Spatial Learning and Memory in Capuchin-Monkeys and Microglial Morphology in the Molecular Layer of the Dentate Gyrus

AUTHORS: Carlos S. Filho1, Camila M. de Lima1, César A. R. Fôro1, Marcus A. de Oliveira1, Nara G. M. Magalhães1, Cristovam G. Diniz2, Daniel G. Diniz1, Pedro F. da C. Vasconcelos3, (*) Cristovam W. P. Diniz1

 

Affiliations:

1Laboratório de Investigações em Neurodegeneração e Infecção, Hospital Universitário João de Barros Barreto, Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Pará, Brasil

2Instituto Federal de Educação Ciência e Tecnologia do Pará, Campus Universitário de Abaetetuba, Abaetetuba, Pará, Brasil

3Instituto Evandro Chagas, IEC, Ananindeua, Pará, Brascil

 

Running title: Dentate gyrus microglial morphological phenotypes and spatial memory

 

(*) Correspondence author: Cristovam Wanderley Picanço Diniz, MD, PhD

E-mail: cwpdiniz@gmail.com Address: Federal University of Pará, Biological Sciences Institute, Laboratory of Investigations in Neurodegeneration and Infection at the University Hospital João de Barros Barreto.

Rua Mundurucus 4487, Bairro de São Braz, Telephone/ Fax: 0055(91)32016756

Zip Code: 66073005 – Belém – Pará – Brazil

 

INTRODUCTION

Microglia seem to be essential for novel object recognition, and the learning-induced formation of glutamatergic synapses is mediated by microglial-derived, neurotrophic factor (BDNF) (Parkhurst et al., 2013). In addition, after training to remember the spatial location of an object, synapse remodeling is evident six hours later, in the molecular layer of the dorsal dentate gyrus (DG-Mol) (Scully et al., 2012), and this event is microglia-dependent (Lim et al., 2013; Zabel & Kirsch, 2013).

Based on observations that synaptic morphological changes seemed to be higher in the target layers of the entorhinal-to-dentate gyrus projections (Scully et al., 2012), and that synaptic remodeling was microglia-dependent (Kettenmann et al., 2013; Wake et al., 2013; Zabel & Kirsch, 2013; Gomez-Nicola & Perry, 2014), we hypothesized that significant microglial morphological changes may be apparent in the DG-Mol layer after spatial learning memory consolidation. Please refer to (Santos-Filho et al., 2014) for detailed description.

METHODS

Four specimens of Cebus apella were assessed individually using a touch screen monitor to present all visuo-spatial stimuli of paired associates learning testing. After tests, animals were euthanized with an overdose of anesthetics. Then, brain tissues were fixed, cut at 100µm thickness and processed for selective microglia immunolabeling. We measured morphometric features from microglia microscopically 3-D reconstructed from the external and middle thirds of the DG-Mol layers, and from the lacunosum molecular layer from CA1 and subjected them to cluster and discriminant analysis. The morphological variables that contributed to cluster formation were analyzed for correlations to PAL performance.

 

RESULTS

Figure 1 illustrates the learning rates for each subject in the first stage of the PAL task. M and S monkeys completed the task at the 3rd and 5th training sessions, respectively, and J and F performed best at the 14th and 19th training sessions, respectively.

 

Figure 1

Figure 1. The learning rates for each subject in the first stage of the PAL task. A normalized scale expresses the learning rate in percentage values as a function of the number of sessions. An arbitrary value of 60% was set as the minimum learning rate value to be achieved by each subject, before the behavioral assessment ended. Individual monkeys are designated F, J, M, and S; for further details see (Santos-Filho et al., 2014).

 

We analyzed 268 microglia from the dentate gyrus of all subjects (F, n = 72; M, n = 73; S, n = 58; and J, n = 65). Microglia were reconstructed from both rostral (172) and caudal (96) regions of the DG-Mol. We performed a cluster analysis (tree clustering method) based on measurements of 22 morphometric features. The results suggested that our sample comprised two main classes of microglia, designated types I and II (Figure 2). On average, compared to type II, type I microglia showed, shorter and thinner branches, smaller surface areas and planar angles, less tortuosity, higher density of branches, and less complexity (lower k-dim).

 

Figure 2Figure 2. Dendrogram of cluster analysis results (tree clustering method) and corresponding representative three-dimensional reconstructions. (Top) Dendrogram groupings of 268 dentate gyrus microglia from all four subjects indicated 2 main microglial types (I and II). Microglia were reconstructed from both rostral (172) and caudal (96) regions of dentate gyrus, and cluster analysis was based on 22 microglia morphometric features. (Middle) Microglial reconstructions represent the average values of morphologic features for types I and II microglia. (Bottom) Microglial reconstructions represent the average values of morphologic features for S, M, F and J monkeys. Note close similarity between S and M average microglia with type I and between F average microglia with type II. J average microglia seems to be in an intermediate position; for further details see (Santos-Filho et al., 2014).

 

Cluster and discriminant analyses were applied to compare microglial morphology from the rostral and caudal regions of the dentate gyrus. In both cases, the results showed that the sampled microglia from those regions also comprised two main classes of microglia. Although these two regions shared five predictive morphological variables we found that significant correlations between PAL performance and microglial morphology were mainly limited to the rostral region.

 

Table 1. Linear and non-linear correlations between performance on the paired associates learning task and morphological features of microglia from the rostral and caudal regions of the dentate gyrus.

TAB1

 

In the rostral region of the dentate gyrus, we found that, compared to type II microglia, type I microglia were, on average, significantly smaller, with thinner branches, less tortuosity, more ramified, and less complex (lower k-dim). When the average DG-Mol microglia of each monkey were compared, we found that F and J monkeys showed more similarity to type II microglia, and M and S monkeys showed more similarity with type I microglia (Figure 2). Furthermore, F samples were dominated by features of type II, and J microglial samples did not show any morphological predominance.

 

Table 2. Linear and non-linear correlations between performance on the paired associates learning task and type I morphological features of microglia from the rostral and caudal regions of the dentate gyrus.

TAB2

 

Next, we performed linear and non-linear correlations between PAL performance and type I morphological features of microglia from the rostral and caudal regions of DG-Mol (Table 2). Note that PAL performance correlated with seven type I microglial morphological features in the rostral region, but no correlations were found with type II in that region. Similarly, PAL performance correlated with three type I microglia morphological features in the caudal region, but no correlations with type II microglia were found (Table 2). No simple correlations were found between PAL performance and the microglial morphological features in the CA1-LMol.

 

DISCUSSION

To cope with spatial learning and memory tasks, the brain must accentuate the differences between old and new experiences, before coding occurs (Schmidt et al., 2012). For that purpose, medial and lateral perforant pathways transmit spatial and non-spatial information to the dentate gyrus, to allow object recognition. Lateral portions of the entorhinal cortex project to the caudal levels of the dentate gyrus and hippocampus, and medial portions of the entorhinal cortex project to the rostral levels (Witter et al., 1989; Witter and Amaral, 1991). In addition, it has been demonstrated in the Rhesus monkey and the rat that the entorhinal cortex also projects to the CA1-LMol layer (Witter & Amaral, 1991; Dolorfo & Amaral, 1998), and those synapses, at least in rats, exhibit synaptic plasticity associated with allocentric spatial learning and memory (Remondes & Schuman, 2002; 2003; 2004; Langston et al., 2010).

In the present study, M and S monkeys were able to learn and remember the spatial locations of objects, and they completed the PAL task in three and five training sessions, respectively. In contrast, F and J subjects were able to complete the PAL task only after 15 and 19 sessions, respectively. PAL performance was correlated with type I, but not type II, microglia in the rostral DG-Mol; therefore, we suggest that type I microglia in the DG-Mol may be important for spatial learning and memory consolidation. Similarly, we found that microglial type I morphological features from the rostral and caudal DG-Mol, but not the CA1-LMol, were correlated with PAL performance. Coherently, microglia promote learning-related synapse formation through BDNF signaling (Parkhurst et al., 2013) and synapse remodeling is evident in the DG-Mol layer, after spatial learning and memory training (Scully et al., 2012).

Thus, we suggest that in both rostral and caudal regions of the DG-Mol of Cebus apella, a selective morphological phenotype of microglia may be associated with spatial learning and memory consolidation.

 

REFERENCES

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