No. of Co-infecting Organisms / patient ‡ 15 [8.0–20.0] 3.5 [0.8–11.3] 0 [0–4.3]
P. aeruginosa co-infection 14.4 ± 19.7 - 0.1 ± 0.3
S. aureus co-infection 3.6 ± 4.8 2.0 ± 4.1 3.7 ± 7.5
H. influenza co-infection 1.8 ± 2.9 0.7 ± 1.6 1.1 ± 3.1
S. pneumonia co-infection 0.6 ± 1.2 0.2 ± 0.5 0.4 ± 1.0
S. maltophilia co-infection 0.9 ± 1.5 2.6 ± 6.6 1.7 ± 6.3
Other organism co-infection 1.4 ± 1.9 3.1 ± 5.9 1.7 ± 3.2
† Total number of different bacterial organisms isolated in that group of patients during the study period.
‡ Number different bacterial organisms isolated excluding those isolates for BCC or PA in those patients who are colonized with these bacteria.
Discussion
This study identified individuals with BCC and PA more frequently require courses of IV antibiotic therapy in comparison to CF adults in whom neither pathogen was identified [NI]. This is in keeping with previous findings in which patients colonized with BCC [9,10] and PA [11,12] were noted to have a more rapid decline in health status with an associated increased number of hospital admissions for treatment of infective exacerbations. Prophylactic antibiotic use was found to be similar in all groups, however their role and the optimal duration of treatment of CF patients remains controversial [13].
The BCC group were also noted to have significant weight loss over the study period, again this has been shown to act as an independent predictor of survival in this group [14]. No significant differences were demonstrated between weight change in the PA and NI groups; it may be that a longer period of study is required to detect significant differences.
On comparison of PA and BCC patients to those in the NI group, investigators have previously demonstrated higher morbidity [10,12]. In this study we have found higher total numbers of positive sputum specimens, total numbers of organisms and increased numbers of co-infecting organisms in the BCC and PA groups in comparison to NI [P < 0.01]. To our knowledge this is the first paper to relate bacterial colonization to the numbers and types of coinfecting organisms isolated during exacerbations. Patients infected with BCC are known to have elevated lung inflammation[15] with an accelerated rate of lung function decline[9], this in part may be due to BCC lipopolysaccharides mediated neutrophil recruitment and subsequent respiratory burst response [16]. This pro-inflammatory status may predispose individuals to increased morbidity and co-infection. BCC infection has also been linked to a compromised host response [17] and combined with a poor nutritional status [14] increases the likelihood of respiratory tract infection in these patients. In this study the types of organisms isolated did not differ significantly between the groups, organisms corresponding to those found on previous studies [1]. It should be borne in mind that these findings relate to the adult CF population in Northern Ireland which is unique in that the majority of B. cepacia complex positive patients are colonized with a single strain (B. cenocepacia ET12).
It is well documented that CF patients experience a more rapid decline in their lung function [18]. However, no significant changes in lung function were seen between the start and end of the study and on comparison between groups. It is possible that this is due to the relatively small group numbers or that a longer follow-up period is required. The prevalence of diabetes mellitus amongst the groups was similar; its presence has recently been associated with a more rapid decline in lung function in CF patients [6]. Surprisingly, the incidence of smoking was highest amongst those with more severe disease in the BCC group.
Investigators have shown evidence of this type of risk taking behaviour in these patients, however it is actually lower when compared than their peer group [19]. This emphasizes the importance of addressing relevant risk factors and behaviour amongst these patients.
Conclusion
Patients with BCC and PA infection have a similar use of antibiotics. Total numbers of organisms isolated and numbers of co-infecting organisms are significantly higher in the BCC group compared to PA, and in the PA group compared to NI patients. Types of co-infecting organisms are similar in all groups of patients.
Abbreviations
BCC Burkholderia cepacia complex
CF Cystic fibrosis
FEV1 Forced expiratory volume in one second
NI 'Neither Isolated' group
No. Number
P Probability
PA Pseudomonas aeruginosa
SAO2 Oxygen Saturation
SD Standard Deviation
SE Standard Error
SPSS Statistical Package for the Social Sciences
SPECIAL CHARACTERS:
< Less than
α Alpha
Competing Interests
The authors' declare that they have no competing interests.
Authors' Contributions
TMcM
Collected data relating to the study and drafted the paper.
AMcD
Participated in the analysis of clinical specimens and contributed to the manuscript content.
JEM
Participated in the study design and coordination.
JSE
Conceived of the study and participated in its design and coordination.
All authors read and approved the final manuscript
Acknowledgements
Dr Chris Patterson [Department Of Epidemiology, Queens University Belfast] performed the statistical analysis of all data.
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| 15601468 | PMC544565 | CC BY | 2021-01-04 16:38:17 | no | Ann Clin Microbiol Antimicrob. 2004 Dec 15; 3:26 | utf-8 | Ann Clin Microbiol Antimicrob | 2,004 | 10.1186/1476-0711-3-26 | oa_comm |
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