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Behavioural characterisation of the robotic mouse mutant
Behavioural Brain Research 181 (2007) 239–247
Research report
Behavioural characterisation of the robotic mouse mutant Peter L. Oliver, David A. Keays, Kay E. Davies ∗ MRC Functional Genetics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX1 3QX, UK Received 31 October 2006; received in revised form 12 April 2007; accepted 19 April 2007 Available online 24 April 2007
Abstract The ataxic mouse mutant robotic is characterised by progressive adult-onset Purkinje cell loss that occurs in a distinctive region-specific pattern. We report the first behavioural characterisation of this mutant and quantify its performance on tests of motor function, locomotor and exploratory activity over a time course that reflects specific stages of cell loss in the cerebellum. Robotic mutants are significantly impaired on the rotarod and static rod tests of coordination and their performance declined during aging. In addition, gait analysis revealed an increase in the severity of the ataxia displayed by mutants over time. Interestingly, spontaneous alternation testing in a T-maze was not significantly affected in robotic mice, unlike other ataxic mutants with more rapid and extensive cerebellar degeneration; robotic therefore provides an opportunity to investigate the necessity of specific Purkinje cell populations for various behavioural tasks. © 2007 Elsevier B.V. All rights reserved. Keywords: Purkinje cell; Cerebellum; Mouse; Motor coordination; Ataxia
1. Introduction The robotic (Rob/+) mouse, named after its distinctive jerky ataxic gait, is an autosomal dominant cerebellar mutant that arose from a large-scale chemical mutagenesis program [46]. Additional features of the phenotype include bilateral cataracts, a shortened lifespan and reduced overall size. A neuropathological screen revealed no abnormalities in the brain other than adult-onset loss of Purkinje cells in the cerebellum in a striking, region-specific pattern with no overt granule cell or inferior olive degeneration detected [22]. The ataxic phenotype is first apparent at approximately 3 weeks of age, and calbindin immunostaining at 3–5 weeks revealed swelling of Purkinje cell bodies and the presence of axonal torpedoes in mutants. Cell loss does not occur, however, until the eighth post-natal week in which the anterior vermal cerebellar lobes are the first to be affected, with typically a 25% reduction in total Purkinje cell number by 10 weeks of age. The characteristic compartmentalised pattern of cell death is more obvious by 20 weeks and by 40 weeks only 30% of the Purkinje cells remain with lobe X completely spared and virtually no cells surviving in lobes I–IV [22]. The causative mutation was identified in a
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highly conserved region of the putative transcription factor Af4, which is expressed specifically in Purkinje cells in the cerebellum. We have since shown that the robotic mutation disrupts the binding of the E3 ubiquitin ligases Siah-1a and -2, preventing the normal rapid turnover of Af4 by the proteasome and causing the subsequent accumulation of the mutant protein [3,47]. This particular pattern of neurodegeneration is distinctive from the classic spontaneous cerebellar mutants such as lurcher (Grid2Lc ) and pcd (Agtpbp1pcd ) that suffer from rapid and almost complete loss of Purkinje cells by 2 months of age [7,40], or staggerer (Rorasg ) and nervous that only display approximately 75 and 40% Purkinje cell loss, respectively [18,52]. Interestingly, however, the distinctive anterior–posterior pattern of cell loss the robotic cerebellum is also seen in the mouse models of the lipidstorage disorder Niemann-Pick disease types A/B (ASMKO) and C (BALB/c npcnih ) although over a much shorter timespan [19,49]. Detailed characterisation of the distinctive neurodegenerative phenotype of these mutants in combination with behavioural testing has provided some insight into the relative contribution of individual neuronal populations to sensorimotor performance, motor learning and more complex paradigms such as eye-blink conditioning (reviewed in [8,23,39]). Here we describe the first behavioural characterisation of the robotic mouse; in addition to quantifying the progression of the ataxia in this mutant, it provides a unique opportunity to investigate
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the necessity of specific Purkinje cell populations for various behavioural tasks. 2. Materials and methods 2.1. Animals The original founder robotic mutant male was identified as the progeny of an ENU-injected BALB/c male and a C3H/HeH female. Sperm from a sixth generation C3H backcross male was used to generate a cohort of mice by in vitro fertilisation. The genotype of individual animals was confirmed by amplifying a 580 base-pair region of Af4 exon 3 from genomic DNA using the polymerase chain reaction (PCR) with the primers 5 -CCACGTCTGACTCAAGACCA and 5 -CATCCTTAGTCTGGTGAGCTTG followed by sequencing. Mice were weaned at 21 days and kept in same sex groups in controlled conditions (12 h light–dark cycle, at 21–22 ◦ C with food and water ad libitum). Cohorts of 10 female robotic mutant and 10 wild-type littermates (+/+) were analysed in the study. All studies were conducted in accordance with the UK Animals (Scientific Procedures) Act 1986.
2.2. Behavioural testing Mice were tested at 6, 10, 20 and 40 weeks of age over a period of 4 days in each case. These particular timepoints were chosen to reflect the progressive stages of Purkinje cell death that have been already characterised and quantified in detail elsewhere [22]. 2.2.1. Locomotor activity Mice were placed individually in cages (40 cm × 20 cm × 20 cm) containing fresh sawdust. The number of beam breaks and cage transitions were recorded in 5 min allocations using a beam splitter (Benwick Electronics) for a total of 35 min. 2.2.2. Footprint analysis To obtain footprints, the hind-feet of mice were coated with non-toxic paint. Each animal was then allowed to walk on absorbent paper along a chamber (50 cm × 9 cm × 8 cm) towards one end that had been closed off with a lid and small entrance to create a dark box 15 cm long. After a single training run with no paint, data were collected from two consecutive runs from five mutant and wild-type mice at each timepoint. Four step parameters were calculated based on previously described methods [2,11,53]. Mean stride length was calculated as the distance travelled divided by the total number of steps, and gait width was determined by measuring the perpendicular distance of a given step to a line connecting the opposite preceding and succeeding steps. The maximum difference in stride length was calculated by measuring the distance between steps on the same side of the body (both right–right and left–left) and the distance of the shortest stride was subtracted from the distance of the longest stride. Finally, linearality, a measure of variation in direction of movement, was calculated by drawing a line perpendicular to the direction of travel for each right footprint. The angle between this perpendicular line and each subsequent right footprint was determined and the differences in angle were calculated between each consecutive step pair. The absolute values of all angle differences were summed and divided by the total number of steps scored. In all cases, the first two and last two steps in the testing apparatus were not used for analysis. 2.2.3. Grip strength Mice were tested using a commercial grip strength monitor (Chatillon). Each animal was held 2 cm from the base of the tail, allowed to grip a protruding metal bar attached to the apparatus with their forepaws, and pulled gently until they released their grip. The maximum force exerted was recorded and averaged for four consecutive trails 30 s apart. 2.2.4. Inverted screen A 45 cm2 screen of wire mesh consisting of 12 mm2 of 1 mm diameter wire surrounded by a 5 cm deep wooden frame was used. The mouse was placed in the centre of the wire mesh screen and the stopwatch started. Immediately
the screen was rotated to the inverted position over 2 s, with the mouse’s head declining first. The screen was maintained 30 cm above a padded surface for 120 s. Mice were timed for how long they remained upside down on the screen, with a maximum score of 120 s being given if the animal did not fall. Three trials were carried at 1 h intervals. 2.2.5. Accelerating rotarod A commercial rotarod device was used (Ugo Basile, Italy) consisting of a grooved plastic beam 5 cm in diameter. Mice were placed on the beam (revolving at the default 5 rpm) facing in the opposite orientation to rotation. After 1 min the rod speed was gradually accelerated to a maximum of 30 rpm over 4 min by electronic control of the motor. The latency before falling was measured up to a maximum total time on the rod of 6 min for three trials at 1 h intervals. 2.2.6. Round static rod coordination A wooden dowel 28 mm diameter and 60 cm in length was fixed to solid support at one end and clamped to bench 60 cm above a padded surface. Mice were placed on the protruding end of the rod, facing away from the bench, and the time taken to turn and face the bench (orientation time), to fall from the rod or for all four paws to reach the supported end (transit time) was recorded up to maximum of 3 min. Any mouse failing to achieve one of the tasks was given a maximum score of 180 s [12]. 2.2.7. Spontaneous alternation A T-maze constructed from dark grey plastic was used, consisting of three arms 30 cm in length, 10 cm wide with walls 29 cm high. Mice were placed at the base of the ‘T’ (starting arm) and allowed to walk to the junction for arm selection. Once the tail had passed beneath the barrier of the chosen arm, it was closed and the mouse was retained in that section for 30 s. The barrier was subsequently raised while the mouse was returned to the starting arm allowing the selection process to be repeated. A total number of 10 trials were carried out over 2 days in blocks of five trials 30 min apart. If no choice had been made after a total of 5 min then a ‘fail’ score was recorded for that particular test.
2.3. Data analysis To analyse the data, repeated measures ANOVA’s were employed executed in the SPSS program. In circumstances where the data failed to meet the assumptions of normality and equality of variance either transformations or non-parametric tests were used.
3. Results 3.1. Weight Mice were weighed on day one of the test battery and robotic mutants were consistently smaller in size and body weight than littermate controls over the testing period (Fig. 1). Statistical analysis revealed that there was not only an effect of genotype (F(1,18) = 813.5, P < 0.0001), but also an effect of age (F(3,54) = 220.2, P < 0.0001) on body mass. In addition, robotic mice failed to gain as much weight as controls as shown by an interaction between age and genotype (F(3,54) = 15.3, P < 0.0001); mutants were over 60% the weight of controls at 6 weeks of age, but were approximately half the weight by the final timepoint. 3.2. Locomotor activity Locomotor activity (LMA) was measured to determine the affect of the ataxia on general movement in an open field (Fig. 2). Mutants consistently exhibited reduced activity at all timepoints,
P.L. Oliver et al. / Behavioural Brain Research 181 (2007) 239–247
Fig. 1. Mean weight of robotic (filled squares) and wild-type (open squares) mice over the time course of the experimental period. Robotic mice are significant smaller than control mice (F(1,18) = 813.5, P < 0.0001). Error bars show S.E.M.
scoring approximately one-third fewer beam breaks at 6–20 weeks of age. The inactivity of mutants was most noticeable at 40 weeks of age, with an almost 50% reduction in the number of beam breaks. The effect of genotype on LMA was highly significant (F(1,18) = 52.8, P < 0.0001); however, based on the total number of beam breaks, a repeated ANOVA showed no effect of age (F(3,54) = 1.3, P > 0.2) or interaction between age and genotype (F(3,54) = 1.7, P > 0.1) on activity in the apparatus. 3.3. Gait analysis To further analyse the progression of the ataxia in mutants, detailed gait analysis was carried out by painting the hind-feet
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of mice and quantifying a number of parameters from the footprint traces. It was noticeable from the patterns obtained in this task that robotic mice used the whole of their rear paws to walk and maintain balance, whereas control animals were more likely to use the toes and the front part of the foot only (Fig. 3A). From analysis of the traces, robotic mice show a large reduction in mean stride distance at all timepoints compared to controls (F(1,8) = 7195, P < 0.0001); this reduction became more pronounced at later timepoints as shown by an interaction between age and genotype ((F(3,24) = 8.6, P < 0.0005) (Fig. 3B)). This shortened stride length may be a result of the ataxia, but this is also undoubtedly a reflection of the reduced body length of mutants, which is approximately 60–65% of wild-type mice; a similar reduction to that seen in mean stride distance (data not shown). Conversely, however, an increase in mean gait width was observed from 10 weeks of age in robotic mice that cannot be accounted for by body size (Fig. 3C). Statistical analysis revealed that although gait width generally increased with age (F(3,24) = 20.6, P < 0.0001), there was significant effect of genotype (F(1,8) = 28.5, P = 0.001) and an interaction between age and genotype (F(3,24) = 5.8, P < 0.005) on this measurement, reflecting the distinctive broad-based walk of this mutant. To ascertain whether the uneven, jerky nature of the robotic ataxic gait became more pronounced during ageing, the maximum stride difference and linearality of movement was calculated (Fig. 3D and E). As the mutant mice became older, the regular alternating gait observed at 6 weeks of age in mutants became progressively erratic, with larger maximum stride differences than controls observed (Fig. 3D). The effect of genotype was highly significant (F(1,8) = 18.6, P < 0.005), although there was no affect of age (F(3,24) = 1.7, P > 0.1) or interaction between age and genotype (age (F(3,24) < 1, P < 0.5) on step length irregularity. There was also no bias in this parameter towards one side, with both right–right and left–left stride measurements showing no significant differences at any time (data not shown). Furthermore, robotic mice showed an increasing tendency to meander along the apparatus as shown by the linearality score, a measurement of regularity of direction (Fig. 3E). This is illustrated by the curved nature of a representative footprint pattern from a robotic mouse at 20 weeks of age compared to a control animal that consistently walked in a much straighter line (Fig. 3A). Repeated measures ANOVA did not, however, find a significant affect of genotype (F(1,8) = 3.8, P > 0.05) or interaction between age and genotype (F(3,24) < 1, P < 0.5) on linearality. 3.4. Grip strength
Fig. 2. LMA expressed as total number of beam breaks in a 35 min period. Robotic mutants (filled squares) exhibit a significant reduction in overall levels activity compared to wild-type mice (open squares) (F(1,18) = 52.8, P < 0.00001). Error bars show S.E.M.
To assess grip strength and climbing ability, parameters that may influence additional tests of motor coordination, the inverted screen test was carried out. At each timepoint, all wild-type and mutant animals were able to maintain their grip on the wire mesh once inverted for the maximum 120 s, even at 40 weeks of age (data not shown). In addition, mice from both genotypes were able to manoeuvre
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Fig. 4. Grip strength testing. The mean maximum forepaw resistance of robotic mutants (filled bars) is significantly lower than wild-type (open bars) mice (F(1,10) = 12.3, P < 0.01), although not progressive with age. Error bars show S.E.M.
themselves around the screen without falling. This suggests that the general grip strength of robotic mice was not impaired to a significant degree despite the presence of overt ataxia. However, to analyse forelimb strength in more quantitative manner, a grip strength meter was used. From these data, a small but significant reduction in grip strength was identified at all four timepoints tested by mutant mice ((F(1,10) = 12.3, P < 0.01) Fig. 4), although this was not significantly influenced by the age of the animals and was not progressive. 3.5. Rotarod
Fig. 3. Gait analysis by hind-foot painting. (A) Representative footprint trails from 20-week-old wild-type (top) and robotic (bottom) mice. (B) Mean stride distance is progressively and significantly shorter in robotic mutants (filled bars) than wild-type mice (open bars) (F(3,24) = 8.6, P < 0.0005). (C) Robotic mice show a significant increase in gait width with age (F(3,24) = 5.8, P < 0.005) and the mean maximum stride difference (D) is also significantly larger in robotic mutants (F(1,8) = 18.6, P < 0.005), although not progressive. (E) The linearality measurement scores suggest that robotic mutants are more inclined to meander along the footprint apparatus than wild-type mice. Error bars show S.E.M.
To quantify the progression of the ataxia and motor coordination in robotic mutants, their performance on an accelerating rotarod was observed. From the mean of three consecutive trials, longitudinal analysis shows that there was a significant effect of age in both genotypes ((F(3,54) = 35.1, P < 0.0001) Fig. 5A). At 6 weeks of age control mice average 223.7 s on the rotarod, which decreased to 116.3 s at 40 weeks. Likewise, the performance of mutant mice decreased from an average of 203.3 s at 6 weeks to 82.0 s at the final timepoint. A repeated measures ANOVA showed that there was also a significant effect of genotype (F(1,18) = 21.2, P < 0.0005), with mutants performing worse than controls, as expected, but no significant correlation between age and genotype (F(3,54) = 1.3, P > 0.1). Both groups of mice did, however, show an improvement in performance over the three individual trials at each timepoint (Fig. 5B).
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3.6. Static rod The static rod test also used to assesses motor coordination and equilibrium although in a situation where there were fewer spatiotemporal constraints than on the rotarod. At 6 weeks of age, there was no significant difference between the groups in the time taken to reach the supported end of the rod (F(1,19) = 0.01, P > 0.5). From 10 weeks onwards, mutants began to perform worse on the beam; by 20 weeks of age, none of the mutants completed the task, and all robotic animals fell during testing at 40 weeks (Fig. 5D). Statistical analysis revealed that overall there was a significant effect of genotype (Z = −2.82, P = 0.005), a significant effect of age (F(3,54) = 4.35, P < 0.01), however, no interaction between age and genotype (F(3,54) = 0.89, P = 0.448). Similar results were obtained when examining orientation times, with robotic mice taking longer than controls to maintain their equilibrium successfully at the end of the rod after 6 weeks of age (Fig. 5C). In general, robotic mutants would attempt to orientate themselves even at 40 weeks of age and it was during this movement that the mice would slip and fall. Overall from the orientation data there was a significant effect of genotype (Z = −2.5, P = 0.01), an effect of age (F(3,54) = 6.8, P = 0.001), and an interaction between age and genotype (F(3,54) = 4.2, P = 0.01). 3.7. Spontaneous alternation
Fig. 5. Motor coordination testing of robotic mutants. (A) Rotaord performance; latency to fall is displayed as a mean of three consecutive trials. Mutants (filled squares) perform consistently worse on the apparatus than wild-type mice (open squares) (F(1,18) = 21.2, P < 0.0005) although both genotypes show a significant reduction in performance over time (F(3,54) = 35.1, P < 0.0001). (B) Mean latency to fall from the rotarod over three individual trials (left to right) showing improvement in both wild-type and robotic mutants. (C and D) Static beam performance; time taken to reach the end of the rod (transit time) and time taken to turn around initially (orientation time) is shown. There was a significant decrease in performance of mutants (filled bars) compared to wild-type mice (open bars) in both parameters (Z = −2.82, P = 0.005; Z = −2.5, P = 0.01, respectively). Error bars indicate S.E.M.
To analyse working spatial memory we employed a freetrail spontaneous alternation protocol. We chose this paradigm because this delayed non-match to place task does not require vision [6]. In addition to the cataracts that are first visible from 2 months of age in robotic mice, the mutant is also on a C3H background which carries a homozygous mutation (rd) causing almost complete degeneration of photoreceptor cells by 30 days of age [5,16]. In the T-maze robotic mice displayed a slightly reduced alternation rate in comparison to wild-type mice in general, however this difference was not significant at any of the four timepoints tested (F(1,18) = 1.8, P > 0.1). Overall there was also a reduction in rates of alternation for both mutants and controls during the 40 weeks period; controls decreased from an average alternation rate of 84–76.3%, while mutants decreased from 81 to 70.3%. Analysis by an ANOVA showed that the decrease in performance with age was significant (F(3,54) = 2.92, P < 0.05), however, there was no genotype by age interaction (F(3,54) = 0.13, P > 0.5; Fig. 6). To determine whether the ataxia in robotic mice lead to a bias in the direction of choice, the total number of left and right turns in the first stage of the experiment was recorded; there was no significant difference in choice between wild-type and robotic mice at any timepoint (data not shown). 4. Discussion We have undertaken the first longitudinal analysis of the robotic mouse mutant on tasks that assess locomotor activity, motor coordination and working spatial memory. This study has demonstrated that robotic mice become progressively inac-
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occurs in a transcription factor does not preclude its influence on metabolic or hormonal pathways. 4.2. Locomotor activity, gait and grip strength
Fig. 6. Spontaneous alternation of robotic mutants. Spontaneous alternation in a T-maze was employed to assess working spatial memory in robotic mice. While there was a significant age related decrease in performance for both wild-type mice (open squares) and mutants (filled squares) (F(3,54) = 2.92, P < 0.05), there was no effect of genotype (F(1,18) = 1.8, P > 0.1).
tive and increasingly uncoordinated compared to control mice as their gait disturbance becomes more pronounced, although they did not display a significant T-maze alternation deficit. A summary of these results is shown in Table 1. 4.1. Weight Robotic mutants can be identified from their littermates during the third post-natal week when their reduced size is first noticeable; the data here show that by 6 weeks of age mutants are approximately 60% of the size of controls throughout adulthood. In addition, their reduced size is in proportion to their mass, with body length, limb length and brain weights on average 60–65% that of control animals (data not shown). Small size is also a feature of other cerebellar mutants, such as homozygous weaver (Grik2wv ) [51] and staggerer [9]; it was concluded from the latter study, as here, that differences in motor function tests are unlikely to be attributed to body mass as the variation between the wild-type and mutant strain was essentially consistent throughout the experiment. It is often speculated that ataxic mutants are smaller then littermates due to competition for food during the early stages of development [38]. This may indeed be the case in robotic mice, however the fact that the mutation
Robotic mice were considerably less active than controls in the LMA apparatus and this effect was more pronounced at the final timepoint tested. This was also reflected in the T-maze apparatus where mutants consistently took longer to complete the task. A reduction in spontaneous activity has also been recorded in similar apparatus in both the weaver and staggerer mutants that also suffer from partial Purkinje cell loss (see below) [36,37]. The decreased activity of robotic mice is unlikely to be exclusively due to an impairment in general exploratory behaviour as they were able to complete the T-maze task successfully, even at 40 weeks of age. This may in part reflect the increasingly erratic nature of their gait as illustrated by the footprint analysis, substantiating general observations during handling that the ataxia in robotic mice is progressive. Attempts were made to quantify this phenomenon further using a holeboard test as used in a time course study of lurcher behaviour [21]. Hilber and Caston showed that lurcher mutants were severely impaired in this task, as quantified by the number and frequency of foot slips, although the deficits did not increase with age. However, attempts to analyse robotic mice in identical apparatus were not interpretable because there was a large discrepancy in walking times between mutants and controls (data not shown). Results from the grip strength tests showed that robotic mice are able to hold their own bodyweight using all four limbs; this observation was also noted during characterisation of the npcnih mutant, even at the end-point of the experiment [56]. In addition, a progressive yet relatively small reduction in forepaw grip strength was observed in robotic mice. This is in contrast to measurements of muscular strength by wire hanging carried out on ageing lurcher mice, where the latency before falling was up to 20 times shorter in mutants compared to age-matched wild-type controls [21]. However, our data does suggest that the relative climbing ability and strength of robotic mice would not be a confounding factor in the interpretation of motor coordination tests. The consistently smaller size of mutants may be masking any grip strength deficiency on the inverted screen test; however, detailed analysis of muscle biopsies has not identified any differences in the fibre size, cytoarchitecture or fibre-type ratios of robotic mice compared to controls (unpublished observations).
Table 1 Summary of results in this study Age (weeks)
Vermal PC loss (%)
LMA
Mean max stride difference
Gait linearality
Grip strength
Rotarod
Spontaneous alternation
6 10 20 40
0 25 50 75
↓ ↓ ↓ ↓↓
↔ ↓ ↓↓ ↓↓↓
↑ ↑↑ ↑↑↑ ↑↑↑↑
↔ ↓ ↓↓ ↓
↓ ↓↓ ↓↓↓ ↓↓
↔ ↔ ↔ ↔
The direction of the arrows indicates the relative average performance or experimental values of robotic mutants vs. age-matched wild-type mice. Percentage Purkinje cell (PC) loss in the cerebellar vermis is shown based on data from Isaacs et al. [22].
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4.3. Motor coordination tests In concordance with numerous studies of spontaneous and transgenic ataxic mouse mutants, robotic mice were impaired on both the accelerating rotarod and static rod tests of motor coordination [11,21,28–31,38,42,54]. Latencies before falling from the rotarod were consistently lower in robotic mice than controls, although this difference was more pronounced at 10 and 20 weeks of age. However, mutants appeared to be motivated to carry out the test, as passive rotation, clinging on to the rod while it freely rotates (as reported elsewhere [42]), was a very rare occurrence. In addition, both robotic and wild-type mice showed increased average latencies before falling over the three successive trials. This is consistent with a number of studies of cerebellar mutants in which absolute performance on the rotatrod is impaired, but motor learning is still present after training [11,21]. Furthermore, wild-type mice showed a general trend of decreased performance on the rotarod during ageing which has also been observed in longitudinal studies of lurcher and staggerer mice prior to, or without, training [9,21]. It has been postulated this phenomenon is a function of muscle strength as opposed to impairments in the acquisition of motor tasks [21], although it has been shown here that there is no obvious correlation between forepaw strength and rotarod performance in control mice up to 40 weeks of age. 4.4. Spontaneous alternation Although robotic mutants consistently alternated at rates 5–10% lower than controls, these figures were considerably above chance at all timepoints tested. This was somewhat surprising considering that delayed spontaneous alternation rates, using two-trial paradigms, are observed in all of the well-known spontaneous cerebellar mutants [4,10,15,24,27,33–38]. It has therefore been suggested that interactions between the cerebellum and the hippocampus and/or vestibular system underlie alternation deficits in rather than simply a function of poor motor coordination [25,44]. Although Af4 is expressed at low levels in the hippocampus, there are no anatomical abnormalities in this region in robotic mice (unpublished observations). Moreover, with no detectable degeneration of granule cell or inferior olive neurons in robotic mice, it is tempting to speculate that their ability to alternate, even when only 30% of the Purkinje cells remain, might reflect uncompromised connectivity related to these spared regions. The cerebellum is implicated in emotional as well as motor behaviour [8,43], and motivation is also likely to impair spontaneous alternation performance after repeated trails; potentially cerebellar pathways related to this response may be intact in robotic mice but impaired in other ataxic mutants with more profound cell loss. 5. Conclusions There has been some debate over the contributions of Purkinje cell loss and connectivity in the cerebellum of ataxic mutants to their relative performance in certain tests of motor coordination. For example, both lurcher and pcd mutants suffer from
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almost complete Purkinje cell loss in addition to severe granule cell death but perform normally on a rectangular stationary beam [28,32,42]. In contrast, staggerer and weaver mice, with approximately 75 and 40% Purkinje cell loss, respectively, fall quicker on the same apparatus [13,54]. This suggests that aberrant Purkinje cell innervation has a greater influence on motor coordination than complete absence. This argument can be extended to locomotor activity; lurcher, pcd and nervous mutants display ambulatory activity that is equivalent or even more pronounced than controls, where ataxia may even be masking further hyperactive behaviour [26,33,35], whereas staggerer and weaver are hypoactive in open field and T-maze tests [13,36,37,54]. These data are in agreement with our findings of robotic, where a significant reduction in locomotor activity was observed even at 6 weeks of age, when only a small percentage of Purkinje cells display any visible pathology. It is therefore interesting that in analysis of npcnih mice at the same age, prior to Purkinje cell death, male mutants travelled a significantly shorter total distance over a 15 min spontaneous locomotor activity test [56]. However, it is clear from our study that the ataxia and motor coordination deficits of robotic mice are progressive as the cerebellar degeneration becomes more severe. This is also the conclusion drawn from a longitudinal analysis of round static beam and rotarod performance of the npcnih mutant [56]. Although the npcnih study was carried out over a much shorter time course (4 weeks) than the robotic behavioural analysis described here (34 weeks), the distinctive anterior–posterior and parasagittal striped pattern of Purkinje cell loss of both mutants is remarkably similar [19,50]. This common cell loss pattern between two seemingly unrelated mutant lines may reflect an underlying susceptibility of particular groups of Purkinje cells to pathological insults. Therefore, both these studies would argue that defects in only a small and localised region of the cerebellum is enough to significantly influence motor function and coordination. Robotic is therefore an excellent opportunity to study this phenomenon, particularly as additional pathological features of the npcnih phenotype, such as defects in peripheral nerve, are likely to influence behavioural testing of motor function and control [20]. Established cerebellar mouse mutants have provided some limited insight into function consequences of region-specific disruption of Purkinje cell loss. For example, nervous mice, in which the lateral cerebellum is predominantly susceptible to cell death, do not show a deficit in the horizontal bar suspension test compared to controls [38]; this may illustrate the importance of that particular cerebellar region to the parameters examined in that specific task. However, robotic provides a potentially more sophisticated tool for the understanding of compartmentalisation in the cerebellum. Although comparatively little is known about the relative contribution of specific lobes to behavioural tasks, work in rodents has shown that Larsell’s lobule HVI and the anterior lobes are critical for normal eye-blink conditioning responses [1,45,55], and human MRI studies have revealed the specific activation of various regions in the cerebellar vermis and hemispheres in working memory tasks [14]. With new insights
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[20] Higashi Y, Murayama S, Pentchev PG, Suzuki K. Peripheral nerve pathology in Niemann-Pick type C mouse. Acta Neuropathol (Berl) 1995;90:158–63. [21] Hilber P, Caston J. Motor skills and motor learning in Lurcher mutant mice during aging. Neuroscience 2001;102:615–23. [22] Isaacs AM, Oliver PL, Jones EL, Jeans A, Potter A, Hovik BH, et al. A mutation in Af4 is predicted to cause cerebellar ataxia and cataracts in the robotic mouse. J Neurosci 2003;23:1631–7. [23] Ito M. Mechanisms of motor learning in the cerebellum. Brain Res 2000;886:237–45. [24] Lalonde R. Delayed spontaneous alternation in weaver mutant mice. Brain Res 1986;398:178–80. [25] Lalonde R. The neurobiological basis of spontaneous alternation. Neurosci Biobehav Rev 2002;26:91–104. [26] Lalonde R, Botez MI. Exploration and habituation in nervous mutant mice. Behav Brain Res 1985;17:83–6. [27] Lalonde R, Botez MI, Boivin D. Spontaneous alternation and habituation in a t-maze in nervous mutant mice. Behav Neurosci 1986;100:350–2. [28] Lalonde R, Botez MI, Joyal CC, Caumartin M. Motor abnormalities in lurcher mutant mice. Physiol Behav 1992;51:523–5. [29] Lalonde R, Filali M, Bensoula AN, Lestienne F. Sensorimotor learning in three cerebellar mutant mice. Neurobiol Learn Mem 1996;65:113–20. [30] Lalonde R, Hayzoun K, Derer M, Mariani J, Strazielle C. Neurobehavioral evaluation of Reln-rl-orl mutant mice and correlations with cytochrome oxidase activity. Neurosci Res 2004;49:297–305. [31] Lalonde R, Hayzoun K, Selimi F, Mariani J, Strazielle C. Motor coordination in mice with hotfoot Lurcher and double mutations of the Grid2 gene encoding the delta-2 excitatory amino acid receptor. Physiol Behav 2003;80:333–9. [32] Lalonde R, Joyal CC, Guastavino JM, Botez MI. Hole poking and motor coordination in lurcher mutant mice. Physiol Behav 1993;54:41–4. [33] Lalonde R, Lamarre Y, Smith AM, Botez MI. Spontaneous alternation and habituation in lurcher mutant mice. Brain Res 1986;362:161–4. [34] Lalonde R, Manseau M, Botez MI. Delayed spontaneous alternation in Purkinje cell degeneration mutant mice. Neurosci Lett 1987;80:343– 6. [35] Lalonde R, Manseau M, Botez MI. Spontaneous alternation and habituation in Purkinje cell degeneration mutant mice. Brain Res 1987;411:187– 9. [36] Lalonde R, Manseau M, Botez MI. Spontaneous alternation and exploration in staggerer mutant mice. Behav Brain Res 1988;27:273–6. [37] Lalonde R, Manseau M, Botez MI. Spontaneous alternation and exploration in weaver mutant mice. Behav Brain Res 1988;31:111–4. [38] Lalonde R, Strazielle C. Motor coordination exploration and spatial learning in a natural mouse mutation (nervous) with Purkinje cell degeneration. Behav Genet 2003;33:59–66. [39] Lalonde R, Strazielle C. Spontaneous and induced mouse mutations with cerebellar dysfunctions: behavior and neurochemistry. Brain Res 2007;1140:51–74. [40] Landis SC, Mullen RJ. The development and degeneration of Purkinje cells in pcd mutant mice. J Comp Neurol 1978;177:125–43. [41] Larouche M, Hawkes R. From clusters to stripes: the developmental origins of adult cerebellar compartmentation. Cerebellum 2006;5:77–88. [42] Le Marec N, Lalonde R. Sensorimotor learning and retention during equilibrium tests in Purkinje cell degeneration mutant mice. Brain Res 1997;768:310–6. [43] Lorivel T, Hilber P. Effects of chlordiazepoxide on the emotional reactivity and motor capacities in the cerebellar Lurcher mutant mice. Behav Brain Res 2006;173:122–8. [44] Newman PP, Reza H. Functional relationships between the hippocampus and the cerebellum: an electrophysiological study of the cat. J Physiol 1979;287:405–26. [45] Nolan BC, Freeman Jr JH. Purkinje cell loss by OX7-saporin impairs excitatory and inhibitory eyeblink conditioning. Behav Neurosci 2005;119:190–201. [46] Nolan PM, Peters J, Strivens M, Rogers D, Hagan J, Spurr N, et al. A systematic genome-wide phenotype-driven mutagenesis programme for gene function studies in the mouse. Nat Genet 2000;25:440–3.
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Research report
Behavioural characterisation of the robotic mouse mutant Peter L. Oliver, David A. Keays, Kay E. Davies ∗ MRC Functional Genetics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX1 3QX, UK Received 31 October 2006; received in revised form 12 April 2007; accepted 19 April 2007 Available online 24 April 2007
Abstract The ataxic mouse mutant robotic is characterised by progressive adult-onset Purkinje cell loss that occurs in a distinctive region-specific pattern. We report the first behavioural characterisation of this mutant and quantify its performance on tests of motor function, locomotor and exploratory activity over a time course that reflects specific stages of cell loss in the cerebellum. Robotic mutants are significantly impaired on the rotarod and static rod tests of coordination and their performance declined during aging. In addition, gait analysis revealed an increase in the severity of the ataxia displayed by mutants over time. Interestingly, spontaneous alternation testing in a T-maze was not significantly affected in robotic mice, unlike other ataxic mutants with more rapid and extensive cerebellar degeneration; robotic therefore provides an opportunity to investigate the necessity of specific Purkinje cell populations for various behavioural tasks. © 2007 Elsevier B.V. All rights reserved. Keywords: Purkinje cell; Cerebellum; Mouse; Motor coordination; Ataxia
1. Introduction The robotic (Rob/+) mouse, named after its distinctive jerky ataxic gait, is an autosomal dominant cerebellar mutant that arose from a large-scale chemical mutagenesis program [46]. Additional features of the phenotype include bilateral cataracts, a shortened lifespan and reduced overall size. A neuropathological screen revealed no abnormalities in the brain other than adult-onset loss of Purkinje cells in the cerebellum in a striking, region-specific pattern with no overt granule cell or inferior olive degeneration detected [22]. The ataxic phenotype is first apparent at approximately 3 weeks of age, and calbindin immunostaining at 3–5 weeks revealed swelling of Purkinje cell bodies and the presence of axonal torpedoes in mutants. Cell loss does not occur, however, until the eighth post-natal week in which the anterior vermal cerebellar lobes are the first to be affected, with typically a 25% reduction in total Purkinje cell number by 10 weeks of age. The characteristic compartmentalised pattern of cell death is more obvious by 20 weeks and by 40 weeks only 30% of the Purkinje cells remain with lobe X completely spared and virtually no cells surviving in lobes I–IV [22]. The causative mutation was identified in a
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highly conserved region of the putative transcription factor Af4, which is expressed specifically in Purkinje cells in the cerebellum. We have since shown that the robotic mutation disrupts the binding of the E3 ubiquitin ligases Siah-1a and -2, preventing the normal rapid turnover of Af4 by the proteasome and causing the subsequent accumulation of the mutant protein [3,47]. This particular pattern of neurodegeneration is distinctive from the classic spontaneous cerebellar mutants such as lurcher (Grid2Lc ) and pcd (Agtpbp1pcd ) that suffer from rapid and almost complete loss of Purkinje cells by 2 months of age [7,40], or staggerer (Rorasg ) and nervous that only display approximately 75 and 40% Purkinje cell loss, respectively [18,52]. Interestingly, however, the distinctive anterior–posterior pattern of cell loss the robotic cerebellum is also seen in the mouse models of the lipidstorage disorder Niemann-Pick disease types A/B (ASMKO) and C (BALB/c npcnih ) although over a much shorter timespan [19,49]. Detailed characterisation of the distinctive neurodegenerative phenotype of these mutants in combination with behavioural testing has provided some insight into the relative contribution of individual neuronal populations to sensorimotor performance, motor learning and more complex paradigms such as eye-blink conditioning (reviewed in [8,23,39]). Here we describe the first behavioural characterisation of the robotic mouse; in addition to quantifying the progression of the ataxia in this mutant, it provides a unique opportunity to investigate
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the necessity of specific Purkinje cell populations for various behavioural tasks. 2. Materials and methods 2.1. Animals The original founder robotic mutant male was identified as the progeny of an ENU-injected BALB/c male and a C3H/HeH female. Sperm from a sixth generation C3H backcross male was used to generate a cohort of mice by in vitro fertilisation. The genotype of individual animals was confirmed by amplifying a 580 base-pair region of Af4 exon 3 from genomic DNA using the polymerase chain reaction (PCR) with the primers 5 -CCACGTCTGACTCAAGACCA and 5 -CATCCTTAGTCTGGTGAGCTTG followed by sequencing. Mice were weaned at 21 days and kept in same sex groups in controlled conditions (12 h light–dark cycle, at 21–22 ◦ C with food and water ad libitum). Cohorts of 10 female robotic mutant and 10 wild-type littermates (+/+) were analysed in the study. All studies were conducted in accordance with the UK Animals (Scientific Procedures) Act 1986.
2.2. Behavioural testing Mice were tested at 6, 10, 20 and 40 weeks of age over a period of 4 days in each case. These particular timepoints were chosen to reflect the progressive stages of Purkinje cell death that have been already characterised and quantified in detail elsewhere [22]. 2.2.1. Locomotor activity Mice were placed individually in cages (40 cm × 20 cm × 20 cm) containing fresh sawdust. The number of beam breaks and cage transitions were recorded in 5 min allocations using a beam splitter (Benwick Electronics) for a total of 35 min. 2.2.2. Footprint analysis To obtain footprints, the hind-feet of mice were coated with non-toxic paint. Each animal was then allowed to walk on absorbent paper along a chamber (50 cm × 9 cm × 8 cm) towards one end that had been closed off with a lid and small entrance to create a dark box 15 cm long. After a single training run with no paint, data were collected from two consecutive runs from five mutant and wild-type mice at each timepoint. Four step parameters were calculated based on previously described methods [2,11,53]. Mean stride length was calculated as the distance travelled divided by the total number of steps, and gait width was determined by measuring the perpendicular distance of a given step to a line connecting the opposite preceding and succeeding steps. The maximum difference in stride length was calculated by measuring the distance between steps on the same side of the body (both right–right and left–left) and the distance of the shortest stride was subtracted from the distance of the longest stride. Finally, linearality, a measure of variation in direction of movement, was calculated by drawing a line perpendicular to the direction of travel for each right footprint. The angle between this perpendicular line and each subsequent right footprint was determined and the differences in angle were calculated between each consecutive step pair. The absolute values of all angle differences were summed and divided by the total number of steps scored. In all cases, the first two and last two steps in the testing apparatus were not used for analysis. 2.2.3. Grip strength Mice were tested using a commercial grip strength monitor (Chatillon). Each animal was held 2 cm from the base of the tail, allowed to grip a protruding metal bar attached to the apparatus with their forepaws, and pulled gently until they released their grip. The maximum force exerted was recorded and averaged for four consecutive trails 30 s apart. 2.2.4. Inverted screen A 45 cm2 screen of wire mesh consisting of 12 mm2 of 1 mm diameter wire surrounded by a 5 cm deep wooden frame was used. The mouse was placed in the centre of the wire mesh screen and the stopwatch started. Immediately
the screen was rotated to the inverted position over 2 s, with the mouse’s head declining first. The screen was maintained 30 cm above a padded surface for 120 s. Mice were timed for how long they remained upside down on the screen, with a maximum score of 120 s being given if the animal did not fall. Three trials were carried at 1 h intervals. 2.2.5. Accelerating rotarod A commercial rotarod device was used (Ugo Basile, Italy) consisting of a grooved plastic beam 5 cm in diameter. Mice were placed on the beam (revolving at the default 5 rpm) facing in the opposite orientation to rotation. After 1 min the rod speed was gradually accelerated to a maximum of 30 rpm over 4 min by electronic control of the motor. The latency before falling was measured up to a maximum total time on the rod of 6 min for three trials at 1 h intervals. 2.2.6. Round static rod coordination A wooden dowel 28 mm diameter and 60 cm in length was fixed to solid support at one end and clamped to bench 60 cm above a padded surface. Mice were placed on the protruding end of the rod, facing away from the bench, and the time taken to turn and face the bench (orientation time), to fall from the rod or for all four paws to reach the supported end (transit time) was recorded up to maximum of 3 min. Any mouse failing to achieve one of the tasks was given a maximum score of 180 s [12]. 2.2.7. Spontaneous alternation A T-maze constructed from dark grey plastic was used, consisting of three arms 30 cm in length, 10 cm wide with walls 29 cm high. Mice were placed at the base of the ‘T’ (starting arm) and allowed to walk to the junction for arm selection. Once the tail had passed beneath the barrier of the chosen arm, it was closed and the mouse was retained in that section for 30 s. The barrier was subsequently raised while the mouse was returned to the starting arm allowing the selection process to be repeated. A total number of 10 trials were carried out over 2 days in blocks of five trials 30 min apart. If no choice had been made after a total of 5 min then a ‘fail’ score was recorded for that particular test.
2.3. Data analysis To analyse the data, repeated measures ANOVA’s were employed executed in the SPSS program. In circumstances where the data failed to meet the assumptions of normality and equality of variance either transformations or non-parametric tests were used.
3. Results 3.1. Weight Mice were weighed on day one of the test battery and robotic mutants were consistently smaller in size and body weight than littermate controls over the testing period (Fig. 1). Statistical analysis revealed that there was not only an effect of genotype (F(1,18) = 813.5, P < 0.0001), but also an effect of age (F(3,54) = 220.2, P < 0.0001) on body mass. In addition, robotic mice failed to gain as much weight as controls as shown by an interaction between age and genotype (F(3,54) = 15.3, P < 0.0001); mutants were over 60% the weight of controls at 6 weeks of age, but were approximately half the weight by the final timepoint. 3.2. Locomotor activity Locomotor activity (LMA) was measured to determine the affect of the ataxia on general movement in an open field (Fig. 2). Mutants consistently exhibited reduced activity at all timepoints,
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Fig. 1. Mean weight of robotic (filled squares) and wild-type (open squares) mice over the time course of the experimental period. Robotic mice are significant smaller than control mice (F(1,18) = 813.5, P < 0.0001). Error bars show S.E.M.
scoring approximately one-third fewer beam breaks at 6–20 weeks of age. The inactivity of mutants was most noticeable at 40 weeks of age, with an almost 50% reduction in the number of beam breaks. The effect of genotype on LMA was highly significant (F(1,18) = 52.8, P < 0.0001); however, based on the total number of beam breaks, a repeated ANOVA showed no effect of age (F(3,54) = 1.3, P > 0.2) or interaction between age and genotype (F(3,54) = 1.7, P > 0.1) on activity in the apparatus. 3.3. Gait analysis To further analyse the progression of the ataxia in mutants, detailed gait analysis was carried out by painting the hind-feet
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of mice and quantifying a number of parameters from the footprint traces. It was noticeable from the patterns obtained in this task that robotic mice used the whole of their rear paws to walk and maintain balance, whereas control animals were more likely to use the toes and the front part of the foot only (Fig. 3A). From analysis of the traces, robotic mice show a large reduction in mean stride distance at all timepoints compared to controls (F(1,8) = 7195, P < 0.0001); this reduction became more pronounced at later timepoints as shown by an interaction between age and genotype ((F(3,24) = 8.6, P < 0.0005) (Fig. 3B)). This shortened stride length may be a result of the ataxia, but this is also undoubtedly a reflection of the reduced body length of mutants, which is approximately 60–65% of wild-type mice; a similar reduction to that seen in mean stride distance (data not shown). Conversely, however, an increase in mean gait width was observed from 10 weeks of age in robotic mice that cannot be accounted for by body size (Fig. 3C). Statistical analysis revealed that although gait width generally increased with age (F(3,24) = 20.6, P < 0.0001), there was significant effect of genotype (F(1,8) = 28.5, P = 0.001) and an interaction between age and genotype (F(3,24) = 5.8, P < 0.005) on this measurement, reflecting the distinctive broad-based walk of this mutant. To ascertain whether the uneven, jerky nature of the robotic ataxic gait became more pronounced during ageing, the maximum stride difference and linearality of movement was calculated (Fig. 3D and E). As the mutant mice became older, the regular alternating gait observed at 6 weeks of age in mutants became progressively erratic, with larger maximum stride differences than controls observed (Fig. 3D). The effect of genotype was highly significant (F(1,8) = 18.6, P < 0.005), although there was no affect of age (F(3,24) = 1.7, P > 0.1) or interaction between age and genotype (age (F(3,24) < 1, P < 0.5) on step length irregularity. There was also no bias in this parameter towards one side, with both right–right and left–left stride measurements showing no significant differences at any time (data not shown). Furthermore, robotic mice showed an increasing tendency to meander along the apparatus as shown by the linearality score, a measurement of regularity of direction (Fig. 3E). This is illustrated by the curved nature of a representative footprint pattern from a robotic mouse at 20 weeks of age compared to a control animal that consistently walked in a much straighter line (Fig. 3A). Repeated measures ANOVA did not, however, find a significant affect of genotype (F(1,8) = 3.8, P > 0.05) or interaction between age and genotype (F(3,24) < 1, P < 0.5) on linearality. 3.4. Grip strength
Fig. 2. LMA expressed as total number of beam breaks in a 35 min period. Robotic mutants (filled squares) exhibit a significant reduction in overall levels activity compared to wild-type mice (open squares) (F(1,18) = 52.8, P < 0.00001). Error bars show S.E.M.
To assess grip strength and climbing ability, parameters that may influence additional tests of motor coordination, the inverted screen test was carried out. At each timepoint, all wild-type and mutant animals were able to maintain their grip on the wire mesh once inverted for the maximum 120 s, even at 40 weeks of age (data not shown). In addition, mice from both genotypes were able to manoeuvre
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Fig. 4. Grip strength testing. The mean maximum forepaw resistance of robotic mutants (filled bars) is significantly lower than wild-type (open bars) mice (F(1,10) = 12.3, P < 0.01), although not progressive with age. Error bars show S.E.M.
themselves around the screen without falling. This suggests that the general grip strength of robotic mice was not impaired to a significant degree despite the presence of overt ataxia. However, to analyse forelimb strength in more quantitative manner, a grip strength meter was used. From these data, a small but significant reduction in grip strength was identified at all four timepoints tested by mutant mice ((F(1,10) = 12.3, P < 0.01) Fig. 4), although this was not significantly influenced by the age of the animals and was not progressive. 3.5. Rotarod
Fig. 3. Gait analysis by hind-foot painting. (A) Representative footprint trails from 20-week-old wild-type (top) and robotic (bottom) mice. (B) Mean stride distance is progressively and significantly shorter in robotic mutants (filled bars) than wild-type mice (open bars) (F(3,24) = 8.6, P < 0.0005). (C) Robotic mice show a significant increase in gait width with age (F(3,24) = 5.8, P < 0.005) and the mean maximum stride difference (D) is also significantly larger in robotic mutants (F(1,8) = 18.6, P < 0.005), although not progressive. (E) The linearality measurement scores suggest that robotic mutants are more inclined to meander along the footprint apparatus than wild-type mice. Error bars show S.E.M.
To quantify the progression of the ataxia and motor coordination in robotic mutants, their performance on an accelerating rotarod was observed. From the mean of three consecutive trials, longitudinal analysis shows that there was a significant effect of age in both genotypes ((F(3,54) = 35.1, P < 0.0001) Fig. 5A). At 6 weeks of age control mice average 223.7 s on the rotarod, which decreased to 116.3 s at 40 weeks. Likewise, the performance of mutant mice decreased from an average of 203.3 s at 6 weeks to 82.0 s at the final timepoint. A repeated measures ANOVA showed that there was also a significant effect of genotype (F(1,18) = 21.2, P < 0.0005), with mutants performing worse than controls, as expected, but no significant correlation between age and genotype (F(3,54) = 1.3, P > 0.1). Both groups of mice did, however, show an improvement in performance over the three individual trials at each timepoint (Fig. 5B).
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3.6. Static rod The static rod test also used to assesses motor coordination and equilibrium although in a situation where there were fewer spatiotemporal constraints than on the rotarod. At 6 weeks of age, there was no significant difference between the groups in the time taken to reach the supported end of the rod (F(1,19) = 0.01, P > 0.5). From 10 weeks onwards, mutants began to perform worse on the beam; by 20 weeks of age, none of the mutants completed the task, and all robotic animals fell during testing at 40 weeks (Fig. 5D). Statistical analysis revealed that overall there was a significant effect of genotype (Z = −2.82, P = 0.005), a significant effect of age (F(3,54) = 4.35, P < 0.01), however, no interaction between age and genotype (F(3,54) = 0.89, P = 0.448). Similar results were obtained when examining orientation times, with robotic mice taking longer than controls to maintain their equilibrium successfully at the end of the rod after 6 weeks of age (Fig. 5C). In general, robotic mutants would attempt to orientate themselves even at 40 weeks of age and it was during this movement that the mice would slip and fall. Overall from the orientation data there was a significant effect of genotype (Z = −2.5, P = 0.01), an effect of age (F(3,54) = 6.8, P = 0.001), and an interaction between age and genotype (F(3,54) = 4.2, P = 0.01). 3.7. Spontaneous alternation
Fig. 5. Motor coordination testing of robotic mutants. (A) Rotaord performance; latency to fall is displayed as a mean of three consecutive trials. Mutants (filled squares) perform consistently worse on the apparatus than wild-type mice (open squares) (F(1,18) = 21.2, P < 0.0005) although both genotypes show a significant reduction in performance over time (F(3,54) = 35.1, P < 0.0001). (B) Mean latency to fall from the rotarod over three individual trials (left to right) showing improvement in both wild-type and robotic mutants. (C and D) Static beam performance; time taken to reach the end of the rod (transit time) and time taken to turn around initially (orientation time) is shown. There was a significant decrease in performance of mutants (filled bars) compared to wild-type mice (open bars) in both parameters (Z = −2.82, P = 0.005; Z = −2.5, P = 0.01, respectively). Error bars indicate S.E.M.
To analyse working spatial memory we employed a freetrail spontaneous alternation protocol. We chose this paradigm because this delayed non-match to place task does not require vision [6]. In addition to the cataracts that are first visible from 2 months of age in robotic mice, the mutant is also on a C3H background which carries a homozygous mutation (rd) causing almost complete degeneration of photoreceptor cells by 30 days of age [5,16]. In the T-maze robotic mice displayed a slightly reduced alternation rate in comparison to wild-type mice in general, however this difference was not significant at any of the four timepoints tested (F(1,18) = 1.8, P > 0.1). Overall there was also a reduction in rates of alternation for both mutants and controls during the 40 weeks period; controls decreased from an average alternation rate of 84–76.3%, while mutants decreased from 81 to 70.3%. Analysis by an ANOVA showed that the decrease in performance with age was significant (F(3,54) = 2.92, P < 0.05), however, there was no genotype by age interaction (F(3,54) = 0.13, P > 0.5; Fig. 6). To determine whether the ataxia in robotic mice lead to a bias in the direction of choice, the total number of left and right turns in the first stage of the experiment was recorded; there was no significant difference in choice between wild-type and robotic mice at any timepoint (data not shown). 4. Discussion We have undertaken the first longitudinal analysis of the robotic mouse mutant on tasks that assess locomotor activity, motor coordination and working spatial memory. This study has demonstrated that robotic mice become progressively inac-
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occurs in a transcription factor does not preclude its influence on metabolic or hormonal pathways. 4.2. Locomotor activity, gait and grip strength
Fig. 6. Spontaneous alternation of robotic mutants. Spontaneous alternation in a T-maze was employed to assess working spatial memory in robotic mice. While there was a significant age related decrease in performance for both wild-type mice (open squares) and mutants (filled squares) (F(3,54) = 2.92, P < 0.05), there was no effect of genotype (F(1,18) = 1.8, P > 0.1).
tive and increasingly uncoordinated compared to control mice as their gait disturbance becomes more pronounced, although they did not display a significant T-maze alternation deficit. A summary of these results is shown in Table 1. 4.1. Weight Robotic mutants can be identified from their littermates during the third post-natal week when their reduced size is first noticeable; the data here show that by 6 weeks of age mutants are approximately 60% of the size of controls throughout adulthood. In addition, their reduced size is in proportion to their mass, with body length, limb length and brain weights on average 60–65% that of control animals (data not shown). Small size is also a feature of other cerebellar mutants, such as homozygous weaver (Grik2wv ) [51] and staggerer [9]; it was concluded from the latter study, as here, that differences in motor function tests are unlikely to be attributed to body mass as the variation between the wild-type and mutant strain was essentially consistent throughout the experiment. It is often speculated that ataxic mutants are smaller then littermates due to competition for food during the early stages of development [38]. This may indeed be the case in robotic mice, however the fact that the mutation
Robotic mice were considerably less active than controls in the LMA apparatus and this effect was more pronounced at the final timepoint tested. This was also reflected in the T-maze apparatus where mutants consistently took longer to complete the task. A reduction in spontaneous activity has also been recorded in similar apparatus in both the weaver and staggerer mutants that also suffer from partial Purkinje cell loss (see below) [36,37]. The decreased activity of robotic mice is unlikely to be exclusively due to an impairment in general exploratory behaviour as they were able to complete the T-maze task successfully, even at 40 weeks of age. This may in part reflect the increasingly erratic nature of their gait as illustrated by the footprint analysis, substantiating general observations during handling that the ataxia in robotic mice is progressive. Attempts were made to quantify this phenomenon further using a holeboard test as used in a time course study of lurcher behaviour [21]. Hilber and Caston showed that lurcher mutants were severely impaired in this task, as quantified by the number and frequency of foot slips, although the deficits did not increase with age. However, attempts to analyse robotic mice in identical apparatus were not interpretable because there was a large discrepancy in walking times between mutants and controls (data not shown). Results from the grip strength tests showed that robotic mice are able to hold their own bodyweight using all four limbs; this observation was also noted during characterisation of the npcnih mutant, even at the end-point of the experiment [56]. In addition, a progressive yet relatively small reduction in forepaw grip strength was observed in robotic mice. This is in contrast to measurements of muscular strength by wire hanging carried out on ageing lurcher mice, where the latency before falling was up to 20 times shorter in mutants compared to age-matched wild-type controls [21]. However, our data does suggest that the relative climbing ability and strength of robotic mice would not be a confounding factor in the interpretation of motor coordination tests. The consistently smaller size of mutants may be masking any grip strength deficiency on the inverted screen test; however, detailed analysis of muscle biopsies has not identified any differences in the fibre size, cytoarchitecture or fibre-type ratios of robotic mice compared to controls (unpublished observations).
Table 1 Summary of results in this study Age (weeks)
Vermal PC loss (%)
LMA
Mean max stride difference
Gait linearality
Grip strength
Rotarod
Spontaneous alternation
6 10 20 40
0 25 50 75
↓ ↓ ↓ ↓↓
↔ ↓ ↓↓ ↓↓↓
↑ ↑↑ ↑↑↑ ↑↑↑↑
↔ ↓ ↓↓ ↓
↓ ↓↓ ↓↓↓ ↓↓
↔ ↔ ↔ ↔
The direction of the arrows indicates the relative average performance or experimental values of robotic mutants vs. age-matched wild-type mice. Percentage Purkinje cell (PC) loss in the cerebellar vermis is shown based on data from Isaacs et al. [22].
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4.3. Motor coordination tests In concordance with numerous studies of spontaneous and transgenic ataxic mouse mutants, robotic mice were impaired on both the accelerating rotarod and static rod tests of motor coordination [11,21,28–31,38,42,54]. Latencies before falling from the rotarod were consistently lower in robotic mice than controls, although this difference was more pronounced at 10 and 20 weeks of age. However, mutants appeared to be motivated to carry out the test, as passive rotation, clinging on to the rod while it freely rotates (as reported elsewhere [42]), was a very rare occurrence. In addition, both robotic and wild-type mice showed increased average latencies before falling over the three successive trials. This is consistent with a number of studies of cerebellar mutants in which absolute performance on the rotatrod is impaired, but motor learning is still present after training [11,21]. Furthermore, wild-type mice showed a general trend of decreased performance on the rotarod during ageing which has also been observed in longitudinal studies of lurcher and staggerer mice prior to, or without, training [9,21]. It has been postulated this phenomenon is a function of muscle strength as opposed to impairments in the acquisition of motor tasks [21], although it has been shown here that there is no obvious correlation between forepaw strength and rotarod performance in control mice up to 40 weeks of age. 4.4. Spontaneous alternation Although robotic mutants consistently alternated at rates 5–10% lower than controls, these figures were considerably above chance at all timepoints tested. This was somewhat surprising considering that delayed spontaneous alternation rates, using two-trial paradigms, are observed in all of the well-known spontaneous cerebellar mutants [4,10,15,24,27,33–38]. It has therefore been suggested that interactions between the cerebellum and the hippocampus and/or vestibular system underlie alternation deficits in rather than simply a function of poor motor coordination [25,44]. Although Af4 is expressed at low levels in the hippocampus, there are no anatomical abnormalities in this region in robotic mice (unpublished observations). Moreover, with no detectable degeneration of granule cell or inferior olive neurons in robotic mice, it is tempting to speculate that their ability to alternate, even when only 30% of the Purkinje cells remain, might reflect uncompromised connectivity related to these spared regions. The cerebellum is implicated in emotional as well as motor behaviour [8,43], and motivation is also likely to impair spontaneous alternation performance after repeated trails; potentially cerebellar pathways related to this response may be intact in robotic mice but impaired in other ataxic mutants with more profound cell loss. 5. Conclusions There has been some debate over the contributions of Purkinje cell loss and connectivity in the cerebellum of ataxic mutants to their relative performance in certain tests of motor coordination. For example, both lurcher and pcd mutants suffer from
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almost complete Purkinje cell loss in addition to severe granule cell death but perform normally on a rectangular stationary beam [28,32,42]. In contrast, staggerer and weaver mice, with approximately 75 and 40% Purkinje cell loss, respectively, fall quicker on the same apparatus [13,54]. This suggests that aberrant Purkinje cell innervation has a greater influence on motor coordination than complete absence. This argument can be extended to locomotor activity; lurcher, pcd and nervous mutants display ambulatory activity that is equivalent or even more pronounced than controls, where ataxia may even be masking further hyperactive behaviour [26,33,35], whereas staggerer and weaver are hypoactive in open field and T-maze tests [13,36,37,54]. These data are in agreement with our findings of robotic, where a significant reduction in locomotor activity was observed even at 6 weeks of age, when only a small percentage of Purkinje cells display any visible pathology. It is therefore interesting that in analysis of npcnih mice at the same age, prior to Purkinje cell death, male mutants travelled a significantly shorter total distance over a 15 min spontaneous locomotor activity test [56]. However, it is clear from our study that the ataxia and motor coordination deficits of robotic mice are progressive as the cerebellar degeneration becomes more severe. This is also the conclusion drawn from a longitudinal analysis of round static beam and rotarod performance of the npcnih mutant [56]. Although the npcnih study was carried out over a much shorter time course (4 weeks) than the robotic behavioural analysis described here (34 weeks), the distinctive anterior–posterior and parasagittal striped pattern of Purkinje cell loss of both mutants is remarkably similar [19,50]. This common cell loss pattern between two seemingly unrelated mutant lines may reflect an underlying susceptibility of particular groups of Purkinje cells to pathological insults. Therefore, both these studies would argue that defects in only a small and localised region of the cerebellum is enough to significantly influence motor function and coordination. Robotic is therefore an excellent opportunity to study this phenomenon, particularly as additional pathological features of the npcnih phenotype, such as defects in peripheral nerve, are likely to influence behavioural testing of motor function and control [20]. Established cerebellar mouse mutants have provided some limited insight into function consequences of region-specific disruption of Purkinje cell loss. For example, nervous mice, in which the lateral cerebellum is predominantly susceptible to cell death, do not show a deficit in the horizontal bar suspension test compared to controls [38]; this may illustrate the importance of that particular cerebellar region to the parameters examined in that specific task. However, robotic provides a potentially more sophisticated tool for the understanding of compartmentalisation in the cerebellum. Although comparatively little is known about the relative contribution of specific lobes to behavioural tasks, work in rodents has shown that Larsell’s lobule HVI and the anterior lobes are critical for normal eye-blink conditioning responses [1,45,55], and human MRI studies have revealed the specific activation of various regions in the cerebellar vermis and hemispheres in working memory tasks [14]. With new insights
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