Characteristic expression of twelve rice PR1 family genes in response to pathogen infection, wounding, and defense-related signal compounds (121/180)

Pathogenesis-related (PR) proteins have been used as markers of plant defense responses, and are classified into 17 families. However, precise information on the majority members in specific PR families is still limited. We were interested in the individual characteristics of rice PR1 family genes, and selected 12 putatively active genes using rice genome databases for expressed genes. All were upregulated upon compatible and/or incompatible rice-blast fungus interactions; three were upregulated in the early infection period and four in the late infection period. Upon compatible rice–bacterial blight interaction, four genes were upregulated, six were not affected, and one was downregulated. These results are in striking contrast to those among 22 ArabidopsisPR1 genes where only one gene was pathogen-inducible. The responses of individual genes to salicylic acid, jasmonic acid, and ethylene induced defense signaling pathways in rice are likely to be different from those in dicot plants. Transcript levels in healthy leaves, roots, and flowers varied according to each gene. Analysis of the partially overlapping expression patterns of rice PR1 genes in healthy tissues and in response to pathogens and other stresses would be useful to understand their possible functions and for use as characteristic markers for defense-related studies in rice.
Introduction
Pathogenesis-related (PR) proteins, which are classified into 17 families, accumulate after pathogen infection or related situations in many plant species (van Loon et al. 2006). Among the PR gene family, PR1 genes have been frequently used as marker genes for systemic acquired resistance in many plant species. However, only a small proportion of PR1 genes have been studied among the many members in this family, and information on the other members is limited. For example, NtPR1a, b, and c genes for acidic proteins, and NtPRB1 (PR-1g) and NtPRB1b genes for basic proteins were reported in tobacco (Nicotiana tabacum) (van Loon and van Strien 1999), but little information is known on other PR1 members. To understand the characteristics and redundancy of the majority of PR1 family members, genome-based studies are necessary. For such studies, dicot Arabidopsis and monocot rice (Oryza sativa L.) plants have been used as the model plants.
In Arabidopsis, 22 genes are listed as predicted PR1 genes that encode homologous proteins to tobacco PR1a protein, which was first reported as an acidic protein in tobacco leaves infected with Tobacco mosaic virus (TMV) (van Loon et al. 2006). Among the 22 genes, only one PR1 gene (At2g14610), which encodes a basic protein, is known to be pathogen-responsive, and the other PR1 genes reportedly did not respond to either bacterial or fungal pathogens (van Loon et al. 2006). From these results, we tend to suppose that only one PR1 gene relates to pathogen resistance in Arabidopsis and the others contribute to other functions.
In rice, the induction of two PR1 genes, OsPR1a and 1b, by blast fungus infection was reported (Agrawal et al. 2001). They encode putative acidic and basic proteins, respectively, and also responded to environmental stresses and treatments with some chemicals (Agrawal et al. 2000a, b). However, for other rice PR1 gene family members, there is only limited information except for their presence and expression: (1) at least 4 signals for possible rice PR1 proteins responsive to anti-tobacco PR1a antibodies were found in an extract of blast fungus-infected rice leaves (Schweizer et al. 1997; Iwai et al. 2007), and (2) 32 predicted PR1 genes were proposed to be present in the rice genome (van Loon et al. 2006).
To study the response of individual rice PR1 genes to pathogens, we selected active rice PR1 genes from the rice genome databases for expressed genes, and studied their induced expression by real time RT-PCR (qPCR). In striking contrast with the result in Arabidopsis, all 12 rice genes selected here were upregulated by blast fungus infection. The levels of constitutive expression in organs and induced expression by different treatments varied according to the gene. In addition to the data on tissue-specific PR1 expression in transgenic rice with a OsPR1 promoter::β-glucuronidase (GUS) fusion gene, we list the characteristics of the 12 rice PR1 genes. This is the first example of a comparison of the expression of the majority of members of a monocot PR family to our knowledge. This information will be useful for further studies on PR genes and on resistance mechanism in rice plants.
Materials and methods
Plant materials
Wild-type (WT) rice (Oryza sativa cv. Nipponbare) and the near isogenic line IL7 (Ise and Horisue 1988), which contains the R gene Pi-i against blast fungus (Magnaporthe grisea) race 003 (Yamada et al. 1976), were grown for 14–16 days in soil (Bonsol No. 1, Sumitomo Chemicals L., Japan) in a greenhouse at 28°C in the day time and 25°C at night. The fourth leaves of plants at the 4-leaf stage were mainly used as the experimental material. The seeds of transformants with OsPR1::GUS were germinated on agar medium containing 30 μg ml−1 hygromycin, transferred to soil at 7 days after imbibition, and grown in the greenhouse. Five-day-old seedlings on agar medium and 2-month-old plants in the greenhouse were used for GUS-staining assays.
Infection with pathogens
Magneporthe grisea race 003 (isolate Kyu-89-241) was grown on oat-meal medium (Difco) for 2 weeks and conidia were induced under BLB light (FL20S BLB, Toshiba) for 2 days at 25°C. The rice seedlings of Nipponbare and IL7 plants at the 4-leaf stage were spray-inoculated with a conidia suspension (1 × 105 conidia ml−1) containing 0.05% Tween 20, and the inoculated plants were incubated under high humidity in the dark for 20 h, and then moved to the greenhouse. Under these conditions, about 100 local lesions were induced per leaf on Nipponbare and IL7. For bacterial blight infection, Nipponbare plants, which are compatible with Xanthomonas oryzae pv. oryzae (Xoo) strain T7174 (race I, MAFF 311018), were inoculated by cutting the leaf top with scissors that had been dipped in a suspension containing 108 cfu/ml of Xoo, and incubated in the greenhouse.
Treatment with chemicals
Plastic pots (15 × 5.5 × 10 cm) with 12 rice seedlings at the 4-leaf stage, were dipped in 500 ml solutions containing 1 mM 1-aminocyclopropane-1-carboxylic acid (ACC) or 3 mM sodium salicylate (SA) solution, and incubated for 24 h in the greenhouse. For jasmonic acid (JA) treatment, pots with 12 seedlings each was put in an air-tight clear plastic box, and a cotton pad with volatile methyl jasmonate (MeJA) dissolved in EtOH was put at the corner of the box to give a final concentration of 100 μM, and incubated for 24 h. The fourth leaves were used for RNA extraction.
Quantitative real-time RT-PCR
To analyze the response of OsPR1 genes to biotic and abiotic stresses, quantitative real-time RT-PCR (qPCR) was conducted using iQ SYBR Green Supermix (BioRad, Hercules, CA, USA) and an iCycler (BioRad) according to the manufacturers’ instructions. At least three independent biological samples were used with specific primers for each individual gene (Table 2). The data were normalized by the value of an actin gene (AK060893), and fold change in the expression level was calculated compared with that of healthy fourth leaves, and standard deviation (SD) values are shown.
Construction of plasmids for transformation of rice
The promoter region of the OsPR1.1 gene (OsPR1#074 in this work) was obtained through PCR amplification from the Nipponbare genome, using a GenomeWalkerTM kit (Invitrogen, CA, USA) in accordance with the manufacturer’s instructions (Accession no. AP008213, Kawahigashi et al. 2007). The amplified fragment was digested by NotI and BamHI and inserted into the pTH2 vector to construct the PR1T::GUS plasmid (Fig. 7a). The PR1T::GUS plasmid contained 1,919 bp of the promoter and the coding region for 16 amino acids at the N-terminus of the OsPR1 gene to express a fused GUS protein effectively (Kawahigashi et al. 2007).
Transformation of rice
Agrobacterium tumefaciens LB4404 was transformed with the constructed vector (Kawahigashi et al. 2007). The transformation of rice was performed by Agrobacterium infection as described by Toki et al. (2006).
Histological GUS analysis
Histological analysis for GUS activity was performed at 37°C essentially as described by Ohshima et al (1990) using a modified reaction mixture: 50 mM phosphate buffer (pH 7.0) containing 1 mM 5-bromo-4-chloro-3-indolyl glucuronide (X-gluc), 5% methanol, 10 μg/ml cycloheximide, and 1 mM dithiothreitol. The reaction was stopped by the addition of ethanol.
Results
Characterization of the rice PR1 gene family
Using the amino acid sequence of the mature tobacco acidic PR1a protein (NtPR1a; 138 amino acids, Ohshima et al. 1987) as the probe, we searched for expressed genes in rice (Oryza sativa cv. Nipponbare) encoding homologous proteins with a similar size range (within 150–200 amino acids) using a full-length cDNA database (http://www.cdna01.dna.affrc.go.jp/cDNA) and rice EST database (http://www.riceblast.dna.affrc.go.jp/). The presence and location of these genes were confirmed in the genome database of rice (http://www.rapdb.dna.affrc.go.jp/). After removing repetitive clones and confirmation of the sequences, finally 12 genes were selected as candidates for active rice PR1 genes, which encode proteins with sequences ≥41% identical to that of tobacco PR1a protein at least in the C-terminal 65 amino acids. Previously, two rice PR1 genes were reported to encode an acidic PR1a protein and a basic PR1b protein (Agrawal et al. 2000a), which were upregulated by blast fungus infection. In addition to the two, we found another 10 active genes on chromosomes 1, 2, 5, 7, 10, and 12 (Table 1). Proposed names were given to distinguish each gene to avoid confusion in this study according to the location on the chromosomes. For example, OsPR1b (AK107926, Os01g28450), located on the 5′ side of the gene for AK121108 (Os01g28500) on chromosome 1, was designated as OsPR1#011 and AK121108 was OsPR1#012. On chromosome 7, four PR1-like genes, OsPR1#071, #072, #073, and #074, were clustered in tandem from the 5′ side to the 3′ side in the sense orientation in this order. The predicted isoelectric point (pI) of each mature protein is shown in Table 1 for reference. The amino acid sequences of the predicted mature proteins for the 12 OsPR1 genes were compared with those of representative acidic and basic PR1 proteins from Arabidopsis (AtPR1-like and AtPRB1) and tobacco (NtPR1a and NtPRB1b), respectively (Fig. 1a). The sequences of the 12 OsPR1s, 2 AtPR1s, and 2 NtPR1s listed here are well conserved. As the N-termini of OsPR1#051, #052, and #121 are not conserved, these regions were eliminated for the alignment in Fig. 1a. Ten amino acid residues (closed circles) are completely conserved among the 12 rice, 2 Arabidopsis, and 2 tobacco genes, indicating they are important for the predicted roles of PR1 proteins.
Table 1Classification of rice PR1 protein genesPlantAccession No. (Symbol name)Locus nameReferencesPIProposed symbolsRiceAK107926 (OsPR1b)Os01g28450Agrawal et al. (2000b)7.6OsPR1#011AK121108Os01g28500–8.7OsPR1#012AK107467Os02g54540–8.5OsPR1#021AK105575Os02g54560–5.9OsPR1#022AK071326Os05g51660–7.1OsPR1#051AK100748Os05g51680–5.5OsPR1#052AK060057Os07g03279–4.2OsPR1#071AK062949Os07g03580–4.7OsPR1#072AK063248Os07g03590–5.8OsPR1#073AU163470 (OsPR1a)Os07g03710Agrawal et al. (2000a)4.4OsPR1#074AU070895Os10g11500–10.7OsPR1#101AK100940Os12g43700–5.1OsPR1#121Arabidopsis(PR-1 like)At2g19990Metzler et al. (1991) 6.0(AtPRB1)At2g14580Santamaria et al. (2001)8.8TobaccoX06361 (NtPR1a) (X12737, X05959, X06930)Ohshima et al. (1987)4.4X66942 (NtPRB1b)Eyal et al. (1992)7.6Fig. 1Characterization of the PR1 gene family in rice plants. a Amino acid sequence alignment of 12 putative PR1 proteins from rice. The predicted mature proteins encoded by these genes were aligned by GENETICS 9.0 (Software Development Co., Tokyo). Because the N-termini of OsPR1#052, #051, and #121 are considerably different from those of the other 9 OsPR1s, these regions were eliminated from the figure and the conserved C-termini among the 12 OsPR1s were aligned. The acidic and basic PR1 proteins from Arabidopsis (AtPR1-like and AtPRB1) and tobacco (NtPR1a and NtPRB1b) were used as representatives of acidic and basic PR1 proteins, respectively. The conserved amino acid residues among the 16 proteins are marked with black circles. Identical amino acids are boxed. b Phylogenetic analysis of rice PR1 proteins based on the mature proteins using UPGMA method. The predicted isoelectric point (pI) for each mature protein is shown on the right. Acidic, basic, and neutral proteins are shown in red, blue, and green, respectively. Refer to the description in a for details
Using the data of Fig. 1a, a phylogenetic tree was constructed (Fig. 1b). Among the 12 OsPR1 proteins, 7 acidic and 4 basic protein members were found (Fig. 1b). The C-terminal region of OsPR1#051 was indicated to be neutral. The PR1 proteins from Arabidopsis and tobacco grouped in one clade (framed), and OsPR1 proteins belonged to different clades.
Responses of OsPR1 genes to blast-fungus infection in young rice plants
Compatible interaction
The response of individual OsPR1 genes to blast-fungus infection was analyzed by qPCR using a set of primers specific for each gene (Table 2). A conidia suspension of M. grisea race 003 was sprayed on the seedlings of Nipponbare at the four-leaf stage. In this system, the fungus infection results in the formation of whitish expanding lesions at 3 days post inoculation (dpi), and increasing lesion size thereafter with conidia formation. The infected plants were severely wilted and died within 7–9 days indicating a typical compatible host–parasite interaction (Sasaki et al. 2004; Iwai et al. 2006). Using triplicate cDNA samples originating from independently prepared RNA templates from inoculated fourth leaves, the expression levels of each gene were compared with that of an actin gene, which was selected as a control gene. Although expression levels of OsPR1 genes in healthy Nipponbare leaves varied according to each gene (0 dpi, white column in Fig. 2a), all OsPR1 genes tested were up-regulated by blast-fungus inoculation at 3 and/or 6 dpi (black column). When compared with mock-inoculation (gray column), the enhanced expression by the fungus was significant for each gene at 6 dpi, at which lesions were vigorously expanding. The transcripts of OsPR1#074, #011, and #012 had already accumulated to near maximal levels at 3 dpi maintaining almost the same levels at 6 dpi, indicating they are rapid response genes to blast fungus infection. However, the transcripts of OsPR1#073, #022, #101, #051, and #121 had not increased significantly at 3 dpi compared with mock-inoculated controls, indicating these are late response genes. Blast fungus infection induced around 1,000-fold transcript accumulation at 6 dpi compared with 0 dpi in 7 genes: OsPR1#074, #011, #071, #073, #052, #072, #101, and #121.
Table 2Primers for qPCR of OsPR1s and actin genesNameForward primerReverse primerOsPR1#074GTATGCTATGCTACGTGTTTATGCGCAAATACGGCTGACAGTACAGOsPR1#011ACGCCTTCACGGTCCATACAAACAGAAAGAAACAGAGGGAGTACOsPR1#071CTTTAACTATGTATGGAGTATGATATAAATGTGTTATTTTCTTCTTTTATTCGAACGACAACOsPR1#012CGCTGTGTGTTTGTGTTATGTCCGTGGTTTTGTCTTTATTTCAATCCOsPR1#073TTATATATGTATGTTCGTATGTATGTATGCTGATGTACTTATTCCATCCGACACOsPR1#021CGCAGCAACCAACCAATCTTGACAGTTGTAGTACTCTTGTAACATCATCOsPR1#022CCACAGAGTTTGTCAGGATTGTCCAGATTGCACACACCTGATTCCOsPR1#052AGCTACCTGTCATTTCTTCATTTCTGCTACTCCAGAAGGAAATTAAAAGOsPR1#072AATTAATACTGGAGTAGATGCATGTACACGAATAACGTACTGTATTCTGTATGOsPR1#121ACCATCGTCGTCGTCTCATCAGCCTCTAGGGCATATCACTAACOsPR1#101TCGCTGCCGCTAGTACATTTCATTAAGATCATTACATGCTTTATTGTTCACOsPR1#051CCTGCCTGCCTTCCTCATTCAGTGAAGATTTGGTTTCCATTGTATTGActinGAGTATGATGAGTCGGGTCCAGACACCAACAATCCCAAACAGAGFig. 2Response of OsPR1 genes to pathogen infection. a Blast fungus infection. A conidia suspension of blast fungus race 003 was spray-inoculated onto Nipponbare (compatible, upper panel), and IL7 (incompatible, lower panel). The inoculated fourth leaves were harvested at 0, 3, and 6 days post inoculation (dpi) for RNA extraction. To semi-quantify the transcript level, RNA samples were subjected to qPCR, and the relative expression levels compared with that of a control actin gene are shown. The transcript levels at time 0, mock-inoculated, and blast-fungus inoculated leaves were shown as white, gray, and black columns, respectively. Bars indicate mean ± SD. The up-regulated genes by blast fungus-infection are marked with arrowheads. Gray arrowheads in the lower panel indicate early responsive genes. b Bacterial blight infection. The fourth leaves of Nipponbare rice plants were inoculated with a suspension of Xoo for bacterial blight disease, and the leaves were harvested at 0, 4, and 8 dpi for RNA extraction. The transcript level was analyzed by qPCR. The relative expression levels compared with that of the control actin gene are shown. Transcript levels at time 0, mock-inoculated, and bacteria-blight inoculated leaves are shown as white, gray, and black columns, respectively. Xoo-upregulated and -downregulated genes are marked with forward and reverse arrowheads, respectively. Bars indicate mean ± SD
Incompatible interaction
IL7 is a near isogenic line of Nipponbare, into which the resistance gene Pi-i for blast fungus race 003 has been introduced. In fungus-inoculated IL7, small HR lesions were induced at 2 dpi with no remarkable fungal development thereafter, indicating a typical incompatible interaction (Sasaki et al. 2004; Iwai et al. 2006). The transcript levels of OsPR1#074, #011, and #012 were maximized at 3 dpi, and slightly reduced at 6 dpi (Fig. 2a, lower part, gray arrowheads), indicating they are early responsive genes to blast fungus infection. The 3 genes had been recognized as early responsive genes in compatible interaction. The levels of OsPR1#074 and #011 transcripts increased by about 1,000-fold at 3 dpi. The transcripts of OsPR1#071, #072, #073, and #101 were higher at at 6 dpi than at 3 dpi, indicating they are late responsive genes (black arrowheads). The level of OsPR1#121 was suppressed by mock-inoculation but the level was not changed by incompatible interaction. OsPR1#051, whose transcript increased in the compatible interaction at 6 dpi, was not clearly upregulated in the incompatible interaction.
Responses of OsPR1 genes to bacterial blight infection in young rice plants
To study the response of the 12 OsPR1 genes to a bacterial pathogen, the fourth leaves of Nipponbare were inoculated with Xoo for leaf blight disease. In the compatible rice–bacterium interaction, the infected region was visualized as wilted necrosis, which spread from the cross cut end to the leaf base with time, and the area of necrosis grew to 2–3 cm long at 8 dpi. At 4 and 8 dpi, 5 cm long leaf pieces from the inoculated point were used to prepare RNA samples. Transcripts of OsPR1#074, #011, and #012, which are early responsive genes to blast fungus (Fig. 2a), accumulated remarkably at both 4 and 8 dpi compared with 0 dpi or a mock-inoculated control (Fig. 2b), indicating they were responsive to Xoo infection (closed forward arrowheads). Expression of OsPR1#021 and #022 were slightly suppressed after the infection compared with mock-inoculated leaves (closed reverse arrowheads). Expression of OsPR1#074, #011, #012, and #101 were upregulated by Xoo infection but other OsPR1 genes did not respond or rather suppressed, while all OsPR1 genes were upregulated in compatible blast fungus interaction (Fig. 2a).
Wound-induced expression of OsPR1 genes in young rice plants
To study the response of OsPR1 genes to wounding, the fourth leaves were wounded by cross cutting. Triplicate samples were extracted at 0, 1, 6, and 24 h after wounding, and used for qPCR. Among the 12 genes, OsPR1#074 was wound-inducible with peak accumulation of the transcript at 6 h after wounding, and OsPR1#051 was found to be an early wound-responsive gene (Fig. 3, forward arrowheads). The OsPR1#121 and #052 transcripts decreased in response to wounding (reverse arrowheads) (Fig. 3).
Fig. 3Response of OsPR1 genes to wounding The fourth leaves were cut into 1 cm length sections for wounding, and floated on water for 0, 1, 6, and 24 h under light, and then the leaf pieces were homogenized for RNA extraction. Relative expression levels compared with that of the control actin gene are shown. The wound-upregulated and -downregulated genes are marked with forward or reverse arrowheads, respectively. Bars indicate mean ± SDFig. 4Organ-specific expression of OsPR1 genes Leaves and roots of the seedlings at the fourth leaf stage (left panel), and flowers of adult plants (right panel) were subjected to qPCR analysis. Relative expression levels compared with that of the control actin gene are shown. The genes that express at a high level in roots and flowers are marked with white and gray arrowheads, respectively. Bars indicate mean ± SD
Organ specific expression of OsPR1 genes
The basal transcript levels in various organs varied according to each gene; in the roots of healthy young rice plants at the 4-leaf stage, the transcript levels of the 5 genes, OsPR1#074, #071, #073, #072, and #101, were clearly higher than in the fourth leaves (Fig. 4) (open arrowheads). In flowers, the basal transcript levels of five genes, OsPR1#074, #011, #012, #121, and #051, were at a high level (gray arrowheads). These results indicate that the organ-specific expression profiles of OsPR1 genes varied considerably. The OsPR1#074, #071, #073, and #072, which are located on chromosome 7, were highly expressed in healthy roots. In healthy flowers the expression levels of five genes, OsPR#074, #011, #012, #121, and #051, were also higher.
Responses of OsPR1 genes to defense signal compounds in young rice plants
The responses of OsPR1 genes to defense signal compounds were studied. Intact young rice plants in soil pots were dipped in water, SA, or ACC solutions, or exposed to the vapor of MeJA. RNA from the fourth leaves was subjected to qPCR (Fig. 5). OsPR1#074 and #101 were upregulated by all three signal compounds tested, while OsPR1#071, #073, #021 and #121 were upregulated by JA but not by ACC or SA. OsPR1#074, #011, and #012, which were early responsive genes to blast fungus (Fig. 2), were SA-inducible. OsPR1#011 was upregulated by SA but suppressed by ACC. Among the six genes that were inducible by the wound signal compound JA, OsPR1#074 and #051 were also upregulated by wounding (Fig. 3). OsPR1#022 and #052 were not particularly sensitive to any of these compounds. OsPR1#011 and #072 were downregulated by ACC and SA respectively, and OsPR1#071 was downregulated by both ACC and SA.
Fig. 5Response of OsPR1 genes to defense-signal compounds ACC, SA, and JA. Expression levels of each OsPR1 gene in fourth leaves at 24 h after treatment with defense-signal compounds were analyzed by qPCR. Relative expression levels compared with that of the control actin gene are shown. The ACC-, SA-, and JA-inducible genes are marked with gray, dark gray, and black arrowheads, respectively. For ACC or SA treatment, plastic pots with rice seedlings were dipped in solutions containing 1 mM ACC or 3 mM SA solution, and incubated for 24 h in the greenhouse. C1 control for ACC- and SA-treatment (leaves of water treated plant); For JA treatment, pots with seedlings each was put in an air-tight clear plastic box, and a cotton pad with volatile methyl jasmonate (MeJA) dissolved in EtOH was put at the corner of the box to give a final concentration of 100 μM, and incubated for 24 h. C2 control for JA-treatment (leaves of plant treated with 0 μM of JA), bars indicate mean ± SD
Comparison of the defense-signaling pathways conferred by ACC, SA, and JA in rice with those in tobacco and Arabidopsis
Using the 12 OsPR1 genes as probes, we analyzed the relationship of responsiveness to three defense signal compounds in rice. In tobacco, SA signaling was antagonistic to JA signaling on PR gene expression: Niki et al. (1998) showed that the expression of acidic PR1, 2, and 3 genes was upregulated by SA-treatment and it was suppressed in the presence of JA in a dose-dependent manner, while the expression of basic PR2, 5, and 6 genes was upregulated by JA and suppressed by SA. Thomma et al. (1998) reported that the JA-signaling pathway is different from the SA-signaling pathway in Arabidopsis. Glazebrook et al. (2003) reported that the expression patterns of some genes revealed mutual inhibition between SA- and JA-dependent signaling using global expression phenotyping analysis of mutant Arabidopsis plants. Synergistic induction by ET and JA was reported in basic tobacco PR genes (Xu et al. 1994). However, the expression patterns of OsPR1 genes in Fig. 5 showed no clear mutual inhibition between SA- and JA-dependent signaling, except for OsPR1#071. We further analyzed the relationship by means of Pearson’s correlation coefficient (Fig. 6). Correlation coefficients for SA versus ACC, ACC versus JA, and SA versus JA were 0.75, 0.61, and 0.22, respectively. The linear correlation coefficient, r, represents the strength and the direction of the linear relationship between two variables. A correlation of +1 means an increasing linear relationship and complete correlation, while −1 means a decreasing linear relationship. Cohen (1988) proposed that |r| below 0.1 is regarded as insubstantial, 0.1 ≤ |r| < 0.3 as weak, 0.3 ≤ |r| < 0.5 as medium, and 0.5 ≤ |r| < 1.0 as strong correlation. While, levels of statistical significance at 5 and 1% (n = 12) are 0.576 and 0.708, respectively. The r values in Fig. 6 suggest synergism between ACC versus SA and ACC versus JA, and low or no correlation between SA versus JA rather than antagonism.
Fig. 6Relationship of the three signaling pathways in OsPR1 genes. Based on the data in Fig. 5, the response of young rice plants to ACC- , SA-, or JA-treatments was compared with other treatments, and the correlation coefficients between the responses to pairs of signals were calculated. See the text for details
Tissue specific expression of a representative OsPR1 gene in rice plants
Analysis of the tissue specific expression of rice PR1 gene by means of histological studies has not been performed previously. To study the tissue specific expression profiles of a representative OsPR1 gene, we selected OsPR1#074 because it clearly responded to both compatible and incompatible fungal interactions, bacterial bright infection, wounding and the 3 defense signal compounds. The promoter sequence of OsPR1#074 was obtained by PCR amplification from the Nipponbare genome. The promoter sequence, containing 1,919 bp, was inserted into the pTH2 vector (Kawahigashi et al. 2007) to construct the plasmid PR1T::GUS, which contains the promoter and the coding region for 16 amino acids of the N-terminus of the OsPR1 protein to express the GUS protein effectively as a translational fusion (Fig. 7a). The OsPR1T::GUS gene was introduced into rice plants (Oryza sativa cv. Nipponbare). Among 30 hygromycin-resistant plants regenerated, three independent lines were selected as representatives exhibiting a negligible level of GUS activity in healthy leaves and more than 30-fold higher levels after wounding or inoculation with a conidia suspension of M. grisea. The OsPR1T::GUS plants of the second or third generation were subjected to histological GUS analysis using X-gluc as the substrate.
Fig. 7Histological analysis of OsPR1#074 gene expression in transgenic rice plants. a Schematic diagram showing the construct of the introduced gene. The promoter sequence of OsPR1#074 was used. b Localized GUS staining in healthy 5 day-old transgenic rice seedlings (a–e). Because similar expression profiles were obtained in three independent transgenic lines, the data from a representative line is shown. f unhulled rice. c GUS staining of 2 month-old transgenic rice plants. a Healthy roots, b roots at 2 days after wounding, c–e leaves at 2 days after wounding. d GUS staining assay of 4th leaves at 5 (a, c, d) or 11 (b) dpi. Black arrows indicate stained mesophyll tissue. Brown arrows indicate the necrotic regions
Figure 7B shows the localization of GUS activity in healthy 5-day-old transgenic rice seedlings. A high level of GUS activity was detected in the primary root (arrows, Fig. 7B a–c) and the root tip (arrowheads), but it was absent or considerably reduced in the elongation zone (white arrowheads). In cross sections of the middle part of the root 80 μm in thickness (Fig. 7B e), a strong GUS activity was detected in the vascular cylinder (vc) and sclerenchyma (sc), and a weak activity in the cortex (co). GUS staining was found in the slit surface of the coleoptile (yellow arrowheads, Fig. 7B a, b), and on the surface of unhulled rice (white arrow, Fig. 7B a, f). In the anther, a weak blue staining was found in the matrix of pollen loculus (data not shown). In the vector control plants, no GUS activity was detected in roots (Fig. 7B d) or any other part analyzed under these in condition (data not shown).
In 2 month-old OsPR1T::GUS plants, no GUS activity was found in healthy roots (Fig. 7C a), while high levels of GUS activity were found in healthy 5 day-old (Fig. 7Ba, b, and c) and 2 week-old (data not shown) plants. Thus, OsPR1 expression in roots is likely to be regulated developmentally. Wounding induced a high level of GUS activity in the roots of 2-month-old plants. When the aerial parts were removed by cross cut from of the plants, a strong GUS activity was induced in the roots of cut plants at 2 days after cutting (Fig. 7C b). In cross sections of the root in the middle region, GUS staining was found in all tissues, including the vascular cylinder, cortex, and sclerenchyma. No detectable GUS activity was found in healthy leaves of transgenic rice plants (data not shown); however, wounding induced a strong GUS activity in the neighbor region of the cut surface especially in the vascular systems including xylem parenchyma (xy) and phloem (ph) (Fig. 7C c–e).
Next, GUS activity in rice leaves infected with blast fungus was analyzed. In the compatible cultivar (WT) lacking the Pi-iresistance gene, enlarged lesions were found 3–5 days after inoculation developing in size with time and with conidia formation on the infected leaf surface. After GUS staining, many blue spots were found at or around the lesions in the leaves 5 days after inoculation (Fig. 7D a). When the leaf of Fig. 7D a was cross cut at the marked line, and shown in Fig. 7D c, the blue staining was found to
localize in the mesophyll cells (black arrows) around the necrotic regions (brown arrows, Fig. 7D d). At 11 days after inoculation, GUS activity was found around the necrotic lesions (Fig. 7D b).
Discussion
We studied here the response of 12 OsPR1 genes to pathogens, wounding, and defense signal compounds and their constitutive expression in organs. This is the first example of a comparative study on the characterization of a set of PR 1 family genes by qPCR, a high throughput method. The 12 genes include PR1a and b, which have been reported as pathogen-inducible genes by Agrawal et al. (2001), and ten of the 12 genes are contained in the 32 listed rice PR1-like genes including at least 3 pseudogenes whose expression profiles were not obvious (van Loon et al. 2006). We searched rice PR1-like genes from full-length cDNA (http://www.cdna01.dna.affrc.go.jp/cDNA) and EST (http://www.riceblast.dna.affrc.go.jp/). Because strongly expressed genes should be easily extracted from the databases for expressed genes, a majority of highly expressed rice PR1-like genes are thought to be contained in the 12 genes. The sequences of the 12 OsPR1 proteins were well conserved with complete matches in 10 amino acid residues in the C-terminal half (Fig. 1a). Because the 10 residues were also very well conserved in all 36 PR1-type proteins selected from 14 different plant species including Brassica napus, Hordeum vulgare, Lycopersicon esculentum, Medicago trunculata, Triticum aestivum, and Zea mays (van Loon and van Strien 1999), these regions in the C-terminus should be important for the putative functions of PR1 proteins in plants.
All 12 OsPR1 genes selected here were clearly upregulated in a compatible rice-blast fungus interaction, indicating that they really are pathogen related. Notable is that the induction pattern of each OsPR1 gene by different pathogen–host interactions was characteristic (Fig. 2). Four genes, OsPR1#074, #011, #012, and #101, responded positively to blast fungus infection in both compatible and incompatible interactions, and also to Xoo infection. Among these, OsPR1#074, #011, and #012 were responsive to blast fungus in the early infection period in both interactions. Conversely, the expression of OsPR1#021 and #022 was reduced by Xoo infection compared with the control after mock-inoculation, which is a wound treatment. Assuming that Xoo infection may inhibit wound-induced expression of these genes, it is consistent that they are wound-inducible (Fig. 3). The above result that “all 12 OsPR1s are characteristically pathogen inducible” is apparently different from the data of Arabidopsis where only one PR1 gene was pathogen-inducible (AT2g14610) among 22 predicted ArabidopsisPR1 genes (van Loon et al. 2006). We studied this result using an AtGenExpress Visualization Tool from http://www.arabidopsis.org/, and confirmed that only the PR1 gene was upregulated by infection with either Pseudomonas syringae pv. tomato DC3000 or Pytophythora infestans, and the other PR1 genes were not. The reason for different responses of PR1s to pathogen infection in rice and Arabidopsis is not clear, but we could speculate that OsPR1s have evolved in the rice genome to provide resistance to a broad range of pathogens, but a limited set of ArabidopsisPR1s has evolved to respond to pathogens.
In rice, the level of free SA in young healthy rice leaves was as high as about 10 μg per g fresh leaf (Silverman et al. 1995), but it was only about 20 ng (Seo et al. 2007) or 100 – 200 ng (Silverman et al. 1995) in tobacco leaves, and about 30 ng (Park et al. 2007) or 150 ng (our unpublished data) in Arabidopsis leaves. Thus, the SA content in rice leaves corresponds to 50- to 500-fold of that in tobacco or Arabidopsis leaves, which is comparable to the induced SA level after HR lesion formation by TMV-infection. Such a high SA level in rice plants could affect OsPR1 expression after pathogen infection, treatment with SA, JA, or ACC, and wounding. Actually, the responses of the OsPR1 genes to defense signal compounds indicate that the signaling pathways in rice conferred by SA and JA were synergistic as well as that by ET and JA, and SA and ET (Fig. 6). This finding is considerably different from the “antagonistic relationship of SA and JA signaling” found in tobacco (Niki et al. 1998) and Arabidopsis (Gupta et al. 2000). In tobacco, acidic PR1 proteins such as NtPR1a, b, and c were inducible by SA (Ohashi and Matsuoka 1987), which is a defense signal compound for systemic acquired resistance. The NtPRB1b (Eyal et al. 1992; Niki et al. 1998) and AtPRB1 (Santamaria et al. 2001) genes, which encode basic PR proteins in tobacco and Arabidopsis, respectively, were reported to respond positively to JA and ET, and negatively to SA. The ArabidopsisPR1 gene (At2g14610) encodes a basic protein that is inducible by SA.
To compare the natures of the OsPR1 genes, the results of the expression of 12 OsPR1 genes are summarized in Fig. 8. It is again impressive that the expression upregulated by various treatments differed considerably depending on the gene, and the transcript levels constitutively found in leaf, root, and flower also varied according to the genes. When comparing the four genes OsPR1#071, #72, #073, and #074, which are clustered in a locus on chromosome 7, we found the following common characteristics: all four genes (1) encode acidic proteins, (2) are constitutively expressed in healthy roots at a high level, and (3) are upregulated by blast fungus-infection. In spite of these similarities, their expression profiles after certain treatments were significantly different from each other. For example, (1) the transcript of OsPR1#074 accumulated at an earlier time period than those of OsPR1#071, #072, or #073 in fungus-infected resistant cultivar IL7, (2) OsPR1#074 but not #071, #072, or #073 was upregulated by Xoo-infection, (3) OsPR1#074, but not OsPR1#071, #072, or #073, was wound-inducible, (4) OsPR1#074 was ACC-, SA-, and JA-inducible, OsPR1#071 was JA-inducible, OsPR1#072 was not inducible by any signal compounds used here, and OsPR1#073 appeared to be suppressed by ACC, (5) OsPR1#074 was expressed in flowers at a high level, but OsPR1#071, #072, and #073 were not.
Fig. 8Comparison of expression characteristics of OsPR1 genes Induction levels of OsPR1 genes by pathogens, wounding, and defense signal compounds, and their constitutive expression levels in organs were compared. The transcript levels in healthy 4th leaves are shown in the right most column. ++ very highly inducible or expressed gene, + highly inducible or expressed gene, ± gene whose induction or expression was not clear, − not inducible or expressed gene
From the expression characteristics of the 12 OsPR1 genes obtained here, the possible contribution of all OsPR1 genes for plant self-defense against pathogen attack was indicated. Although the functions of OsPR1 genes are not fully understood, PR1 proteins in some plant species have been reported to have antifungal activity including tobacco acidic PR1a overexpression that induced increased tolerance to two oomycete pathogens in tobacco (Alexander et al. 1993), and basic PR-1 proteins of tobacco and tomato, which have an antimicrobial activity against Phytophythora infestans (Niederman et al. 1995). Thus, the redundant expression of the 12 OsPR1 genes may effectively contribute to defend the attack of various pathogens.
The expression levels of the 12 genes in healthy tissues and wounded leaves were also very different according to each particular gene. The data in Fig. 7 shows a representative example of tissue- and organ-specific expression of OsPR1. The level of GUS activity in transgenic rice plants carrying the OsPR1T::GUS gene was confirmed to be similar to the OsPR1#074 transcript level as shown in tobacco PR1a::GUS plants (Yamakawa et al. 1998), in which the GUS gene was expressed as a similar translational fusion gene described in Fig. 7A. GUS analysis using independent transgenic lines showed OsPR1#074 expression was developmentally regulated in roots and constitutively expressed in young rice plants, but not in 2-month old plants, in which the expression was strongly upregulated by wounding (Fig. 7A, B). The wound-induced OsPR1#074 expression in adult plants was localized mainly in the vascular system, which is important as the sensing tissue of the water pressure change after wounding and as the site of bacterial pathogen propagation.
The information on the 12 PR1 genes obtained here will be useful for further studies on self-defense mechanisms in rice plants, and for the usage of each PR1 gene as a characteristic marker gene.

rice
bacterial blight
pathogen resistance
histological analysis
pr protein
defense gene expression


Reconstituted HDL infusion restores endothelial function in patients with type 2 diabetes mellitus

To the Editor: HDL-cholesterol is inversely correlated with cardiovascular events in all major epidemiological studies [1]. HDL-increasing strategies have demonstrated that HDL-cholesterol increase is associated with decreased cardiovascular risk in high-risk individuals such as patients with type 2 diabetes [2]. Endothelial dysfunction, a hallmark of type 2 diabetes patients, has been shown to predict future cardiovascular events [3]. Therefore, we investigated the effect of reconstituted HDL (rHDL) on endothelial function measured both acutely (4 h after infusion) and 7 days after infusion in type 2 diabetes patients. Control volunteers were measured only at baseline and 4 h after infusion.
Seven non-smoking patients with type 2 diabetes (four men and three women, BMI 24.4 ± 1.6 kg/m2) and seven matched control volunteers (four men and three women, BMI 22.9 ± 1.8 kg/m2) were enrolled. Inclusion criteria for type 2 diabetes patients were: (1) fasting glucose >7.0 mmol/l; (2) no insulin therapy; and (3) triacylglycerol and LDL-cholesterol levels <2.0 and <3.5 mmol/l, respectively. Matched control individuals were volunteers who were recruited via advertisements. The presence of macrovascular disease (ECG abnormalities, abnormal ankle–brachial index or a history of cardiovascular events) served as exclusion criteria. Female participants were postmenopausal and not using hormone replacement therapy. The study protocol was performed at least 4 weeks after discontinuation of vasoactive medication, including ACE inhibitors, angiotensin receptor blockers and non-steroidal anti-inflammatory drugs. None of the patients or control volunteers used lipid-lowering medication. The Internal Review Board of the Academic Medical Center approved the study and all individuals gave written informed consent.
Vascular function was assessed using venous occlusion strain-gauge plethysmography (EC-4; Hokanson, Washington, DC, USA) [4]. Forearm blood flow (FBF), expressed as ml min−1 100 ml−1 forearm tissue volume (FAV), was measured simultaneously in both arms. FBF responses to cumulative doses of the endothelium-dependent vasodilator serotonin (Sigma, Poole, UK; 0.6, 1.8 and 6 ng 100 ml−1 FAV min−1), the endothelium-independent vasodilator sodium nitroprusside (SNP; Spruyt Hillen, IJsselstein, the Netherlands; 6, 60, 180 and 600 ng 100 ml−1 FAV min−1), and the competitive inhibitor of endothelial nitric oxide (NO) synthase NG-monomethyl-l-arginine (l-NMMA; Kordia, Leiden, the Netherlands; 50, 100, 200 and 400 μg 100 ml−1 FAV min−1) were measured. Agents were administered intra-arterially for 6, 4 and 8 min at each dose, respectively. Average FBF values of the measurement (cannulated) and control arm were obtained from the last six measurements of each measurement period. The three different infusion blocks proceeded after a 15 min rest period or until FBF had returned to baseline. The ratio of flow in the infused measurement (M) and non-infused control (C) arm was calculated for each recording (M/C ratio). The average value of the M/C ratio was calculated from these four to six M/C ratios, thus providing an internal control by excluding systemic factors from influencing the results [5]. Subsequently, a venous catheter was inserted in the contralateral arm for administration of rHDL (CSL-111; CSL Bioplasma, Parkville, VIC, Australia) at a dose of 80 mg/kg body weight over a period of 4 h. Subsequently, the infusion blocks were repeated. Blood samples were drawn from the individuals after a 12 h overnight fast, and at 4 h and 7 days after rHDL infusion. Descriptive statistics between the two groups were compared by two-tailed independent Student’s t tests or non-parametric tests, depending on skewedness of the data. Analysis of measurements for individuals between baseline and 4 h as well as baseline and 7 days after rHDL infusion was performed by two-way ANOVA for repeated measures with Bonferroni correction.
Infusion of rHDL was well tolerated and no adverse events were recorded. Characteristics of type 2 diabetes patients and control volunteers during each measurement are listed in Table 1. Baseline FBFs were not significantly different between type 2 diabetes patients and control individuals (Table 1). Intra-arterial infusion of serotonin increased FBF in a dose-dependent manner in both groups (see Fig. 1a). At baseline, the FBF response to serotonin was attenuated in type 2 diabetes compared with control volunteers (M/C ratio in type 2 diabetes: 1.5 ± 0.2 vs controls: 2.5 ± 0.3; p < 0.05). Four hours after rHDL infusion in type 2 diabetes, FBF response to serotonin increased significantly (M/C ratio to 1.9 ± 0.2; p < 0.05 compared with baseline). rHDL infusion had no significant effect on serotonin-induced vasodilation in control volunteers. In type 2 diabetes, 7 days after rHDL infusion serotonin responses had returned to baseline values (7 days, 1.5 ± 0.2). At baseline, the maximal vasoconstrictor response to L-NMMA was blunted in type 2 diabetes compared with control volunteers (M/C ratio controls 0.6 ± 0.1 vs type 2 diabetes 1.0 ± 0.2; p < 0.05, Fig. 1b). After rHDL infusion, the L-NMMA response improved in type 2 diabetes compared with baseline (0.7 ± 0.1; p < 0.05). Although not significant, 7 days after rHDL infusion there still was a tendency towards improvement in type 2 diabetes (M/C ratio 0.8 ± 0.1 compared with baseline). rHDL infusion had no effect on L-NMMA response in control individuals. Finally, SNP responses were lower in type 2 diabetes vs control individuals at baseline (M/C ratio in type 2 diabetes, 3.0 ± 0.6 vs control individuals, 5.3 ± 0.7; p < 0.05, Fig. 1c) and rHDL infusion had no effect on SNP responses.
Fig. 1Change in M/C ratio after stimulation with the endothelium-dependent vasodilator serotonin (a), NO inhibitor L-NMMA (b) and the endothelium-independent vasodilator SNP (c) before (white circles) and 4 h (black circles) and 7 days (diamonds) after rHDL infusion in type 2 diabetes patients as well as in control volunteers (before, white triangle; after 4 h, black triangle). bp < 0.05 for type 2 diabetes patients compared with control individuals and ap < 0.05 for changes after rHDL within type 2 diabetes patients. Data are means ± SEMTable 1Clinical characteristics of type 2 diabetes patients and control volunteers Type 2 diabetes patients (n = 7)Control volunteers (n = 7)Baseline4 h7 daysBaseline4 hWaist circumference (cm)101 ± 5  90 ± 6† Metabolic syndrome6/7  0/7 Systolic BP (mmHg)148 ± 12146 ± 11143 ± 7135 ± 16138 ± 12Diastolic BP (mmHg)78 ± 1379 ± 1382 ± 883 ± 985 ± 8Heart rate (beats per min)65 ± 967 ± 966 ± 1261 ± 964 ± 8Total cholesterol (mmol/l)5.6 ± 0.46.5 ± 1.45.4 ± 1.35.3 ± 0.46.7 ± 1.3LDL-cholesterol (mmol/l)2.9 ± 0.63.3 ± 1.13.1 ± 1.03.0 ± 0.73.9 ± 1.2HDL-cholesterol (mmol/l)1.1 ± 0.22.7 ± 0.7*1.6 ± 0.61.2 ± 0.32.5 ± 0.4*ApoA-I (g/l)1.2 ± 0.12.8 ± 0.4*1.5 ± 0.31.2 ± 0.22.7 ± 0.4*Triacylglycerol (mmol/l)1.5 ± 0.41.6 ± 0.61.6 ± 0.50.8 ± 0.3†1.8 ± 1.3*Glucose (mmol/l)8.3 ± 1.26.8 ± 1.77.3 ± 1.35.2 ± 0.4†4.9 ± 0.2hsCRP (mg/l)3.5 ± 1.64.4 ± 1.73.6 ± 0.81.0 ± 0.9†1.8 ± 1.1ASAT (U/l)22.5 ± 2.520.4 ± 4.022.4 ± 4.320.2 ± 1.821.4 ± 2.7ALAT (U/l)31.2 ± 6.926.0 ± 9.727.8 ± 8.714.9 ± 1.7†15.6 ± 3.2Basal FBF (ml 100 ml−1 FAV min−1)4.1 ± 2.03.7 ± 0.83.9 ± 1.32.6 ± 0.92.8 ± 0.7Values are means ± SD*p < 0.05 compared with baseline within one group; †p < 0.05 between type 2 diabetes and control volunteers ALT, alanine aminotransferase; ASAT, aspartate aminotransferase; hsCRP, high-sensitivity C-reactive protein
In conclusion, the present study confirms that basal and stimulated NO bio-availability is reduced in type 2 diabetes patients compared with control volunteers. Besides hyperglycaemia-induced reactive oxygen radical formation, other components of type 2 diabetes-associated dyslipidaemia such as small dense LDL are known to influence endothelial function in type 2 diabetes [3, 6]. Moreover, the high prevalence of metabolic syndrome in our patients is in line with previously published data [7]. rHDL resulted in a significant improvement of endothelial function within several hours in type 2 diabetes patients. More importantly, there was still a tendency towards improved NO availability 7 days after infusion, at a time when apolipoprotein A-I (ApoA-I) increase had largely disappeared. Acute HDL-increasing strategies are actively being pursued for further reducing cardiovascular burden [8], and thus far the lack of selective and potent HDL-increasing drugs has limited the success of the HDL-cholesterol increase concept. Our reported beneficial effects of ApoA-I infusion may lend further support to the development of ApoA-I-increasing strategies, also for patients with type 2 diabetes.

type 2 diabetes mellitus
hdl-cholesterol
therapy
vascular function


Effects of the cannabinoid CB1 receptor antagonist rimonabant on distinct measures of impulsive behavior in rats

Rationale Pathological impulsivity is a prominent feature in several psychiatric disorders, but detailed understanding of the specific neuronal processes underlying impulsive behavior is as yet lacking.
Introduction
Despite its relative recent discovery, the central endocannabinoid system has been implicated in a variety of behaviors such as food intake (for review, see Di Marzo and Matias 2005) and nociception (for review, see Cravatt and Lichtman 2004), and in mediating the reinforcing properties of drugs of abuse (for review, see De Vries and Schoffelmeer 2005). In addition, the endocannabinoid system has been shown to play an important role in various cognitive processes. In this respect, memory encoding, retrieval, and extinction processes have received most interest (e.g., Marsicano et al. 2002; Takahashi et al. 2005; Varvel et al. 2005; for review, see Lichtman et al. 2002), presumably due to the high expression level of CB1 receptors in the hippocampal formation (Egertova and Elphick 2000; Tsou et al. 1998). High densities of CB1 receptors are also present in frontal cortical and striatal regions (Egertova and Elphick 2000; Tsou et al. 1998), suggesting involvement of the endocannabinoid system in executive functions that appear to be largely controlled by frontal corticostriatal systems (for review, see Miller and Cohen 2001). Indeed, several clinical and preclinical observations have demonstrated that Δ9-tetrahydrocannabinol (THC), the principle active cannabinoid of Cannabis sativa and other synthetic cannabimimetics impair selective attention (Arguello and Jentsch 2004; Solowij et al. 1995; Verrico et al. 2004) and behavioral flexibility (Egerton et al. 2005; Hill et al. 2006), alter time estimation (Han and Robinson 2001, McDonald et al. 2003), and impair working memory (Ilan et al. 2004; Jentsch et al. 1997). Nonetheless, to date, little is known about the involvement of the endocannabinoid system in other executive functions such as inhibitory control processes subserving impulsivity.
Pathological levels of impulsive behavior are important features in attention-deficit/hyperactivity disorder, substance-related disorders, bipolar disorders and personality disorders (American Psychiatric Association 2000). Further elucidating the neurobiological basis of impulsivity may therefore enhance our understanding of these psychiatric disorders. It is becoming increasingly clear, however, that the concept impulsivity is multifaceted and covers various distinct and independent measures. These measures range from poor inhibitory control (impulsive action) to probability and delay aversion or impulsive choice (Barkley 1997; Evenden 1999; Moeller et al. 2001). Recent studies have implicated CB1 receptors in some of these measures of impulsivity. For instance, it has been shown that acute THC impairs response inhibition in healthy volunteers, whereas time estimation and impulsive choice were not affected (McDonald et al. 2003). On the other hand, it has been demonstrated more recently that marijuana acutely increases risk taking in volunteers (Lane et al. 2005). Collectively, these data suggest a role of the cannabinoid system in impulsivity, although its precise role therein is still unclear.
The present experiments were aimed at further elucidating the importance of cannabinoid CB1 receptor activation on distinct measures of impulsivity. To this end, we tested the effects of the potent and selective CB1 receptor antagonist rimonabant (SR141716A; Rinaldi-Carmona et al. 1994) and agonist WIN55,212-2 (D’Ambra et al. 1992) on impulsive behavior in various operant paradigms measuring different and presumably independent aspects of impulsivity (for review, see Winstanley et al. 2006), namely, (1) the five-choice serial reaction time task to measure inhibitory control; (2) the delayed reward paradigm to measure impulsive choice, and (3) the stop-signal paradigm to measure response inhibition.
Materials and methods
Subjects
In total, 48 male Wistar rats were obtained from Harlan CPB (Horst, The Netherlands). At the start of the experiments, animals were 12 weeks old, weighed approximately 250 g, and were housed in pairs in macrolon cages (42.5 × 26.6 × 18.5 cm; l × w × h) under a reversed 12 h light/dark cycle (lights on at 7:00 p.m.) at controlled room temperature (21 ± 2°C) and relative humidity of 60 ± 15%. Animals were maintained at approximately 90% of their free-feeding weight, starting 1 week before the beginning of the experiments by restricting the amount of standard rodent food pellets (Harlan Teklad Global Diet, Blackthorn, UK). Water was available ad libitum throughout the entire experiment. All experiments were conducted with the approval of the animal ethical committee of the Vrije Universiteit, Amsterdam, The Netherlands.
Apparatus
Experiments were conducted in 12 identical rat five-hole nose poke operant chambers with stainless steel grid floors (MED-NPW-5L, Med Associates, St. Albans, VT, USA) housed in sound-insulating and ventilated cubicles. Set in the curved wall of each box was an array of five circular holes, 2.54 cm in diameter, 2.2 cm deep, and 2.25 cm above floor level. Each hole was equipped with an infrared detector located across each nose poke unit 1.0 cm from the front, and a yellow LED stimulus light (6.4 mm in diameter). Rodent food pellets (45 mg, Formula P, Research Diets, New Brunswick, NJ, USA) could be delivered at the opposite wall via a dispenser. In addition, the chamber could be illuminated by a white houselight, and sound stimuli were generated using a programmable audio generator (ANL-926, Med Associates). A computer equipped with MED-PC version 1.17 (Med Associates) controlled experimental sessions and recorded data. Animals were tested once daily from Monday until Friday, during the dark phase of the light/dark cycle.
Behavioral procedures
Separate groups of n = 16 animals were trained for each different paradigm, and for all procedures, a similar habituation and magazine training protocol was used. This protocol consisted of a habituation exposure to the boxes for 20 min with the houselight on and the food cup containing three food pellets for two consecutive sessions. Subsequently, in the next two sessions, in total, 100 pellets were delivered with an average delay of 15 s, to allow the animals to associate the sound of pellet delivery with reward.
Five-choice serial reaction time task
A more detailed description of training in the 5-CSRTT in our laboratory has been reported previously (Van Gaalen et al. 2006a). In short, rats were trained 5 days per week to detect and respond to a brief visual stimulus in one of five holes to obtain a food reward. Each session terminated after 100 trials or 30 min, whichever occurred first. Initially the duration of this stimulus was 32 s and was gradually decreased to 1 s over sessions until animals reached stable baseline performance (accuracy >80% correct choice and <20% errors of omission). Responding during stimulus presentation or within the limited hold (LH) period of 2 s was counted as a correct response. Incorrect, premature responses and errors of omission (no responses or a response after the LH) did not lead to the delivery of a food reward and resulted in a 5-s time-out period during which the houselight was extinguished, whereas perseverative responses, i.e., repeated responding during the presentation of the stimulus, were measured but did not have any programmed consequences. Two different measures of inhibitory control were measured, namely, (1) the number of premature responses before the onset of the visual stimulus, reflecting aspects of loss of inhibitory control and (2) the number of perseverative responses into the stimulus hole after correct choice, presumably measuring aspects of compulsive behavior. In addition, the following other behavioral parameters were measured that reflect task performance, namely, (3) accurate choice, i.e., percentage correct responses calculated as [number correct trials / (correct + incorrect trials)] × 100; (4) latency to make a correct choice, i.e., the mean time between stimulus onset and nose poke in the illuminated hole; (5) omission errors, i.e., the number of omitted trials during a session; and (6) feeder latency, i.e., the latency to collect a pellet following correct choice.
Delayed-reward paradigm
In addition to the 5-CSRTT, the delayed reward paradigm, as employed in our laboratory, has also been described more elaborately (Van Gaalen et al. 2006b). Briefly, rats were trained 5 days per week in this paradigm. In the final stages of training and during drug testing, a session was divided into 5 blocks of 12 trials, each block starting with 2 forced trials during which, after initiating the trial through a nose poke into the central hole, either the left hole or the right hole was illuminated in a counterbalanced fashion. In the next ten trials, the animals had a free choice, and both the left and right hole were illuminated. Poking into one position resulted in the immediate delivery of a small reinforcer (one food pellet), whereas a nose poke into the other position resulted in the delivery of a large, but delayed, reinforcer (four food pellets). If an animal did not make a response during this choice phase within 10 s, an intertrial interval was initiated, and the trial was counted as an omission. The position associated with the small and large reinforcer was always the same for each individual and counterbalanced for the group. The delay for the large reinforcer progressively increased within a session per block of 12 trials as follows: 0, 10, 20, 40, and 60 s. Responding into non-illuminated holes during the test was recorded, but had no programmed consequences. The behavioral measure to assess task performance, i.e., the percentage preference for the large reinforcer as a function of delay, was calculated as: the number of choices for the large reinforcer / (number choices large + small reinforcers) × 100. In addition, we calculated the total number of omitted choice trials within a session.
Stop-signal paradigm
Shaping During initial shaping for two consecutive sessions, both the middle nose poke hole and the hole immediately adjacent to the right or left were illuminated (counterbalanced for all subjects). A nose poke into either one of the two active holes extinguished the visual stimuli in both holes and resulted in delivery of a pellet. After an intertrial interval of 10 s, the next trial started. Nose poking within this intertrial interval period did not have any programmed consequences. A session ended when the rat had earned 100 pellets or after 30 min, whichever occurred first.
Shaping: go trials During the next phase, only the stimulus light in the middle nose poke hole was illuminated (start stimulus). A response into the active middle hole switched off the stimulus light and was followed by the illumination of the stimulus light (go stimulus) in the hole immediately adjacent to the left or right. A nose poke into this illuminated hole switched off the stimulus light and resulted in the delivery of a pellet. After an intertrial interval of 10 s, the next trial started. Responding in the start stimulus hole during presentation of the go stimulus was counted as perseverative start pokes, whereas prestimulus responses into the go stimulus hole resulted in a timeout period of 5 s. Subsequently, the response requirements into the start stimulus hole before onset of a go stimulus were varied into a variable ratio 2 schedule (VR2, i.e., either FR1, FR2, or FR3) to avoid the development of a prepotent response pattern from start stimulus to go stimulus hole and to ensure that animals waited until the appearance of a go stimulus. During this phase, rats were trained until they reliably completed 200 successful go trials. Following this phase, a LH period was introduced for the go stimulus and only during this period was the go stimulus present. Initially, the LH was set at 5 s, and in subsequent sessions, was individually titrated to meet performance criterion of 80% successful hits and <20% prestimulus responses. Omissions of a go stimulus response within the LH resulted in a 5-s time-out period, during which both the houselight and stimulus light were turned off. From initial shaping to criterion performance in this phase, 26 sessions were required.
Shaping: introduction stop signal During the final training phase, a stop signal was introduced in 25% of all trials. Initially, this stop signal (duration, 50 ms; frequency, 4,500 Hz; intensity 80 dB) was contingent with the appearance of the go signal. Responding during the onset of the stop signal or during the LH immediately extinguished the go stimulus and houselight, turned off the stop signal, and was followed by a 5-s time-out. In contrast, if the animal successfully refrained from responding during a stop trial, a pellet was delivered. Initially, the LH during stop and go trials were equal; however, when performance during stop trials was below 80% successfully inhibited stop trials, the LH during stop trials was lowered over sessions in steps of 100–200 ms until animals improved performance. Subsequently, the LH was then gradually increased in these individuals over sessions until the LH during both go and stop trials were equal. As soon as animals reached the criterion of approximately 90% successfully inhibited stop trials, delays for the onset of the stop signal were introduced. The stop-signal delays (SSD) were presented in a pseudorandom order, and to compensate for differences between rats, SSDs were based on each individual rats’ mean reaction time on go trials in the preceding drug-free training session. SSDs were calculated as follows: mRT minus either 25, 50, 100, 200, or 400 ms. In addition, an equal amount of zero delays were presented during sessions. Drug testing commenced upon stable baseline performance for at least five consecutive sessions, i.e., 80% accuracy during go trials and a significant SSD-dependent decrease in correctly inhibited stop trials. It took 30 sessions before animals reached stable baseline performance after the introduction of the stop signal; therefore, from initial shaping to stable baseline performance in the stop-signal paradigm, in total, 56 sessions were required.
Stop-signal paradigm: estimation stop-signal reaction time and correction for omissions during go trials
Calculations to estimate the stop-signal reaction times (SSRT) and a correction for omission errors were adapted from Logan (1994) and Solanto et al. (2001). For estimating the SSRT, data of the three SSDs of 200, 100, and 50 ms were used, as the probability of correct inhibition on these intervals was within the range of 0.2 < p < 0.8, and thus, most informative for estimating SSRT (Band et al. 2003). For each of the three intervals, the probability of responding was calculated including a correction for nonresponses based on the number of omissions during the go trials, the latter, as omissions cannot be distinguished from successful inhibitions during stop trials. The following formula, adapted from Solanto et al. (2001) was used for these calculations:
where x is the number of stop-signal trials at each delay interval; correct inhibitions are the number of correctly inhibited trials, and y is the probability of omissions during the go trials within the entire session. To calculate SSRTs, reaction times on all go trials were rank ordered. From this list with RTs, the “nth” RT was taken, where “n“ was obtained by multiplying the total number of go trials by the probability of responding for a particular SSD. This RT value approximates the latency between onset of the go stimulus and completion of the stopping process. The SSRT for each interval is then obtained by subtracting the SSD interval from this RT. The average estimated SSRT that is used for the analyses in the present study is calculated by taking the mean of each SSRT at the three SSDs (200, 100, and 50 ms).
Drugs
SR141716A was generated and kindly donated by Solvay Pharmaceuticals (Weesp, The Netherlands), whereas WIN55,212-2 was purchased from Tocris Biosciences (Bristol, United Kingdom). Both SR141716A and WIN55,212-2 were dissolved as described previously in a mixture of ethanol, Tween80, and sterile saline (ratio 1:1:18; cf. De Vries et al. 2001). In all experiments, SR141716A was injected 30 min before testing, whereas WIN55,212-2 was injected 20 min before testing. In all paradigms, the order of testing the drugs was (1) SR141716A and (2) WIN55,212-2. The drug combination was only performed in the five-choice serial reaction time task and followed the studies with SR141716A and WIN55,212-2. Drugs were freshly prepared each day before testing and intraperitoneally injected in a volume of 1 ml/kg bodyweight according to a Latin square design for both the dose–response studies and the drug combination on Tuesdays and Fridays, with baseline training sessions on the other weekdays.
Statistical analyses
Data were subjected to repeated measures analysis of variance (ANOVA) with drug dose (all paradigms), delay to large reinforcer (delayed reward paradigm), and stop-signal delay (stop-signal paradigm) as within subjects variables using the Statistical Package for the Social Sciences version 11 (SPSS, Chicago, IL, USA).
The homogeneity of variance across groups was determined using Mauchly’s tests for equal variances, and in case of violation of homogeneity, corrected, and therefore, more conservative Huynh–Feldt probability values were used for subsequent analyses. In the stop-signal paradigm, some further exploratory analyses were performed between mean go reaction times and the limited hold period using a bivariate Pearson correlation. In addition, the estimated SSRT data were also subjected to a median split analysis to assess whether SR141716A or WIN55,212-2 had differential effects on response inhibition in individuals with relatively “fast” or “slow” stopping abilities. In case of statistically significant main effects, further post hoc comparisons were conducted using Student–Newman–Keuls Tests. The level of probability for statistically significant effects was set at 0.05.
Results
Effects of SR141716A and WIN55,212-2 on measures of inhibitory response control in the five-choice serial reaction time task
The number of premature responses, a measure of inhibitory control reflecting impulsive behavior, was dose dependently decreased by SR141716A [Fig. 1a; F3,45 = 12.24, p < 0.001], and further post hoc analyses revealed that all doses significantly lowered premature responding compared with vehicle, whereas the highest dose (3.0 mg/kg) even further lowered the number of premature responses compared with 1.0 mg/kg SR141716A. In contrast, perseverative responding after correct choice, a different measure of inhibitory control reflecting compulsive behavior, was not affected at any dose [Fig. 1b; F3,45 = 1.84, p = 0.15]. Attentional function was improved by SR141716A, and further analyses revealed that only 0.3 mg/kg SR141716A significantly increased the percentageof accurate choice from approximately 77% under vehicle conditions to 82% as shown in Table 1 [F3,45 = 3.43, p = 0.025]. In addition, a marginal, but significant, increase in the latency to make a correct choice was also detected, and further comparisons showed that only the high dose of 3.0 mg/kg significantly slowed correct response reaction time [Table 1; F3,45 = 4.53, p = 0.007]. The latency to collect a food reward and errors of omission, however, were not affected at any dose [Table 1; feeder latency: F3,45 = 0.39, p = 0.65 and omissions: F3,45 = 1.30, p = 0.29].
Fig. 1Effects of SR141716A (a, b) and WIN55,212-2 (c, d) on different measures of inhibitory control in the 5-CSRTT. Data depict mean (±SEM) numbers of premature responses (a, c) and perseverative responses after correct choice (b, d). *p < 0.05 and **p < 0.005 vs vehicleTable 1Effects of SR141716A and WIN55,212-2 on measures of attentional function and motivation in the 5-CSRTT. Data depict mean ± SEMSubstances Accuracy (%)Response latency (ms)Omissions (%)Feeder latency (ms)SR141716AVehicle77.2 ± 2.4336 ± 95.4 ± 1.1954 ± 480.3 mg/kg82.0 ± 1.9*338 ± 105.6 ± 1.0954 ± 441.0 mg/kg80.5 ± 2.5347 ± 145.1 ± 0.7937 ± 433.0 mg/kg80.2 ± 2.3374 ± 14*7.3 ± 1.0939 ± 37WIN55,212-2Vehicle 77.7 ± 2.5324 ± 73.9 ± 0.7956 ± 530.3 mg/kg79.6 ± 2.3333 ± 103.4 ± 0.7910 ± 411.0 mg/kg77.2 ± 2.3380 ± 23*14.4 ± 4.3*943 ± 443.0 mg/kg76.8 ± 2.7399 ± 17**20.2 ± 4.1**1212 ± 138*p < 0.05**p < 0.005 vs vehicle
WIN55,212-2 did neither change the number of premature responses nor the number of perseverative responses after correct choice [Fig. 1c and d; premature responses: F3,45 = 1.02, p = 0.39 and perseverative responses: F3,45 = 1.64, p = 0.21; respectively]. Furthermore, as indicated in Table 1, WIN55,212-2 did not change accurate choice or the latency to collect a pellet after correct choice [accurate choice: F3,45 = 0.72, p = 0.54 and feeder latency: F3,45 = 3.19, p = 0.052]. In contrast, errors of omission were increased, and latencies to make a correct choice were lengthened by WIN55,212-2 [omissions: F3,45 = 7.44, p = 0.002 and correct response latency: F3,45 = 6.59, p = 0.004].
As shown in Fig. 2, the decrements in premature responding by 3.0 mg/kg SR141716A were prevented in the presence of WIN55,212-2 at a dose of 1.0 mg/kg [F3,45 = 7.33, p < 0.001]. In addition, this selected dose of WIN55,212-2, by itself, did not affect inhibitory control as indicated by the absence of an effect of this dose on the number of premature responses.
Fig. 2Coadministration of WIN55,212-2 at 1.0 mg/kg (WIN1) prevents the effects of 3.0 mg/kg SR14716A (SR3) on inhibitory control in the 5-CSRTT. All data are depicted as mean (±SEM). *p < 0.05 and **p < 0.005
SR141716A and WIN55,212-2 do not affect decision making in the delayed reward paradigm
Stable baseline performance on the delayed reward paradigm occurred after approximately 30 training sessions on a full delay range (0, 10, 20, 40, and 60 s), and therefore, we commenced drug testing from session 35 onwards. A clear, highly significant delay-dependent decrement in the percentage preference for the large reinforcer was observed [Fig. 3; delay: F4,60=270.22, p < 0.001]. Nonetheless, SR141716A did not shift the preference for a large reinforcer over delays at any of the tested doses [dose: F3,45 = 1.56, p = 0.21 and dose × delay: F12,180 = 1.24, p = 0.29]. In addition, SR141716A also did not change the total numbers of omitted choice trials, i.e., the failures to start a trial during the choice phase [dose: F3,45 = 3.10, p = 0.052].
Fig. 3Effects of SR141716A (a) and WIN55,212-2 (b) on the mean (±SEM) percentage preference for the large reinforcer in the delayed reward paradigm
In the WIN55,212-2 experiments, at the highest dose, in total, five animals omitted all choice trials of some delays, and therefore, were excluded from all analyses of the delay discounting data. Similar to SR141716A, increasing the delay highly significantly shifted the preference from large to the small reinforcer [Fig. 4; delay: F4,40 = 179.06, p < 0.001]. Although there was no overall effect of WIN55,212-2 on decision making [dose: F3,30 = 0.68, p = 0.57], there was a dose by delay interaction effect suggesting a shift in preference for the large reinforcer over delays [dose × delay: F12,120 = 2.61, p = 0.011]. Nonetheless, further post hoc comparisons revealed no differences between vehicle and any of the other doses. The number of omitted choice trials was not affected by WIN55,212-2 [dose: F3,30 = 1.61, p = 0.21].
Fig. 4Effects of SR141716A on response inhibition as measured in the stop-signal paradigm. Data are depicted as mean (±SEM) percentage of correctly inhibited stop trials with varying SSDs before the mean go RT (a), go reaction times (b) and estimated stop signal reaction times from SSDs 200, 100, and 50 ms (c). **p < 0.005 vs vehicle
Effects of SR141716A and WIN55,212-2 on response inhibition in the stop-signal paradigm
Under baseline conditions in the stop-signal paradigm, there was stable individual variation in mean reaction times during go trials over consecutive sessions ranging from 290 to 470 ms (mean: 365 ms; standard deviation: 58 ms). Likewise, the limited hold periods during go and stop trials also stably varied and ranged from 400 to 1,100 ms (mean: 594 ms; SD: 167 ms). A highly significant positive correlation indicated that individuals with “shorter” limited hold periods also displayed shorter mean go reaction times compared to individuals with “longer” limited hold periods that were slower in go reaction speed [r = 0.85, p < 0.001].
Response inhibition, as displayed in the inhibition function curve in Fig. 4a, was not affected by SR141716A at any of the tested doses [dose: F3,45 = 1.28, p = 0.29 and dose × SSD: F12,180 = 1.25, p = 0.26]; however, the percentage of correctly inhibited stop trials significantly declined with decreasing stop-signal delays [SSD: F4,60 = 39.24, p < 0.001]. SR141716A did slow mean reaction times during go trials by approximately 30 ms [Fig. 4b; dose: F3,45 = 7.93, p = 0.001], and further analyses indicated that both the 1.0 and 3.0 mg/kg dose significantly slowed reaction times compared with vehicle. Nonetheless, the number of omissions during go trials were not significantly affected [mean ± SEM: vehicle = 16.2 ± 3.0; 0.3 mg/kg = 18.1 ± 1.2; 1.0 mg/kg = 17.7 ± 1.7 and 3.0 mg/kg = 23.3 ± 2.6; dose: F3,45 = 1.76, p = 0.17]. Lastly, the average estimated SSRT across three stop-signal delays, putatively reflecting response inhibition, was not significantly changed by SR141716A at any dose [Fig. 4c; dose: F3,45 = 1.47, p = 0.24]. Furthermore, a median split analysis (median SSRT: 238 ms) did not reveal differential effects of SR141716A on the estimated SSRT in individuals with relatively “fast” vs “slow” stopping abilities [slow vs fast stopper: F1,14 = 9.54, p = 0.008; dose: F3,42 = 1.04, p = 0.38 and dose × slow vs fast stopper: F3,42 = 2.48, p = 0.074].
In the WIN55,212-2 experiments, at the highest dose, in total, four animals did not start any go trials and were therefore excluded from all stop-signal data analyses. Response inhibition, as displayed in the inhibition function curve in Fig. 5a, was affected by WIN55,212-2 [SSD: F4,44 = 23.51, p < 0.001; dose: F3,33 = 5.12, p = 0.005 and dose × SSD: F12,132 = 1.11, p = 0.36], and further comparisons revealed that 0.3 mg/kg WIN55,212-2 significantly deteriorated the percentage of correct inhibition compared to vehicle. Mean reaction times during go trials were slowed by WIN55,212-2 [Fig. 5b; dose: F3,33 = 15.07, p < 0.001], and further analyses indicated that 1.0 and 3.0 mg/kg WIN55,212-2 significantly slowed mean reaction times compared to vehicle. Nonetheless, the number of omissions during go trials were not significantly affected [mean ± SEM: vehicle = 14.8 ± 1.9; 0.3 mg/kg = 23.2 ± 4.8; 1.0 mg/kg = 26.1 ± 5.4 and 3.0 mg/kg = 34.7 ± 7.0; dose: F3,33 = 3.06, p = 0.073]. Lastly, the average estimated SSRT across three stop-signal delays, putatively reflecting response inhibition, was not significantly changed by WIN55,212-2 at any dose [Fig. 5c; dose: F3,33 = 2.06, p = 0.13]. Furthermore, a median split analysis (median SSRT: 227 ms) did not reveal differential effects of WIN55,212-2 on the estimated SSRT in individuals with relatively “fast” vs “slow” stopping abilities [slow vs fast stopper: F1,10 = 9.57, p = 0.011; dose: F3,30 = 2.12, p = 0.12 and dose × slow vs fast stopper: F3,30 = 1.30, p = 0.29].
Fig. 5Effects of WIN55,212-2 on response inhibition as measured in the stop-signal paradigm. Data are depicted as mean (±SEM) percentage of correctly inhibited stop trials with varying SSDs before the mean go reaction times (a), go RTs (b) and estimated stop signal reaction times from SSDs 200, 100, and 50 ms (c). *p < 0.05 and **p < 0.005 vs vehicle
Discussion
To our knowledge, this is the first report demonstrating behavioral effects of a CB1 receptor agonist and antagonist on independent measures of impulsivity. Thus far, evidence pointing towards cannabinoid involvement in impulsivity mainly originates from studies in which effects of THC or marijuana were tested in human volunteers (Lane et al. 2005; McDonald et al. 2003). Our data obtained in the 5-CSRTT strongly suggest that inhibitory control is modulated by an endogenous cannabinoid tone, as premature responding was dose dependently decreased by SR141716A. This notion is further supported by the observation that in the presence of WIN55,212-2, the effects of 3.0 mg/kg SR141716A on premature responding were no longer observed. Furthermore, the finding that the CB1 receptor agonist WIN55,212-2, by itself, did not impair inhibitory control by increasing the number of premature responses is in keeping with previous findings (Arguello and Jentsch 2004). It should be noted though, that in the study by Arguello and Jentsch (2004), SR141716A (dose range: 0.1–1.0 mg/kg), by itself, did not change premature responding in a lateralized reaction time task, thereby, contrasting our findings. Nonetheless, differences in baseline performance may have contributed to the discrepancy in findings, as premature responding in well-trained rats in the task by Arguello and Jentsch (2004) was low (approximately 3 per session) compared to premature responding in well-trained rats in the present study (approximately 22 per session). Presumably, a floor effect may have masked the effects of SR141716A on premature responding in the lateralized reaction time task.
Together, our data indicate that performance in the 5-CSRTT is associated with profound occupation of CB1 receptors by endogenously released cannabinoids. As might then be expected, the endocannabinoid uptake inhibitor AM404 did not affect inhibitory response control in the 5-CSRTT (unpublished data). In contrast to premature responding, perseverative responding after correct choice, a different measure of inhibitory control that putatively reflects compulsive behavior (Robbins 2002), was neither altered upon activation nor blockade of CB1 receptors. This finding further underscores the dissociation between premature and perseverative responding in the 5-CSRTT that has been shown to have a neuroanatomical basis as well (Chudasama et al. 2003).
The endocannabinoid system interacts with many other central neurotransmitters systems including the cholinergic, GABAergic, glutamatergic, and opioid systems (Schlicker and Kathmann 2001; Schoffelmeer et al. 2006). Moreover, cannabimimetics have been shown to indirectly modulate the release of dopamine and glutamate in corticostriatal regions (e.g., Cheer et al. 2004; Szabo et al. 1999; Tanda et al. 1997; Xi et al. 2006), most notably in the nucleus accumbens, and particularly, these effects have been linked to the involvement of the cannabinoid system in addiction (for review, see De Vries and Schoffelmeer 2005). Although it is beyond the scope of the present study and additional experiments are required to substantiate this, one might speculate that the present findings are explained by these modulatory effects of the cannabinoid system on mesolimbic dopamine release, as inhibitory control processes in the 5-CSRTT have been shown to depend upon dopamine receptor activation within the nucleus accumbens (Cole and Robbins 1987; Pattij et al. 2007). On the other hand, explanations for the beneficial effects of SR141716A on inhibitory control may be due to the role of the endocannabinoid system in feeding behavior (for review, see Di Marzo and Matias 2005). Accordingly, it has been demonstrated that SR141716A reduces intake of normal and palatable food (e.g., Arnone et al. 1997; Freedland et al. 2000; Thornton-Jones et al. 2005) and the motivation to obtain food (Solinas and Goldberg 2005). Nonetheless, despite these reported effects of SR141716A on food intake, anorexic effects of this compound seem unlikely to explain the current data, as both primary indices of food-motivated behavior in the 5-CSRTT, namely, errors of omission and feeder response latencies, were not changed.
It is interesting to note that we also observed a moderate beneficial effect of SR141716A on visuospatial attention, as it increased the level of accurate choice at 0.3 mg/kg. Likewise, it has been shown that SR141716A improves social recognition and spatial and aversive memory at comparable dose ranges between 0.3 and 3.0 mg/kg (Lichtman 2000; Takahashi et al. 2005; Terranova et al. 1996; Wolff and Leander 2003). Although the mechanisms responsible for the beneficial effects of disrupting endocannabinoid signaling on mnemonic and attentional processes need to be elucidated further, it is interesting to note that SR141716A increases, among others, cholinergic neurotransmission in the medial prefrontal cortex in vivo (Tzavara et al. 2003). This finding may be particularly relevant for attentional function, as performance in the 5-CSRTT has been shown to be accompanied by increments in acetylcholine release in the medial prefrontal cortex (Passetti et al. 2000), whereas decrements in acetylcholine release in this brain region have been shown to correlate with poor visuospatial attention (McGaughy et al. 2002).
A different measure of impulsivity we studied was impulsive choice in the delayed reward paradigm. Conceptually, impulsive choice differs from inhibitory control in the 5-CSRTT and rather reflects a cognitive decision-making process, as rats have to weigh the immediate vs delayed outcomes of their behavior. Impulsive choice then is reflected in insensitivity towards the delayed larger reward and a preference for the small immediate reward. In the present experiments, we observed that neither SR141716A nor WIN55,212-2 did have any effects on impulsive choice. In accordance with these findings, it has been shown recently that THC did not change delay and probability discounting in human volunteers (McDonald et al. 2003), and together, these data suggest that the (endo)cannabinoid system is not critically involved in impulsive choice. Our observations do contrast with previous findings indicating that marijuana elevates risk-taking behavior in volunteers (Lane et al. 2005). Risk taking, however, differs from delay and probability aversion in that it is most likely to occur when the behavioral options may result in losses or have aversive consequences (Rachlin et al. 1986). In the delayed reward paradigm and the paradigm used by McDonald et al. (2003), subjects could only win and not lose reward (food or money, respectively), whereas only the magnitude of the reward depended upon choice. It is therefore possible, that these procedural and conceptual differences explain the discrepancy in findings, and furthermore, that delay aversion and risk-taking processes are differentially regulated by the (endo)cannabinoid system.
While response inhibition has been shown to be impaired in human volunteers after THC administration (McDonald et al. 2003), neither disruption of endocannabinoid signaling nor administration of a CB1 receptor agonist had clear observable behavioral effects on stop-signal task performance. In agreement with the assumption of a “race model” between “go” and “stop” processes (Logan 1994) and previous stop-signal data in rats (Eagle and Robbins 2003a, b), the probability of successfully inhibiting a response during a stop trial decreased in rats when the onset of the stop signal was delayed in time. Although SR141716A did not shift this inhibition curve as a function of increasing stop-signal delays, the low dose WIN55,212-2 (0.3 mg/kg) significantly deteriorated the ability to inhibit responding with increasing stop-signal delays. However, the primary parameter in this paradigm, the average estimated stop-signal reaction time, or simply put, speed of stopping, was not changed by any dose of SR141716A or WIN55,212-2. In addition, median split analyses revealed no differential drug effects in individuals with “slow” vs “fast” stopping abilities as has been reported previously for the effects of d-amphetamine on stopping speed in both rats and humans (De Wit et al. 2002; Feola et al. 2000). Collectively, our findings suggest that response inhibition processes, as measured in the stop-signal paradigm, are not under control of CB1 receptors. In the present study, the speed of stopping was estimated from separate SSRTs obtained from three stop-signal delays (200, 100, and 50 ms before mean go RT), and consistent with this estimation method, there was some variability in the SSRTs depending on the delay (Band et al. 2003; Logan 1994), with shorter stop-signal delays resulting in SSRTs of approximately 140 ms and longer delays resulting in SSRTs of approximately 300 ms. However, the variability in SSRTs over delays was similar across all doses of both drugs (data not shown), thus, ruling out the possibility that this variability may have masked effects of SR141716A or WIN55,212-2 on response inhibition. In contrast to the measures of response inhibition, reaction times during performance on go trials were slowed by both compounds in line with their effects in the 5-CSRTT. With regard to these effects of SR141716A, in the absence of changes on omission errors in both the 5-CSRTT and the stop signal paradigm, they cannot be solely interpreted in terms of motor effects. Rather, the minor but significant increase in response latencies of approximately 30 ms in both paradigms (Table 1 and Fig. 4b) may indicate changes in information processing speed induced by blockade of endocannabinoid signaling. However, this notion is not confirmed by previous sensorimotor gating data (Mansbach et al. 1996; Martin et al. 2003). In contrast, the effects of 1.0 and 3.0 mg/kg WIN55,212-2 on reaction times in the stop-signal paradigm may in part be secondary to changes in locomotor activity, as these doses also increased the number of omissions in the 5-CSRTT.
Remarkably, although both the stop-signal paradigm and 5-CSRTT measure aspects of impulsive action, only inhibitory control in the 5-CSRTT was improved by SR141716A. Impulsive action in the latter paradigm is mainly measured as the inability to inhibit inappropriate (premature) responses, whereas in the stop-signal paradigm impulsive action is reflected in the inability to inhibit ongoing behavior, i.e., the inability to stop a behavioral response that has just been initiated. Our observations support the notion of different and separable forms of inhibitory control, and moreover, suggest a differential role for the endocannabinoid system therein. Likewise, lesion studies have suggested that different brain regions are involved in inhibitory control measured in either the 5-CSRTT or stop-signal paradigm, as for instance, lesions of the nucleus accumbens or subregions of the medial prefrontal cortex have been shown to impair inhibitory control in the 5-CSRTT (Christakou et al. 2004; Chudasama et al. 2003; Muir et al. 1996) and not in the stop-signal paradigm (Eagle and Robbins 2003b).
In summary, the present study provides evidence for a differential involvement of the endocannabinoid system in independent measures of impulsivity, as SR141716A primarily affected inhibitory control, and neither impulsive choice nor response inhibition, whereas WIN55,212-2 only slightly affected response inhibition. In this regard, the present data add to existing clinical and preclinical evidence demonstrating that distinct measures of impulsivity can be dissociated at a pharmacological and neuroanatomical level (e.g., Chudasama et al. 2003; De Wit et al. 2002; McDonald et al. 2003; Winstanley et al. 2004). Our findings may be of particular interest with respect to the heterogeneity observed in attention-deficit/hyperactivity disorder (Sonuga-Barke 2002) and suggest that possible novel pharmacotherapies targeted at the cannabinoid system may benefit the subtype resulting from poor inhibitory control, but not the motivational style, or delay aversion, subtype.

cannabinoid
cognition
inhibitory control
impulsive choice
response inhibition
sr141716a
win55,212-2