The influence of low-grade glioma on resting state oscillatory brain activity: a magnetoencephalography study

Purpose In the present MEG-study, power spectral analysis of oscillatory brain activity was used to compare resting state brain activity in both low-grade glioma (LGG) patients and healthy controls. We hypothesized that LGG patients show local as well as diffuse slowing of resting state brain activity compared to healthy controls and that particularly global slowing correlates with neurocognitive dysfunction. Patient and methods Resting state MEG recordings were obtained from 17 LGG patients and 17 age-, sex-, and education-matched healthy controls. Relative spectral power was calculated in the delta, theta, upper and lower alpha, beta, and gamma frequency band. A battery of standardized neurocognitive tests measuring 6 neurocognitive domains was administered. Results LGG patients showed a slowing of the resting state brain activity when compared to healthy controls. Decrease in relative power was mainly found in the gamma frequency band in the bilateral frontocentral MEG regions, whereas an increase in relative power was found in the theta frequency band in the left parietal region. An increase of the relative power in the theta and lower alpha band correlated with impaired executive functioning, information processing, and working memory. Conclusion LGG patients are characterized by global slowing of their resting state brain activity and this slowing phenomenon correlates with the observed neurocognitive deficits.
Introduction
Low–grade glioma (LGG) patients constitute 25% [1] of the glioma patient population and have a survival of 5–10 years [2]. The optimal treatment for this patient group is still a matter of debate [3–5]. The first clinical feature of this disease is epilepsy in two-third of the patients [1, 4]. The majority of LGG patients also suffer from cognitive deficits, which tend to have a global character and cannot only be explained by the tumor localization alone [6, 7].
Higher cognitive functions depend on the integrated activity of several specialized brain areas. Functional imaging techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) are used to characterize oscillatory activity in patients with neurodegenerative diseases, including Alzheimer’s [AD; 8–10] and Parkinson’s disease [PD; 11–13]. Oscillations are a feature of neuronal brain activity and the synchronization of the oscillatory activity (reflecting the interactions of neuronal activity) is a likely mechanism for neuronal communication. For an overview of the relation between oscillatory brain activity and neurocognitive function [see 14, 15].
Magnetoencephalography (MEG) is a relatively novel way to capture the dynamics of the electromagnetic fields within the brain, and combines excellent temporal and spatial resolution. Comparison of MEG in AD patients and healthy controls showed more delta and theta activity in the patient population especially in the temporo-parietal region [16]. Another study compared spectral power in four different groups (severe AD, moderate AD, Lewy Body dementia, and healthy controls) and showed that the healthy control population had the largest rhythmic activity in the alpha band. The moderate AD population and the patients with Lewy Body dementia had the rhythmic predominance in the pre-alpha frequency band (7–9 Hz), whereas the severe AD showed a shift towards the slow-band (3–7 Hz) [17]. In PD patients, MEG has been used by Kotini [18], who showed slowing of the alpha rhythm in the patient population compared to healthy controls, and Bosboom [19], who demonstrated slowing in the theta, beta, and gamma frequency band in non-demented PD patients with a further slowing (and involvement of delta and alpha band) in the demented PD patient population.
It is suggested that the increase in slow wave activity corresponds with neurocognitive deficits in AD as well as in PD [13, 16, 19, 20].
MEG-studies have previously been performed in brain tumor patients. Oshino [21] found an increase in delta and theta band activity especially in the vicinity of the tumor and in the surrounding regions of edema. In the majority of these patients, the increase correlated with the clinical symptoms of these patients, assessed with a routine neurological examination, but neurocognitive function were not formally investigated. Although the focal lesion in brain tumor patients contrasts with the generalized brain degeneration in AD and PD, there is evidence that brain tumors can cause functional disturbances in areas remote from the tumor [22–25]. De Jongh [22] found focal clusters of delta activity near the tumor area but also in the contralateral hemisphere. An evaluation of fast MEG waves in brain tumor patients showed dipoles located in the parietal and occipital area, whereas the tumors were located in the parietal, temporal, and frontal area [23]. In this study, these dipoles were found remote from the tumor area and even in the contralateral hemisphere. Bartolomei [24, 25] found differences in resting state functional connectivity in the same brain tumor population within several frequency bands when compared to healthy controls. Interestingly, in agreement with de Jongh’s results, these differences were not limited to the tumor area and were more obvious in those with a tumor in the left hemisphere.
Since higher cognitive functions are presumed to depend on the integrated activity of several specialized brain areas, it is suggested that neurocognitive deficits may have a stronger correlation with diffuse alterations in resting state brain oscillatory activity than focal abnormalities in the brain tumor population.
In the present MEG-study, power spectral analysis of oscillatory brain activity was used to compare resting state brain oscillatory activity in both LGG patients and healthy controls.
We hypothesize that (1) LGG patients show, in addition to the local abnormalities at the tumor site, global slowing of the resting state brain activity compared to healthy controls which will vary between patients with a tumor in the left or right hemisphere (2) changes in resting state brain oscillatory activity reflect an intermediate level between the impact of tumor and tumor-related treatment on the one hand (‘input’) and the neurocognitive deficits (‘output’) on the other hand and that diffuse slowing is correlated with higher neurocognitive dysfunction.
Materials and methods
Patients and controls
Twenty-three LGG patients were asked to participate in this study. Patients were eligible if: (a) they had a suspected or histologically confirmed LGG; (b) there was no radiological (confirmed by MR or CT scan) and/or clinical tumor progression in the previous 6 months; (c) they did not use medication possibly interfering with neurocognitive function, other than anti-epileptic drugs (AEDs).
Patients were recruited from the VU University Medical Center (VUmc) and the Academic Medical Center (AMC), both tertiary referral centres in Amsterdam for brain tumor patients, after the institutional ethical review boards of both participating hospitals approved the study protocol. Relatives of the patients were asked to participate as healthy controls. Healthy controls were eligible if they: (a) did not suffer from any neurological disease; (b) did not use any medication that might influence cognitive function. For patients who could not provide a healthy control participant, VU University Medical Center staff members were included.
Magnetoencephalography
MEG recordings were obtained using a 151-channel whole-head MEG system (CTF systems; Port Coquitlam, British Columbia, Canada) while participants were seated inside a magnetically shielded room (Vacuumschmelze GmbH, Hanau, Germany). Magnetic fields were recorded during a no-task, eyes-closed resting state. Metal artefacts were avoided as much as possible. A third-order software gradient [26] was used with a recording passband of 0.25–125 Hz and a sample frequency of 312.5 Hz. At the beginning, middle and end of each recording, the head position relative to the coordinate system of the helmet was recorded by leading small alternating currents through three head position coils attached to the left and right preauricular points and the nasion on the subject’s head. Head position changes up to approximately 1.5 cm during a recording condition were accepted.
For this study, 149 of the 151 channels could be used. MEG recordings were converted to ASCII files. From these ASCII files four artefact free epochs of 13 s per subject (4096 samples) were carefully selected by visual analysis by one of the authors (IB).
Magnetic field frequencies ranging from 0.5 to 80 Hz were recorded. The MEG data were digitally filtered off-line in the following frequency bands: delta (0.5–4 Hz), theta (4–8 Hz), lower alpha (8–10 Hz), upper alpha (10–13 Hz), beta (13–30 Hz), gamma (30–50 Hz).
The MEG channels were grouped into 10 distinct regions, respectively left and right frontal region, left and right temporal region, left and right parietal region, left and right occipital region and left and right central region, as shown in Fig. 1. Fast Fourier transformation was separately applied for every patient and control on the four epochs in the above mentioned frequency bands. The results of the four epochs were averaged for each participant and the mean relative power for each MEG region were used for the analysis.
Fig. 1Distribution of MEG regions
Neurocognitive assessment
Patients and controls participated in neurocognitive assessment (see Table 1). The total duration of the assessment varied between 1 and 2 h. Individual patient’s test scores were converted to z-scores, using the means and standard deviations of the age-, sex- and education-matched healthy controls as a reference.
Table 1Description of neuropsychological test batteryLetter-digit substitution test [27] This test provides a measure of psychomotor performance that is relatively unaffected by intellectual prowess and is suitable for groups with an age range exceeding 60 years. The number of items written down in 90 s is registered, as in the decrease in performance when graphomotor speed is involvedVisual verbal learning test [27] This version of the Rey Auditory Verbal Learning Test calls for various aspects of verbal learning and recall. Measures used for analysis are memory performance on trial 1 as indicator of immediate recall, total recall after five trials, delayed recall and recognition after 20 min as indicators of memory consolidation into long-term memory, and a delta score as a measure of learning capacityStroop color-word test [27] This test is a selective attention task aiming at measuring interference susceptibility and consists of three subtasks with increasing task complexityCategoric word fluency [28]A simple task requiring the generation of words from semantic categories (animals) within a limited timeConcept shifting test [29]This test, which has two conditions of complexity, predominately measures functions associated with executive function, especially visual scanning and conceptual tracking. The motor component of this task is measured by three dummy conditions in which no cognitive capacity except for graphomotor speed is requiredMemory comparison test (MCT) Selective attention, mental concentration, memory and information processing
To reduce data, individual scores on these tests were summarized into six cognitive domains, namely information processing speed, psychomotor function, attention, verbal memory, working memory, and executive functioning. Construction of these domains has previously been reported [30], and was based on a Principal Component Analysis using Varimax rotation with Kaiser normalization performed on the z-scores of a large group of healthy controls [31]. The domains found are commonly used in neurocognitive practice and research.
Statistical analysis
Differences between both groups in the distribution of age, sex, and education were analyzed by means of chi-square tests. Mann–Whitney nonparametric U-tests were used to investigate whether patients’ standardised z-scores on neurocognitive tests in the overall measure of cognition differed significantly from healthy control z-scores.
Because of the non-normal distribution of the relative power, Mann–Whitney nonparametric U-tests were used to determine possible differences between the patient population and healthy controls.
To assess the association between relative power and cognition within the patient group, Spearman’s correlation coefficient (ρ) was calculated between the relative power in the different MEG regions and z-scores of the six neurocognitive domains for all separate frequency bands.
Results
Patient characteristics
From the initial patient group, six patients were excluded, four patients due to metal artefacts on the MEG measurements, one due to severe epileptic seizures, and one due to tumor progression at the time of registration. The final analyses were performed on a sample of 17 patients and 17 matched healthy control participants.
Due to the matching procedure, there were no significant differences between patients and healthy controls in age (M = 42.7, SD = 11.2 in patients, M = 42.6, SD = 12.7 in healthy controls, p = 0.99), and educational level (M = 5.2, SD = 1.8 in patients, M = 5.5, SD = 1.8 in healthy controls, p = 0.64). The male–female ratio between the two groups did not differ significantly (p = 0.37). Fourteen of the 17 LGG patients had a histologically confirmed LGG, clinically and radiologically stable for more than six months before inclusion, whereas the other 3 patients were suspected of having LGG, and stable for more than six months. The patients in our study were diagnosed several years ago (mean 8 years, range: 1–19 years). One year after MEG registration two of the three patients with suspected LGG were operated on because of increasing epilepsy frequency.
Eleven of those 16 patients underwent debulking, whereas 3 patients underwent a stereotactic biopsy and another two patients had an open biopsy. Of the 16 patients with a histologically confirmed LGG, the pathological diagnosis was grade II astrocytoma in ten patients, oligodendroglioma grade II in four patients and oligoastrocytoma grade II in two patients.
Seven of the seventeen patients underwent radiotherapy with prior chemotherapy in two patients (1 patient with 5 cycles of PCV and 1 patient with 2 cycles of PCV and 3 cycles of temozolomide). Eleven patients had left hemisphere tumors and 6 patients had right-sided tumors. The specific localisation of the tumor is shown in Table 2.
Table 2Tumor lateralization and localizationLeft hemisphereRight hemisphereTumor locationNo. of patientsTumor locationNo. of patientsLeft frontal4Right frontal2Left parietal3Right frontoparietal3Left temporal3Right insular region1Left parieto-occipital 1Total11Total6
In the patient group, all but one patient used AED mono- or poly-therapy. Six of the 16 patients on AED were free of seizures, while the other 10 patients were still having seizures.
Differences between patient and healthy control group
Neurocognitive functioning
As expected, patients performed poorer than healthy controls on the neurocognitive test battery (controls M = 0.00). More specifically, patients showed a significantly lower psychomotor function (M = −0.50, SD = 0.75 versus SD = 0.55, p = 0.044), working memory capacity (M = −1.43, SD = 1.62 versus SD = 0.91, p = 0.002), information processing speed (M = −0.85, SD = 0.88 versus SD = 0.97, p = 0.018) and attention (M = −1.92, SD = 3.87 versus SD = 0.68, p = 0.003). Patients’ performance on the other two cognitive domains did not differ significantly from controls (Fig. 2).
Fig. 2Patients’ z-scores on the six neurocognitive domains and on total neurocognitive functioning. Note: * p < 0.05, ** p < 0.01. A = attention, EF = executive functioning, IPS = information processing speed, PF = psychomotor function, VM = verbal memory, WM = working memory. Performance is relative to that of age-, sex-, and education-matched healthy controls (represented by the 0-line). A higher score (i.e. approaching 0) means better performance
Spectral analysis
Global spectral analysis. The mean relative power of the LGG patient population showed a significant decrease (Mann–Whitney U, p = 0.034) in the gamma band (30–50 Hz) compared with healthy controls.
Spectral analysis of frequency bands within each MEG region. An increase (Mann–Whitney U; p = 0.014) in relative power in the LGG patients compared to the healthy controls was found in the left parietal region within the theta frequency band (4–8 Hz). A decrease in relative power was seen in the left and right central region (Mann–Whitney U; both p = 0.024) and left and right frontal region (Mann–Whitney U; both p = 0.005) of the LGG patient population compared to the healthy controls within the gamma band (30–50 Hz), as shown in Table 3 and Fig. 3.
Table 3Significant differences in relative power between patients and controls per frequency band and accompanying statisticsPatientsControlspMSDMSDThetaLeft parietal0.1310.0530.0920.0260.014GammaLeft central0.0640.0270.0950.0410.024Right central0.0660.0330.1020.0470.024Left frontal0.0480.0270.0880.0430.005Right frontal0.0500.0300.0830.0370.005Significant higher relative power is depicted in bold and in italicsFig. 3The significant differences in relative power between the patient group and the healthy controls within the different frequency bands. Green area: significant higher relative power in the patient group compared to the healthy controls. Red area: significant lower relative power in the patient group compared to the healthy controls
Influence of tumor lateralization on relative power. Eleven patients had left hemisphere tumors and 6 patients had a tumor in the right hemisphere. Patients with a tumor in the left hemisphere showed an increase in theta activity in the left and right central region (Mann–Whitney U; respectively p = 0.001 and p = 0.014), left and right parietal region (respectively p = 0.004 and p = 0.029), and left and right temporal region (respectively p = 0.022 and p = 0.027) when compared to the healthy control population. Patients with left-sided tumors also showed a decrease in relative power in the gamma band in the left and right central region (respectively p = 0.023 and p = 0.048) and left and right frontal region (respectively p = 0.011 and p = 0.019).
Patients with a tumor in the right hemisphere showed a significant increase in relative power of delta activity in the right parietal region (Mann–Whitney U; p = 0.050) compared to the healthy control population. These patients also showed a decrease in relative power in the gamma band in the left and right frontal region (respectively p = 0.005 and p = 0.025), right temporal region (p = 0.042), and left and right occipital region (respectively p = 0.025 and p = 0.014), as shown in Table 4 and Fig. 4.
Table 4Significant differences in relative power between patients with a tumor in the left or right hemisphere and controls per frequency band and accompanying statisticsPatientsControlspMSDMSDLeft hemisphereThetaLeft central0.1410.0390.0950.0180.001Right central0.1330.0410.0960.0220.014Left parietal0.1490.0570.0920.0260.004Right parietal0.1410.0650.0890.0290.029Left temporal0.1070.0490.0720.0190.022Right temporal0.1000.0430.0710.0230.027GammaLeft central0.0630.0260.0950.0410.023Right central0.0690.0320.1020.0470.048Left frontal0.0480.0270.0880.0430.011Right frontal0.0530.0310.0830.0370.019Right hemisphereDeltaRight parietal0.4240.1440.3030.1370.050GammaLeft frontal0.0500.0280.0880.0430.005Right frontal0.0450.0310.0830.0370.025Right temporal0.0420.0220.0700.0450.042Left occipital0.0400.0290.0770.0320.025Right occipital0.0480.0360.0990.0370.014Significant higher relative power is depicted in bold and in italicsFig. 4The significant differences in relative power between the patients with a tumor in the left or right hemisphere compared to the healthy controls within the different frequency bands. Green area: significant higher relative power in the patient group compared to the healthy controls. Red area: significant lower relative power in the patient group compared to the healthy controls
Since these results could be influenced by differences in tumor volume, we evaluated the tumor size of participating patients. We used T1-weigthed MRI and defined tumor size as the product of the two largest perpendicular diameters of tumor hypointensity [32]. We found no significant differences in the tumor size between left- and right-sided tumors.
Associations between patient’s neurocognitive functioning and relative power in distinct MEG regions
In the patient population, negative associations between neurocognitive functioning and relative power were found in the theta and lower alpha band. An increase in the relative power in the theta band within the left frontal (−0.606; p = 0.010) and the right frontal region (−0.490; p = 0.046) was correlated with a poorer executive functioning. In the same frequency band (4–8 Hz), an increase in theta band power was correlated with poorer information processing within the left central and left frontal region (respectively −0.515; p = 0.034 and −0.592; p = 0.012). Within the lower alpha band in the right temporal region, an increase in relative power was correlated with a disturbed working memory (−0.508; p = 0.037).
Discussion
The primary goal of the present study was to evaluate whether LGG patients show (in addition to the well known MEG slowing around the tumor) diffuse slowing in resting state brain activity. The secondary goal of the study was to investigate whether this slowing is correlated with neurocognitive dysfunction. By means of MEG-registrations we have demonstrated that LGG patients have slowing of the resting state brain activity when compared to healthy controls. The decrease in relative power was mainly found in the gamma frequency band in the bilateral frontocentral MEG regions. Regarding the low frequency bands, an increase in relative power was found in the theta frequency band in the left parietal region. Correlations of neurocognitive functioning with the relative power in the patient population showed clear associations in the lower alpha and theta band, increased slowing correlating with poorer performance.
We chose to use the relative power instead of the absolute power, because relative power is less influenced by the distance between the MEG sensor and the underlying neural populations. The distance is variable due to head position in the helmet and the thickness of the skull. Lower variance of power values in subject groups are expected by using the relative power instead of the absolute power. A second reason for us to use this method is the fact that by using this method we are able to compare our results with those of other patient cohorts at our MEG center [19, 20]. This includes comparisons with other brain tumor data sets but also the comparison of our results with neurodegenerative diseases (including AD and Parkinson’s disease (PD)). This will give us an opportunity to learn more about differences in resting state oscillatory brain activity in the different diseases and at different disease stages. A limitation of this method of analysis is that an increase in one frequency band can cause a decrease in another band, although this might not be the observed pattern in absolute measures.
In previous studies, MEG has been used for power analysis in patients with neurodegenerative diseases, such as AD and PD. They showed similar results, namely a rhythmic predominance in the lower frequency bands, as was found in our study with LGG patients [16–20]. There are only few studies performed in brain tumor patients, however.
De Jongh [22] demonstrated that both a higher tumor malignancy and larger tumor volume were associated with higher signal powers in the delta band. In our study, we did not find increased oscillatory brain activity in the delta band. It might be that the conflicting results are due to differences in methodology of both studies. In contrast with De Jongh’s study [22], we analysed the relative power, which means that the power is influenced by the power in the other frequency bands and therefore group differences in a specific frequency band could results from changes in that specific band or in the absolute power of the other frequency bands. Furthermore, we evaluated diffuse delta activity instead of clusters of dipoles in the vicinity of the tumor. Apart from these technical differences, De Jongh’s patient population consisted of a more heterogeneous group of brain tumor patients. They found a higher delta power in the high-grade gliomas compared to those patients with a low-grade glioma and a delta signal power decrease after surgery. Our patient population consisted exclusively of patients with a low-grade glioma, and all but three MEG registrations were done after surgery, which might explain the lack of changes in the delta band in the present study. It also suggests that changes in the gamma and theta band may be more sensitive indicators of brain dysfunction in LGG.
In another study of De Jongh [23], the authors found focal clusters of fast MEG waves outside the tumor area and even in the contralateral hemisphere. They concluded that the asymmetry possibly reflects features of normal background activity, since asymmetry has already been demonstrated in healthy subjects for the alpha activity which is usually dominant in the right hemisphere. Since De Jongh used a single dipole analysis instead of our analysis of diffuse power changes and, again, analysed a group of patients with diverse brain tumors, it is difficult to compare the results of both studies.
Oshino and colleagues [21] used synthetic aperture magnetometry (SAM) to analyze oscillatory activity recorded by the MEG. SAM is an alternative method investigating the distribution of reconstructed sources over different cortical regions. In contrast to other source localization methods, beamforming does not rely on averaging and therefore allows analysis of evoked and induced brain activity. In a group of 15 patients with various types of primary or secondary brain tumors, they found an increase in delta and theta band activity especially in the vicinity of the tumor and in the surrounding regions of edema. In the majority of these patients, the increase in the delta band correlated well with the clinical symptoms, assessed with a routine neurological examination. The results were not correlated with neurocognitive functioning because they stated that there is much individual variation in brain activity related to neurocognitive function and they expected it difficult and sometimes impossible for these patients to undergo the neurocognitive battery. In contrast to their results, we only found an increase in theta activity and decrease in the gamma frequency band. As mentioned before we analyzed the relative power and also our patient population with only LGG patients is not comparable to the patients with various brain tumors in the study of Oshino. Although, different methods to reconstruct sources can show significant discrepancies, we also found spectral changes in the contralateral hemisphere.
By evaluating separately those patients with a tumor localisation in the left or the right hemisphere, we found more significant differences in patients with a tumor in the left hemisphere compared to healthy controls. In agreement with our previous study [24], in which we evaluated the functional connectivity (functional connectivity is a statistical correlation between time series of brain activity recorded over distinct regions which are assumed to reflect interactions between the brain regions) in patients with diverse primary brain tumors, we also found that patients with a tumor in the left hemisphere showed more differences in this functional connectivity compared to those with right-sided tumors.
Why those patients with a tumor in the left hemisphere do show more differences in oscillatory activity is not known. It is possible that the left hemisphere (usual the dominant hemisphere) acts differently to the tumor compared to those located in the right hemisphere. It is also possible that patients with a tumor in the left hemisphere are in a different stage of their disease compared to those with a tumor in the right hemisphere. Since our sample size is rather small, this could be reflected on the observed results. On the other hand is it possible that our results reflect hemispheric asymmetry which has been found in the healthy population previously. Gootjes et al. [33] found hemispheric differences in functional connectivity in healthy young adults during resting state.
The neurocognitive domains of psychomotor function, working memory, information processing speed and attentional tasks were impaired in LGG patients. These results corroborate previous studies indicating a decline in neurocognitive functioning in LGG patients [6, 34–38]. Several research groups have suggested that the increase in slow wave activity corresponds with neurocognitive deficits in AD and PD. Fernandez [16] found right parietal delta activity to be significantly associated with variability in the Cambridge Examination for Mental Disorders in the elderly (CAMCOG) score, whereas the left temporal theta activity predicted the variability of Mini Mental State Examination (MMSE) results. Sinanovic [13] found a positive correlation between diffuse changes in spectral power and MMSE scores in the PD patients with dementia. Bosboom [19] found a negative correlation in the non-demented PD patients between theta power and CAMCOG scores in both occipital and right temporal region but expected these associations, given the large number of relations that were analyzed, likely to be caused by coincidence. In the demented PD group they found no significant correlations. Stoffers [20] showed a negative association between lower alpha power and the performance on perseveration-related tasks in the non-demented PD patients.
Evidently, this study has its limitations. First of all, the patient group is rather small as mentioned before. Secondly, although our patient population consisted of only LGG patients, they did not all receive the same treatment. In our study we hypothesized changes in oscillatory brain activity to be the intermediate between the impact of the tumor and its treatment on the one hand and the neurocognitive deficits as the output on the other hand. This study showed that irrespective of the different treatment options, changes in oscillatory brain activity can be found in brain tumor patients which is associated with neurocognitive function. To get informed on the influence of the different tumor treatments on brain activity and its relation with neurocognitive function is very interesting. A longitudinal study of these effects (e.g. surgery, radiotherapy and chemotherapy) on brain activity is currently under way. The goal of that study is to explore correlations between changes in neurocognitive function and changes in functional brain dynamics during the disease course.
Our study is the first to correlate the relative power with neurocognitive functioning in brain tumor patients. The observed correlations were quite strong and showed that an increased activity in the theta and lower alpha band is correlated with impaired executive functioning, information processing and working memory. This is a first step in unraveling the underlying mechanisms of neurocognitive dysfunction in brain tumor patients. MEG power analysis gives us an interesting tool to assess functional alterations in the patient’s brain in the course of disease and to evaluate its relationship with neurocognitive functioning.

low-grade glioma
meg
cognition
power analysis


What Obstetrician-Gynecologists Think of Preconception Care

Objectives: To describe obstetrician-gynecolog-ists’ opinions of preconception care (PCC) and ascertain patient uptake for this service. Methods: A questionnaire was mailed to 1105 ACOG members in August 2004. Results: There was a 60% response rate. Most physicians think PCC is important (87%) and almost always recommend it to women planning a pregnancy (94%); 54% do so with women who are sexually active. Around a third (34%) thought their patients usually do not plan their pregnancies and 49% said very few pregnant patients came in for PCC. Of those who obtain PCC, they were believed to do so more likely to assure a healthy pregnancy (83%) than because of an elevated risk for birth defects (20%). Of 11 issues presented, cigarette smoking and folic acid supplementation were rated the most important for PCC counseling; exercise and environmental concerns were the least important. Conclusions: Physicians are willing to provide PCC but few patients are accessing such services.
Introduction
When poor pregnancy outcomes occur, they frequently have been set in motion long before the first prenatal visit. Fortunately, many of the factors contributing to less than ideal birth outcomes can be managed and brought under control prior to conception. Preconception care (PCC) consists of the identification of those conditions that could affect a future pregnancy or fetus and that may be amenable to intervention [1]. ACOG recommends that all health encounters during a woman's reproductive years, particularly those that are a part of PCC, should include counseling on appropriate medical care and behavior to optimize pregnancy outcomes [1]. Through PCC, physicians can advise patients on numerous behavioral and lifestyle changes, ranging from exercise and weight control to the use of prescribed medications and illicit drugs. They may also address carrier screening for heritable genetic disorders and the options available for helping to avoid an affected birth. For example, it is well known that women with a chronic disease such as diabetes have an increased risk of congenital abnormalities in their offspring, and they are known to have improved birth outcomes when they plan their pregnancies and utilize PCC [2]. Another prime example of the success of PCC efforts is the reduction in the number of pregnancies affected by neural-tube defects following national efforts to increase consumption and supplementation of folic acid by women of childbearing potential [3] through education and food fortification. Despite such improvements, fewer than 50% of women are aware of the benefits of taking folic acid supplements around the time of conception [4], the majority of women with diabetes do not plan their pregnancies [5], many women continue to drink alcohol regularly during pregnancy [6] and almost 50% of pregnancies in the U.S. are unplanned [7].
PCC has had its successes, but clearly more work needs to be done. The importance of PCC in promoting maternal and fetal health has long been recognized, and national recommendations and guidelines for PCC are being developed [8]. One aspect of successful development of PCC guidelines involves knowledge of obstetrician-gynecologists’ current practices and opinions regarding PCC and what they perceive to be barriers to successful implementation of PCC. The purpose of this study was to assess the practices and opinions of obstetrician-gynecologists regarding PCC, and how frequently they perceive their patients to avail themselves of such services.
Materials and methods
Questionnaires were mailed in August 2004 to 1105 American College of Obstetricians and Gynecologists (ACOG) Fellows and Junior Fellows in Practice. Of these subjects, 605 were members of the Collaborative Ambulatory Research Network (CARN), and 500 were a computer-generated random sample of ACOG members who had not received a survey from ACOG during the previous two years (Non-CARN). Members of CARN are practicing obstetrician-gynecologists who have volunteered to participate in survey studies on a regular basis. CARN was established to facilitate assessment of clinical practice patterns and aid the development of educational materials. Two reminder mailings were sent to non-respondents and questionnaires returned by December 31, 2004 were included in the survey. This protocol has typically resulted in a total sample size of >450, which is sufficient to detect differences between groups of <0.5 standard deviation with power of 80% and significance at the 0.05 level [9].
The survey recorded demographic details of physicians and their patient population, and assessed practices and opinions regarding PCC. A questionnaire concerning screening for aneuploidy was included in the same mailing; both questionnaires shared a demographics section containing 9 questions on one page. The PCC questionnaire contained 11 questions, some multi-part, on two pages. Both questionnaires were brief and resulted in a final document of 3 double-sided pages, a standard length for our full-length questionnaires. Seven of the 11 questions on PCC involved rating the frequency of an activity (e.g., always, usually, occasionally, never) or degree of agreement with a statement, and three questions were of a multiple-choice format. The questionnaires were developed in consultation with medical specialists and were pilot tested on a sample of practicing obstetrician-gynecologists prior to final distribution.
The data were analyzed using a personal computer-based software package (SPSS® 12.0, SPSS Inc., Chicago, IL). Descriptive statistics were computed for the measures used in the analyses, which are reported as mean ± SEM. Student's t test was used to compare group means of continuous variables. Factor Analysis was conducted on two sets of rating variables (Agreement with 6 statements regarding PCC, and Importance of counseling on 11 issues during PCC and during routine care), and factors with eigenvalues greater than one were treated with a varimax rotation. Differences in ratings on the Importance scales were examined with multivariate analysis of variance. Differences on categorical measures were assessed using χ2. Group differences on ordinal measures were assessed using the Mann-Whitney U test or Kruskal Wallis χ2. Related-sample differences on ordinal measures used the Wilcoxon signed ranks test. Correlations including an ordinal measure used the Spearman's rho coefficient. All analyses were tested for significance using an alpha of 0.05.
Results
Demographics
A total of 670 questionnaires were returned. Data from 18 respondents were judged invalid (physician retired, returned to sender), resulting in a valid response rate of 60% (652/1087), 432 from CARN members (72.1% response rate) and 220 from Non-CARN (45.1% response rate). Physicians responded from every state of the United States except Maine, as well as the District of Columbia, Puerto Rico, Canada, and overseas military installations. The respondents’ mean age (47.32±0.39) and the proportion of males to females (males = 55%) closely matched those of the larger population to whom the survey was sent (46.99±0.32, males = 55%) and of ACOG Fellows and Junior Fellows in Practice as a whole (47.64, males = 58%).
The remaining analyses are limited to the 88.8% (579) of respondents who indicated that their primary medical specialty was gynecology or general obstetrics and gynecology. This selection was made to best assess the practices of non-subspecialist obstetricians and gynecologists, rather than those in maternal fetal medicine, reproductive endocrinology, or other subspecialties, to whom patients may be referred after pregnancy has already been attempted. Of these physicians, 83.9% (486) practice obstetrics (ObGyn), and 16.1% (93) do not (GynOnly); the term ‘physicians’ is used when referring to both ObGyns and GynOnlys. See Table 1 for physician demographics. CARN and Non-CARN differed on only one non-demographic item (CARN were more likely than Non-CARN to discuss carrier screening with all patients who present for PCC. See results.); thus data were collapsed across these two groups.
Table 1Physician demographicsTotal (n=579)Gender (%) Males53.9 Females46.1CARN67.5Non-CARN32.5Age in years, mean (SEM)*47.12 (0.42) Males50.88 (0.54) Females42.76 (0.54)Years in practice, mean (SEM)15.22 (0.41)Deliveries in 2003, mean (SEM)129.65 (3.13)Practice location (%) Urban, inner city10.7 Urban, non-inner city29.3 Suburban32.1 Mid-sized town19.4 Rural6.9 Other1.6Practice type (%) Ob/Gyn partnership/gp51.4 Solo practice22.5 Multi-specialty10.4 University full-time faculty and practice8.5 Other7.2Patient ethnicity—mean (SEM) % of patients Non-Hispanic white62.29 (1.10) African-American16.78 (0.79) Hispanic13.35 (0.78) Asian/Pacific Islander3.83 (0.29) Native American1.29 (0.23)*P < 0.001: Males older than females; CARN older than non-CARN.
Defining preconception care
Physicians were asked whether they would define PCC as specialized or routine care. The great majority (86.7%) defined it “As specialized pre-pregnancy care that focuses on issues not typically addressed during a routine exam which are specific to ensuring an optimal pregnancy outcome.” Far fewer (13.3%) defined it as “The same as routine well-woman care that occurs during the reproductive years, prior to a pregnancy.” Those who defined PCC as routine tended to be in practice longer than those defining it as specialized (17.55±1.16 yrs versus 14.84±0.45 yrs. t(555)=2.22; P < 0.05), independent of gender.
Recommending PCC
The vast majority of physicians (97.3%) indicated that they provide PCC for their patients. Physicians were asked how frequently they recommend PCC to different groups of women of childbearing age (see Table 2). Three quarters (75%) always recommend PCC to patients planning a pregnancy, and 9 in 10 (89.9%) always recommend it to diabetic patients planning a pregnancy. Physicians who defined PCC as routine were more likely than those who defined it as specialized to recommend PCC to women who are sexually active (‘always’ or ‘usually’: routine = 63.4%, specialized = 52.1%. M-W U=13989.5; P<0.05) and to women who are using birth control (‘always’ or ‘usually’: routine = 48.6%, specialized = 32.8%. M-W U=13002; P < 0.01).
Table 2Percent of physicians indicating how frequently they recommend PCC to different groups of womenHow frequently do you recommend preconceptioncare to the following women of childbearing age?AlwaysUsuallyOccasionallyNeverDiabetic women planning a pregnancy89.97.91.70.5Women who are planning a pregnancy75.019.25.00.8Obese women planning a pregnancy61.527.09.52.0Women indicating they want children in the future38.543.516.02.0Women who are sexually active19.134.938.67.4Women who are using birth control11.524.145.918.5
Opinions about PCC
Physicians were asked their level of agreement with several statements regarding PCC (see Table 3). The majority agreed (4–5 on 5 point scale) that PCC is an important issue (87.3%) and that it has a positive effect on pregnancy outcomes (83.5%), though only 20.7% agreed that it is a high priority in their workload. Three quarters (76.8%) thought they have appropriate training to provide PCC. Half agreed that there is not enough time to provide preconception care visits to all women of childbearing age (51.4%), and that time devoted to PCC is not reimbursed (49.8%). Based on factor analysis, the first three items were combined to produce a single mean score for the ‘positive’ aspects of PCC (Factor I), and the second set of three items for the ‘negative’ aspects of PCC (Factor II). There were significant positive correlations between agreement on Factor I and frequency of recommending PCC in all groups of women (all P’s < 0.001); there were significant negative correlations between agreement on Factor II and frequency of recommending PCC in all groups of women (all P’s < 0.01). In other words, the more strongly they agreed that PCC was important/positive/high priority, the more frequently they recommended PCC. Looking at the individual components of the two factors, lack of reimbursement for PCC was the only item that did not correlate with frequency of recommending PCC in any group of women.
Table 3Percent of physicians indicating how strongly they agree with several statement regarding PCCStronglyStronglyagreedisagreeMean on 5 pt Six statements regarding opinions about PCC54321scale (SEM)Factor I: positive aspects Preconception care is an important issue47.439.910.51.30.94.32 (0.034) Preconception care has a positive effect on pregnancy outcomes44.738.814.31.11.14.25 (0.035) Preconception care is a high priority in my workload6.813.942.327.99.12.82 (0.043)Factor II: negative aspects Time devoted to preconception care is not reimbursed24.725.130.814.05.53.50 (0.051) There is not enough time to provide preconception care visits to all women of childbearing age22.628.819.718.910.03.35 (0.056) I do not have appropriate training to provide preconception care3.05.214.735.741.41.92 (0.044)
Counseling in PCC
Almost a third (31.9%) of physicians said they discuss screening for aneuploidy with All patients who present for preconception counseling. Of those not selecting All, 80.7% said they did so with patients at risk for aneuploidy. Almost one-in-ten (9.8%) said they did not discuss aneuploidy. The majority (54.4%) of physicians said they discuss carrier screening for heritable genetic disorders (e.g., CF, sickle cell anemia) with All patients who present for preconception counseling, and, of those not selecting All, 84.8% said they did so with patients who have a family history of heritable genetic disorders or other risk factors. Only 4.3% said they did not discuss carrier screening.
Physicians were presented with 11 health-related issues (e.g., exercise, nutrition, over the counter drug use) and were asked to rate how important counseling was for each issue during a routine office exam and during preconception care (see Table 4). (Note: Physicians were not asked to rank order the issues in terms of relative importance, nor were they asked to compare the issues in terms of routine or PCC; such calculations were made based on analyzing the mean ratings for each issue.) Physicians rated all issues as more important in PCC than in routine care, although the difference for Exercise was marginal (Wilcoxon signed ranks test. Exercise: P=0.052. All other P’s < 0.001). All issues were rated as very important for PCC, with cigarette smoking and folic acid supplementation rated the most highly important issues in PCC. Cigarette smoking and illegal drug use were rated the most important issues for counseling in routine care. Whereas folic acid supplementation ranked second in importance for PCC, it ranked tenth for routine care.
Table 4Percent of physicians indicating how important they think counseling is on several issues for patients who (a) come in for a routine office exam, and (b) those who are planning a pregnancy(Rank of meanImportanceVery importantNot importantMean (SEM) within a or b)Neutral [3–5]on 7 pt scaleFactor I Folic acid supplements:  (a) routine exam(10)36.852.510.74.88 (0.07)  (b) preconception care(2)96.43.4.26.83 (0.03)Factor II Cigarette smoking:  (a) routine exam(1)89.210.60.26.57 (0.03)  (b) preconception care(1)98.41.60.06.86 (0.02) Illegal drug use:  (a) routine exam(2)83.215.21.66.40 (0.05)  (b) preconception care(3)94.55.10.46.74 (0.03) Alcohol consumption:  (a) routine exam(7)49.146.54.45.40 (0.06)  (b) preconception care(4)88.511.50.06.57 (0.03)Factor III Obesity:  (a) routine exam(3)76.123.70.26.11 (0.04)  (b) preconception care(7)82.717.30.06.30 (0.04) Exercise:  (a) routine exam(4)67.031.41.65.87 (0.05)  (b) preconception care(10)68.331.00.75.94 (0.05) General Nutrition:  (a) routine exam(6)51.246.72.15.51 (0.06)  (b) preconception care(8)82.217.50.46.28 (0.04)Factor IV Chronic diseases:  (a) routine exam(5)63.834.91.25.79 (0.05)  (b) preconception care(6)88.310.80.96.45 (0.04) Family health history (inherited disorders):  (a) routine exam(8)51.543.94.65.38 (0.06)  (b) preconception care(5)90.69.20.26.55 (0.04) Over the counter and prescription drug use:  (a) routine exam(9)42.051.46.55.08 (0.06)  (b) preconception care(9)81.617.70.76.27 (0.04) Environmental concerns:  (a) routine exam(11)19.966.313.94.25 (0.06)  (b) preconception care(11)56.139.14.85.47 (0.06)
The 11 issues were grouped into 4 factors, for both routine care and PCC (see item labels, Table 4). How physicians defined PCC had a significant effect on importance ratings overall, controlling for years in practice [F(8, 539)=3.437; P < 0.005]. However, the only individual factor on which ratings were distinctly different was folic acid supplementation during routine care: physicians who defined PCC as routine were more likely than those defining it as specialized to rate it as very important (49.3% vs 34.8%) during routine care [t(556)=2.53; P < 0.02].
Gender, controlling for years in practice, also had an overall effect on ratings [F(8, 541)=4.066; P < 0.001]: females tended to rate most items as more highly important than did males.
Patient use of PCC
Of the physicians who provide PCC, a third (32.3%) said that patients ‘rarely’ present for PCC, almost half (48.5%) said they ‘sometimes’ do, and 17.3% said patients ‘frequently’ present for PCC. Over a third (34.7%) agreed that their patients usually do not plan their pregnancies. Almost half (49.1%) of ObGyns said ‘few’ or ‘none’ of their pregnant patients came in for PCC prior to the pregnancy, and almost two thirds (63.1%) said ‘most’ or ‘all’ of their pregnant patients initially made contact with them once they were already pregnant. See Table 5. The frequency with which patients reportedly present for PCC was positively correlated with physician agreement that their patients plan their pregnancies (P < 0.001), as well as with how frequently physicians recommend PCC to the 6 different patient groups listed in Table 2 (all P’s < 0.001, except ‘Diabetic,’ P=0.01).
Table 5Patient use of pre-pregnancy planningPercent of physicians selecting a particular response to questionnaire items:How frequently do you have patients present for PCC?Never or rarelySometimesFrequently32.348.517.3Women that I see usually do not plan their pregnancies Agree (1–2)Neutral (3)Disagree (4–5) (on a 5 point scale)34.733.831.6How many of your pregnant patients came in for NoneFewSomeHalf or More preconception care before they became pregnant? (OB)3.345.838.312.6How many of your pregnant patients initially made AllMostManyHalf or Fewer contact with you once they were already pregnant? (OB)9.453.718.018.9
Physicians were asked to indicate how many of the patients who presented for PCC did so for each of three reasons (7 point scale: None Few Some Half Many Most All). Over four-fifths (82.7%) of physicians indicated that more than half of the patients do so to ensure a healthy pregnancy (mean 5.63±0.062 on 7 pt scale). In contrast, 41.9% said more than half do so because of difficulties conceiving (mean 4.05±.058), and 20.0% said more than half do so because of an elevated risk of a birth defect or developmental disorder (mean 3.13±0.064).
Discussion
The purpose of this study was to examine obstetrician-gynecologists’ opinions and beliefs about PCC, and how frequently they perceived their patients to be utilizing PCC. Our findings document that most physicians think it is an important issue, that it has a positive effect on pregnancy outcomes, and that they are appropriately trained to provide it (Table 3). However, respondents reported that few of their patients seek PCC. Explanations for this lack of patients seeking PCC likely include the fact that almost 50% of pregnancies in the U.S. are unplanned [7], the lack of third party reimbursement for PCC visits, and patients’ rather poor understanding of the potential benefits of PCC to the lifetime health of future offspring. We did find an association between the frequency with which physicians recommend PCC and the frequency with which patients present for PCC. It is also possible that some women receiving routine health care prior to a first pregnancy see an internist, family physician, or nurse practitioner for such care, and only seek the care of an obstetrician-gynecologist once they become pregnant. Thus, it is possible that our study of obstetrician-gynecologists does not reflect the full extent to which women may be seeking PCC. However, a recent study of primary care providers (not obstetrician-gynecologist) also found that few patients were receiving PCC [10].
Stumbling blocks to increasing utilization of PCC may include physicians’ beliefs that time devoted to PCC is not reimbursed and that there is not enough time to provide PCC to all women of childbearing age. Regarding this latter point, opinions differ within the medical community as to who the target population for PCC should be. Three general target groups include women at high risk for poor birth outcome, women planning a pregnancy, and all women of childbearing age [11]. In this study, the vast majority of physicians said they frequently recommend PCC to diabetic and obese women planning a pregnancy, as well as to women generally who are planning a pregnancy (see Table 2). Far fewer obstetrician-gynecologists said they frequently recommend PCC to women described simply as sexually active (54%) or to those using birth control (36%). This suggests that our survey sample population views women who have consciously decided to plan a pregnancy as the most appropriate target group for PCC. Almost 9 in 10 (87%) physicians defined PCC as specialized pre-pregnancy care focusing on issues not typically addressed during a routine exam, and the remaining 13% defined it as routine well-woman care occurring during the reproductive years, prior to a pregnancy. Those who defined it as routine were more likely to offer such counseling to women who were sexually active or using birth control than were those defining it as specialized care.
One aspect of PCC includes counseling women on topics that encourage a healthy pregnancy, although many such topics are clearly relevant to the general health of any woman. Physicians were asked to rate eleven topics in terms of their importance for counseling during PCC and routine care, such as exercise, nutrition, and over the counter drug use (see Table 4). All were considered very important topics for counseling in PCC, with cigarette smoking and folic acid supplementation rated the most highly important for PCC. For counseling in routine care, folic acid supplementation received the second lowest score of all, with under two-fifths (37%) of physicians rating it as important. This points to the need for continued national attention to increase consumption and supplementation of folic acid.
Our study has limitations that should be acknowledged. The response rate was 60%, and our findings are based on the responses of 579 non-subspecialist obstetricians and gynecologists. While our study may be subject to non-response bias, we believe that our findings are reliable. The typical response rate in these survey studies is approximately 35–60%, and our response rate was at the high end of expected participation. In addition, the responses were derived from geographically diverse locations and from physicians from different practice types reflecting the influence of physician location and practice type. Whereas physicians who were more interested in the topic of the survey may have been more likely to respond, a subset of our subject pool was comprised of CARN members who respond to several questionnaires a year covering a wide variety of topics; it is unlikely that Preconception Care is a topic of greater interest to this group than to the group of randomly selected ACOG members. CARN members differed significantly from non-CARN subjects on only one non-demographic response, and the mean age and male to female ratio of respondents closely matched those of the larger group to whom the survey was sent and of ACOG Fellows and Junior Fellows as a whole, all of which suggests that response bias was minimized.
Recent studies have recommended offering PCC “opportunistically,” incorporating information relevant to preconception care into routine well-woman visits [10, 12]. Most topics relevant to preventing congenital birth defects are also important to the overall health of women, pointing out the compatibility of the two counseling contexts. The one exception to this in our questionnaire was folic acid supplementation, and it would need greater emphasis in routine care to take advantage of opportunistic counseling. We found that those physicians who defined PCC as “the same as routine well-woman care” differed from those defining it as “specialized” in several ways, including increased importance of counseling on folic acid supplementation during routine care. A recent study found that topics such as vitamin supplements and alcohol use were rarely discussed during routine care by a high proportion of obstetrician-gynecologists, and by an even higher proportion of family practitioners [13]. They also found that physician recommendations influenced women's decisions to take folic acid.
Obstetrician-gynecologists recognize the importance of preconception care and provide this care for their patients. However, lack of third party reimbursement, lack of time during office visits due to competing demands, and lack of consumer awareness pose barriers to effective implementation of PCC. The fact that almost half of all pregnancies in the U.S. are unplanned poses an even greater challenge. Continued efforts are needed to raise awareness of the importance of PCC by consumers, health care providers, third party carriers, and policy makers.

obstetrician-gynecologist
preconception care
folic acid
survey
routine care


Identification of an OsPR10a promoter region responsive to salicylic acid

Orysa sativa pathogenesis-related protein 10a (OsPR10a) was induced by pathogens, salicylic acid (SA), jasmonic acid (JA), ethephon, abscisic acid (ABA), and NaCl. We tried to analyze the OsPR10a promoter to investigate the transcriptional regulation of OsPR10a by SA. We demonstrated the inducibility of OsPR10a promoter by SA using transgenic Arabidopsis carrying OsPR10a:GFP as well as by transient expression assays in rice. To further identify the promoter region responsible for its induction by SA, four different deletions of the OsPR10a promoter were made, and their activities were measured by transient assays. The construct containing 687-bp OsPR10a promoter from its start codon exhibited a six-fold increase of induction compared to the control in response to SA. Mutation in the W-box like element 1 (WLE 1) between 687 and 637-bp from TGACA to TGAAA completely abolished induction of the OsPR10a promoter by SA, indicating that the WLE 1 between −687 and −637 of OsPR10a promoter is important in SA-mediated OsPR10a expression. We show for the first time that the W-box like element plays a role in SA mediated PR gene expression.
Introduction
Plants have developed defense mechanisms to recognize pathogens and subsequently activate defense-related genes, such as pathogenesis-related proteins (PR proteins). The major families of PR proteins have been grouped into at least 14 different classes, primarily on the basis of their amino acid sequences (Van Loon and Van Strien 1999). Although the biological and/or biochemical functions of many PR proteins remain unclear, PR2 (β-1, 3-glucanase activity) and PR3 (chitinase) proteins have been shown to inhibit fungal growth (Woloshuk et al. 1991; Sela-Buurlarge et al. 1993). These responses are not limited to pathogen attack and can be induced by defense signaling molecules such as SA, JA and ET (Dempsey et al. 1999; Pieterse and van Loon 1999). To study the defense signaling in plants, many groups have isolated promoters of PR proteins in several plant species, such as Arabidopsis, tobacco, pepper, and rice (Malnoy et al. 2003; Hong et al. 2005; Li et al. 2005; Liu et al. 2005a; Lee and Hwang 2006).
Expression profiles of PR proteins, such as OsPR1, OsPR10 and OsPR1b were reported. Originally, OsPR10a was known to be induced by probenazole and thus, was called a probenazole-inducible gene, PBZ1 (Midoh and Iwata 1996). Later, PBZ1 was renamed as OsPR10a because it shares a similar sequence with (has sequence similarity to) PR-10 proteins. The investigators reported that OsPR10a is only induced by probenazole but not by ethephon, NAA, SA, NaCl or mannitol in rice leaves. In contrast, Rakwal et al. (2001) reported that OsPR10a is induced by JA, SA, and ABA but not by IAA or GA in light. Ryu et al. (2006) found similar results using RT-PCR. Chen et al. (2006) reported that the elicitor derived from Magnaporthe grisea induces OsPR10a. However, there has been only one study on OsPR10a and OsCHNIII promoters, even though many reports are available for the expression profile of PR genes in rice (Rakwal et al. 2001; Hashimoto et al. 2004; Chen et al. 2006; Ryu et al. 2006). The authors reported that OsPR10a and OsCHNIII promoters are induced by an elicitor derived from Magnaporthe grisea by a transient assay in vitro. However, the cis elements were not analyzed.
Most promoters induced by pathogens or SA contain the W-box, GCC box, RAV1 AAT, or ASF1 motif, etc. (Li et al. 2005; Lee and Hwang 2006; Sohn et al. 2006). Their cis-elements have been identified by series deletion of the promoter and site directed mutagenesis of its plausible site. Maleck et al. (2000) analyzed the transcriptome of Arabidopsis under defense inducing conditions, and they studied induced promoters such as PR1. The W-boxes ((T)TGACC/T) are enriched in the PR1 regulon promoter. They also described that the W-box like element ((T)TGACA) is also enriched in PR-1 regulon promoters even though there is no evidence that WRKYs bind to this motif (Maleck et al. 2000).
Transcription factors that can recognize the cognate cis element were identified by methods such as the gel-mobility shift assay, yeast-one hybrid, transient assay in plant. WRKY, ERF, RAV, bZIP, MYB, etc. have been shown to be involved in the defense signaling (Rushton et al. 1996, 2002; Eulgem et al. 1999; Kirsch et al. 2001; Heise et al. 2002). The interaction of a transcription factor to its cognate cis element is a key step in the process of defense signaling. Among transcription factors, WRKY proteins are the most extensively studied in defense signaling (Eulgem et al. 1999; Robatzek and Somssich 2001; Shimono et al. 2007). Asai et al. (2002) reported that AtWRKY22 and AtWRKY29 regulate FLS2-mediated defense signaling. The complex of TGA factor and NPR1 binds to the LS7 in the PR-1 promoter of Arabidopsis (Johnson et al. 2003). Furthermore, the TGA/NPR1 complex is as well conserved in rice as in Arabidopsis (Fitzgerald et al. 2005). Recently, there are three reports that OsWRKY45, OsWRKY71 and OsWRKY03 regulate the defense signaling in rice, respectively (Liu et al. 2005b, 2006; Shimono et al. 2007), implicating that WRKYs also binds to the W boxes in rice as it does in Arabidopsis.
In this study, we analyzed the expression profile of OsPR10a. We isolated its promoter and analyzed its cis-elements. We also identified the WLE1 (TGACA) controlling induction of OsPR10a promoter by SA. This is the first report that the W-box like element actually plays a role in SA-mediated defense signaling.
Material and methods
Plant materials
Rice seedlings (Oryza sativa cv. Hwachung; seeds from Dr. Wan-He Ye, NIAST, Suwon, South Korea) were grown in a greenhouse at 28°C for 3 weeks. Three-week-old rice seedlings were washed, incubated in tap water for 2 days, and then treated with SA, JA, ethephon, ABA, or NaCl at 1 mM, 100 μM, 100 μM, 100 μM, and 200 mM, respectively. Rice leaves were harvested at the times indicated in the figures. For bacterial inoculations, a strain of Xanthomonas oryzae pv oryzae KXO98 (Xoo; obtained from Korean Agricultural Culture Collection, KACC, Suwon, South Korea) incompatible to O. sativa cv. Hwachung was grown in PSA medium (10 g peptone, 10 g sucrose, 1 g sodium–glutamate, and 15 g agar per L) for 2 days and then resuspended in 1 mM MgCl2 to a final OD600 of 0.5. Xoo was sprayed on 3-week-old rice seedlings. After inoculation, plants were incubated in a humidity chamber for 24 h. Samples were taken at the times indicated in the figures and were immediately frozen in liquid nitrogen and stored at −80°C until further analysis.
Isolation of OsPR10a promoter
Based on an annotation of the rice genome, a −1000 bp fragment of the OsPR10a promoter was obtained by PCR from rice genomic DNA using an OsPR10a gene-specific primer sets. These primers were designed from the Genbank sequence AL845342. PCR was performed for 30 cycles under the following condition: 94°C for 30 s, 53°C for 1 min, and 72°C for 1 min, followed by a final extension at 72°C for 7 min. The primer sets are as follows:
5′-AAAAAGCAGGCTTGTTTTGAATGCTGGAATGATAA-3′, and 5′-AGAAAGCTGGGTCACTGAAGATATAATCTA-3′.
The underlined sequences match the attB1 and attB2 sites for the Gateway cloning system (Invitrogen, Carlsbad, CA, USA). A 1000 bp amplified PCR product was cloned into pDONR221 to make an entry clone by BP clonase (Invitrogen); successful insertion was confirmed by sequencing.
Promoter-LUC constructs
The reporter constructs used in the transient expression assays in this study were prepared according to the following procedure. For a 1.0 kb OsPR10a promoter, 1000-bp upstream from the start codon of OsPR10a was cloned by BP reaction into pDONR221 to make the −1000 bp PR10a promoter entry clone described in the previous section. −1000-OsPR10a:LUC was created by LR reaction with the −1000-PR10a entry clone and promoter destination vector (attB1-ccdB-Cmr-attB2-LUC, unpublished results; Invitrogen). On the basis of W-boxes involved in the activation of defense genes in plants to construct the deleted OsPR10a:LUC construct, we amplified the OsPR10a promoter region using these sense primers: −818: 5′-AAAAAGCAGGCTCGTGACATCAGATTGAGTAT-3′−687: 5′-AAAAAGCAGGCTCGATAAAGGGTATTTGTTTA-3′−637: 5′-AAAAAGCAGGCTACCTATCATCTAAAAGCATT-3′.
We also used the antisense primer 5′-AGAAAGCTGGGTCACTGAAGATATAATCTA-3′. PCR was performed for 30 cycles under the following condition: 94°C for 30 s, 53–55°C for 1 min, and 72°C for 1 min, followed by a final extension at 72°C for 7 min.
The sequences underlined match attB1 and attB2 sites in the Gateway cloning system. These −818, −687, and −637 bp amplified PCR products were cloned into pDONR221 to make entry clones by BP clonase and confirmed by sequencing. 818-, 687-, and 637-OsPR10a:LUC were created by LR reaction with the 818-, 687-, and 637-OsPR10a entry clones and the promoter destination vector (attB1-ccdB-Cmr-attB2-LUC, unpublished results).
Site directed mutagenesis
The mutagenized reporter constructs used in the transient expression assays in this study were prepared according to the manufacturer’s instruction (Stratagene, La Jolla, CA, USA). For the mutagenized 687 bp-OsPR10a promoter, −1000-OsPR10a entry clone (20 μg) was added to 1 μL of 10× reaction buffer, 1 μL (1 ρmole) phosphorylated specific mutagenic primer sets, 0.5 μL 10 mM dNTPs, 0.6 μL Quick solution; and the volume was adjusted to 10 μL with sterile deionized water. The solution was added to 0.3 μL of Pfu Turbo DNA polymerase (2.5 units/μL). The reaction matrix mix was used for subsequent PCR. Specific PCRs were performed for 18 cycles under the following conditions: 95°C for 50 s, 60°C for 50 s, and 68°C for 1 min, followed by a final extension at 68°C for 7 min. After generation of the mutgenic double stranded plasmid containing staggered nicks, the product was treated with Dpn I and incubated at 42°C for 60 min. The nicked plasmid incorporating the desired mutations was purified with phenol and chloroform extraction and ligated with T4 DNA Ligase at 16°C for overnight. Five microliters of the mutated plasmid was transformed into E.coli (DH5α) cells. After transformation, the plasmid DNA was isolated from the mutagenic transformant and confirmed by sequencing. For PCR of mutant strand synthesis reaction, the following mutagenic primer pairs were used: 5′-TGAAATGTAGTCGTACCTATCA-3′ and 5′-TGCTCTGAGATGGGTCTAAACA-3′.
Particle bombardment and transient expression assays
Leaf bombardments were performed in a Biolistic PDS-1000/He particle delivery system using 1100-p.s.i. rupture disks (BioRad, Hercules, CA, USA). Plasmid DNAs for particle bombardment were prepared as described by the manufacturer’s instructions. For reporters, −1000-bp OsPR10a:LUC and 818-, 687-, 637-OsPR10a:LUC and m687-OsPR10a:LUC were used; 35S:RLUC was used as an internal control to normalize LUC activities between samples after bombardments. About 2 cm lengths of one-week old rice seedlings grown in the dark were cut and incubated on a plate in 1/2 MS medium overnight (Murashige and Skoog 1962). Tungsten particles coated with 1000-, 818-, 687-, 637-OsPR10a:LUC, or m687-OsPR10a:LUC, and the internal control were delivered into leaf segments by the particle delivery system (BioRad). Leaf segments were incubated at 28°C for 24 h with buffer (1/2 MS medium) or with 1 mM SA and then harvested. Leaf segments were ground in liquid nitrogen and dissociated in 1× passive buffer. The luciferase activities from protein extracts were measured by a dual luciferase system (Promega, Madison, WI, USA) with a luminometer (Aureon Biosystems, Vienna, Austria).
RT-PCR analysis
Leaf samples were ground to powder in liquid nitrogen, and total RNA was extracted using the Trizol reagent according to the manufacturer’s instructions (Invitrogen). For reverse transcription, total RNA (1 μg) was added to 1 μL of oligo (dT)16 and 1 μL of gene specific primer sets (0.5 ρmole); and the volume was adjusted to 15 μL with sterile deionized water. The solution was incubated at 70°C for 5 min, then immediately transferred to ice before the addition of 35 μL of reverse transcriptase master mix containing 10 μL 5× buffer, 3 μL 0.1 M DTT, 5 μL 10 mM dNTPs, 1 μL (200 units/μL) M-MLV RTase (Promega) and 0.2 μL (40 units/μL) RNasin (Promega). The reaction was incubated at 42°C for 90 min before heat inactivation at 65°C for 10 min. Two microliters of each reverse transcriptase reaction was used for subsequent PCR. Gene specific PCRs were performed for 35 cycles under the following conditions: 94°C for 30 s, 53°C for 1 min, and 72°C for 1 min, followed by a final extension at 72°C for 7 min. Samples were visualized on 1.2% agarose gels. For RT-PCR analysis of OsPR10a genes in rice, the following primer pairs were used: OsPR10a (D38170)5′-GCTACAGGCATCAGTGGTCA-3′ and 5′-GACTCAAACGCCACGAGAAT-3′,OsActin (XM469569) 5′-TCCATCTTGGCATCTCTCAG-3′and 5′-GTACCCGCATCAGGCATCTG-3′.
Generation of transgenic Arabidopsis, induction with SA, and fluorescence microscopy
OsPR10a:GFP was constructed by LR reaction with pBGWFS7 (Gateway™; Department of Plant Systems Biology, VIB-Ghent University, Belgium) and the OsPR10a promoter entry clone described in the previous section, and then transformed into Agrobacterium tumefaciens GV3101 for Arabidopsis. Arabidopsis (Columbia ecotype) was transformed with A. tumefaciens GV3101 carrying OsPR10a:GFP::GUS and 35S:GFP(35S:pBGWFS7::GUS) as an internal control. A bacterial suspension of A. tumefaciens GV3101 carrying OsPR10a:GFP::GUS and 35S:GFP::GUS was sprayed on the unopened flowers of Arabidopsis. T1 plants were screened by 0.3% Barstar spray (Misung, Daejeon, South Korea). Samples were taken from independent T1 plants for RT-PCR analysis. T2 plants were also screened by 0.3% Barstar spray. Three individual T2 plants in each line were used for induction with 1 mM SA at 28°C for 72 h. After induction, transgenic plants carrying OsPR10a:GFP and 35S:GFP were examined by fluorescence microscopy using an Olympus SZX-RFL3 (Olympus Optical Co., LTD, Tokyo, Japan). Excitation and emission filters SZX-FGFP and SZX-FGFPA were used for GFP and GFPA (Ex 460–490/Em510- for GFP and Ex460–490/Em510–550 for GFPA). Images were captured with a JP/FV300 camera (Olympus).
Results
Expression patterns of OsPR10a in response to different stimuli
Several research groups have reported some discrepancy for the expression patterns of OsPR10a to some stimuli. Here we looked at the expression of OsPR10a by RT-PCR, which is a more sensitive method than reported previously. First, we tested whether OsPR10a is induced by a pathogen, as reported previously (Midoh and Iwata 1996; Ryu et al. 2006; Fig. 1a). Induction of OsPR10a started at 6 h and reached a maximum at 48 h after Xoo infection, as reported previously (Ryu et al. 2006). We also determined whether OsPR10a was induced by biotic elicitors such as SA, JA, and ethephon. For the fist time, we show that OsPR10a is induced by ethephon (Fig. 1b). Witzh regard to abiotic stress treatments, OsPR10a was induced by NaCl and ABA (Fig. 1c). Taken together, we conclude that OsPR10a is induced by the pathogen Xoo, SA, JA, ethephon, NaCl, and ABA.
Fig. 1Expression pattern of OsPR10a in rice leaves treated with Xoo and five compounds. a Three-week-old rice seedlings were infected with Xoo and were harvested at 0, 6, 12, 24, and 48 h. b, c Three-week-old rice seedlings were treated with SA, JA, ethephon, ABA, or NaCl and were harvested at 0, 6, 12, 24, and 48 h. Total RNA was isolated from each sample, and RT-PCR was performed using OsPR10a specific primer pair. Transcript levels of OsActin show that equal amounts of RNA were used in the RT-PCR samples
The OsPR10a promoter is induced by SA treatment as shown in transient-assay system
We analyzed the expression of the OsPR10a gene to pathogens and various phytohormones. We focused on the SA-mediated response of the OsPR10a gene. To investigate how the OsPR10a gene was transcriptionally regulated by SA, we isolated the OsPR10a promoter in a 1.0 kb genomic DNA fragment upstream from the start codon of the OsPR10a gene by PCR. To analyze whether the 1.0 kb OsPR10a promoter was activated by SA as expected by its expression pattern, we carried out a transient assay using particle bombardment. The 1.0 kb fragment of OsPR10a promoter was used to make a reporter construct (OsPR10a:LUC). Its schematic diagram is shown in Fig. 2a. OsPR10a:LUC was introduced into rice leaves by particle bombardment; leaf segments were then treated with either buffer or SA. Protein extracts were prepared from samples after 24 h post-treatment, and their relative luciferase activities were measured. OsPR10a promoter activities were expressed as relative luciferase activities. Figure 2b shows a representative graph out of more than three independent experiments. The absolute values from each experiment were different, but the relative ratios from each sample were similar. Luciferase activity in the SA-treated sample was about two-fold higher than in non-treated (control) samples (Fig. 2b). This result indicates that the OsPR10a promoter is activated by SA, based on its expression profile.
Fig. 2OsPR10a promoter activity in response to SA: a schematic representation of the OsPR10a promoter in the reporter construct. b A transient assay showing the OsPR10a promoter in response to SA. OsPR10a:LUC was bombarded into rice leaves, which were then incubated in MS medium or MS medium containing 1 mM SA at 28°C for 24 h. Protein extracts were made by dissociation in passive lysis buffer as described in “Materials and methods”. Relative luciferase activity is the ratio of the value obtained with the SA-treated OsPR10a:LUC divided by the value obtained with the buffer-treated OsPR10a:LUC. Bars indicate the standard error of three replicates
The OsPR10a promoter is induced by SA treatment in stably transformed Arabidopsis
We further investigated whether SA, as seen in the transient assay, activates the OsPR10a promoter using a transgenic approach. A 1.0 kb fragment of the OsPR10a promoter was cloned into a promoter-less GFP::GUS expression vector to make a OsPR10a:GFP::GUS construct, and was then introduced into Arabidopsis by Agrobacterium-mediated transformation (Fig. 3a). Transgenic Arabidopsis plants (T1) were screened by spraying with 0.3% Barstar and then with self-crossing. Induction of the OsPR10a promoter by SA was analyzed by GFP fluorescence in T2 transgenic Arabidopsis seedlings treated with either SA or buffer (Fig. 3b). In these GFP filter images (>510 nm), transgenic Arabidopsis carrying OsPR10a:GFP::GUS exhibited an orange fluorescence in the SA-treated sample because the green fluorescence from GFP was mixed with the red fluorescence from plants themselves (Fig. 3b, middle panel). The green fluorescence from GFP is shown more clearly using a GFPA filter (510–550 nm; bottom panel of Fig. 3b). As shown in Fig. 3b, the OsPR10a promoter was clearly activated by SA in transgenic Arabidopsis.
Fig. 3Fluorescence images of Arabidopsis transgenic plants carrying OsPR10a:GFP: a schematic diagram of OsPR10a:GFP::GUS fusion construct. b Induction of OsPR10a promoter by SA. OsPR10a:GFP::GUS was introduced into Arabidopsis by Agrobacterium-mediated transformation. Transgenic Arabidopsis seedlings carrying the OsPR10a:GFP::GUS was examined using fluorescence microscopy after SA treatment at 72 h. Non-transgenic Arabidopsis seedling was used as a control (left panel at mock and SA treatments). Shown are the bright-field images (upper panel Bright), the green fluorescent images using GFP filter (middle panel GFP) and the GFPA filter (bottom panel GFPA). Images are representatives from two independent experiments. The experiments were repeated at least twice
Analysis of cis-elements of OsPR10a promoter
In order to find a cis-acting element of the promoter in response to SA, an analysis was done using the PLACE program (a database for PLAnt Cis-acting Elements located at http://www.dna.affrc.go.jp/cDNA/place) (Fig. 4). Among many putative cis elements, we only indicated cis-elements in boxes known to be related to defense inducers, such as the pathogens, SA, JA, and ethephon, of the OsPR10a gene shown in Fig. 1 (Shinshi et al. 1995; Eulgem et al. 1999; Kagaya et al. 1999). The OsPR10a promoter analyzed by the PLACE program contains four W-boxes, whose detail sequences are different; there are one canonical W-box ((T)TGACC/T) and three W-box like elements (WLE 1) containing TGAC core (TGACA). There would be more W-box like elements in defense gene regulon promoters. Therefore, we decided to name TGACA as the W-box like element 1 (WLE1). In addition, there are three RAV1AAT elements, and one ASF1 motif element (Fig. 4). The W-box, RAV1AAT, and ASF1 motif are known to be cis-elements of the WRKY, RAV1, and bZIP proteins, respectively (Abe et al. 1997; Chen and Chen 2002; Yamamoto et al. 2004). The WRKY, RAV1, or bZIP proteins might be involved in the response of the OsPR10a promoter to SA. In addition to them, there are many cis-elements involved in ABA responsiveness, even though they are not indicated in Fig. 4. These elements might be involved in the induction of OsPR10a by ABA as shown in Fig. 1c. Interestingly, there is no cis-element, such as the JA responsive element (JERE) (AGACCGCC) or the ethylene response element (ERE) (AGCCGCC), which is the binding site for ethylene response element binding proteins (EREBP), despite the fact that OsPR10a was induced by JA and ethephon.
Fig. 4Putative cis-acting elements in 1.0 kb OsPR10a promoter. The putative cis-elements are indicated in boxes and its name is given above each element. Arrows indicate the direction of the cis-element. W-box WRKY transcription factor binding site; RAV1AAT RAV transcription factor binding site; ASF1 motif bZIP factor binding site; WLE1 putative WRKY transcription factor binding site
Deletion analysis of the OsPR10a promoter to identify the regions responsible for the induction by SA
To identify the region of the OsPR10a promoter involved in the response to SA, we made serial deletions of the OsPR10a promoter by PCR (Fig. 5a). Deletions, beginning with the locations −818, −687, and −637, were fused to the LUC coding sequences and 3′ nopaline synthase gene terminator (Fig. 5a). These four constructs were tested for SA inducibility of the OsPR10a promoter by introducing them into rice leaves using particle bombardment and then treating them with either buffer or SA for 24 h. Protein extracts were made from the bombarded leaves and their luciferase activities were measured (Fig. 5b). In the case of the 1.0 kb OsPR10a:LUC construct, luciferase activity was increased up to two fold over the control with SA treatment but not in the 818:LUC construct, indicating that there is a weak positive cis-element in region I between −1000 and −818 bp of OsPR10a promoter (Fig. 5a). One ASF1 motif was found in region I. The exact positive element in this region has not yet been identified. Luciferase activity in the 687:LUC construct was increased up to sixfold with SA treatment, indicating that there is a negative element in region II between −818 and −687 bp of the OsPR10a promoter. There is only one WLE1 with the TGAC core (TGACA) and one RAV1AAT element in region II that is known to be bound by transcription factors associated with the defense signaling (Fig. 4). Besides this, there are many putative cis-elements in region II (data not shown). Therefore, the exact negative element has not yet been determined. In the 637:LUC construct, there was only about a two-fold increase in luciferase activity with SA treatment, indicating that there is at least one positive element between −687 and −637 bp (region III) and another one between −637 and 1 bp (region IV) of the OsPR10a promoter. There is only one WLE1 containing the TGAC core (TGACA) in region III, suggesting that this element may play an important role in the strong inducibility of the 687:LUC construct by SA (Table 1). The W-box, RAV1AAT, and WLE1 are found in region IV of the OsPR10a promoter, and at least one of them can act as a weak positive element.
Fig. 5Deletion analysis of OsPR10a promoter: a schematic diagrams of serial deletion constructs of OsPR10a promoter. The numbers to the left of each construct indicate the distance from the start codon ATG. The predicted cis-elements ( W-box, RAV1AAT, and ASF1motif) are indicated by their respective abbreviations. The start codon, ATG, is written in bold. b Luciferase activity in deletion constructs of the OsPR10a promoter. Each deletion construct OsPR10a:LUC was bombarded into rice leaves, which were incubated in MS liquid medium or MS medium containing 1 mM SA at 28°C for 24 h. Protein extracts were made by dissociation in passive lysis buffer as described in “Materials and methods”. Bars indicate the standard error of three replicates. The values are the ratio of the value obtained from each deletion constructs of OsPR10a promoter treated with SA or buffer divided by the value obtained from 1.0 kb OsPR10a promoter construct treated with bufferTable 1The list of putative cis-acting elements of the OsPR10a promoter in region IIIRegionaPositionbcis-Elements (#)/putative factor cIII−687 to −637GT1CONSENSUS(1)/GT-1, WLE1 (1)/WRKYaOsPR10a promoter was divided into four regions depending on the presence of W-box. The region III between −687 and −637 bp of OsPR10a promoterbindicates the distance of upstream from the start codon of OsPR10acPutative cis-acting elements in region III of OsPR10a promoter were analyzed using PLACE (a database for PLAnt Cis-acting Elements located at the web site (http://www.dna.affrc.go.jp/cDNA/place)
Mutation of a W-box like element in OsPR10a promoter abolished its SA inducibility
SA inducibility of the 687:LUC construct is the highest among deletion constructs, and only one WLE1 with the TGAC core is present in region III. To further verify this, the WLE1 in region III was mutagenized from TGAC to TGAA (Fig. 6a). Eulgem et al. (1999) reported that the WRKY protein couldn’t bind to a TGAA sequence; therefore, this mutation prevents the association of WRKY to the WLE1 of the OsPR10a promoter. Interestingly, SA inducibility of 687 bp-OsPR10a promoter was completely abolished in the mutagenized 687 bp-OsPR10a promoter, indicating that this WLE1 is involved in the SA inducibility of OsPR10a.
Fig. 6The effect of the mutation in the WLE1 of OsPR10a promoter region III. a Sequences of the WLE1 (the −659 to −644 bp) in the OsPR10a promoter and the mWLE1 with the TGAAA instead of TGACA. The WLE1 sequence is underlined and bolded. The asterisk represents the mutated base in the WLE1. b Luciferase activity in 687:LUC and m687:LUC in rice leaves. Bar indicates the standard error of the three replicates
Discussion
Plant defense mechanisms to pathogen attack have been extensively studied in Arabidopsis; however, it is not well studied in rice. To study the defense mechanisms in rice, we tried to understand the transcriptional regulation of OsPR10a because OsPR10a has been used as a marker of induction for the defense response in rice (Ryu et al. 2006; Chen et al. 2006). OsPR10a was originally cloned by Midoh and Iwata (1996). They reported that OsPR10a was induced by Magnaporthe grisea and probenazole but not by ethephon, NAA, SA, NaCl, mannitol, and wound. More recently, Rakwal et al. (2001) reported that OsPR10a was induced by various phytohormones, such as SA, JA, and ABA, but not by IAA and GA. There were discrepancies between these two reports. In our study, we have shown that OsPR10a was induced by Xanthomonas oryzae pv. oryzae, phytohormones, such as JA, SA, ethephon, ABA and NaCl, but not by IAA and GA. In the case of JA, our result is consistent with the previous report (Rakwal et al. 2001). However, in the case of SA, our result is consistent with the findings by Rakwal et al. (2001), but not with that one by Midoh and Iwata (1996). There might be some differences in the method of SA treatment. We treated rice seedlings with SA by the soil drenching method because our previous result, based on the expression of OsPR1 gene, indicated that spraying rice leaves with SA does not reliably induce the defense response. In the case of ethephon, our data are also not consistent with the results from Midoh and Iwata (1996). Their data on the expression of OsPR10a were generated by Northern blots, whereas our results were generated by a more sensitive method, RT-PCR. Our result is the first report on the response of OsPR10a to ethephon. Our data suggest that OsPR10a is induced by three different defense signaling transducers (SA, JA, and ethephon). For abiotic stress treatments, OsPR10a was induced by NaCl and ABA. In the case of NaCl, our result also differs from the data shown by Midoh and Iwata (1996). We think that there is a sensitivity difference due to the detection methods of OsPR10a mRNA between RT-PCR and Northern hybridization as in the case of ethephon. In the case of ABA, our result is consistent with a report by Rakwal et al. (2001). Taken together, we conclude that OsPR10a is induced by the pathogens, SA, JA, ethephon, NaCl, and ABA.
In this study, we focused on SA mediated induction of OsPR10a because SA mediated defense signaling is the most well studied in Arabidopsis. The OsPR10a promoter was isolated to study the transcriptional regulation of OsPR10a gene. Gene activity was induced by SA in a transient assay system as expected by its expression profile. Chen et al. (2006) reported that it was induced by an elicitor derived from Magnaporthe grisea as shown in a transient assay system. SA might be involved in elicitor-mediated defense signaling, yet there was no report on the activity of OsPR10a promoter in plants. Our data now have shown that the OsPR10a promoter was also activated by SA in stably transformed plants, as we have seen in a transient-assay system.
The cis-acting elements of the OsPR10a promoter were analyzed to find the elements responsible for its induction by SA. It resulted in many putative cis-acting elements. The transcription factors which play an important role in defense signaling are WRKY, ERF, bZIP, MYB, RAV1, etc. (Ruston and Somssich 1998; Singh et al. 2002; Sohn et al. 2006). Therefore, we searched binding sites in the OsPR10a promoter for WRKY, ERF, bZIP, MYB, and RAV1. We found W-box, RAV1AAT element, and ASF1 motif element. The W-box, RAV1AAT, and ASF1 motif are known to be the binding sites of the WRKY, RAV1, and bZIP proteins, respectively (Abe et al. 1997; Chen and Chen 2002; Yamamoto et al. 2004). This suggests that the WRKY, RAV1, or bZIP proteins might be responsible for the induction of OsPR10a promoter by SA. We also found several ABRE sequences that are known to be responsible for ABA responsiveness of the gene (Shinozaki and Yamaguchi-Shinozaki 1996). This element might be involved in the induction of OsPR10a by ABA. There is no cis-element, such as JERE or ERE even-though OsPR10a was induced by JA and ethephon. Induction of OsPR10a by JA and ethephon appears to occur indirectly through some other transcription factors bound to the OsPR10a promoter.
Based on cis-elements found in OsPR10a promoter, three different deletion constructs (818:LUC, 687:LUC, and 637:LUC) were made. Induction of the OsPR10a promoter by SA was completely abolished using the 818:LUC construct, indicating that at least one weak positive element exists in region I. In the 687:LUC construct, there was approximately a sixfold increase compared to the 1.0 kb OsPR10a promoter construct. This suggests that at least one negative element exists in region II. In the 637:LUC construct, its activity was dramatically reduced compared to the 687:LUC construct, suggesting that there is a positive element in region III. Induction of the promoter by SA was also maintained in the 637:LUC construct, suggesting a positive element is present in region IV. In region III, there were a number of available cis elements in the OsPR10a promoter (Table 1). However, only one WLE1 with the TGAC core was present in region III. Its nucleotide sequence is different from the canonical W-boxes ((T)TGACC/T) (Maleck et al. 2000). However, they also described that the WLE1 (TGACA) is enriched in PR-1 regulon promoters. To verify involvement of the WLE1 in response to SA, its sequences were mutagenized (Eulgem et al. 1999). The mutation of the WLE1 from TGAC to TGAA in region III completely abolished the induction of the 687:LUC construct by SA. This suggests that the WLE1 is important in the expression of the OsPR10a gene in response to SA. This is the first finding that the WLE1 (TGACA) is important in SA mediated PR gene expression. Interaction of transcription factors and cis-acting elements constitute a key step in the defense signaling. The OsTGA factor interacts with OsNPR1 as reported in Arabidopsis (Chern et al. 2001; Yu et al. 2001). These authors suggest that NPR1-mediated defense signaling in Arabidopsis is conserved in rice. However, they did not report the identity of the target gene of this complex. Liu et al. (2005b) reported that OsWRKY12 induces the expression of OsNPR1 and OsPR1b; however, they did not show evidence that OsWRKY12 directly regulates the expression of OsNPR1 and OsPR1b since their experiments utilized transgenic plants over-expressing OsWRKY12. Here, we suggest that WRKY may play a major role in SA-mediated OsPR10a expression in rice. However, we cannot exclude involvement of other transcription factors in SA-mediated expression of OsPR10a. In the near-future, we will carry out electrophoretic mobility assays of the WRKY proteins to the WLE1 described in this study. We will further address what kinds of WRKY proteins regulate the OsPR10a promoter and identify the different partners required for SA-mediated OsPR10a expression.

ospr10a promoter
salicylic acid
cis-acting element
salicylic acid induction