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The immune marker CD68 correlates with cognitive impairment in normally aged rats
Neurobiology of Aging xxx (2013) 1e6
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The immune marker CD68 correlates with cognitive impairment in normally aged rats Mark Farso, Caroline Ménard, Jessica Colby-Milley, Rémi Quirion* Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, Québec, Canada
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a b s t r a c t
Article history: Received 12 November 2012 Received in revised form 8 February 2013 Accepted 15 February 2013
The relationship between heightened neuroinflammation and cognitive decline in the normally aged brain is still debatable, as most data are derived from insult-related models. Accordingly, the aim of the current study was to determine whether a link could be established for 2 immune markers at the posttranscriptional level; CD68 and MHC-II, in a normally aged (24-month-old) rat population discriminated for their learning abilities. Using the Morris Water Maze (MWM) task, aged rats were divided into aged learning-impaired (AI) or -unimpaired (AU) groups. Western immunoblots of hippocampal tissue revealed a significant increase of CD68 in AI rats compared to the AU group. Moreover, up-regulated CD68 expression correlated with increased latency times in the MWM task. Immunofluorescence for CD68 revealed intense staining in the white matter regions and CA3 subregion of the hippocampus in the AI group. Despite expression of MHC-II in the AI group, no correlation was found. Overall, these data suggest that CD68 could play a role associated with cognitive decline in a subgroup of the normally aged population. Ó 2013 Elsevier Inc. All rights reserved.
Keywords: Normally aged rat Learning-impairment Hippocampus CD68 MHC-II White matter
1. Introduction Epidemiological studies show increased prevalence of cognitive decline occurring in the aging (i.e., 65 years) population (DeCarli, 2003; Ward et al., 2012). Interestingly, these studies also reveal that not all aged individuals will necessarily develop such impairment, therefore inciting the need to elucidate the mechanisms behind successful (cognitive) aging. A similar phenomenon is observed in an animal model of normal aging in which aged (24-month-old) rats can be distinguished into 2 main subpopulations: aged memory-unimpaired (AU) and aged memory-impaired (AI) based on hippocampal-dependent spatial learning and memory (Brouillette and Quirion, 2008; Geinisman et al., 2004; Ménard and Quirion, 2012; Rowe et al., 2007). Moreover, mechanistic studies in this model indicate alterations, in the AI group, of specific hippocampal signaling pathways that could be responsible for their cognitive decline (Brouillette and Quirion, 2008; Ménard and Quirion, 2012; Rowe et al., 2007). Heightened neuroinflammatory responses are associated with brain aging, indicating a possible role for these phenomena in agerelated cognitive decline (Corona et al., 2011; David et al., 1997; Lu * Corresponding author at: Douglas Mental Health University Institute, Department of Psychiatry, McGill University, 6875, LaSalle Boulevard, Verdun (Montréal), QC, H4H 1R3, Canada. Tel.: þ1 514 761 61312934; fax: þ1 514 762 3034. E-mail address: [email protected] (R. Quirion). 0197-4580/$ e see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neurobiolaging.2013.02.008
et al., 2004; Viviani and Boraso, 2011). Brains of aged animals exhibit increased levels of cytokines/chemokines (Bodles and Barger, 2004; Lynch et al., 2010), implicating a role for the activation of microglia during aging. As microglia are considered to be the main producers of these inflammatory molecules (Hanisch and Kettenmann, 2007; Kettenmann et al., 2011), their involvement in the aged brain with learning and memory is thus likely to be crucial (Lynch et al., 2010; Yirmiya and Goshen, 2011). Evidence for enhanced microglial activity in the aged brain in comparison with younger counterparts has been previously shown via increased expression of major histocompatibility complex class II antigens (MHC II) (Sheffield and Berman, 1998), CD68 (Kullberg et al., 2001) and other immune markers (Griffin et al., 2006). In addition, microglia from aged animals display enlarged cell bodies and shortened processes (Perry et al., 1993). The link between neuroinflammatory activity and age-associated cognitive deficit is evidenced through gene microarray analyses of the hippocampus, in which a causal relationship between inflammatory gene responses and normally age-associated cognitive deficit was convincingly established (Blalock et al., 2003; Haberman et al., 2011). However, studies involving post-transcriptional inflammatory activity with age-related learning impairments are still rare. Therefore, the aim of the present study was to determine whether increased neuroinflammation, at the protein level, bears a correlation to decreased cognitive status in a population of normally aged (24-month old; 24-mo) rats. These aged rats were
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discriminated for their learning ability using the Morris Water Maze task, their hippocampi subsequently assessed for protein levels of 2 immune markers known to be up-regulated in the aged brain; CD68 and MHC-II, followed by a correlation analysis to determine a possible link to aged-impaired rats. 2. Methods 2.1. Animals Male LongeEvans rats were purchased from Charles River Laboratories (St Constant, QC, Canada) and were housed at the Douglas Mental Health University Institute animal facility until 6 months or 24 months old. Animals (2 per cage) were maintained on a 12-h light/dark cycle with ad libitum access to food (Purina Lab Chow; Mondou, Montreal, QC, Canada) and water. Animal care, surgery, and handling procedures were approved by the McGill University Animal Care Committee (protocol no. 3589) and the Canadian Council for Animal Care. 2.2. Morris Water Maze task Male Long-Evans rats of 6 months (i.e., young; Y; used as a reference point) or 24 months (i.e., aged rats) were tested for spatial memory using the Morris Water Maze (MWM) task, a hippocampal-dependent behavioral task (Morris, 1984). In accordance with Ménard and Quirion (2012), rats were timed (i.e., the latency, in seconds) to find a submerged platform located below
the surface of opaque water, with 3 trials for 5 consecutive days, followed by the first probe test on day 5. This protocol was repeated the following week to determine discrimination reversal, a form of inhibitory learning, which tests for the formation of new memories and moreover is affected during aging (Wilson et al., 2006). Seven days later, a third probe test was performed for the evaluation of memory consolidation and retrieval (data not shown). A timeline illustrating the behavior protocol is shown in Fig. 1A. Classification criteria were used for the learning curves and probe tests to accurately distinguish between AU and AI rats, and are extensively explained by Ménard and Quirion (2012). In brief, aged rats were distinguished as impaired when the time taken to find the hidden platform on both days 4 and 5 was over 25 seconds (average of 3 trials per day). The learning probe test was then used to confirm memory impairment via platform crossings and time spent in the target and opposite quadrants. A criterion of 2 or fewer crossings over the hidden platform position during the learning probe test (total of 60 seconds) was used. Moreover, aged rats with spatial memory deficits swam randomly in the pool during the probe tests, as assessed by the time spent in the target versus opposite quadrants. The reverse memory paradigm (in the second week of training) assessed inhibitory learning whereby the platform was moved to the opposite quadrant whereas the position of visual cues did not change. Rats were then trained to find the new hidden platform location for 4 consecutive days (3 trials per day), followed by a second probe test. The third probe test evaluated memory consolidation and retrieval. Brain tissue from all 3 test groups was collected for Western immunoblotting or immunofluorescence.
Fig. 1. Discrimination of aged rats as either AI or AU. The timeline for the behavior protocol (A). Mean latency time (in seconds) for each training day of the MWM task in week 1 (B) and week 2 (D) indicated a significantly (p < 0.0001) slower learning curve for AI rats compared to that of AU rats, which remained at Y levels. The probe test on day 5 (C) and day 11 (E) confirmed this discrimination, as AI rats showed significantly (p < 0.001) fewer platform crossings compared to AU and Y rats. * p < 0.05; ** p < 0.001.
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2.3. Western immunoblotting Considering the timeline of training (i.e., 3 weeks) involving consolidation of memories, adjacent cortices (the neocortex) of the hippocampus were included with hippocampal lysates (Wang and Morris, 2010). Soluble lysate was prepared from hippocampal homogenate and its protein content determined as previously reported (Ménard and Quirion, 2012). Proteins from the soluble lysate were separated via gel electrophoresis (20 mg protein per lane at 180 V for 2 hours at room temperature [RT]) and then followed by their transfer onto Hybond-C nitrocellulose membranes (Amersham Biosciences, Baie d’urfe, QC, Canada) (80 V for 2 hours on ice) (Farso et al., 2011). After blocking in 5% skim milk (dissolved in Trisbuffered saline [TBS] with 0.05% Tween 20 [0.05% TBST], 1 hour at RT), the membranes were subsequently incubated with the primary antibodies: anti-mouse CD68 (AbD Serotec, Raleigh, NC) (1:50) or anti-mouse MHC-II (Abcam, Cambridge, MA) (1:500) (both in 0.05% TBST with 5% skim milk) overnight at 4 C. Incubation with horseradish peroxidase (HRP)econjugated goat-anti mouse secondary antibody (Santa Cruz Biotechnology, Santa Cruz, CA) (1:15,000 in 0.05% TBST with 5% skim milk; 1 hour at RT) then followed. b-actin (Santa Cruz Biotechnology, Santa Cruz, CA) (1:5000 in 0.05% TBST with 5% skim milk; 1 hour at RT) served as the loading control. Relevant optical densities were normalized against b-actin and then expressed as a percentage of Y. Samples were tested in quadruplicates from each rat group (6 rats per group), and the means were pooled and graphed using the GraphPad Prism software V. 2.4. Immunofluorescence for CD68 Coronal sections (20-mm thick) at the level of the dorsal hippocampus of AI rats were fixed (4% paraformaldhyde; 10 minutes at RT), permeabilized (0.2% TritonX 100 diluted in 0.1 mol/L phosphate-buffered saline [PBS]; 10 minutes at RT), blocked (10% normal goat serum [NGS] in 0.1 mol/L PBS with 0.05% Tween-20 [0.05% PBST]), and exposed to the primary, CD68 (1:100 in 10% NGS and 0.05% PBST, at 4 C, overnight), followed by the secondary Alexa Fluor 568 goat anti-mouse IgG antibody (Invitrogen, Carlsbad, CA) (1:200 in 1% NGS and 0.05% PBST, at RT, 2 hours in the dark). Coverslips were then added to the sections with Fluoromount-G (Southern Biotech, Birmingham, AL). Immunoreactivity in the hippocampus and neocortex was visualized using the Zeiss Axio Observer microscope and digital photographs were taken using Zeiss Axio Vision software (version 4.7.1.0). 2.5. Statistical analysis All data are expressed as the mean SEM and were analyzed by 1-way analysis of variance (ANOVA) followed by the Bonferroni post hoc test. Significance was reached at values of p < 0.05 or p < 0.0001. 3. Results 3.1. Characterization of the normally aged rat population as either AI or AU The mean latency to find the submerged platform in the MWM (Fig. 1B) task was significantly higher (approximately 60 seconds) for a subpopulation of aged rats (p < 0.001) from days 2 to 5 of training, and thus classified as AI (Fig. 1B). This group had a significantly slower learning curve compared to a subset of aged rats (labeled as AU) that exhibited the same latency times as Y rats (with the exception on day 2; between 10 and 40 seconds). The probe test
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(learning probe) at the end of week 1 (Fig. 1C) confirmed an impairment of spatial memory for the AI group, as the number of platform crossings was significantly lower (approximately 50%; p < 0.001) for AI rats compared to AU and Y rats. The inhibitory learning task (Fig. 1D) showed a strongly significant lack of discrimination of reversal abilities for the AI subgroup, as the mean latency was significantly higher (between 40 and 60 seconds) in this group from days 8 to 11 (p < 0.001) compared to the AU and Y group (between 10 and 20 seconds). The second probe test (reverse probe; Fig. 1E) showed that the number of platform crossings was significantly lower (approximately 70%e80%; p < 0.001) for AI rats compared to AU and Y rats, thus validating memory impairments for multiple cognitive processes in this subgroup. 3.2. Correlation between learning impairment observed in the AI subgroup and increased CD68 expression Quantification of Western immunoblots for neuroimmune mediators indicated that both CD68 and MHC-II were up-regulated in the aged rats, but that only CD68 correlated with learning impairment. Compared to that in Y and AU rats, the expression of CD68 (100% 14% and 117% 13%, respectively) was significantly (p < 0.05) increased approximately 2.5-fold in the AI subgroup (252% 62%; Fig. 2A). CD68 expression in AU rats remained at levels similar to those of the Y group. In the total population of aged rats consisting of AU and AI groups, the percentages distribution of expression of CD68 in the AI group (ranging between 165% and 550%) correlated (Pearson r coefficient of 0.3320; p < 0.05) with higher mean latency scores on day 5 (ranging between 30 and 50 seconds) in the same group (Fig. 2B), indicating that increased levels of CD68 correlated well with the decreased learning ability in the subgroup of aged rats. Expression of MHC-II in the AI subgroup (272% 34%) was significantly (p < 0.05) increased (approximately 3-fold) compared to Y rats (100% 18%) but not the AU group (207% 41%) (Fig. 2C). MHC-II did not correlate with learning impairment, as the percentage distribution of expression of MHC-II in the AI group (ranging between 180% and 500%) were similar to those of the AU group (ranging between 150% and 450%), and thus no correlation could be reached (Pearson r coefficient of 0.0303; p ¼ 0.5886) with the mean latency scores on day 5 (Fig. 2D). 3.3. AI rats exhibit a distinct population of CD68-positive cells in the hippocampus and adjacent white matter regions We then set out to investigate the distribution of CD68 in the AI subgroup, to couple its correlation with learning impairment to its presence in the hippocampus, as this brain region is the main center on which the MWM task depends. Immunofluorescence for CD68 has previously been shown to be punctate in nature (McKay et al., 2007), which was also observed in the current study, thus validating our immunohistochemistry methodology. Overall, the observations suggested that the involvement of CD68 with learning impairment is likely to be predominantly within the hippocampal formation, as more prominent immunoreactivity for CD68 was seen in the hippocampus and white matter regions (WMRs) compared to the adjacent cortical regions. In the AI subgroup, intense and abundant staining for CD68 was observed in (1) hippocampal white matter regions: fimbria (fi) (Fig. 3A), deep cerebral white matter (dcw) and alveus (alv) surrounding the CA1 and CA2 subregions (Fig. 3B); (2) pyramidal cell layer of the CA2 subregion (Fig. 3B); and (3) molecular layer of dentate gyrus (MoDG) (Fig. 3C). Compared to these regions, CD68 was not as prominently present in the CA1 subregion (Fig. 3C), the primary somatosensory (S1) and secondary somatosensory (S2) cortices (cx) (Fig. 3D and E, respectively) and the motor cortex (Mcx) (Fig. 3F). Compared to the AI group, CD68
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Fig. 2. Quantification and correlation studies with the immune markers, CD68, and MHC-II. Quantification of Western immunoblots for CD68 (A) and MHC-II (C) indicated that although both are significantly (p < 0.05) increased in AI rats compared to Y, only CD68 is significantly (p < 0.05) up-regulated in AI versus AU rats. Correlation analyses for CD68 (B) and MHC-II (D) revealed that increased latency time observed in AI rats significantly correlated with CD68 (r2 ¼ 0.3320, p < 0.05) but not MHC-II. * p < 0.05; ** p < 0.0001.
was less prominent in AU rats, and negligible immunoreactivity was observed in Y rats (data not shown). 4. Discussion The current study provides evidence that, in a normally aged rat population, an association may exist between an increase in selected neuroinflammatory-like alterations and learning impairment. Our results suggest the possible involvement of CD68, but not MHC-II, in learning impairment. Although focused on 2 immune markers, these findings could imply a diversity of inflammatory signaling, potentially through the heterogeneous activity of microglia (Hanisch and Kettenmann, 2007), occurring in the aged rat population with cognitive deficits. We report that increased levels of CD68, a glycoprotein expressed on lysosomal membranes of activated microglia/macrophages (McKay et al., 2007), correlates with the cognitive deficit in the same subgroup of aged rats (i.e., the AI subgroup). These results confirm, yet also extend, the role of CD68 in such a population, as recent work by Blau et al. (2012) demonstrated that, in normal aged rats, increased mRNA levels of CD68 correlated with reduced synaptic plasticity. We also show that the additional immune marker, MHC-II, was elevated at its protein level in AI rats but did not correlate with cognitive impairment; in agreement with VanGuilder et al. (2011), who found that increased mRNA for MHC-II and its structural components had no correlation with their learning-impaired rats. Increasing evidence is pointing toward the concept of heightened neuroimmune activity in WMRs with the dysregulation of myelin-mediated signaling and loss of white matter integrity as 1 potential cause for the cognitive demise occurring during normal aging (Bendlin et al., 2010; Blalock et al., 2003; Huang et al., 2009; Rowe et al., 2007; VanGuilder et al., 2012). Activated microglia is
likely to play an important role in the above paradigm, given their toxic action in demyelinating diseases (Hanisch and Kettenmann, 2007; Merson et al., 2010). Induction of antigen presentation (via MHC-II) on microglia in response to myelin destruction may cause an increased inflammatory status ultimately leading to neuronal demise (Blalock et al., 2003). An enhanced inflammatory status, as observed through the activation of microglia, is shown in hippocampal WMRs of aged animals (Sheffield and Berman, 1998; Sloane et al., 1999). Although these animals were not discriminated for their cognitive ability, the suggestion was brought forward of an association between inflammation in WMRs and cognitive decline in aged animals (Sheffield and Berman, 1998; Sloane et al., 1999). Our findings with CD68 may lend further support for the aforementioned concept, as CD68-positive microglia has been implicated in myelin destruction (De Groot et al., 1997). In the subgroup of aged rats in which hippocampal CD68 expression correlated with cognitive deficit, the WMRs surrounding the hippocampus of these rats also showed the presence of CD68, particularly in the fimbria region (including alveus), which is part of the fimbriaefornix formation. The fimbriaefornix is involved with hippocampal information processing (Kesner, 2007) as well as the axonal output region for the CA3 (Kesner, 2007), and is shown to deteriorate with age (Jang et al., 2011) and negatively affect cognition when damaged (Sarro et al., 2011; Walker and Olton, 1984). Therefore, it could be hypothesized that at late age a relationship exists between heightened microglial-mediated immune activity and dysregulation of white matter processing involving myelin deficiency (in the fimbria/alveus region), consequently impairing learning capabilities due to decreased synaptic transmission (Blalock et al., 2003). Alternatively, the presence of CD68 in the WMRs of the AI rats may indeed reflect phagocytosis of myelin, as activated microglia have been shown to phagocytise this sheath
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Fig. 3. CD68 immunofluorescence in the AI hippocampus and adjacent neocortex. Immunoreactivity of CD68 in the AI brain was observed in the fimbria (fi) of the hippocampus (A) and deep cerebral white matter (dcw) and alveus (alv) (B), the CA2 subregion (A and B), and in the molecular dentate gyrus (MoDG) (C). Staining was less prominent in the adjacent cortical regions: S2 cx (D), S1 cx (E), and Mcx (F). Abbreviations: cg, cingulum; CPu, caudate putamen; Mcx, motor cortex; S1 cx, primary somatosensory cortex; S2 cx, secondary somatosensory cortex. Scale bar ¼ 100 mm.
ultimately reducing their inflammatory responses (Liu et al., 2006). Hence, these cells could take on a protective role. Therefore, further mechanistic studies could help to elucidate the precise role of CD68-mediated immune responses with myelin-mediated signaling of the fimbria in the cognitively-impaired population. 5. Conclusion Our results suggest the involvement of neuroinflammatory-like signaling, possibly through the activation of microglia. The correlation seen with CD68 and learning impairment in AI rats may thus provide for an inflammatory mechanism that distinguishes a normally aged population as cognitively impaired. Disclosure Statement There are no actual or potential conflicts of interest. Acknowledgements We thank Drs Bruno Giros and Salah El Mestikawy for the use of their Zeiss Axio Observer microscope, and Mira Thakur for proofreading and editing the manuscript. This project was funded by the Canadian Institutes of Health Research (CIHR) (to R.Q.). References Bendlin, B.B., Fitzgerald, M.E., Ries, M.L., Xu, G., Kastman, E.K., Thiel, B.W., Rowley, H.A., Lazar, M., Alexander, A.L., Johnson, S.C., 2010. White matter in aging and cognition: a cross-sectional study of microstructure in adults aged eighteen to eighty-three. Dev. Neuropsychol. 35, 257e277. Blalock, E.M., Chen, K.C., Sharrow, K., Herman, J.P., Porter, N.M., Foster, T.C., Landfield, P.W., 2003. Gene microarrays in hippocampal aging: statistical
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Neurobiology of Aging journal homepage: www.elsevier.com/locate/neuaging
The immune marker CD68 correlates with cognitive impairment in normally aged rats Mark Farso, Caroline Ménard, Jessica Colby-Milley, Rémi Quirion* Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, Québec, Canada
a r t i c l e i n f o
a b s t r a c t
Article history: Received 12 November 2012 Received in revised form 8 February 2013 Accepted 15 February 2013
The relationship between heightened neuroinflammation and cognitive decline in the normally aged brain is still debatable, as most data are derived from insult-related models. Accordingly, the aim of the current study was to determine whether a link could be established for 2 immune markers at the posttranscriptional level; CD68 and MHC-II, in a normally aged (24-month-old) rat population discriminated for their learning abilities. Using the Morris Water Maze (MWM) task, aged rats were divided into aged learning-impaired (AI) or -unimpaired (AU) groups. Western immunoblots of hippocampal tissue revealed a significant increase of CD68 in AI rats compared to the AU group. Moreover, up-regulated CD68 expression correlated with increased latency times in the MWM task. Immunofluorescence for CD68 revealed intense staining in the white matter regions and CA3 subregion of the hippocampus in the AI group. Despite expression of MHC-II in the AI group, no correlation was found. Overall, these data suggest that CD68 could play a role associated with cognitive decline in a subgroup of the normally aged population. Ó 2013 Elsevier Inc. All rights reserved.
Keywords: Normally aged rat Learning-impairment Hippocampus CD68 MHC-II White matter
1. Introduction Epidemiological studies show increased prevalence of cognitive decline occurring in the aging (i.e., 65 years) population (DeCarli, 2003; Ward et al., 2012). Interestingly, these studies also reveal that not all aged individuals will necessarily develop such impairment, therefore inciting the need to elucidate the mechanisms behind successful (cognitive) aging. A similar phenomenon is observed in an animal model of normal aging in which aged (24-month-old) rats can be distinguished into 2 main subpopulations: aged memory-unimpaired (AU) and aged memory-impaired (AI) based on hippocampal-dependent spatial learning and memory (Brouillette and Quirion, 2008; Geinisman et al., 2004; Ménard and Quirion, 2012; Rowe et al., 2007). Moreover, mechanistic studies in this model indicate alterations, in the AI group, of specific hippocampal signaling pathways that could be responsible for their cognitive decline (Brouillette and Quirion, 2008; Ménard and Quirion, 2012; Rowe et al., 2007). Heightened neuroinflammatory responses are associated with brain aging, indicating a possible role for these phenomena in agerelated cognitive decline (Corona et al., 2011; David et al., 1997; Lu * Corresponding author at: Douglas Mental Health University Institute, Department of Psychiatry, McGill University, 6875, LaSalle Boulevard, Verdun (Montréal), QC, H4H 1R3, Canada. Tel.: þ1 514 761 61312934; fax: þ1 514 762 3034. E-mail address: [email protected] (R. Quirion). 0197-4580/$ e see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neurobiolaging.2013.02.008
et al., 2004; Viviani and Boraso, 2011). Brains of aged animals exhibit increased levels of cytokines/chemokines (Bodles and Barger, 2004; Lynch et al., 2010), implicating a role for the activation of microglia during aging. As microglia are considered to be the main producers of these inflammatory molecules (Hanisch and Kettenmann, 2007; Kettenmann et al., 2011), their involvement in the aged brain with learning and memory is thus likely to be crucial (Lynch et al., 2010; Yirmiya and Goshen, 2011). Evidence for enhanced microglial activity in the aged brain in comparison with younger counterparts has been previously shown via increased expression of major histocompatibility complex class II antigens (MHC II) (Sheffield and Berman, 1998), CD68 (Kullberg et al., 2001) and other immune markers (Griffin et al., 2006). In addition, microglia from aged animals display enlarged cell bodies and shortened processes (Perry et al., 1993). The link between neuroinflammatory activity and age-associated cognitive deficit is evidenced through gene microarray analyses of the hippocampus, in which a causal relationship between inflammatory gene responses and normally age-associated cognitive deficit was convincingly established (Blalock et al., 2003; Haberman et al., 2011). However, studies involving post-transcriptional inflammatory activity with age-related learning impairments are still rare. Therefore, the aim of the present study was to determine whether increased neuroinflammation, at the protein level, bears a correlation to decreased cognitive status in a population of normally aged (24-month old; 24-mo) rats. These aged rats were
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discriminated for their learning ability using the Morris Water Maze task, their hippocampi subsequently assessed for protein levels of 2 immune markers known to be up-regulated in the aged brain; CD68 and MHC-II, followed by a correlation analysis to determine a possible link to aged-impaired rats. 2. Methods 2.1. Animals Male LongeEvans rats were purchased from Charles River Laboratories (St Constant, QC, Canada) and were housed at the Douglas Mental Health University Institute animal facility until 6 months or 24 months old. Animals (2 per cage) were maintained on a 12-h light/dark cycle with ad libitum access to food (Purina Lab Chow; Mondou, Montreal, QC, Canada) and water. Animal care, surgery, and handling procedures were approved by the McGill University Animal Care Committee (protocol no. 3589) and the Canadian Council for Animal Care. 2.2. Morris Water Maze task Male Long-Evans rats of 6 months (i.e., young; Y; used as a reference point) or 24 months (i.e., aged rats) were tested for spatial memory using the Morris Water Maze (MWM) task, a hippocampal-dependent behavioral task (Morris, 1984). In accordance with Ménard and Quirion (2012), rats were timed (i.e., the latency, in seconds) to find a submerged platform located below
the surface of opaque water, with 3 trials for 5 consecutive days, followed by the first probe test on day 5. This protocol was repeated the following week to determine discrimination reversal, a form of inhibitory learning, which tests for the formation of new memories and moreover is affected during aging (Wilson et al., 2006). Seven days later, a third probe test was performed for the evaluation of memory consolidation and retrieval (data not shown). A timeline illustrating the behavior protocol is shown in Fig. 1A. Classification criteria were used for the learning curves and probe tests to accurately distinguish between AU and AI rats, and are extensively explained by Ménard and Quirion (2012). In brief, aged rats were distinguished as impaired when the time taken to find the hidden platform on both days 4 and 5 was over 25 seconds (average of 3 trials per day). The learning probe test was then used to confirm memory impairment via platform crossings and time spent in the target and opposite quadrants. A criterion of 2 or fewer crossings over the hidden platform position during the learning probe test (total of 60 seconds) was used. Moreover, aged rats with spatial memory deficits swam randomly in the pool during the probe tests, as assessed by the time spent in the target versus opposite quadrants. The reverse memory paradigm (in the second week of training) assessed inhibitory learning whereby the platform was moved to the opposite quadrant whereas the position of visual cues did not change. Rats were then trained to find the new hidden platform location for 4 consecutive days (3 trials per day), followed by a second probe test. The third probe test evaluated memory consolidation and retrieval. Brain tissue from all 3 test groups was collected for Western immunoblotting or immunofluorescence.
Fig. 1. Discrimination of aged rats as either AI or AU. The timeline for the behavior protocol (A). Mean latency time (in seconds) for each training day of the MWM task in week 1 (B) and week 2 (D) indicated a significantly (p < 0.0001) slower learning curve for AI rats compared to that of AU rats, which remained at Y levels. The probe test on day 5 (C) and day 11 (E) confirmed this discrimination, as AI rats showed significantly (p < 0.001) fewer platform crossings compared to AU and Y rats. * p < 0.05; ** p < 0.001.
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2.3. Western immunoblotting Considering the timeline of training (i.e., 3 weeks) involving consolidation of memories, adjacent cortices (the neocortex) of the hippocampus were included with hippocampal lysates (Wang and Morris, 2010). Soluble lysate was prepared from hippocampal homogenate and its protein content determined as previously reported (Ménard and Quirion, 2012). Proteins from the soluble lysate were separated via gel electrophoresis (20 mg protein per lane at 180 V for 2 hours at room temperature [RT]) and then followed by their transfer onto Hybond-C nitrocellulose membranes (Amersham Biosciences, Baie d’urfe, QC, Canada) (80 V for 2 hours on ice) (Farso et al., 2011). After blocking in 5% skim milk (dissolved in Trisbuffered saline [TBS] with 0.05% Tween 20 [0.05% TBST], 1 hour at RT), the membranes were subsequently incubated with the primary antibodies: anti-mouse CD68 (AbD Serotec, Raleigh, NC) (1:50) or anti-mouse MHC-II (Abcam, Cambridge, MA) (1:500) (both in 0.05% TBST with 5% skim milk) overnight at 4 C. Incubation with horseradish peroxidase (HRP)econjugated goat-anti mouse secondary antibody (Santa Cruz Biotechnology, Santa Cruz, CA) (1:15,000 in 0.05% TBST with 5% skim milk; 1 hour at RT) then followed. b-actin (Santa Cruz Biotechnology, Santa Cruz, CA) (1:5000 in 0.05% TBST with 5% skim milk; 1 hour at RT) served as the loading control. Relevant optical densities were normalized against b-actin and then expressed as a percentage of Y. Samples were tested in quadruplicates from each rat group (6 rats per group), and the means were pooled and graphed using the GraphPad Prism software V. 2.4. Immunofluorescence for CD68 Coronal sections (20-mm thick) at the level of the dorsal hippocampus of AI rats were fixed (4% paraformaldhyde; 10 minutes at RT), permeabilized (0.2% TritonX 100 diluted in 0.1 mol/L phosphate-buffered saline [PBS]; 10 minutes at RT), blocked (10% normal goat serum [NGS] in 0.1 mol/L PBS with 0.05% Tween-20 [0.05% PBST]), and exposed to the primary, CD68 (1:100 in 10% NGS and 0.05% PBST, at 4 C, overnight), followed by the secondary Alexa Fluor 568 goat anti-mouse IgG antibody (Invitrogen, Carlsbad, CA) (1:200 in 1% NGS and 0.05% PBST, at RT, 2 hours in the dark). Coverslips were then added to the sections with Fluoromount-G (Southern Biotech, Birmingham, AL). Immunoreactivity in the hippocampus and neocortex was visualized using the Zeiss Axio Observer microscope and digital photographs were taken using Zeiss Axio Vision software (version 4.7.1.0). 2.5. Statistical analysis All data are expressed as the mean SEM and were analyzed by 1-way analysis of variance (ANOVA) followed by the Bonferroni post hoc test. Significance was reached at values of p < 0.05 or p < 0.0001. 3. Results 3.1. Characterization of the normally aged rat population as either AI or AU The mean latency to find the submerged platform in the MWM (Fig. 1B) task was significantly higher (approximately 60 seconds) for a subpopulation of aged rats (p < 0.001) from days 2 to 5 of training, and thus classified as AI (Fig. 1B). This group had a significantly slower learning curve compared to a subset of aged rats (labeled as AU) that exhibited the same latency times as Y rats (with the exception on day 2; between 10 and 40 seconds). The probe test
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(learning probe) at the end of week 1 (Fig. 1C) confirmed an impairment of spatial memory for the AI group, as the number of platform crossings was significantly lower (approximately 50%; p < 0.001) for AI rats compared to AU and Y rats. The inhibitory learning task (Fig. 1D) showed a strongly significant lack of discrimination of reversal abilities for the AI subgroup, as the mean latency was significantly higher (between 40 and 60 seconds) in this group from days 8 to 11 (p < 0.001) compared to the AU and Y group (between 10 and 20 seconds). The second probe test (reverse probe; Fig. 1E) showed that the number of platform crossings was significantly lower (approximately 70%e80%; p < 0.001) for AI rats compared to AU and Y rats, thus validating memory impairments for multiple cognitive processes in this subgroup. 3.2. Correlation between learning impairment observed in the AI subgroup and increased CD68 expression Quantification of Western immunoblots for neuroimmune mediators indicated that both CD68 and MHC-II were up-regulated in the aged rats, but that only CD68 correlated with learning impairment. Compared to that in Y and AU rats, the expression of CD68 (100% 14% and 117% 13%, respectively) was significantly (p < 0.05) increased approximately 2.5-fold in the AI subgroup (252% 62%; Fig. 2A). CD68 expression in AU rats remained at levels similar to those of the Y group. In the total population of aged rats consisting of AU and AI groups, the percentages distribution of expression of CD68 in the AI group (ranging between 165% and 550%) correlated (Pearson r coefficient of 0.3320; p < 0.05) with higher mean latency scores on day 5 (ranging between 30 and 50 seconds) in the same group (Fig. 2B), indicating that increased levels of CD68 correlated well with the decreased learning ability in the subgroup of aged rats. Expression of MHC-II in the AI subgroup (272% 34%) was significantly (p < 0.05) increased (approximately 3-fold) compared to Y rats (100% 18%) but not the AU group (207% 41%) (Fig. 2C). MHC-II did not correlate with learning impairment, as the percentage distribution of expression of MHC-II in the AI group (ranging between 180% and 500%) were similar to those of the AU group (ranging between 150% and 450%), and thus no correlation could be reached (Pearson r coefficient of 0.0303; p ¼ 0.5886) with the mean latency scores on day 5 (Fig. 2D). 3.3. AI rats exhibit a distinct population of CD68-positive cells in the hippocampus and adjacent white matter regions We then set out to investigate the distribution of CD68 in the AI subgroup, to couple its correlation with learning impairment to its presence in the hippocampus, as this brain region is the main center on which the MWM task depends. Immunofluorescence for CD68 has previously been shown to be punctate in nature (McKay et al., 2007), which was also observed in the current study, thus validating our immunohistochemistry methodology. Overall, the observations suggested that the involvement of CD68 with learning impairment is likely to be predominantly within the hippocampal formation, as more prominent immunoreactivity for CD68 was seen in the hippocampus and white matter regions (WMRs) compared to the adjacent cortical regions. In the AI subgroup, intense and abundant staining for CD68 was observed in (1) hippocampal white matter regions: fimbria (fi) (Fig. 3A), deep cerebral white matter (dcw) and alveus (alv) surrounding the CA1 and CA2 subregions (Fig. 3B); (2) pyramidal cell layer of the CA2 subregion (Fig. 3B); and (3) molecular layer of dentate gyrus (MoDG) (Fig. 3C). Compared to these regions, CD68 was not as prominently present in the CA1 subregion (Fig. 3C), the primary somatosensory (S1) and secondary somatosensory (S2) cortices (cx) (Fig. 3D and E, respectively) and the motor cortex (Mcx) (Fig. 3F). Compared to the AI group, CD68
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Fig. 2. Quantification and correlation studies with the immune markers, CD68, and MHC-II. Quantification of Western immunoblots for CD68 (A) and MHC-II (C) indicated that although both are significantly (p < 0.05) increased in AI rats compared to Y, only CD68 is significantly (p < 0.05) up-regulated in AI versus AU rats. Correlation analyses for CD68 (B) and MHC-II (D) revealed that increased latency time observed in AI rats significantly correlated with CD68 (r2 ¼ 0.3320, p < 0.05) but not MHC-II. * p < 0.05; ** p < 0.0001.
was less prominent in AU rats, and negligible immunoreactivity was observed in Y rats (data not shown). 4. Discussion The current study provides evidence that, in a normally aged rat population, an association may exist between an increase in selected neuroinflammatory-like alterations and learning impairment. Our results suggest the possible involvement of CD68, but not MHC-II, in learning impairment. Although focused on 2 immune markers, these findings could imply a diversity of inflammatory signaling, potentially through the heterogeneous activity of microglia (Hanisch and Kettenmann, 2007), occurring in the aged rat population with cognitive deficits. We report that increased levels of CD68, a glycoprotein expressed on lysosomal membranes of activated microglia/macrophages (McKay et al., 2007), correlates with the cognitive deficit in the same subgroup of aged rats (i.e., the AI subgroup). These results confirm, yet also extend, the role of CD68 in such a population, as recent work by Blau et al. (2012) demonstrated that, in normal aged rats, increased mRNA levels of CD68 correlated with reduced synaptic plasticity. We also show that the additional immune marker, MHC-II, was elevated at its protein level in AI rats but did not correlate with cognitive impairment; in agreement with VanGuilder et al. (2011), who found that increased mRNA for MHC-II and its structural components had no correlation with their learning-impaired rats. Increasing evidence is pointing toward the concept of heightened neuroimmune activity in WMRs with the dysregulation of myelin-mediated signaling and loss of white matter integrity as 1 potential cause for the cognitive demise occurring during normal aging (Bendlin et al., 2010; Blalock et al., 2003; Huang et al., 2009; Rowe et al., 2007; VanGuilder et al., 2012). Activated microglia is
likely to play an important role in the above paradigm, given their toxic action in demyelinating diseases (Hanisch and Kettenmann, 2007; Merson et al., 2010). Induction of antigen presentation (via MHC-II) on microglia in response to myelin destruction may cause an increased inflammatory status ultimately leading to neuronal demise (Blalock et al., 2003). An enhanced inflammatory status, as observed through the activation of microglia, is shown in hippocampal WMRs of aged animals (Sheffield and Berman, 1998; Sloane et al., 1999). Although these animals were not discriminated for their cognitive ability, the suggestion was brought forward of an association between inflammation in WMRs and cognitive decline in aged animals (Sheffield and Berman, 1998; Sloane et al., 1999). Our findings with CD68 may lend further support for the aforementioned concept, as CD68-positive microglia has been implicated in myelin destruction (De Groot et al., 1997). In the subgroup of aged rats in which hippocampal CD68 expression correlated with cognitive deficit, the WMRs surrounding the hippocampus of these rats also showed the presence of CD68, particularly in the fimbria region (including alveus), which is part of the fimbriaefornix formation. The fimbriaefornix is involved with hippocampal information processing (Kesner, 2007) as well as the axonal output region for the CA3 (Kesner, 2007), and is shown to deteriorate with age (Jang et al., 2011) and negatively affect cognition when damaged (Sarro et al., 2011; Walker and Olton, 1984). Therefore, it could be hypothesized that at late age a relationship exists between heightened microglial-mediated immune activity and dysregulation of white matter processing involving myelin deficiency (in the fimbria/alveus region), consequently impairing learning capabilities due to decreased synaptic transmission (Blalock et al., 2003). Alternatively, the presence of CD68 in the WMRs of the AI rats may indeed reflect phagocytosis of myelin, as activated microglia have been shown to phagocytise this sheath
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Fig. 3. CD68 immunofluorescence in the AI hippocampus and adjacent neocortex. Immunoreactivity of CD68 in the AI brain was observed in the fimbria (fi) of the hippocampus (A) and deep cerebral white matter (dcw) and alveus (alv) (B), the CA2 subregion (A and B), and in the molecular dentate gyrus (MoDG) (C). Staining was less prominent in the adjacent cortical regions: S2 cx (D), S1 cx (E), and Mcx (F). Abbreviations: cg, cingulum; CPu, caudate putamen; Mcx, motor cortex; S1 cx, primary somatosensory cortex; S2 cx, secondary somatosensory cortex. Scale bar ¼ 100 mm.
ultimately reducing their inflammatory responses (Liu et al., 2006). Hence, these cells could take on a protective role. Therefore, further mechanistic studies could help to elucidate the precise role of CD68-mediated immune responses with myelin-mediated signaling of the fimbria in the cognitively-impaired population. 5. Conclusion Our results suggest the involvement of neuroinflammatory-like signaling, possibly through the activation of microglia. The correlation seen with CD68 and learning impairment in AI rats may thus provide for an inflammatory mechanism that distinguishes a normally aged population as cognitively impaired. Disclosure Statement There are no actual or potential conflicts of interest. Acknowledgements We thank Drs Bruno Giros and Salah El Mestikawy for the use of their Zeiss Axio Observer microscope, and Mira Thakur for proofreading and editing the manuscript. This project was funded by the Canadian Institutes of Health Research (CIHR) (to R.Q.). References Bendlin, B.B., Fitzgerald, M.E., Ries, M.L., Xu, G., Kastman, E.K., Thiel, B.W., Rowley, H.A., Lazar, M., Alexander, A.L., Johnson, S.C., 2010. White matter in aging and cognition: a cross-sectional study of microstructure in adults aged eighteen to eighty-three. Dev. Neuropsychol. 35, 257e277. Blalock, E.M., Chen, K.C., Sharrow, K., Herman, J.P., Porter, N.M., Foster, T.C., Landfield, P.W., 2003. Gene microarrays in hippocampal aging: statistical
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