Medial prefrontal serotonin in the rat is involved in goal-directed behaviour when affect guides decision making

Rationale Across species, serotonin (5-HT) depletion in the prefrontal cortex (PFC) has been shown to cause impaired performance on tests of cognitive flexibility and the processing of affective information (e.g. information with an ‘emotional’ content). While recent work has explored the specific role of the orbital PFC herein, the role of the medial PFC remains unclear.
Introduction
Certain aspects of cognitive flexibility (i.e. the ability to adapt behaviour, see for instance, Fuster 1980; Kolb 1984; De Bruin et al. 1994; Dalley et al. 2004) depend critically on central serotonin (5-HT). Recent experiments in humans with tryptophan depletion, a method resulting in a transient decrease in 5-HT synthesis, show impairments in the ability to learn changed stimulus–reward associations (Rogers et al. 1999) and in the performance on tests of cognitive flexibility (Rubinsztein et al. 2001). Lesion studies in non-human primates corroborate these findings, showing that it is the 5-HT innervation of the prefrontal cortex (PFC) that is critical for adapting behaviour when stimulus–reward contingencies are reversed in a two-choice discrimination (reversal learning; Clarke et al. 2004, 2005). Similar observations have been made after central 5-HT depletion in rats, indicating that 5-HT depletion leads to impaired flexibility (Harrison et al. 1999), increased perseverative responding (Beninger and Phillips 1979; Morgan et al. 1993) and impaired impulse inhibition (Winstanley et al. 2004, 2006).
In addition to effects on cognitive flexibility, tryptophan depletion has also been reported to lead to impairments in affective processing (i.e. the ability to evaluate and integrate emotional content, see for instance, Hariri et al. 2006) and reward discrimination (Rubinsztein et al. 2001; Rogers et al. 2003; see also Roberts and Wallis 2000). Such changes in affective processing (see also Remijnse et al. 2005) have, however, not yet been reported in studies involving selective prefrontal 5-HT lesions in either primates or in rats. Elucidation of the neural substrates underlying this special role of 5-HT in affective processing is one aim of the current study.
A second issue that is central to studies of the PFC in cognitive flexibility is that of specific contributions of the different cortical sub-areas. Evidence for a functional dissociation within the PFC regarding cognitive flexibility comes from studies across species, and it is important to note that the functional differentiation (specificity) of the anatomical subdivision of the rat brain PFC is, to a large extent, comparable to that of the primate PFC (Uylings et al. 2003). In terms of homology between the primate and rodent PFC, we believe, given the data reviewed in Uylings et al. (2003), that the rat medial PFC (mPFC) shows a functional overlap with both lateral and medial frontal regions of the primate brain.
The orbital PFC has been shown to support reversal learning in both primates (Iversen and Mishkin 1970; Butters et al. 1973; Dias et al. 1996; Clarke et al. 2004; Kringelbach et al. 2003; Hornak et al. 2004) and rats (Schoenbaum et al. 2002; Chudasama and Robbins 2003; McAlonan and Brown 2003) and is implicated in the coding of reward-related information in non-human primates (Hikosaka and Watanabe 2000; Tremblay and Schultz 2000; Schultz 2006), rats (Schoenbaum et al. 2003; Bohn et al. 2003) and humans (O’Doherty et al. 2001; Kringelbach 2005).
The mPFC, on the other hand, supports rule switching and attentional set shifting in rats (De Bruin et al. 1994; Joel et al. 1997; Ragozzino et al. 1999a,b; Birrell and Brown 2000). This functional dissociation between reversal learning and attentional set shifting is also observed in primates, in which the latter is dependent on the lateral PFC (Dias et al. 1996; see also Robbins 2005). However, this particular distinction between PFC functions might be rather specific to visual- and odour-based learning in rats (Chudasama and Robbins 2003; Schoenbaum et al. 2002) and primates (Clarke et al. 2005, 2007), as several groups have reported an involvement of the mPFC in reversal learning using spatial discriminations (Kolb et al. 1974; Li and Shao 1998; Salazar et al. 2004; De Bruin et al. 2000). Furthermore, the mPFC has been shown to be involved in the initial processing of reward-related information, as recording of single neuron activity indicated this area to be involved in the coding of reward information in relation to spatial cues (Hok et al. 2005). From this, we conclude that the mPFC may have a specific role in spatial reversal learning. On a more general level, these findings have been paralleled and extended by human studies. Knutson et al. (2005), for example, reported on mPFC-mediated reward coding, and evidence for a mPFC involvement in implementation of that (reward) knowledge in existing stimulus–reward contingencies comes from Ridderinkhof et al. (2004).
Given the fact that 5-HT is involved in reversal learning supported by the orbital PFC (see above) and that 5-HT lesions lead to functional deficits such as perserverative responding and loss of impulse inhibition (Beninger and Phillips 1979; Soubrié 1986; Harrison et al. 1999; Dalley et al. 2002; Winstanley et al. 2004, 2006), which are also observed after lesioning of the mPFC (Morgan et al. 1993; Passetti et al. 2002; Salazar et al. 2004), we feel that investigation of the involvement of 5-HT in the mPFC in spatial reversal learning is warranted. This forms the second main aim of these experiments.
Given the primate literature on the involvement of 5-HT in affective processing and of the mPFC in the implementation of reward information, we hypothesise that the medial prefrontal 5-HT is selectively involved in cognitive flexibility when affect guides decision making (i.e. when ‘emotional’ content drives decision making). Moreover, based on the ‘reversal’ literature, we hypothesise that 5-HT in the mPFC is especially important for successful behavioural adaptation when the stimulus modality is spatial but not odour-based.
To test our hypotheses, we conducted an experiment in which we selectively destroyed 5-HT terminals in the mPFC of male Wistar rats by means of local infusion of the toxin 5,7-dihydroxytryptamine (5,7-DHT). Both sham controls and lesioned animals were then assessed on a series of behavioural tests in which functions thought to underlie cognitive flexibility and affective processing were addressed (De Bruin et al. 2000; Schoenbaum et al. 2006).
To explore the hypothesis that mPFC 5-HT is involved in affective processing, we tested the impact of altered reward value on reversal learning. For half of the sham and control animals, we switched the reward presented after a correct response from non-preferred reward pellets to preferred pellets at the time of the first reversal. Our expectation was that the introduction of this affective shift, on top of the reversal, would cause lesioned animals to show altered task performance because of an inability to accurately process the altered reward information.
For the hypothesis that stimulus modality determines specific involvement of the mPFC, we tested reversal learning across different stimulus modalities.
In line with our studies on mPFC lidocaine inactivation (De Bruin et al. 2000), we subjected the animals to a test of spatial discrimination and reversal learning. In addition, we included a second, operant, odour-based go/no-go reversal task. Based on our hypothesis regarding selective involvement of the mPFC in spatial learning, we expected impaired reversal learning in the spatial but not in the odour-guided task in lesioned animals.
To gain more insight in the mechanisms that might underlie impaired cognitive flexibility, we tested the effect of the lesion on extinction learning and impulse inhibition, and we assessed the animals’ behaviour during extinction of the ‘spatial’ task and responding on ‘go’ and ‘no-go’ trials in the ‘odour’ task.
Materials and methods
All experiments were approved of by the Animal Experimentation Committee of the Royal Netherlands Academy of Arts and Sciences and were carried out in agreement with Dutch Laws (Wet op de Dierproeven 1996) and European regulations (Guideline 86/609/EEC).
Subjects
Subjects were 32 male outbred Wistar rats (Harlan/CPB, Horst, The Netherlands), weighing 175–200 g upon arrival. The animals were socially housed in groups of four in standard type IV Macrolon cages, where they were kept under a reversed day/night cycle (dimmed red light from 7 a.m. until 7 p.m., white light from 7 p.m. until 7 a.m.) for the duration of the experiment. After surgery, the animals were food restricted (16 g/animal per day) to maintain their body weight at 90% of free-feeding weight. Water was available ad libitum throughout the experiment.
Surgery
Two weeks after arrival, the animals were randomly assigned to either the experimental lesion groups or the sham groups and subjected to surgery. The aim was to selectively destroy the serotonergic (5-HT) innervation of the mPFC. To prevent loss of dopaminergic (DA) and noradrenergic (NA) terminals in the target area because of the treatment, all animals were intraperitoneally injected with 20 mg/kg Desipramine HCl (Sigma, Zwijndrecht, The Netherlands) dissolved in MilliQ water (10 mg/ml) 30 min before surgery. The weight of the animals at the time of surgery was 247–289 g. Anaesthesia was induced with 5% isoflurane in oxygen and maintained for the duration of the surgery with 2% isoflurane. For the surgical procedure, the animals were mounted in a stereotactic frame with the toothbar set at −2.5 mm. Bilateral stainless steel cannulas (outside diameter 0.3 mm), connected to a microinfusion pump (801 syringe pump, Univentor, High Precision Engineering, Zejtun, Malta) with flexible PEEK tubing (0.51 mm outside diameter, 0.013 mm inside diameter; Aurora-Borealis, Schoonebeek, The Netherlands), were then placed in the mPFC at an angle of 12°, anterioposterior +30, lateral ±16; ventral −40 (Paxinos and Watson 1998).
After cannula placement, 0.5 μl of either 5,7-DHT (5,7-DHT creatinine sulphate salt, Sigma; 32 μg/μl, dissolved in 0.1% ascorbic acid) or vehicle was infused into the target area over a period of 1 min. After infusion, the cannulas were kept in place for an additional 2 min to allow for diffusion. The wound was closed with surgical stitches after the cannulas were removed from the brain. Post-operative pain reduction was achieved with Finadyne (flunixin meglumide 50 mg/ml, Schering-Plough, Segré, France), 0.01 ml/100 g body weight subcutaneously, approximately 2 h after surgery. After surgery, the animals were returned to their home cages.
Apparatus
Two behavioural tasks were used. For the first (two-lever discrimination) task, locally constructed operant chambers were equipped with a set of two retractable levers positioned to the left and right of a food dispenser (distance lever–feeder, 10 cm). A house light was placed on the opposite wall, two lights were positioned above the levers, signalling their presentation, and one inside the food dispenser, signalling the delivery of a 45 mg food pellet (Noyes Formula AI or P, Research Diets, New Brunswick, NJ). Nose pokes were detected by an infrared sensor located inside the food well. Operation of the Skinner boxes was controlled by a personal computer running the Med-pc™ software (Med Associates, Sanddown Scientific, Middlesex, UK).
For the second (odour-based go/no-go) task, commercially available operant chambers (Med Associates, Sanddown Scientific) were equipped with a retractable lever and an odour-sampling unit positioned on opposing walls (spaced 34 cm apart), allowing the presentation of distinct (non-aversive natural) odours. A food dispenser, from the same company, was positioned next to the odour-sampling port. Trial lights indicated odour, lever and reward presentation.
Pellet rewards
To manipulate the affective value of the rewards, we used two different types of pellet rewards (Noyes ™ AI and P; for additional information see www.researchdiets.com) that were shown to be differentially preferred by the animals (see “Pellet preference”). Before the start of the experiment, all animals were familiarised with both types of pellets in their home cage. During the reversal phases of both tasks, the preferred pellet reward was only available to half of the animals (for groups, see below).
Pellet preference
To establish whether male Wistar rats showed preference for a specific food pellet, we pre-tested three types of rewards (Noyes ™ formula AI, P, and FP; see Fig. 1) in a separate group of eight animals. The testing apparatus was a standard-size T-maze with trap doors in which both arms were baited. Five daily habituation sessions were given during which the animals were allowed to freely explore the entire T-maze and familiarise themselves to the position of the two different rewards that were tested that day. Subsequently, the animals were given the choice between the two arms, each baited with one of the three types of reward (15 trials). In these trials, the trap door was lowered after an arm entry was made to prevent the rat from entering the second arm. All three possible combinations of pellets were given over 3 consecutive days to all rats. To avoid ‘sequence’ effects, the rats were pseudo-randomly assigned to a particular pellet order. The animals were housed and food restricted as the experimental animals.
Table 1Results of HPLC analysis of sham and 5-HT-lesioned animals in seven brain areas Treatment5-HTDANAmPFC—15,7-DHT63.2 ± 8.6 (9.1%)**35.5 ± 7.1 (66.9%)417.6 ± 41.1 (66.6%)**Sham691.5 ± 119.553.1 ± 8.0627.0 ± 56.8mPFC—25,7-DHT68.8 ± 14.9 (28.0%)**60.9 ± 11.1 (93.0%)446.3 ± 41.8 (104.8%)Sham245.3 ± 46.565.5 ± 5.0425.8 ± 25.4mPFC—35,7-DHT42.5 ± 6.9 (18.0%)*71.1 ± 19.1 (70.3%)272.6 ± 23.0 (74.1%)Sham236.0 ± 70.1101.1 ± 33.7367.7 ± 48.0olPFC—15,7-DHT319.1 ± 125.2 (107.2%)11.3 ± 1.6 (87.5%)239.4 ± 15.7 (93.4%)Sham297.6 ± 28.012.9 ± 1.8256.4 ± 13.0olPFC—25,7-DHT175.6 ± 40.5 (80.4%)23.7 ± 3.4 (74.2%)293.6 ± 42.9 (126.6%)Sham218.3 ± 32.332.0 ± 4.5231.8 ± 14.0olPFC—35,7-DHT278.2 ± 96.5 (107.7%)77.1 ± 14.0 (94.7%)235.5 ± 19.7 (93.5%)Sham258.2 ± 25.681.4 ± 9.8251.9 ± 20.4M-25,7-DHT138.7 ± 19.2 (72.1%)*53.4 ± 6.9 (74.6%)199.4 ± 14.7 (112.3%)Sham192.3 ± 15.171.5 ± 10.2177.5 ± 9.5Tissue concentrations of serotonin (5-HT), dopamine (DA) and noradrenaline (NA) in three adjacent (anterior–posrterior) medial prefrontal (mPFC 1–3) and orbital prefrontal (olPFC 1–3) areas, and control area (M-2) in sham (n = 16) and 5,7-DHT- (n = 14) lesioned animals. Group averages (±SEM) are presented in ng/g*Significant group differences p < 0.01**Significant group differences p < 0.05
Experimental procedure
Approximately 9 days after surgery, a period that a pilot experiment had indicated to be necessary to achieve maximum 5-HT depletion (data not shown), the behavioural experiments started. On experimental days, which typically lasted from 9 a.m. to 5 p.m., the animals were transferred to the experimental room, where they remained in their home cages until testing. In both the animal facility and the experimental room, a radio played to mask background noises.
Before the behavioural testing, both lesion and sham groups were further divided to create a total of four groups. Two groups, one lesion and one sham, received non-preferred pellets throughout the experiment, and two groups started receiving non-preferred pellets but only received preferred pellets from the start of the first reversal.
The two behavioural tasks were applied consecutively in the same order to all animals. At the start of the second task, all animals received non-preferred pellets again.
Behavioural task 1. Two-lever discrimination task. (weeks 1–4)
This task was used to assess the effects of mPFC 5-HT depletion on the acquisition of an operant response, discrimination between two spatial stimuli and reversal and extinction of the acquired responses (De Bruin et al. 2000).
First, the animals were trained, in a maximum of nine (64 trials) shaping sessions, to press a lever for a food reward. During this shaping phase, a single lever was presented at each trial (randomly the left or right lever). In the course of the sessions, the animals were required to increase the number of lever responses per trial from a fixed ratio (FR) of one to three responses to obtain a reward. All of the trials in the two-lever spatial discrimination and reversal task (including shaping) were discrete choice trials; that is, the lever(s) were retracted upon responding or, in case of an omission, at the end of the trial duration (60 s). The inter-trial interval was set at 25 s.
Thereafter, a second lever was introduced, and the animal had to learn to press either the left or right lever to obtain a food reward (two-lever discrimination). Pressing the incorrect lever or omitting a response (i.e. not responding within 60 s) led to trial termination and an inter-trial interval timeout. After reaching criterion, i.e. obtaining 90% of the food pellets in a session, the animals were subjected to serial reversals. For two of the four groups, this coincided with the switch from non-preferred to preferred pellets. During this reversal phase, the rewarded and non-rewarded levers were reversed daily for 4 consecutive days. The final phase was an extinction phase in which both levers were presented but none were rewarded.
On both the two-lever discrimination and the serial reversal tests, the animals were tested twice daily: a morning and an afternoon session. The extinction phase consisted of a single daily session for 4 consecutive days. All sessions comprised eight blocks (with eight trials each), which were divided for analysis into two sets of four blocks. Session duration ranged from 45 min (two-lever discrimination) to 2 h (extinction).
Behavioural task 2. Odour go/no-go inhibition task (weeks 5–9)
This task was used to assess the acquisition of a two-odour discrimination, the inhibition of lever pressing and the reversal of the acquired discrimination.
In this task, the animals were first trained to sample an odour in an odour port and subsequently press a lever once for a food reward (FR1). This was achieved in four (30 trial) sessions over 2 days. Once they acquired these two operant behaviours, they were trained in six (30 trial) daily sessions to discriminate between two odours, one indicating a ‘go’ trial in which the animal was required to press the lever to obtain a food reward and another odour that signaled a ‘no-go’ trial in which the animal had to withhold pressing the lever to obtain a food reward. Failure to press the lever on a ‘go’ trial or inhibit a lever response on a ‘no-go’ trial, as well as a failure to sample the odour or respond within 60 s (omission), triggered a 30-s inter-trial timeout during which the house light was turned off and no pellet was rewarded. The final phase was a single reversal in which the stimulus–response contingencies were switched and the animals were required to reverse their previously acquired go/no-go responses. For two of the four groups, this coincided with the switch from non-preferred to preferred pellets. In this final phase, the animals were tested in 24 (45 trial) daily sessions over 12 days. The number of trials per session during this phase was increased to 45 to facilitate task acquisition.
Behavioural measures
Pellet preference test The mean cumulative number of arm entries/pellet choices per rat were grouped and compared over all, 15 trials, sessions.
General activity To assess the possible effects of the lesion on general activity, the groups were compared regarding average number of head entries into the food dispenser and lever presses per block. These measures were taken during testing on the two-lever discrimination task. To exclude the influence of performance on these activity measures, data from the last acquisition session (both sets) were used. In this session, the performance of both groups was stable and statistically identical.
Tests of cognitive flexibility The data obtained from both behavioural tasks were taken as measures of performance in the following fashion: the amount of obtained rewards as a fraction of the amount of obtainable rewards was taken as an index of overall performance on all test phases except the extinction phase of the two-lever discrimination. For this phase, the total number of lever responses was taken as the index, and, as this measure is prone to interference from pre-existing differences between groups in the total number of lever presses, the amount of lever presses per block of eight trials was expressed as a percentage of the mean number of lever presses per group in the same block on the previous day.
Response accuracy as an index of the amount of response errors was scored as the number of correct lever presses relative to the total amount of lever presses. This measure, together with the number of lever omissions, was taken to explain sub-optimal performance.
Performance on the two-odour go/no-go task was further divided into success and failure of responding on the ‘go’ trials and the ‘no-go’ trials as a measure of inhibition.
The effects of reward type (preferred vs non-preferred pellets) on reversal learning were measured by comparing performance scores of the reversal phase of both groups.
The success of task acquisition of any test phase was established by calculating the aforementioned performance. In case of the extinction phase, the success on the test was measured by the total number of lever presses.
Lesion evaluation
Neurochemistry After all experimental procedures were completed, the animals were numbed with a mixture of O2/CO2, after which they were decapitated. Dissection of the brain and removal of the regions of interest immediately followed decapitation. Both the medial (prelimbic, infralimbic and anterior cingulate cortex) and orbitolateral (ventral orbital, ventrolateral orbital, lateral orbital, agranular insular) PFC were completely removed in three adjacent (anterior–posterior from frontal pole to genu of the corpus callosum) slices as well as a single control area (motor cortex, M-2; Paxinos and Watson 1998; Uylings et al. 2003). After dissection, all samples were stored in −80°C until analysis. All medial and orbitolateral PFC areas were analysed separately to gain more insight into the spread of the lesion. Tissue from both hemispheres were, however, analysed together.
Tissue samples were weighed and homogenised in ice-cold 0.1 mol/l perchloric acid and centrifuged for 15 min at 14,000 rounds/min. After centrifuging, the unfiltered supernatants were transferred to an autosampler and 20-μl aliquots were injected onto a column for high-performance liquid chromatography (HPLC) analysis (Waters 600E pump and Waters 717plus autosampler, Waters Chromatography b.v., The Netherlands; Decade VT-03 electrochemical detector, Antec Leyden, The Netherlands) and Shimadzu Class-vp™ software (version 5.03, Shimadzu Duisburg, Germany).
The mobile phase consisted of 0.06 mol/l sodium acetate, 9 mmol/l citric acid, 0.37 mmol/l heptanesulphonic acid and 12.5% methanol. The flow rate was kept constant at 0.65 ml/min. Separation of 5-HT, DA and NA from other components was achieved with a Supelcosil column (LC-18-DB 25 cm × 4.6 mm × 5 μm), with a 2-cm guard column of the same material (Supelco Superguard ™, Supelco, USA) kept at a constant temperature of 28°C. Quantification was achieved by means of electrochemical detection (oxidation potential set at +0.65 V). The transmitter/metabolite content was calculated against a calibration curve of external standards.
The lowest detectable concentration in the supernatant of our main transmitter of interest, 5-HT, was 800 pg/ml, at a signal to noise ratio of 2:1.
Data analysis
Pellet preference The data of the pellet-preference experiment were analysed using a Friedman rank-order test with the cumulative number of choices per pellet as rank sums. Post-hoc testing consisted of two-group Mann–Whitney U comparisons. p values (two-tailed) were set at p < 0.01.
Behavioural tasks 1 and 2 Behavioural data taken during these experiments were analysed using SPSS for Windows (version 11.0; SPSS, Gorinchem, The Netherlands). Shaping data from both behavioural tasks were analysed as the number of trials needed to reach a performance level of greater than 90%. Acquisition and reversal data obtained from both behavioural tasks were arcsine transformed to reduce the interference from ceiling effects (Microsoft Excel, Seattle, WA). The data of the extinction phase of the two-lever discrimination, where no ceiling effect was present, were not transformed. Based on previous results, in which effects of local mPFC inactivation were often observed only in one half of a learning session, we divided the data of each session of the two-lever discrimination task in two sets of four blocks, each consisting of eight trials (De Bruin et al. 2000). In contrast, data from the go/no-go task execution were analysed between sessions and not between sets or blocks because the observed learning in this task was slow and group differences spread out over multiple days. Because of technical problems, some data points were lost.
For the data of both tasks, a repeated-measures analysis of variance (ANOVA) was used with treatment (control/lesion) and pellet (AI/P) as ‘between-subjects’ factors and block as the ‘within-subjects’ factor. When a ‘between-subjects’ effect was found, a one-way ANOVA and Student–Newman–Keuls (SNK) test was used to test for group differences. For the shaping and acquisition phases (which preceded the introduction of the different pellets), the behavioural data of both control groups and lesion groups were combined. This was done to increase power and because both control and lesion groups received the same pellet. In case a treatment effect was found, an independent-samples t test was used as the post-hoc test. Activity measures over the last acquisition session were analysed in a similar fashion.
Neurochemical data Transmitter tissue content was analysed against a calibration curve of external standards. Group comparisons were made using one-way ANOVA, and post-hoc testing was done with an independent-samples t test.
Results of the 5,7-DHT mPFC lesion experiment
Pellet preference Figure 1 shows the mean cumulative scores or arm entries for the three pellet types tested. Comparison of rank sums using a Friedman rank-order test revealed significant group differences (F(9,2) = 12.667, p = 0.002). Repeated post-hoc Mann–Whitney U tests revealed a food preference for pellet type ‘P’ over pellet types ‘FP’ and ‘AI,’ p < 0.001.
Fig. 1Pellet preference test. Rats were tested over 3 consecutive days in a T-maze for pellet preference. Mean cumulative number of arm entries/pellet choices is displayed. Asterisk, different from ‘AI’ and ‘FP,’ p < 0.001
Neurochemical examination Postmortem tissue analysis of brain tissue revealed that 5-HT levels were reduced in lesioned animals compared to control rats in all mPFC areas. Table 1 summarises the results. Spread to neighbouring areas was limited to a small but significant reduction of 5-HT in M-2. Pre-surgery desipramine administration prevented significant loss of DA terminals in all inspected areas, although reductions in both medial and orbital areas were observed. A modest but significant reduction in NA was found in the most frontal mPFC area (med-1). Two animals were excluded from the experiment and further data analysis for showing insufficient loss of 5-HT in the mPFC (i.e. less than 30%) or loss of transmitter in neighbouring areas (i.e. more than 50% of both DA and NA).
Body weight and general activity Post-surgery body weight did not differ between the lesion group and animals that underwent sham surgery (data not shown). The behavioural measures, head entries into the food dispenser and average number of lever presses, taken to assess overall activity of the treatment groups, did not differ, respectively (F(1,28) = 0.719, p = 0.404 and F(1,28) = 0.001, p = 0.977). Table 2 shows the averages for both parameters.
Table 2General activity measures ShamLesionHead entries into food dispenser15.48 ± 1.4317.38 ± 1.76Lever presses26.71 ± 0.7126.73 ± 0.39The average number (±SEM) of head entries into the food dispenser and the average number of lever presses for sham control and lesioned animals. No statistical differences of the lesion on these parameters were observed.
Behavioural analysis
Effects of mPFC 5-HT depletion on the two-lever discrimination task
Shaping During the initial shaping phase, the animals learned to press a lever for a food reward. No differences between any of the groups were observed on number of trails needed to reach criterion (i.e. >90% of pellets obtained; F(3,28) = 1.597, p = 0.215). Shaping took maximally nine sessions spread out over 5 days. Performance, expressed as a percentage of obtained rewards, exceeded 90% before initiation of the next test phase.
Two-lever discrimination Shaping on the operant task was followed by a two-lever discrimination. A main effect of block, showing that learning took place, was found over all blocks of the morning session (F(7,168) = 17.019, p = 0.000) after which performance levelled off. Initially, the overall performance of the sham controls fell behind that of lesioned animals (interaction effect), which made fewer errors in the second set of the morning session (F(1,24) = 5.599, p = 0.026; see Fig. 2). An independent-samples t test over this set showed blocks 2 and 3 to differ between lesion and control groups, respectively (t = −2.509; p = 0.025 and t = −2.534; p = 0.024). No difference in the number of omissions was observed (F(1,24) = 1.045, p = 0.316). Group differences had disappeared by the second session.
Fig. 2Acquisition of the two-lever discrimination. Depicted is the percentage of correct responses for both lesion and control groups on the first acquisition day. White and grey bars indicate sets of four blocks (32 trials). Asterisk indicates group differences, p < 0.05
Overall performance over the second set of the final acquisition session showed that all groups (four) had obtained at least 90% of the rewards and did not differ significantly in performance (F(1,23) = 0.980, p = 0.334; see Fig. 3a).
Fig. 3Performance on the final acquisition phase and initial reversal. a Performance on the final acquisition sessions. All groups obtained at least 90% of the rewards over the last set of the afternoon session, and no group differences were found. b Performance over the first reversal sessions. Control animals that switched to preferred pellets fell behind the other groups. White and grey bars indicated sets of four blocks (32 trials). Asterisk indicates group differences, p < 0.05
Spatial reversal After reaching criterion on the two-lever discrimination, the animals were subjected to four serial reversals. As each group (lesion and control) was now divided into a non-preferred- and preferred-pellet group, the number of groups was four from now on. A main effect of block (F(7,133) = 51.715, p = 0.000; not treatment or pellet, p > 0.3) was observed over the morning session of the first reversal (F(1,19) = 51.715, p = 0.000). During the afternoon session, a block effect over both sets taken together indicated that learning continued (F(7,140) = 22.669, p = 0.000). Main effects on performance for both treatment (F(1,23) = 5.245, p = 0.032) and pellet (F(1,23) = 6.158, p = 0.021) as well as a treatment/pellet interaction (F(1,23) = 5.701, p = 0.026) were observed over the first set. A one-way ANOVA with SNK post-hoc test revealed that sham animals receiving preferred pellets obtained fewer pellets than any other group in blocks 1 (F(3,27) = 4.163, p = 0.017), 2 (F(3,27) = 5.176, p = 0.007) and 4 (F(3,27) = 5.520, p = 0.012; see Fig. 3b). No differences for treatment, pellet or interaction were found over the second set of the afternoon session (p > 0.3). A covariate analysis revealed that the effects found during the reversal could not be attributed to a pre-existing performance difference between groups.
To examine if the group differences could be attributed to either an increase in errors or an increase in omissions, further analyses were performed. The data indicated that the control animals that were switched to preferred pellets exhibited a decrease in accurate lever pressing, i.e. increased errors (F(1,24) = 4.638, p = 0.042; see Fig. 4), as opposed to increased omissions (F(1,24) = 1.606, p = 0.213), in this group in the first set of the afternoon session. After the initial reversal, all groups performed in similar fashion in the three subsequent reversals.
Fig. 4Omissions and response accuracy in the first reversal of the two-lever spatial discrimination task. The graph shows the percentage omissions (top) and percentage correct responses (bottom) during the initial reversal. Decreased lever press accuracy (increased errors) of control animals that receive preferred pellets is seen during the first set of the afternoon session. White and grey bars indicated sets of four blocks (32 trials). Post-hoc testing revealed the second block of trials in this set to be statistically different from all other groups. No differences were observed for the percentage of omissions
Extinction After four consecutive reversals, the animals were subjected to an extinction phase during which lever pressing was no longer rewarded. When comparing the total amount of lever presses corrected for baseline lever pressing, all groups showed a steady decrease over both sets of each of the four sessions (main effect of block: F(7,175) between 29.237 and 10.048; p = 0.000; see Fig. 5). There were, however, no main effects of treatment or pellet or any interaction effect between the groups (p > 0.2). Over the course of 4 days, lever pressing was never totally extinguished. Analysis of the uncorrected extinction data yielded identical results.
Fig. 5Lever responding during extinction of the two-lever spatial discrimination task. The cumulative number of lever presses per group during the extinction phase of the two-lever discrimination task, when lever pressing is no longer rewarded is shown. Although a decrease is seen over each individual session, lever pressing is never totally abolished. White and grey bars indicated sets of four blocks (32 trials). No significant group differences were found
Effects of mPFC 5-HT depletion on the odour-based go/no-go task
Shaping During the initial shaping phase, the animals learned to sample an odour and press a lever for a reward. Both groups acquired the responses (i.e. >90% of pellets obtained) in maximally eight sessions, spread out over 6 days, without showing any treatment effect (F(3,28) = 0.667, p = 0.590).
Two-odour discrimination After shaping, the animals were trained, over six sessions, to respond to one odour with a lever press, while inhibiting a lever press after sampling a second, different, odour. Results show an overall increase in performance over these sessions, i.e. a main effect of block (F(5,140) = 16.074, p = 0.00) for all groups (data not shown). No effect of treatment was found (F(1,28) = 1.375, p = 0.251). Response preference for either the ‘go’ or ‘no-go’ response were not observed.
Odour reversal After the acquisition of the odour discrimination, the stimulus–response contingencies were reversed, and the animals had to switch their response to the odours. In this treatment, the two groups were again divided into sub-groups for preferred and non-preferred pellets. In contrast to the quickly acquired spatial reversal, the odour reversal took 12 sessions to acquire (reaching ‘pre-reversal’ performance). Figure 6 shows the performance of all groups.
Fig. 6Performance on the final acquisition phase and reversal of the two-odour go/no-go discrimination task. a Performance over the four final acquisition sessions. All groups obtained at least 90% of the rewards over the final four acquisition sessions, and no group differences were found. b Performance over the reversal sessions. Control animals that were rewarded non-preferred pellets fell behind the other groups. Asterisk indicates group differences, p < 0.05
Over 6 consecutive test days (12 sessions), main effects of both session (F(1,10) = 40.163, p = 0.000) and treatment (F(1,10) = 9.025, p = 0.013) as well as a treatment/pellet interaction were observed (F(1,10) = 5.764, p = 0.037). Further analysis (one-way ANOVA and SNK post-hoc test) revealed that in sessions 7 (F(3,13) = 6.851, p = 0.009), 8 (F(3,13) = 3.898, p = 0.044) and 9 (F(3,13) = 5.563, p = 0.017), control animals receiving non-preferred pellets obtained fewer rewards than all other groups.
Further analysis of the data showed that unlike the initial reversal task, none of the groups differed in response accuracy (F(1,10) = 0.158, p = 0.699). However, a treatment/pellet interaction (F(1,10) = 5.898, p = 0.036) was observed for the average number of omissions (see Fig. 7). Post-hoc testing showed this measure in sessions 8 and 10 to differ significantly between sham animals on non-preferred rewards and all other groups, respectively (F(3,13) = 4.647, p = 0.028 and F(3,13) = 4.194, p = 0.037).
Fig. 7Omissions and response accuracy during reversal learning of the two-odour go/no-go discrimination task. Mean percentage of omissions (top) and percentage correct responses (bottom) per group during the ‘odour-reversal’ are shown. An increase in the number of omissions is seen for control animals that received non-preferred pellets on sessions 8 and 10. No differences were observed for the percentage of correct responses
Discussion
With the current experiment, we found support for our hypothesis that medial prefrontal 5-HT is selectively involved in cognitive flexibility when affect guides decision making. Local depletion of mPFC 5-HT prevented the reversal learning impairments that were induced by a change in the reward value during operant behaviour. This effect was observed both in spatial two-lever discrimination reversal learning and during the reversal of the odour-based go/no-go task. In the former, control animals that were switched to preferred pellets showed increased erroneous lever pressing compared to all other groups, whereas in the latter, non-switched control animals showed increased omissions. Apparently, the lesion rendered the animals less sensitive to changed reward value. Similar effects, however, were not observed during extinction learning, when rewards were no longer available.
Taken together, these data suggest that depletion of mPFC 5-HT impairs goal-directed behaviour by rendering rats less capable of responding to a change in reward value (incentive learning; Balleine and Dickinson 1998; Balleine 2005; Niv et al. 2006), without disturbing their ability to detect the presence or absence of a reinforcer and to utilise this information to adapt behaviour accordingly.
The data moreover indicate that this effect occurs independent of the stimulus modality of the behavioural tasks, thereby opposing our hypothesis that reversal deficits induced by mPFC 5-HT lesioning would be limited to spatial tasks, as opposed to odour-guided ones.
In the current experiment, we lesioned 5-HT terminals in the mPFC by means of a local microinjection of the toxin 5,7-DHT. HPLC analysis showed that this procedure was successful as a marked loss of serotonergic innervation (70–90%), compared to sham controls, which was observed in the target area. This reduction was observed without spread to neighbouring areas whilst sparing both DA and NA innervation.
Two behavioural paradigms were employed to study cognitive flexibility and affective processing. First, we used a task previously developed in our lab with which we showed mPFC involvement in spatial reversal learning (De Bruin et al. 2000). A second, symmetrically reinforced go/no-go odour reversal task was newly designed for the current experiments. These tasks were chosen to give insight into reversal learning across different stimulus modalities and to allow the study of different aspects of reversal learning, such as inhibitory processes involving both response extinction (spatial discrimination task) and impulse inhibition (odour-based, go/no-go task). Data showed that whilst the stimulus discriminations for both tasks were relatively quickly acquired, reversal learning took significantly longer for the odour stimuli. Whereas the ‘spatial’ lever press reversal was acquired within a day, the ‘odour’ go/no-go reversal took up to 12 days. These differences are in line with existing literature on the acquisition of the spatial lever-press task (De Bruin et al. 2000; Van der Meulen et al. 2003) and a comparable odour-based go/no-go task (Schoenbaum et al. 2006).
Our hypothesis that medial prefrontal 5-HT depletion would have a stronger impact on reversal learning in the ‘spatial’ task than in the ‘odour’ task could not be proven.
When comparing the performance of lesioned animals to that of control animals, we were unable to find any indication for impaired reversal learning on either task. Not only did 5-HT lesioned animals make fewer errors during the initial acquisition, these animals showed to be fully capable of acquiring both types of reversals, demonstrating intact cognitive flexibility across stimulus modalities when the reward identity remains constant. These findings suggest that in rats, the acquisition of a spatial reversal task, which depends on the mPFC, does not depend on 5-HT innervation of that area. This contrasts the finding that in primates, the orbital PFC involvement in visual reversal learning does depend on 5-HT.
To test our hypothesis that mPFC 5-HT is involved in affective processing, we used a pellet switch to induce a change in affective value of the reinforcer during both reversal tasks. The rationale for this was that the impact of the serotonergic lesion in the mPFC on cognitive flexibility would be greater in a situation where the affective value of the reward can be used to guide decision making. Whereas in primate literature, the term ‘affective shifts’ has been used for a switch in the value associated with the stimulus (Dias et al. 1996), we wanted to introduce a switch in affective value of the reinforcer. Similar to the experiments of Tremblay and Schultz (2000), we changed the relative reward value from non-preferred, although still salient, to preferred (see also Watanabe 1996), at the time of the reversal.
To determine food preference, food-deprived rats were pre-tested on three types of food reward in a T-maze, where all rats chose Noyes ™ pellet P over AI. These were subsequently implemented in the task as ‘preferred’ and ‘non-preferred’ rewards, respectively, to examine the effect of this affective shift in both groups during reversal learning. To exclude a ‘novelty’ effect, the animals were acquainted with both types of pellets before testing. As we observed pellet switch-induced performance effects in control rats during both tasks, we conclude that the pellet rewards were sufficiently different. The possibility that the lesion induced an indifference to either reward does not seem likely. If lesioned animals were in fact indifferent to reward outcome, they would have performed as non-switched control animals throughout the experiment, but this was not the case (see below).
During initial ‘spatial’ reversal learning, lesioned animals behaved as non-switched controls, showing a quick reversal, without performance deficits on reversal learning per se. We did, however, observe that switching intact animals to preferred rewards resulted in reduced performance on the reversal task compared to lesioned animals. These results suggest that in intact animals behavioural adaptation is at least partly guided by reward value and, in this particular case, further complicates acquisition of the reversal.
A second instance of such a pellet-switch-induced performance deficit was observed during the ‘odour’ reversal. Whereas the initial reversal impairment in switched control animals was due to increased errors, during the ‘odour’ reversal, non-switched control animals fell behind in performance because of increased omissions.
As the pellet switch did not affect performance of lesioned animals at any time, we pose that an inability to use affective information (i.e. pellet value) in lesioned animals underlies the observed group differences. These data suggest that initial behavioural adaptation guided by altered reward outcome is dependent on medial prefrontal 5-HT.
In the following section, we will first discuss why, in our opinion, a number of possible mechanisms cannot explain the observed effects induced by the pellet switch.
We will then propose how our observations fit and extend current literature.
Among the possible causes for these effects, altered associative learning is not a likely one. Although lesioned animals did make fewer errors during the acquisition of the spatial discrimination task, an effect that has also been observed by others (e.g. Ward et al. 1999), and suggests possible improved stimulus–outcome learning, performance of neither lesioned group (switch or non-switched) exceeded performance of the control animals during the subsequent testing. Moreover, the performance impairments observed in the control group occurred only during the early stages of reversal learning, leaving the acquisition of subsequent (spatial) reversals or stimulus–outcome learning unaffected (see also Harrison et al. 1999).
Despite reports on involvement of both the mPFC (Salazar et al. 2004, Winstanley et al. 2006) and 5-HT (Beninger and Phillips 1979; Soubrié 1986) in impulsive responding and extinction processes, our tests indicate that reduction in mPFC 5-HT does not lead to decreased extinction learning or increased impulsivity. No effects of the lesion were observed during extinction of lever pressing or increased lever pressing during the go/no-go inhibition task. Although these results do not seem to fit the current literature on 5-HT in impulsivity, recent findings by Chamberlain et al. (2006) in human subjects show that impulse inhibition might be mediated via the NA system, rather than the classically implicated 5-HT. Therefore, the possibility that reversal learning improved in lesioned animals through altered inhibition or extinction processes is unlikely. Furthermore, subsequent analysis showed no difference between groups on general motor activity, as assessed by the average number of lever presses and nose pokes into the food dispenser.
The possibility that a motivational effect of 5-HT depletion underlies the observed deficits does not seem likely either. Although the control animals showed an increase in the number of omissions towards the end of the go/no-go task, an effect suggesting decreased motivation towards the end of the experiment for the non-preferred reward, none of the groups (lesion or sham) showed any sign of increased or decreased lever pressing during the preceding test phases, suggesting that motivational processes were intact in lesioned animals. These findings are supported by other reports where motivational changes after general 5-HT depletion in humans (Evers et al. 2005), primates (Clarke et al. 2004) and rodents (Harrison et al. 1999) were not observed either.
Earlier work by others (e.g. Roberts et al. 1994) suggests that (global) 5-HT depletion may induce increased reinforcing efficacy for both psychomotor stimulants and food rewards (but also see Tran-Nguyen et al. 2001). The evidence for such an effect is, however, inconclusive, and the observed changes can also be ascribed to increased impulsivity and insensitivity to punishment (e.g. Harrison et al. 1997). The current data, moreover, does not support these findings, as lesioned animals showed ‘normal’ extinction learning, whereas decreased extinction learning would be expected in case of increased reinforcing efficacy.
From these findings, we conclude that the behavioural effects are not due to a generalized decrement of overall functioning but are rather specific to an inability to effectively incorporate reward value. These results suggest that mPFC 5-HT depletion induce a specific deficit in goal-directed behaviour that is limited to the ability to use current reward value to guide behaviour (Balleine and Dickinson 1998; Niv et al. 2006).
Human literature provides a framework in which we think the current findings can be placed. Miller (2000) has described the PFC as a control centre over overt behaviour that regulates ‘active maintenance of patterns of activity that represent goals and the means to achieve them.’ Later work by Miller and Cohen (2001) and Ridderinkhof et al. (2004) extends this model by incorporating different cortical sub-areas and describes how these areas connect to combine (reward-related) information and lead to goal-directed behaviour. In this model, the orbital PFC can be seen as the area where reward-related information is encoded (as discussed in “Introduction”) and the mPFC functions as an area that regulates or signals the orbital PFC to implement performance adjustments (based on reward information).
A unified model for the rodent PFC, similar to that of Miller for the human PFC, has not been proposed, but the available experimental data obtained in rodent areas that show functional overlap with the human mPFC (Uylings et al. 2003) parallel and extend these findings. Balleine and Dickinson (1998), for example, showed that lesions of the prelimbic area (part of the mPFC) render the animals insensitive to variations in the contingency between a response and a specific reward. Moreover, the same authors (De Wit et al. 2006) showed that inactivation of the mPFC leads to impaired resolving of response conflict through loss of inhibitory function, without any apparent impairment in discriminative function for foods or loss of reinforcing function of food pellets and sugar. Further indications for a parallel between the human and rodent mPFC comes from Cardinal et al. (2001), who showed that lesions of the mPFC yielded what Dalley et al. (2004) later described as a flattening of the shift from large-delayed rewards to small-immediate rewards. Although the authors ascribed this to a ‘timing impairment,’ these findings fit the described, human, model of mPFC-mediated performance monitoring and adjustment of behaviour. Finally, Hok et al. (2005) reported on neurons recorded in the mPFC that encode the motivational salience of places. These results add to the experimental evidence presented earlier of orbital PFC-mediated reward encoding by showing that reward-related information is also processed in the mPFC. Together, the presented rodent data support a role for the mPFC in the monitoring, processing and implementation of reward-related information, as suggested for the human mPFC.
Experiments concerning the underlying neurochemical mechanisms involved in the processing of reward-related stimuli have mainly focused on DA. Direct evidence for DA involvement comes from measurements of DA ventral tegmental area neurons in non-human primates that show reactivity to reward (Schultz 1998), probability of reward (Fiorillo et al. 2003) and adaptive neural firing in response to reward-predicting stimuli (Tobler et al. 2005). These findings are further supported by a recent study that shows midbrain activation in humans during reward anticipation (Wittmann et al. 2005).
The presented data, however, show that cognitive flexibility, when affect guides decision making, might be dependent on intact serotonergic projections. Although we cannot rule out possible compensatory mechanisms, our analysis shows that DA innervation in the lesioned animals remained intact. Support for our findings can be found in human studies where global depletion of 5-HT through tryptophan depletion caused altered processing of reward cues, lesading to unfavourable choice behaviour in a gambling task (Rogers et al. 2003).
The current experiments support previous notions on the mPFC as a performance monitoring structure, responsible for the implementation of adjustments in cognitive control. Moreover, they extend the current model by implicating 5-HT, in addition to DA, in cognitive control.
With regards to the initial research questions whether cognitive flexibility is dependent on mPFC 5-HT, the present data suggest that in both the spatial and the odour tasks, the lesioned animals are fully capable of acquiring a reversal, show appropriate extinction learning in the absence of a reinforcer and exert normal inhibitory control. The current observations, however, also suggest that when the value of a reinforcer changes but not its presence or absence, mPFC 5-HT is necessary to adapt the behaviour accordingly.

cognitive flexibility
reversal learning
affective processing
5,7-dht
medial prefrontal cortex


AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data

AntiJen is a database system focused on the integration of kinetic, thermodynamic, functional, and cellular data within the context of immunology and vaccinology. Compared to its progenitor JenPep, the interface has been completely rewritten and redesigned and now offers a wider variety of search methods, including a nucleotide and a peptide BLAST search. In terms of data archived, AntiJen has a richer and more complete breadth, depth, and scope, and this has seen the database increase to over 31,000 entries. AntiJen provides the most complete and up-to-date dataset of its kind. While AntiJen v2.0 retains a focus on both T cell and B cell epitopes, its greatest novelty is the archiving of continuous quantitative data on a variety of immunological molecular interactions. This includes thermodynamic and kinetic measures of peptide binding to TAP and the Major Histocompatibility Complex (MHC), peptide-MHC complexes binding to T cell receptors, antibodies binding to protein antigens and general immunological protein-protein interactions. The database also contains quantitative specificity data from position-specific peptide libraries and biophysical data, in the form of diffusion co-efficients and cell surface copy numbers, on MHCs and other immunological molecules. The uses of AntiJen include the design of vaccines and diagnostics, such as tetramers, and other laboratory reagents, as well as helping parameterize the bioinformatic or mathematical in silico modeling of the immune system. The database is accessible from the URL: .
Introduction
There is a vast, and ever increasing, volume of important information that has accumulated from decades of experimental analysis within immunology. This will only become compounded as high-throughput techniques begin to impinge upon the immunological biosciences. The only efficient way for this information to be properly utilized requires the development of databases that store it and systems that use it. Although the type of data archived may alter from case to case, nonetheless the creation, use, and manipulation of databases containing biologically important information is the most crucial feature of current bioinformatics, both as it supports the genomic and post-genomic revolutions and as a discipline in its own right. There is nothing new in developing databases focusing on immunology: many spotlighting the in-depth sequence analysis of individual immunomacromolecules have existed for some time [1]. Functional or epitope-orientated databases are a more recent development. Examples include the now defunct MHCPEP database [2], FIMM [3], SYFPEITHI [4], the HIV sequence database [5], the HLA ligand database [6], the EPIMHC database [7], and the MHCBN database [8].
An epitope is any molecular structure that can be recognised by the immune, or other biological, system. Epitopes, or the antigen from which they are derived, can be composed of protein, carbohydrate, lipid, nucleotide, or a combination thereof. It is through recognition of foreign, or non-self, epitopes that the immune system can identify and, hopefully, destroy pathogens. Hitherto, peptide epitopes have been the best studied, and have, traditionally, have been categorized as either T cell or B cell epitopes. T cell epitopes are peptides presented to the cellular arm of the immune system via the MHC-peptide-TCR complex. B cell epitopes represent surface regions of an antigen that are bound by soluble or membrane-bound antibodies. If this region of a protein antigen is comprised of residues distally separated within the primary structure, and brought into local proximity by protein folding, then it is termed a discontinuous or conformational B cell epitope. Linear or continuous B cell epitope residues are sequential in both primary structure and thus as a region on the proteins' surface. Such epitopes are predominantly identified by antigen-specific antibody cross-reactivity with peptides.
There is a need to create a databank for the wider disciplines of immuno-vaccinologists, which can act as a central repository and resource. Our aim is to complement other databanks [2-8] and thus we have developed AntiJen, a computational information resource for immunology and vaccinology that integrates quantitative kinetic, thermodynamic and biophysical data, with functional and cellular information. AntiJen v2.0, a development of our earlier database system JenPep [9,10], contains functional data on T cell and B cell epitopes. Moreover, the B cell archive is now sub-divided into linear and conformational epitopes. These epitopes form the basis of the humoral immune response and, unlike T cell epitopes, methods of prediction are often inaccurate [11]. A more in-depth B cell epitope archive should aid the development of prediction strategies. Antigen recognition by the Major Histocompatibility Complex (MHC) is vital to T cell activation hence, the inclusion of thermodynamic data on the binding of peptides to MHC molecules and T Cell Receptor (TCR) binding to peptide-MHC (pMHC) complexes. This data is complemented by kinetic data based on the same molecular interactions. Data on antigen processing and presentation is also included in AntiJen. Binding data derived from peptide interactions with the Transporter Associated with Antigen Processing (TAP transporter) are included in the archive. Additionally, quantitative specificity data from position-specific peptide libraries is included. AntiJen also incorporates thermodynamic data on protein-protein interactions, within an immunological context, such as co-receptor and superantigen binding, plus interactions with the MHC. All of these interactions are, potentially, key factors for the successful computational design of vaccines.
AntiJen also contains biophysical data, including diffusion coefficients and cell surface copy numbers, on a variety of immunological molecules. Such data provides insight into the number of target receptors, which is an important, if under explored, component of binding between cells. Indeed, the number of molecules expressed on the membrane can alter depending on disease. The final addition to the databank focuses upon antigen binding to antibodies. One key innovation is a greatly increased compendium of experimental conditions, which, in conjunction with a greatly enhanced search capacity, consolidates our databases as a unique, value-added data source, fostering developments within both in silico immunology and the wider community of immunovaccinology. The database is available from the URL: .
Database development
Relative to the database system used for JenPep [9,10], the interface to AntiJen is entirely new, having been completely rewritten. AntiJen has been designed and implemented using postgreSQL, a system comprising a relational database and database server, and has thus established increased database robustness, creating an improved infrastructure for foreseeable issues of data storage and data growth. Data within AntiJen is structured into twenty-four normalised tables. Each is category specific and holds either statistical or experimental data. Additional tables accommodate the keyword data – which powers our protein-orientated antigen search and allows integration of the BLAST search – and there is also a structural data table to accommodate links to external structural databases. The user interface consists of a series of HTML forms. The search requests from these forms target PERL scripts integrated with SQL which in turn query the database.
Database content
Compared to its progenitor JenPep [9,10], the data archived in AntiJen v2.0 has grown considerably in depth (additional data types such as experimental conditions), breadth (addition of new data to existing databases), and scope (addition of extra sub-databases containing novel kinds of information). Additions to AntiJen have been derived from exhaustive searching of the primary literature, to give a dataset of > 31,000 entries. AntiJen v2.0 now consists of 11 sub-databases; details of the different databases are given in Table 1. The relative sizes of the databases and the growth from JenPep are summarised in Table 2.
Table 1
AntiJen sub-databases and content.
DATABASE
CONTENT
T Cell Epitopes
Contains T Cell epitope peptides (known binders).
B Cell Epitopes
Contains B Cell linear and conformational epitope peptides.
MHC-Peptide
Binding data relating to antigenic peptides and MHC interactions.
TCR
Binding data relating to antigenic peptides – TCR – MHC interactions.
TAP
Binding data relating to antigenic peptides and TAP interactions.
Kinetics
Kinetic binding data for MHC peptide interactions.
IPPI
Binding data for a collection of immunological protein interactions.
Diffusion Coefficient
Collection of Diffusion and Friction coefficients for surface peptides.
Copy Number
Number/Abundance of cell surface molecules.
Peptide Libraries
Relative binding data for antigenic peptide amino acid substitutions.
Antibody-Peptide
A variety of antibodies known to bind proteins.
Table 2
Size of AntiJen relative to JenPep. The number of peptides for each category in the AntiJen database is given, distinguishing between class I and class II categories, where appropriate. Growth versus JenPep 1 and 2, the progenitors of AntiJen, is included. For certain data categories, most obviously TAP binding data, re-evaluation of the quality of data within JenPep has seen it decrease rather than increase, however the expansion of the data is clearly seen.
DATABASE
JenPep v1.0
JenPep v2.0
AntiJen v1.0
AntiJen v2.0
Class 1
Class 2
Total
Class 1
Class 2
Total
Class 1
Class 2
TOTAL
Class 1
Class 2
TOTAL
T cell epitotes
1266
795
2061
2060
1158
3218
2247
1578
3825
2402
1585
4158
MHC peptide binding
3196
2652
5848
6411
5925
12336
6853
7772
14625
7304
8114
15454
TAP peptide binding
432
441
408
1106
B cell epitotes
816
1295
3541
TCR – peptide-MHC
49
375
124
594
527
253
782
MHC peptide kinetics
704
243
947
897
294
1150
IPPI
805
2675
Copy Number
161
243
414
Diffusion coefficients
759
Peptide Libraries
897
Antibody
395
AntiJen contains both generic and dataset-specific data. For each entry, we record the peptide sequence (eg. YTSDYFISY) of the epitope using the standard one-letter code, its length (9 in this case), and, by linking to the sequence database Swiss-Prot  or NCBI , the antigen to which the peptide sequence most closely matches (in the case of YTSDYFISY: C-ests-1 (p54), SWISS-PROT code P41156). The description of the antigen is, wherever possible, obtained directly from the literature. AntiJen is also linked to PUBMED. This allows us to record the original citation associated with the data. For example, for YTSDYFISY, we cite: Journal of Immunol 1994 volume 152 pages 3913–3924, PUBMED ID 8144960. For the T cell epitope, MHC ligand, and TCR-pMHC complex categories, we also record, for each peptide, the MHC restriction in terms of the host species, class (class I vs. class II), and, where the data is available, the serotype and allele. For YTSDYFISY, these data would be human, class I, HLA-A1, and A*0101.
Entries within AntiJen are, in turn, linked to external databases, which enables further in-depth cross referencing. As we have said, protein sequence identifiers, which may be the source of an antigenic peptide or immunological co-receptor, link directly to details in the Swiss-Prot database [12] or the NCBI protein database. Journal references can be viewed via a link to the PUBMED database , and thus to full literature references, where available. AntiJen also links to structural data, currently derived from the MPID database [13] and the Protein Data Bank [14]. The database aims to provide access to background data where available.
Allele identifiers serve as a link to the IMGT/HLA database  at the European Bioinformatics Institute [15]. Inherent variability in the way MHC alleles are named within the primary literature  prevents us from unambiguously standardizing nomenclature within AntiJen. HLA nomenclature follows that of the HLA Informatics Group . An allele is named using a defined pattern. For example, for HLA-A*0101: the HLA-A refers to the HLA locus; the first 01 to the serologically recognized A1 antigen and the final 01 to the individual HLA allele protein sequence. AntiJen stores the antigen classification (i.e. HLA-A1) and, when available, the specific allele. We have often encountered problems with the nonstandard allele reporting. A 4-digit HLA name necessarily implies the two digit serological antigen, a two digit classification clearly does not imply a specific allele.
During database compilation, a sequence search allows us to identify the protein from which an epitope sequence originates. However, because epitopes are generally short, their sequences may be present in several potential antigens: in orthologues, paralogues, or in totally unrelated sequences. As epitopes are processed from whole proteins via a complex proteolytic pathway, one can use the sequence context to infer preferred proteasomal or endoplasmic protease cleavage patterns, but not if its context is defined incorrectly. Moreover, assuming that AntiJen is used subsequently to assign the antigenic status of proteins, wrongly identifying particular proteins as antigens can lead to the percolation of annotation errors [16,17].
AntiJen is, where possible, a quantitative database archiving continuous measures of binding. This is a fundamental feature of several sub databases, such as the MHC ligand and pMHC-TCR databases. The binding of an immunological macromolecule to a peptide or other biomacromolecule is quantified as are other receptor-ligand interactions:
R+L↔RL
Here R is the receptor (an MHC or TCR), L the ligand (peptide or pMHC), and RL, the receptor-ligand complex (pMHC or pMHC-TCR complex). The rate of the forward reaction is proportional to [L] [R]. The rate of the reverse reaction is proportional to [RL] as no other species are involved in dissociation. At equilibrium, the forward and reverse rates are equal, and so using kon and koff as the respective constants:
kon[R][L] = koff[RL]
Rearranging:
Here KD is the equilibrium dissociation constant, which represents the concentration of ligand that occupies 50% of the equilibrium receptor population, and KA is the equivalent association constant.
Experimentally, the measurement of equilibrium dissociation constants is often addressed using radio-ligand binding assays. Saturation radio-ligand binding assays measure equilibrium binding, at a range of peptide concentrations, to establish affinity (Ka) and receptor number (Bmax). Competitive binding experiments determine binding at a single labelled ligand concentration in the presence of a range of concentrations of unlabelled ligand. AntiJen records a hierarchy of different binding measures in its different sub-databases. Equilibrium constants are the most dependable and sit atop this hierarchy. Next come IC50 values, which can be obtained from a competitive radio-ligand or fluorescence assay. These are the Fmost commonly reported binding measures.
Values obtained from radio-ligand or fluorescence methods may be significantly different. IC50 values for a peptide may vary between experiments depending on the intrinsic affinity and concentration of the standard radiolabelled reference peptide, as well as the intrinsic affinity of the test peptide. IC50 values vary with the equilibrium dissociation constant, at least within a single experiment. In practice, the variation in IC50 is often small enough that values can be compared between experiments. For the peptide discussed above, YTSDYFISY, the radiolabelled IC50 value recorded in AntiJen is 5.3 nM. BL50 values are also obtained from a peptide binding assay and are commonly reported. They are the half maximal binding levels calculated from mean fluorescence intensities of peptides binding to MHCs bound on the surface of RMA-S or T2 cells. Cells, pre-incubated with peptides, are labelled with a fluorescent monoclonal antibody. An overview of the thermodynamic and kinetic binding data within AntiJen is given in Table 3.
Table 3
AntiJen Thermodynamic and Kinetic Data. An overview of the 6 AntiJen databases that provide binding data. It must also be noted that several of the databases contain additional data not present in any of the other databanks.
MHC-Peptide
Kinetics
IPPI
TAP
pMHC-TCR
Antibody
TOTAL
IC50
8562
0
247
1000
0
4
9813
Kon
0
188
563
0
157
87
995
Koff
0
146
610
0
150
101
1007
KD
359
156
1143
16
227
70
1971
Ka
65
0
37
0
28
132
262
t1/2
0
207
72
0
148
0
427
AntiJen also now contains experimental conditions, such as temperature and pH. A summary of this data is given in Table 4. The accuracy of data depends greatly upon the experimental method used. The grouping of data with respect to specific experimental techniques allows a more thorough assessment of training sets. Figure 1 shows the distribution of data for each type of analysis with respect to each database. The MHC Kinetics and TAP databases highlight the problems outlined above. The kinetics database contains data determined from over 14 methods while the TAP database is derived from 4 methods, with radiolabelled assays accounting for 80% of the data.
Table 4
Experimental conditions and associated information archived in AntiJen. Number of recorded experimental conditions stored within the AntiJen database. For each condition (temperature, pH, etc.) we show here the number of entries within a particular sub-database. [Standard] is the concentration of labelled standard peptide in an assay. Likewise, [competitor] is the concentration of competitor peptide within a competition assay. [peptide] is the concentration of peptide in a kinetic experiment. The Method category refers to a standard procedure used to perform a particular assay. Differences in the number of recorded data, relative to figures in Table 1, arise primarily from the omission of key details from particular papers. Archiving of experimental conditions is on-going.
DATABASE
TOTAL
pH
Temperature
[standard]
Stand. peptide seq.
[competitor]
Method
[peptide]
MHC Binding
15454
6679
9831
10893
12796
5007
1251
MHC Kinetics
1150
677
1101
1149
606
TAP Binding
1106
22
243
1092
1101
86
981
TCR-pMHC
782
426
632
668
IPPI
a
2675
726
1371
2600
Copy Number
414
183
278
414
Peptide Libraries
897
897
897
Diffusion Coefficient
759
321
668
736
Antibody
395
119
115
372
Figure 1
The distribution of experimental methods applied within each database. The number of different experimental methods and the abundance of data relating to the method is shown within the figures. The 'OTHERS' category refers to methods for which there is a relatively small number of entries.
The compilation process has highlighted the considerable inconsistency within the immunological literature regarding the recording of such fundamental data. AntiJen contains, however, a direct, verbatim transcription of data from the primary literature. As such, we do not attempt, as a matter of policy, the comprehensive and retrospective correction of potential errors. To undertake such correction would only compound any errors, introducing the kind of percolating inconsistencies so much a feature of other database systems [16,17]. Further inaccuracies may stem from our logistic inability to verify data independently, therefore we must trust those values reported in the literature.
Subsidiary Databases in AntiJen
The AntiJen database contains a number of sub-databases. Each of these contains data on different aspects of the biological function and/or biophysical properties of different classes of immunomacromolecule. We describe the nature and content of each sub database below.
B Cell Epitopes
Epitopes are the principal chemical moieties recognized by the immune system. Although the importance of non-peptide epitopes, such as carbohydrates and lipids, is now increasingly well understood, peptidic B cell and T cell epitopes remain the principal tools by which the intricacy of the immune response can be explored. B cell epitopes are regions of the surface of a protein, or other biomacromolecule, recognized by soluble or membrane-bound Antibody molecules. In developing AntiJen, we have discarded the contents of our previous B cell archive and constructed one de novo. It contains an entirely new data set with a substantially different data structure. There are two forms of epitopes: linear and discontinuous. A linear B cell epitope is composed of a single stretch of sequential residues. A discontinuous B cell epitope is composed of sequentially separate residues brought into close proximity in a conformationally-dependent arrangement. The data we archive is primarily focused upon linear epitopes. This is due to the far greater amount of experimental data available for linear epitopes, which reflects both the relatively facile experiments needed to identify them and an implicit belief in their utility as potential vaccine epitopes. By contrast, discontinuous epitopes are thought to be more prevalent within folded proteins, but are far more challenging to determine experimentally. The archive catalogues the sequence of binding peptides, and also gives the length and source: TTGDVIASS, a 9 amino acid peptide from Escherichia coli non-fimbrial adhesion. Residues identified as important in binding to the antibody are recorded. This may correspond to a whole peptide or a subsequence, such as TTGDVI in the above example. The peptides are also categorized in terms of their relative observed immunodominance. Antibodies host organism and isotype are recorded. The current B cell epitope archive contains 3,541 epitopes.
T Cell Epitopes
T cell epitopes are short peptides bound by major histocompatibility complexes (MHC) and subsequently recognized by T cells. Epitopes recognized by both CD4+ and CD8+ T cells are included in the database. Such epitopes can be identified in many different ways. However, this diversity of measurement imposes a certain need for consistency, necessitating the requirement for recording a range of different experimental methods. The archive has expanded to include 4,158 entries. The entries contain the epitopes, ranging in length from 4 to 38 amino acids, peptide information, detailing the source, with links to Swiss-Prot and the corresponding MHC restriction data such as Serotype, Allele and Class. Additionally, the peptides are categorized in to groups such as Allergens, Bacterial, Cancer, Human, Viral and Self peptides.
MHC – Peptide binding
AntiJen continues to archive quantitative data on the thermodynamics of peptide interactions [18,19], and it has expanded in number and content, with additions such as experimental conditions, plus specific Standard and Competitor peptide concentrations used in the assays. The current archive contains 15,454 entries. The sequence of the binding peptide, along with the source, plus relevant MHC restriction data is recorded. The restriction alleles currently include those from Human, Mouse, Rat, Rhesus Monkey, Cotton-top Tamarin, and Chimpanzee. AntiJen contains IC50 values, binding affinity measurements from competitive binding assays, for which the standard and competitor peptides and concentrations are recorded, plus BL50 values, calculated from peptide stabilizing assays. Where possible, antibodies and the concentrations used to calculate BL50 values are archived. Additionally, but on a somewhat smaller scale, equilibrium association (KA) and dissociation (KD) constants are recorded for peptide-MHC interaction. Melting temperatures (Tm) and signal wavelength are also recorded; this is the temperature and wavelength at which 50% of the MHC protein is denatured as measured by circular dichroism. AntiJen also records so-called Weak/Non-binders. This indicates that the peptide has been tested in an MHC restriction assay and has been found to exhibit a binding affinity, i.e. an IC50 value > 10,000 nM for a radio-ligand assay, so low that it can be categorized as inactive.
pMHC-T Cell Receptor interaction
The TCR sub-database contains 782 entries, which records thermodynamic and kinetic binding data for the interaction of peptide-MHC (pMHC) complexes with TCRs. Different MHCs exhibit a distinct selectivity for certain peptide sequences. T cell receptors, in their turn, also exhibit different affinities for peptide-MHC complexes. The entries contain epitope, peptide source and MHC restriction data, as described above, plus TCR structure information, located at the MMBD database . Furthermore, any mutations are noted and a designated name for the TCR is archived. In addition, the peptides are recorded as either agonists or antagonists. The binding data is given as equilibrium constants (KD), EC50 values, rate of association (Kon), rate of dissociation (Koff), association constant (KA) and the half-life (t1/2) of the TCR-peptide interaction.
TAP Binding
This dataset contains binding data for the interactions between peptides and the TAP transporter, one of the principle steps in antigen presentation. As with the peptide-MHC database, the data is established from competitor binding experiments, based on labelled assays. Therefore, standard and competitor peptide sequences and their concentrations are recorded. The binding data is given as IC50 and KD values. The database currently contains 1,106 entries, with peptides from Human, Rat and Mouse sources. Based on IC50 values > 10,000 nM, the peptides are categorized as weak/non-binders. The entries have increase in number from the level found in JenPep, although several entries were removed in an effort to increase the accuracy and consistency of the archive (Table 2).
Peptide-MHC Kinetics
AntiJen's kinetics sub-database, which contains 1,150 entries, mostly relates to Class I MHC data. It records measurements for forward and reverse rate constants for complexation events. This complements the thermodynamic measurements on peptide-MHC binding described above. The data currently focuses upon both the half-lives of binding interactions, as well as association and dissociation rate constant values (Kon and Koff) for the recorded epitopes. Additionally, concentrations of the peptide, MHC and TAP are archived. The half-life for radioisotope labelled β2-microglobulin dissociation from an MHC class I complex, as measured at 37°C, is also archived. This is a kinetic measurement rather than a thermodynamic one, although it is often assumed that the greater the half-life the stronger the peptide-MHC complex. The half-life (t1/2) equals:
Here the t1/2 corresponds to the dissociation of the MHC-β2 microglobulin complex rather than the kinetics of the protein-ligand interaction, but is still peptide dependent, as well as kinetic in nature.
Immunological Protein-Protein Interactions
The immune system is built around protein interactions therefore we developed another new sub-database which deals with Immunological Protein-Protein Interactions (IPPI). This archive contains 2,675 entries based on a variety of binding data, such as Kon and Koff rates, for a range of macromolecules implicated in physiological or pathological interactions, as well as KD, KA and IC50 values. The molecules include receptors such as CD4 or CD8 molecules, superantigens and other microbial virulence factors, cytokine receptors and cell adhesion molecules. The entries detail both protein partners involved in the binding interaction, with links provided to the NCBI-Entrez database. Additional data for MHC receptors is archived, whereby the reactive epitope is recorded and the co-receptors are categorized into viral, bacterial and self peptides. MHC data outlined in the previous databases is given, where appropriate including any mutations to the MHC.
Antibody – Protein Binding
AntiJen also contains a further sub-database, which comprises thermodynamic data relating to antibody-antigen binding. The dataset contains 395 entries for antigen proteins and antibodies, mostly derived from viral and mammalian sources. Reported values were obtained using radiolabelled assays and BIAcore analysis. This archive should aid in the selection of antibodies and peptides for in vitro studies. The entries list the antibodies and the binding/kinetic data, consisting of KD and KA values and to a lesser extent Kon, Koff and IC50 values.
Peptide Libraries
This archive further complements our MHC binding databases by indicating the relative contribution of residues within peptide libraries to MHC binding. 897 entries contain quantitative specificity data derived from position specific peptide libraries [20]. This catalogues the relative effect on affinity, in the form of IC50, log relative SD50 and log SI values, of all substitutions, at all peptides positions, against a random sequence backdrop. All of the libraries relating to a known peptide binder are designated a name within AntiJen, this usually consists of the author and year of publication. The archive contains the core peptide, along with the mutation position and the substituted amino acid. The corresponding MHC data is given as mentioned above.
Diffusion Co-efficients
To further increase the range of data archived, AntiJen also contains 759 records of cellular biophysical data, in the form of diffusion co-efficients, recorded as cm2s-1, for a diversity of cell surface molecules, including MHCs (Mouse and Human), viral peptides and other receptors [21]. The molecules are either chemically or fluorescently labelled and then measured using one of two methods: Single Particle Tracking (SPT) or Fluorescence Photobleaching Recovery (FPR). SPT monitors the lateral motion of a labelled molecule while FPR measures the rate of subsequent infiltration from a photobleached section of the membrane. Friction co-efficient data is also given, which measures of the velocity and force applied to an antibody-coated bead. Records contain the cell or cell type where diffusion is occurring, the name of the diffusing protein along with the form of labelling applied. Furthermore, specific experimental data is given such as antibody bead size. In this case the diffusion of the beads is monitored. Increasingly, data relating to photobleaching is included, such as beam power, bleaching duration, pre- and post-bleach time, etc..
Copy Numbers
The final sub-database contains 414 measures of cell surface populations of different molecules, called cell surface copy numbers hereafter. This database focuses on an array of molecules, including Class I MHCs (22) and TCRs [23]. The entries are given as number and type of MHC molecules, number of MHC-peptide complexes or abundance of peptides associated with each MHC serotype, generally defined by mass spectroscopy. The entries list the cell type, the antibody bound to the MHC and, if appropriate, the binding epitope. This only applies to the number of MHC-peptide complexes and the abundance of peptides associated with each MHC.
Searching the database
Search mechanisms within AntiJen are significantly improved and allow either a detailed or a broad search from a simple user interface. From our experience with JenPep, we recognize that accessibility to the data in a user friendly manner is a vital requirement, and have improved our current search mechanisms and developed new search interfaces. Two different search mechanisms are available. One is based on BLAST [24] and the other is a bespoke system, allowing several alternative searches. Within a typical search, the user-entered search criteria are carried from an HTML form to a category specific PERL/SQL script, which performs the database queries.
The BLAST search allows querying of a peptide or nucleotide sequence against the proteins contained in AntiJen; all entries containing data within AntiJen which are relevant to a protein sequence are linked via the BLAST output. A local database of protein sequences is searched with BLASTP or BLASTX [24] using the BLOSUM64 matrix. All BLAST control variables are fixed. An HTML Front-End (Figure 2), where a sequence can be entered or uploaded, connects to a web server-based PERL/CGI scripts, which interacts with BLAST. An annotated version of the default BLAST output is produced and links to AntiJen entries via SWISS-PROT [12] accession codes, which act as a query within a Keyword search. This allows AntiJen entries to be viewed directly from the BLAST output.
Figure 2
Overview of the different search methods within AntiJen. The example search is focused upon an MHC ligand. The MHC ligand data can be searched directly (A) from a link on the AntiJen homepage, a broad search and specific search is available. A search for the epitope AMALLRLPLV, has one hit (D), this leads to the entry (G). All of the other sub-databases can be searched in this manner from the homepage. The two other searches are more generalised. A Keyword search (B) carries out a broad search on the whole database, for the criteria – Bacteria. This search gives 139 hits (E) and all of the sub-database entries can be selected from this output. The final search method is a BLAST search (C). The peptide (or nucleotide) sequence is queried against a local protein database. The output (F) provides links to the sub-databases relative to the protein.
At present, peptide string, keyword, and protein name index searches are available within the bespoke system, which allows querying of individual peptide sequences or at the level of whole protein antigens. An overview of the AntiJen search systems is given in Figure 2 and 3. Epitopes, MHC, TCR, TAP peptide binding and kinetics databases can all be searched using sequence strings. The search protocol first returns an epitope list and a count of epitope matches. Subsequently, experimental criteria can be accessed for each selected epitope. Peptides can be searched using an amino acid orientated query: a sparse peptide string, similar in form to a peptide binding motif [3] or a PROSITE pattern [25], is used to identify all matching sequences. See Figure 4. Alternately, a list of protein antigens within AntiJen can be searched using keywords; the thermodynamic binding data such as MHC-peptide, TCR-pMHC and TAP, plus the B and T cell archives, related to the search criteria can then be selected from the matches based upon the SWISS-PROT accession codes [12], displaying all corresponding entries in the database.The other search method, allows the IPPI, diffusion co-efficients, peptide libraries, antibody-protein and copy number sub-databases to be searched using an index method, within a user-friendly HTML drop-down menu. Each of these methods can also be moderated using subsidiary search filters, data size ranges, and result presentation alternatives, such as peptide length or IC50 values. Minimum and maximum values can be used to restrict results, as can selection of MHC restriction alleles.
Figure 3
Searchable database types within AntiJen. The database contains 3 types of searchable sub database: a set of Antigens searchable by keyword, various databases of functional and thermodynamic data searchable by peptide sequence, and a database of immunological protein-protein interactions searchable through an index. Peptide sequence searches can be explicit or "motif" based. Searches can also be focussed by setting the value ranges for properties, such as IC50 etc, recorded in the databases. Currently there is a link between all the thermodynamic and kinetic databases and between them and the database of antigens. Only antigens with data in one of the other sub-databases are included in the antigen database. Links to external databases are also indicated. The BLAST search provides an overall search of the databases except the Protein-Protein Interactions archive.
Figure 4
Sparse peptide sequence search. Examples of the two search types available in AntiJen: (Search 1) a substring query and (Searches 2 and 3) PROSITE-like sparse queries allowing sets of variable (indicated by asterisks) and alternative (encased in square brackets) amino acids in the search. In our example, each query is an extension of the previous search. The initial search type returns a single hit, which is a weak binder. By introducing variable amino acid positions within the query string, the second query permits access to a larger data set with 39 hits being returned. The third search utilises both variable and alternative amino acid request functionality and returns 1996 hits. The number of entries returned can be reduced by specifying the epitope length, limiting IC50 values and restricting by one MHC allele. This search is constrained to peptides of amino acid length 9, which returns 280 hits. Constraining further by MHC allele HLA-A*0201, reduces this to 120. An additional constraint using the IC50 data filter, and requesting values below 500 only (the epitope range), reduces this again to 60.
Discussion
The name given to our new database, AntiJen, reflects a shift from a peptide orientated database structure, which was inherent within our earlier JenPep database, to one which can properly balance its focus on both protein antigens and isolated peptides. As such, it represents an important, integrated, immunological data resource. AntiJen now provides broad insight into both T cell and B cell mediated antigen recognition. In addition, through the auspices of the IPPI sub-database, the database also throws light on co-stimulation by co-receptors and gives important insights into the innate immune response. Our approach to protein-protein interactions, focusing on measured affinities, complements other methods, such as the Yeast-2-Hybrid system, which, while giving greater volumes of data, has problems of accuracy [26]. This, while not devoid of experimental artifact, gives a usefully different perspective on cataloguing protein-protein interactions. The further addition of weak binding notation to the MHC-peptide and TAP provides a greater overview of the nature of antigenic epitopes. This is further improved by the addition of the peptide libraries database, whereby key peptide residues can be highlighted. New databases have expanded the breadth of AntiJen to include biophysical data such as diffusion co-efficients and cellular data such as abundance of molecules. The antibody-antigenic protein sub-database will also provide a key resource for in vivo and in vitro studies, aiding in the selection of antibodies and peptide/protein targets.
AntiJen distinguishes itself from the other specific binding databases [2-8] in several ways. Firstly, more data is recorded; our MHC-peptide database contains over 2,000 more entries than MHCPEP [2] and 10,000 more entries than EPIMHC [7]. Additionally, we have not restricted our archive to only high binders or to a specific category, as seen in EPIMHC and the HIV sequence databases [5]. Furthermore, AntiJen is currently a curated database, which is constantly expanding.
Most obviously, AntiJen is useful in the design of epitope and subunit vaccines. Additionally, AntiJen is helpful in the design of clinical diagnostics and other laboratory reagents, such as the selection of peptides for tetramer design. AntiJen is also useful in the parameterization of mathematical models in theoretical immunology [27]. The redevelopment of the database has focused not only on content, but also on infrastructure. The current system, based on epitope string, keyword and index searches, along with an overall BLAST search, plus the redesigned HTML interface, leads to much greater accessibility and usability. Finally, the database acts as a repository of quantitative, continuous data, for the development of data-driven in silico predictive models, such as prediction of epitopes and MHC binding [18,19,28,29] through QSAR modeling.
Future work
Future tasks in the development of AntiJen, fall into two principle categories: eliminating deficiencies, errors, and inconsistencies within the database and simultaneously reinforcing it by expanding its depth, breadth, and scope. We also need to monitor updates within external databases, so that any alterations are mirrored within the archive. Like all other such repositories, AntiJen is prone to both systematic and random errors within the data accumulation process. User feedback and our interactions with immunologists will hopefully address persisting errors. Deficiencies in our database include our current inability to encode chemically or post-translationally modified peptides, non-natural MHC mutants and non-amino acid peptidomimetic MHC ligands. Additionally, it would also be interesting to complement our existing data on TAP binding with information on antigen presentation pathways, such as proteasomal and cathepsin cleavage patterns. Moreover, the compilation of B cell or antibody epitope data is an area ripe for robust development. Linear and conformational B cell epitopes are very much larger in number than our current compilation, leaving us scope to greatly increase recorded epitopes.
Conclusion
The development of a database is always a work in progress. Not simply because the easily accessible literature is typically always increasing, but also because of the desire to capture as much of the existing, but hidden, literature, as possible. In the post-genomic era, the database has formed the bedrock and language of bioinformatics; increasingly databases are coming to underpin our modern understanding of biology as a whole. Traditionally, databases have arisen as a response to need, answering the individual and idiosyncratic questions posed by biologists. However, the history of bioinformatics databases has shown the extraordinarily diverse ways in which archived data can be used.
In creating AntiJen, we were motivated partly by our desire, and the desire of collaborators, to use the data within it to build predictive in silico models [16,17,28,29], and partly by a more altruistic desire to generate a useful, integrated database system with a quantitative focus. AntiJen has many potential uses throughout the immunological discipline, from immunoinformaticans to experimental immunologists and vaccinologists. By increasing the degree to which data is machine readable and web accessible, we open up new, and previously unthought-of, avenues for the bioinformatic exploration of immunological data.
AntiJen is a primary data resource, amongst the most complete of its kind, yet, like SWISSPROT [12] or GenBank [30] decades ago, it is still relatively small and offers much scope for improved annotation. We see the database as a foundation from which to consolidate, through time, thus achieving a comprehensive resource of immunological data.

vaccines
b cell epitopes
mhc
antibodies
t cell epitopes
tcr
mhc-peptide binding