### Sex Influences Loss of Dendritic and Synaptic Plasticity in the Sporadic AD Animal Model Induced by ICV-STZ

Dendrite complexity (Li et al., 2008) and synaptic plasticity (Kasai et al., 2010) are neurobiological basis for learning and memory. We determined the effect of ICV-STZ on neuronal integrity, by examining levels of the dendritic marker MAP2. For male

rats, the semi quantitative results showed a strongly reduced mean fluorescence intensities (MFIs) of MAP2 immunoreactivity in the pyramidal neurons of CA1 region of the hippocampus in ICV-STZ rats compared to vehicle control (**Figures 2A,B**). However, in female rats, MAP2 immunoreactivity showed that ICV-STZ had no effect on dendritic number compared to control (**Figures 2A,C**). We also examined alterations in dendritic spines using Golgi staining. Mushroom-type spines in the CA1 of ICV-STZ treated male rats decreased remarkably compared to control (**Figures 2D,E**), but the number of mushroom-type dendritic spines were not altered in the ICV-STZ treated female rats compared to control (**Figures 2D,F**).

Normal synaptic function is contingent upon the stable expression of synaptic proteins. Therefore, we evaluated several key synapse-associated proteins using Western blotting. ICV-STZ treatment remarkably suppressed the expressions of presynaptic synapsin I, synaptagmin and postsynaptic PSD95, PSD93, NR2A, and NR2B in male rats (**Figures 3A,B**). Nonetheless, there is no any significant difference between vehicle and ICV-STZ treated female rats (**Figures 3C,D**). These data demonstrate that ICV-STZ induces loss of dendritic and synaptic plasticity in male, but not in female rats.

#### Sex Influences Tau Hyperphosphorylation and GSK-3β Activity in the Sporadic AD Animal Model Induced by ICV-STZ

Abnormal hyperphosphorylation and accumulation of Tau play a key role in AD pathology (Wang and Liu, 2008), and hyperphosphorylated tau causes dendritic loss and neurodegeneration (Wang et al., 2010). In addition, ICV-STZ treatment induces tau hyperphosphorylation in rats (Zhou et al., 2013). In this study, we also explored whether ICV-STZ induces tau hyperphosphorylation in male or female rats, respectively. We detected a significantly increasing tau phosphorylation at the Ser199/202(AT8), Ser262, Ser396, and Ser404 sites in ICV-STZ treated male rats (**Figures 4A,B**). Conversely, in the female rats, ICV-STZ did not induce tau hyperphosphorylation (**Figures 4C,D**).

GSK-3β is the first identified and critical tau kinase (Singh et al., 1995), therefore we evaluated the total level and the activity-dependent phosphorylation of GSK-3β. In male rats, we found that the p-GSK-3β (Ser9) (the inactive form) level was remarkably decreased, while the level of total GSK-3β and p-GSK-3β (Tyr216) (the active form) didn't change (**Figures 5A,B**). In female rats, no significant difference was observed between ICV-STZ treated and vehicle control (**Figures 5C,D**). Taken together, these findings suggest that ICV-STZ activates GSK-3β and consequently leads to hyperphosphorylation of tau protein in male rats, but does not elicit these demonstrable pathological alterations in female rats.

### Sex Influences the Activity of BACE1 and Aβ Production in the Sporadic AD Animal Model Induced by ICV-STZ

Another characterized histology of AD is extracellular senile plaques, which are composed of aggregated protein Aβ initiated by β-secretase (BACE1) (Alafuzoff et al., 1987; Vassar et al., 2009). We employed β-secretase Activity Assay Kit and Aβ40/42 Elisa Kit to detect BACE1 activity and Aβ levels of hippocampus. In ICV-STZ treated male rats, both BACE1 activity and Aβ40 level were significantly increased compared to control rats, while Aβ42 showed ascendant trend without significant difference (**Figures 6A,C,D**). However, in female rats, BACE1 activity and Aβ40/42 levels were not altered in both ICV-STZ and vehicle treated rats (**Figures 6B,E,F**). Thus, these data strongly support that ICV-STZ increases BACE1 activity and augments Aβ

rats. Scale bar = 100µm. (B,C) Quantification of MAP2 immunofluorescence. The data were expressed as mean ± *SD* (*n* = 3). (D) Representative photomicrographs of dendritic spines in the hippocampal CA1 region. Scale bar = 5µm. (E,F) Quantification of mushroom-type dendritic spines. The data were expressed as mean ± *SD* (*n* = 3). \*\*\**P* < 0.001 vs. the vehicle control. Data were analyzed using *t*-test.

production in male rats, while did not exhibit these toxic effects in female rats.

### Estradiol Levels in Serum and Hippocampus of ICV-STZ Treated Male and Female Rats

Previous studies have shown that estradiol reduces Aβ production via reducing total BACE1 activity, and decreases tau hyperphosphorylation by mediated GSK-3β activity (Singh et al., 1999; Zhang et al., 2008). To investigate whether estradiol influences the generation of the sporadic AD animal model induced by ICV-STZ, we measured estradiol levels in serum and hippocampus of ICV-STZ treated male and female rats. Shapiro–wilk test showed that all of the p-values were > 0.05, indicating estradiol levels in serum (Supplementary Table 4) and hippocampus (Supplementary Table 5) according with normal distribution from each group. And then, a general linear model was used for two-way ANOVA (Supplementary Tables 6, 7) followed by post-hoc comparison. We found that estradiol

levels of serum (**Figure 7A**) and hippocampus (**Figure 7B**) of female rats were much higher than that of male rats, while no difference was observed between the groups with same sex. The data suggest that high estradiol might protect from STZ induced neurotoxic effects in female rats.

#### DISCUSSION

Nowadays, AD is a major public health problem, which has been considered as a multifactorial disease associated with several etiopathogenic mechanisms (Iqbal and Grundke-Iqbal, 2010). The first step for a rational drug design is to study etiopathogenic mechanisms and to develop animal models based on these mechanisms. The late-onset sporadic form of AD, which mechanisms still remain unclear due to its multi-etiopathological factors, accounts for over 95% of all cases. However, few experimental animal model of sporadic AD badly limit the studies on its pathogenesis and drug development (Agrawal et al., 2011; Iqbal et al., 2013).

The majority of current animal models of AD are generated as familial one, which express human genes mutations, such as Aβ and tau related gene manipulation. However, animal model of familial AD cannot sufficiently exhibit all pathological alterations and processes (Chen et al., 2013). Therefore, experimental models that faithfully mimic the pathology of sAD are essential to study its mechanism and assess the effectiveness of the therapeutic strategies. Previous research has showed that sAD

\*\**P* < 0.01 vs. the vehicle control.

is being recognized as an insulin resistant brains state (Valente et al., 2010; Bitel et al., 2012; Kamat et al., 2016). Therefore, a non-transgenic animal model generated by ICV-STZ has been proposed as a representative model of sAD. The ICV-STZ rats develop insulin resistant brains state associated with sAD like neuropathological changes and memory impairment (Carro and Torres-Aleman, 2004; Valente et al., 2010; Agrawal et al., 2011; Bitel et al., 2012; Chen et al., 2013; Iqbal et al., 2013; Kamat et al., 2016). Although, the mechanisms underlying ICV-STZ evoked AD pathology remain unknown, ICV-STZ rats have been used in many labs as an experimental model of sAD. For more than 20 years, although some of therapeutic strategies displayed very good effectiveness for AD in ICV-STZ animal models, the same therapies were hard to be reproduced on memory deficits in clinical trials with sAD patients (Salkovic-Petrisic et al., 2013). Thus, it is necessary to re-evaluate the ICV-STZ animal model once again.

  Similar to previous studies, the ICV administration of STZ induced cognitive deficits and loss of synaptic plasticity in male rats, but these neurotoxic effects were not observed in female rats. Thus, the ICV-STZ is only for generating animal model of sAD in male, but not in female rats. Consequently sex differences should be considered in AD researches in the future.

Estrogen reduces Aβ level by down-regulating total β secretase activity through MARK/ERK pathway, and modulates Aβ degradation (Pike, 1999; Singh et al., 1999; Vassar et al., 2009). In the present study, we found that ICV-STZ increased BACE1 activities and Aβ40/42 production in male rats, but these alterations were not observed in female rats. The studies have demonstrated that the number of NFTs consisting of hyperphosphorylated tau is positively correlated with the degree of clinical dementia (Iqbal and Grundke-Iqbal, 1991; Iqbal et al., 2008; Luo et al., 2014). Estrogens attenuate tau hperphosphorylation through kinases and phosphatases, such as the GSK-3β, Wnt, and PKA pathways (Zhang et al., 2008). The

ICV-STZ model in male rats shows hyperphosphorylation of tau and an increase of GSK-3β activity, but these tau pathologies are not observed in female rats. Together, sex hormones might account for functional discrepancy of ICV-STZ in the two sexes.

A large body of evidence shows that women have a higher incidence of AD than men happening after menopause, which suggest that estrogen might protect against AD pathology. Hormones have long been known to play key roles in regulating learning and memory and ample evidence has demonstrated that estradiol affects hippocampal morphology, plasticity, and memory (Packard, 1998; Brinton, 2009; Foster, 2012; Cui et al., 2013). Studies in the aromatase knock-out mouse suggest that estradiol induced spine and spine synapse formation in hippocampus, not in the cortex or the cerebellum (Zhou et al., 2014). Previous studies point to a role of hippocampus-derived estradiol in synaptic plasticity in cultured slices and in vivo, not just the role of gonads-derived estradiol (Zhou et al., 2010; Vierk et al., 2012). In addition, dendritic spines of CA1 pyramidal neurons vary during estrus cyclicity, which likely results from cycle of estradiol synthesis in the hippocampus, since gonadotropin releasing hormone regulates estradiol synthesis in the hippocampus in a dose-dependent manner (Woolley et al., 1990; Prange-Kiel et al., 2008, 2013). In the present study, 10 rats employed in each group is a small sample size. Therefore, we performed Shapiro–wilk test which showed that samples from each group was in accord with normal distribution. Relatively we found that hippocampal estradiol level of female rats is almost four times higher than that of male rats in both vehicle and ICV-STZ treated groups, which is in accordance with previous studies by using mass spectrometry (Fester et al., 2012). This implies that high estradiol levels in female rats might protect them from the ICV-STZ induced cognitive deficits and neurodegenerative pathologies, including synaptic damage, Aβ deposition, and tau hyperphosphorylation in hippocampus.

In reported literature, optimal female performance occurred during the phase of estrus on the spatial learning and memory, and the least efficient performance occurred during proestrus (Vina and Lloret, 2010). Since we did not determine the estrus stage of the control animals and cognitive ability and synaptic density are optimal during proestrus, the estradiol protective effects on hippocampal plasticity and memory would very likely have been greater if we had had exclusively taken proestrus female rats.

Although, the mouse- and monkey ICV-STZ models have also been developed, ICV-STZ rats are still widely used and employed to evaluate the therapeutic potential of drugs and

FIGURE 6 | Sex influences activity of BACE1 and Aβ production in the sporadic AD animal model. (A,B) BACE1 activity was determined using β-Secretase Activity Assay Kit. (C–F) Aβ40/42 levels were quantified through ELISA. The data were expressed as mean ± *SD* (*n* = 5). \**P* < 0.05 vs. the vehicle group. Data were analyzed using *t*-test.

non-drug therapies in numerous laboratories. Cognitive deficits and AD-like pathology, such as neuroinflammation, brain insulin resistance, tau hyperphosphorylation, Aβ overproduction, have been found both in female mice and monkeys (Chen et al., 2014; Park et al., 2015). Liu et al. have reported that STZ inhibits the Ras/ERK signaling cascade and decreased the phosphorylation of CREB, and induces cognitive impairment in rats (Liu et al., 2013). However, the study from Diao et al. shows the genderand EC-dependent levels of proteins from the protein synthetic, chaperoning, and degradation machinery (Diao et al., 2007). Accordingly, it is necessary to re-evaluate the STZ-induced cognitive alterations between male and female rats. In the present study, we found that ICV-STZ remarkably results in cognitive impairments and AD like pathological alterations in the Sprague-Dawley male rats, but not in the female rats. It may conceivably be related with the gender- and EC-dependent levels of proteins from the protein synthetic, chaperoning, and degradation machinery, and consequently regulates tau related kinases and APP cleavage. Its molecular mechanism is worth further discussing. Our findings provide novel insights suggesting that sex differences exist in ICV-STZ rats which have been used as sporadic AD animal model for about 20 years. Therefore, our study encourages investigators to comply with National Institutes of Health policies to include females in biomedical research and to be aware that adding females to a study is not as simple as adding just another group.

#### AUTHOR CONTRIBUTIONS

XW and JW designed the experiments. JB performed the experiments and analyzed data. JB, YM, BZ, RL, ZZ, JW, and XW discussed and interpreted the results. JB, YM, and XW wrote the paper. All authors have approved the final version of the manuscript.

#### REFERENCES

#### ACKNOWLEDGMENTS

This study was supported in parts by grants from Natural Science Foundation of China (81571255 and 31528010), the Hubei Province Key Technology R&D Program (2015BCE094) and the Academic Frontier Youth Team Project of HUST.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi. 2017.00347/full#supplementary-material

Supplementary Figure1 | The experiments were designed as shown above.

dimorphism in vivo but not in vitro. J. Steroid Biochem. Mol. Biol. 131, 24–29. doi: 10.1016/j.jsbmb.2011.11.010

and in cerebral energy metabolism in adult rats. Behav. Neurosci. 112, 1199–1208. doi: 10.1037/0735-7<IP_ADDRESS>9

roles of oestrogen receptor subtypes. Neuroendocrinol. J. 26, 439–447. doi: 10.1111/jne.12162

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Bao, Mahaman, Liu, Wang, Zhang, Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Corrigendum: Sex Differences in the Cognitive and Hippocampal Effects of Streptozotocin in an Animal Model of Sporadic AD

Jian Bao1 , Yacoubou A. R. Mahaman1 , Rong Liu1 , Jian-Zhi Wang1,2, Zhiguo Zhang3 , Bin Zhang4 and Xiaochuan Wang1,2 \*

*1 Key Laboratory of Ministry of Education of China for Neurological Disorders, Department of Pathophysiology, School of Basic Medicine and the Collaborative Innovation Center for Brain Science, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, 2 Co-innovation Center of Neuroregeneration, Nantong University, Nantong, China, 3 School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, 4 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States*

Keywords: Alzheimer's disease (AD), animal model, Streptozotocin (STZ), sex differences, learning and memory

#### **A Corrigendum on**

Edited and reviewed by:

*Mohammad Amjad Kamal, King Fahad Medical Research Center, King Abdulaziz University, Saudi Arabia*

> \*Correspondence: *Xiaochuan Wang<EMAIL_ADDRESS>
Received: *24 August 2019* Accepted: *25 September 2019* Published: *15 October 2019*

#### Citation:

*Bao J, Mahaman YAR, Liu R, Wang J-Z, Zhang Z, Zhang B and Wang X (2019) Corrigendum: Sex Differences in the Cognitive and Hippocampal Effects of Streptozotocin in an Animal Model of Sporadic AD. Front. Aging Neurosci. 11:275. doi: 10.3389/fnagi.2019.00275*

#### **Sex Differences in the Cognitive and Hippocampal Effects of Streptozotocin in an Animal Model of Sporadic AD**

by Bao, J., Mahaman, Y. A. R., Liu, R., Wang, J.-Z., Zhang, Z., Zhang, B., et al. (2017). Front. Aging Neurosci. 9:347. doi: 10.3389/fnagi.2017.00347

In the original article, due to the authors' oversight, there were several mistakes in **Figure 3C** and **Figure 4C** as published. The image "PSD95 of Female" in **Figure 3C** was inadvertently replaced with the image "GSK3β" in Figure 5C. The images "AT8 of Female" and "PS262 of Female" in **Figure 4C** were inadvertently replaced with image "PS404 of Female" from **Figure 4C**. The image "Tau5 of Female" in **Figure 4C** was inadvertently replaced with image "Tau5 of Male" in **Figure 4A**. The corrected **Figures 3C** and **4C** appear below. The quantification for the above-mentioned blots have been done in the corrected **Figures 3D** and **4D**, which all have no significant difference between control and STZ groups as in the published original version of the article.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Copyright © 2019 Bao, Mahaman, Liu, Wang, Zhang, Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

data were expressed as mean ± *SD* (*n* = 4). \*\*\**P* < 0.001 vs. the vehicle control. Data were analyzed using *t*-test.

FIGURE 4 | Sex influences tau hyperphosphorylation in the sporadic AD animal model. (A,C) Western blot analysis of the protein levels of AT8, PS262, PS396, PS404, and Tau5 and (B,D) their quantitative analysis for male or female rats. The data were expressed as mean ± *SD* (*n* = 4). The phosphorylation level of tau was normalized to total tau level probed by tau5. The total level of tau was normalized DM1A. \*\*\**P* < 0.001 vs. the vehicle control. Data were analyzed using *t*-test. \*\**P* < 0.01 vs. the vehicle control.

# Differential Roles of Environmental Enrichment in Alzheimer's Type of Neurodegeneration and Physiological Aging

Vladimir V. Salmin1 , Yulia K. Komleva2,3, Natalia V. Kuvacheva2,3, Andrey V. Morgun4 , Elena D. Khilazheva2,3, Olga L. Lopatina2,3, Elena A. Pozhilenkova2,3 , Konstantin A. Shapovalov1 , Yulia A. Uspenskaya3 and Alla B. Salmina2,3 \*

1 Department of Medical and Biological Physics, Krasnoyarsk State Medical University named after Prof. V.F. Voino-Yasenetsky, Krasnoyarsk, Russia, 2 Department of Biochemistry, Medical, Pharmaceutical and Toxicological Chemistry, Krasnoyarsk State Medical University named after Prof. V.F. Voino-Yasenetsky, Krasnoyarsk, Russia, 3 Research Institute of Molecular Medicine and Pathobiochemistry, Krasnoyarsk State Medical University named after Prof. V.F. Voino-Yasenetsky, Krasnoyarsk, Russia, 4 Department of Pediatrics, Krasnoyarsk State Medical University named after Prof. V.F. Voino-Yasenetsky, Krasnoyarsk, Russia

#### Edited by:

Athanasios Alexiou, Novel Global Community Educational Foundation (NGCEF), Hebersham, Australia

#### Reviewed by:

Daniel Ortuño-Sahagún, Centro Universitario de Ciencias de la Salud, Mexico Maik Gollasch, Charité Universitätsmedizin Berlin, Germany

> \*Correspondence: Alla B. Salmina<EMAIL_ADDRESS>
Received: 01 May 2017 Accepted: 14 July 2017 Published: 26 July 2017

#### Citation:

Salmin VV, Komleva YK, Kuvacheva NV, Morgun AV, Khilazheva ED, Lopatina OL, Pozhilenkova EA, Shapovalov KA, Uspenskaya YA and Salmina AB (2017) Differential Roles of Environmental Enrichment in Alzheimer's Type of Neurodegeneration and Physiological Aging. Front. Aging Neurosci. 9:245. doi: 10.3389/fnagi.2017.00245 Impairment of hippocampal adult neurogenesis in aging or degenerating brain is a well-known phenomenon caused by the shortage of brain stem cell pool, alterations in the local microenvironment within the neurogenic niches, or deregulation of stem cell development. Environmental enrichment (EE) has been proposed as a potent tool to restore brain functions, to prevent aging-associated neurodegeneration, and to cure neuronal deficits seen in neurodevelopmental and neurodegenerative disorders. Here, we report our data on the effects of environmental enrichment on hippocampal neurogenesis in vivo and neurosphere-forming capacity of hippocampal stem/progenitor cells in vitro. Two models – Alzheimer's type of neurodegeneration and physiological brain aging – were chosen for the comparative analysis of EE effects. We found that environmental enrichment greatly affects the expression of markers specific for stem cells, progenitor cells and differentiated neurons (Pax6, Ngn2, NeuroD1, NeuN) in the hippocampus of young adult rats or rats with Alzheimer's disease (AD) model but less efficiently in aged animals. Application of time-lag mathematical model for the analysis of impedance traces obtained in real-time monitoring of cell proliferation in vitro revealed that EE could restore neurosphere-forming capacity of hippocampal stem/progenitor cells more efficiently in young adult animals (fourfold greater in the control group comparing to the AD model group) but not in the aged rats (no positive effect of environmental enrichment at all). In accordance with the results obtained in vivo, EE was almost ineffective in the recovery of hippocampal neurogenic reserve in vitro in aged, but not in amyloid-treated or young adult, rats. Therefore, EE-based neuroprotective strategies effective in Aβ-affected brain could not be directly extrapolated to aged brain.

Keywords: environmental enrichment, neurogenesis, neurosphere, Alzheimer's disease, aging, time-lag model

## INTRODUCTION

fnagi-09-00245 July 24, 2017 Time: 12:57 # 2

Environmental enrichment (EE) is considered as an environment with numerous sensorimotor, cognitive, and social stimulations able to affect brain plasticity, to restore brain functional reserves, and to facilitate establishment of novel connections actual for preventive or rehabilitation strategies. Modeling EE is currently widely used in experiments aimed to study synaptogenesis, neurogenesis, and brain connectivity (Komleva Iu et al., 2013). EE stimulates neurogenesis and synapse turnover, integration of newly-formed cells into neuronal ensembles, modifies epigenetic mechanisms controlling resistance to oxidative stress, modulates production of neurotransmitters and molecules with neurotrophic activity, prevents apoptosis, suppresses neuroinflammation, affects neuron-glia interactions, thereby enhancing cognition, learning, and social communications in animals or humans with or without brain pathology (Herring et al., 2011; Cotel et al., 2012; Komleva Iu et al., 2013; Leger et al., 2015; Grinan-Ferre et al., 2016; Stuart et al., 2017). These effects well-correspond to those observed in people practicing so-called cognitive training because of their professional duties or personal habits and demonstrating good preservation of cognitive functions even at the eldest period of life (Mora, 2013). However, action of EE depends on experimental conditions, duration of exposure, severity of brain injury, age, gender etc., thereby, the observed effects of EE on neuroplasticity might be different and even unexpected (Ming and Song, 2011; Valero-Aracama et al., 2015).

Growing number of experimental and clinical findings suggest that EE might serve as an effective strategy to restore the functional capacity of damaged brain, i.e., in Alzheimer's disease (AD), in neurodevelopmental disorders, after stroke, etc. (Janssen et al., 2014; Polito et al., 2014; Cioni et al., 2016; Rosbergen et al., 2016). Particularly, prevention or treatment of Alzheimer-type neurodegeneration is one of the most challenging questions in the modern neuroscience which is directly linked to the controlled modulation of hippocampal plasticity (Balietti et al., 2012) In such context, EE provides a lot of premises for its effective application: prevention of hippocampal astroglial dysfunction in the AD transgenic mice (Rodriguez et al., 2013), up-regulation of brain-derived growth factor expression in the hippocampus of senescenceaccelerated prone mice (Yuan et al., 2012), prevention of amyloid β (Aβ) deposition and memory impairment in AD model mice (Maesako et al., 2012), modulation of hippocampal synaptic proteins expression (Barak et al., 2013).

In contrast, EE action in aging brain is not deciphered in detail, even aging itself is the certain risk for AD development (Avila et al., 2010). Moreover, precise molecular mechanisms of EE effects on neuroplasticity in aged or AD brain remain to be unclear or even contradictory (Herring et al., 2011; Cotel et al., 2012; Bezzina et al., 2015; Huttenrauch et al., 2016). Impairment of hippocampal adult neurogenesis in aging is a well-known phenomenon caused by the shortage of brain stem cell pool, alterations in the local microenvironment within the neurogenic niches, or deregulation of stem cell development (Ali

et al., 2015; Moraga et al., 2015). It is well-known that there is an obvious contrast between Alzheimer-type neurodegeneration and physiological aging, however, both of them are associated with progressive cognitive deficits and impairment of brain plasticity. Healthy aging brain is characterized by moderate decline in neurogenesis affecting the structure and function of the entorhinal-hippocampal circuit which is in the focus of neurodegeneration seen in AD due to the toxic action of Aβ, oxidative stress, excitotoxicity, and neuroinflammation (Hollands et al., 2016). In physiologically aging brain, no significant changes in the number of neural stem cells (NSCs) residing within the hippocampal neurogenic niche have been detected (Hattiangady and Shetty, 2008). In contrast, gradual decrease of NSCs and neural progenitor cells (NPCs) number has been reported in the experimental APP/PS1/nestin-GFP triple transgenic mouse model of AD (Zeng et al., 2016). Thus, the earliest stages of adult neurogenesis (stem cell maintenance, self-renewal, and proliferation) are significantly affected in AD neurodegeneration but not in healthy aging. Since enhancing neurogenesis was proposed as a therapeutic approach in neurodegeneration (Hollands et al., 2016), one may assume that EE could produce different effects on aging-associated and AD-compromised hippocampal neurogenesis. Despite several experimental and clinical observations on EE action in elderly subjects with or without AD, little information is available on the comparative efficacy of EE in AD and healthy aging.

So, whether EE-induced changes are equally beneficial in aging brain and in AD? The subject has received increased attention with the deciphering the biological mechanisms of latelife brain alterations: reduced neurogenesis and synapse turnover (Mostany et al., 2013), suppressed release of neuropeptides and neurotrophic factors (Forlenza et al., 2015), appearance of white matter lesions and pathological blood–brain barrier permeability (Marin-Padilla and Knopman, 2011; Firbank et al., 2012). Several studies suggest that EE affects epigenetic mechanisms controlling neuroplasticity, promotes remyelination, or reduces glia-supported neuroinflammation in the neurogenic niches in the aged brain (Williamson et al., 2012; Yang et al., 2012; Neidl et al., 2016). However, alterations in neurogenesis and the capacity of so-called "neurogenic reserve" may have different patterns and degree of progression in normal aging and in AD (Esiri and Chance, 2012).

Assessment of neurogenesis represents an appropriate system to analyze the activity of factors with presumptive neuroprotective properties. In vivo, neurogenesis can be studied with the approach based on the detection of markers expression in cells at different stages of development within the neurogenic niches or along their migratory paths in the brain (Roybon et al., 2009). Apart from this, some neurogenesis-associated events are easily reproduced in vitro due to ability of neurogenic nondifferentiated cells to produce non-adherent spherical clusters of cells known as neurospheres (NS). NS-forming capacity is a parameter which is widely used for the assessment of neurogenic potential of neural stem/progenitor cells as well as the action of regulatory molecules, neurotoxic substances, or drug candidates (Pacey et al., 2006). Positive effects of EE on neurogenesis in vivo have been shown (Monteiro et al., 2014; Clemenson et al., 2015),

however, there are very limited data on NS development in vitro in AD or aging (Heo et al., 2007; Diaz-Moreno et al., 2013).

Recently, we have demonstrated EE-mediated enhancement of neurosphere-forming capacity of NSCs obtained from the brains of AD rat model (Kuvacheva et al., 2015). It should be mentioned that analysis of data obtained with NS culture systems is hampered by the relative shortage of tools for adequate interpretation within the context of cell cycle- and proliferationrelated events. Modeling the cell growth kinetics has been in the focus of researchers for a long time (Baker et al., 1998), but mathematic analysis of kinetic features of stem cells behavior in several subsequent NS generations in vitro was undertaken in few studies only (Ma et al., 2007). Development of correct mathematical models of NS establishment in the short-term experimental conditions (when reduction of cell population due to cell death might be contingently neglected) would provide new opportunities for rapid and effective analysis of endogenous and exogenous stimuli targeting neurogenesis. Moreover, current achievements in the application of NS cultures for AD modeling in vitro (Choi et al., 2013; Ghate et al., 2014) or novel attempts to restore brain functions with NSCs transplantation therapy (Waldau and Shetty, 2008) determine the needs in the appropriate mathematical modeling of NS-generation in vitro to clarify mechanisms underlying neurogenesis modulation in a given microenvironment.

Thus, we may entertain the hypothesis that: (i) effects of EE on neurogenesis are different in healthy brain aging and in brain affected by Alzheimer's type of neurodegeneration; (ii) combination of in vivo and in vitro approaches to the assessment of neurogenesis with the mathematical modeling of neurogenic reserve could help us to identify the neurogenesis stage which is a main "target" for the action of EE in normal and damaged brain. Therefore, the goal of this study was to compare the effects of in vivo EE in the Alzheimer's type of neurodegeneration and physiological brain aging with the special focus on hippocampal neurogenesis in vivo and NS-forming capacity of hippocampal stem/progenitor cells in vitro.

## Experimental Procedures

#### Modeling Environmental Enrichment In Vivo Wistar male rats (n = 64) were maintained in standard cages with water and standard diet with free access to food and water. The following groups of animals were included in the experiment: (i) young adult rats, 7–9 months old (n = 40) kept under standard conditions (SC, n = 20) or in the environmental enrichment (EE, n = 20); (ii) elderly rats, 23–25 months old (n = 24) kept under SC or in the EE. In SC, rats were housing in the cages sized 50 cm × 30 cm × 18 cm (five animals in one cage). In EE, rats were housing for 60 days in cages sized 78 cm × 48 cm × 39 cm (10 animals in one cage) equipped with various devices to provide

#### Modeling Alzheimer-Type Neurodegeneration

In the group of young adult rats, 10 animals kept under SC and 10 animals kept in EE have been used for modeling AD with intracerebral bilateral stereotaxic-guided (Narishige Scientific

extensive physical and explanatory activity (tunnels, houses, hammocks, stairs, boxes, wheels) (Jankowsky et al., 2003).

Instrument Lab) injections of 5 µl (2 µg/µl) aggregated Aβ1- 42 (Sigma-Aldrich) into CA1 zones of hippocampus at the day 60 of SC or EE housing according to the protocol described (Li et al., 2011). For this procedure, anesthesia with chloral hydrate (0.35 g/kg) has been applied, and the following stereotaxic coordinates have been chosen: A/P = 3.0 mm, M/L = ± 2.2 mm, D/V = 2.8 mm. Establishment of AD model (Aβ deposition and cognitive impairment) has been confirmed with Thioflavin S staining of brain tissue and neurobehavioral phenotyping of animals (data not shown).

#### Assessment of Hippocampal Neurogenesis

Expression of markers in NSCs, progenitor cells and differentiated neurons (Pax6, Neurogenin 2, NeuroD1, NeuN) was assessed in the hippocampus of animals in all experimental groups according to the standard protocol of immunohistochemistry with antigen unmasking (proteinase K) procedure. Briefly, 2 µm hippocampal slices were obtained after transcardial perfusion of anesthesized rats with 0.1 M PBS (pH 7.4) and 4% PFA followed by hippocampus fixation in 10% buffered PFA for 24 h. The following primary antibodies have been used: Pax-6 (Abcam, ab78545, mouse monoclonal), 1:100, Neurogenin2 (Abcam, ab26190, rabbit polyclonal), 1:50, NeuroD1 (Abcam, ab60704, mouse monoclonal), 1:100, NeuN (Millipore, ABN78, rabbit polyclonal), 1:500. Alexa-conjugated secondary antibodies were used in 1:500 dilution: Alexa Fluor 488 chicken anti-mouse (for Pax6 detection), Alexa Fluor 488 donkey anti-rabbit (for Neurogenin2 detection), NeuN (rabbit polyclonal) – Alexa Fluor 488 donkey anti-rabbit (for NeuroD1 detection), and Alexa Fluor 488 chicken anti-rabbit (for NeuN detection) on slices. Images were visualized with Olympus CX41 microscope and analyzed with Image J software. Corrected total cell fluorescence (CTCF) was calculated for all the images.

#### Isolation of Adult Hippocampal Cells with the NS-Forming Capacity

At the day 70 of housing (10 days after AD modeling in the corresponding group of animals), isolation of hippocampus was performed in anesthesized rats according to the standard protocol described (Kuvacheva et al., 2015). After brain tissue dissociation and isolation of hippocampal cells, counting of the cells for further adjusting the viable cell concentrations was done with the Scepter CellCounter (Millipore). Assessment of proliferation of obtained hippocampal cells in vitro has been performed by culturing them in NeuroCult R NS-A Proliferation Medium with bFGF and EGF (Stemcell). On the next day after cell plating, development of neurospheres as transparent freefloating cell aggregates with surface microspikes was assessed with phase-contrast microscopy. At the day 3 of NS culture, cells were carefully harvested from the flasks, centrifuged, and suspended in the fresh NeuroCult R NS-A Proliferation Medium.

#### Real-Time Analysis of Cell Proliferation In Vitro

Further real-time analysis of cell proliferation kinetics was done using xCElligence system (Roche) with the gold microelectrodecovered microtiter plates. This method allows electrical impedance monitoring in the real-time manner. For this,

FIGURE 1 | Neurogenesis in the subgranular and granular layers of the dentate gyrus in experimental groups (intact rats, vehicle – sham-operated rats, AD model – Aβ-treated rats, aging rats, SC – standard conditions, EE – environmental enrichment). (A–D) Measurement of the CTCF index of different neurogenesis markers between groups under standards conditions (N = 10/per group, one-way ANOVA; Turkey's multiple comparisons test was run between groups). (A) Pax6 CTCF index (p < 0.01; one-way ANOVA). (B) Ngn2 CTCF index (p < 0.0001; one-way ANOVA). (C) NeuroD1 CTCF index (p < 0.0001; one-way ANOVA). (D) NeuN CTCF index (p < 0.0001; one-way ANOVA). Asterisks indicate statistical differences between groups. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001. (E–H) Measurement of the CTCF index of different neurogenesis markers between groups under standards conditions compared to environmental enrichment (N = 10/per group, statistical significance was determined by two-way ANOVA with a Sidak's test multiple comparisons test). (E) Pax6 CTCF index [Interaction p < 0.05, F(3,72) = 2.573, two-way ANOVA]. (F) Ngn2 CTCF index [Interaction ns; environmental factor p < 0.0001, F(1,72) = 68.85, group factor p < 0.0001, F(3,72) = 130.7, two-way ANOVA]. (G) NeuroD1 CTCF index [Interaction p = 0.0001, F(3,72) = 7.91, two-way ANOVA]. (H) NeuN CTCF index [Interaction p < 0.0001, F(3,72) = 47.92, two-way ANOVA]. Asterisks indicate statistical differences between groups. ∗p < 0.05; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001.

1.2–2.5 × 104 cells/ml were cultured in NeuroCult R NS-A Proliferation Medium (100 µl per well, four wells for each series) at +37◦C, 5% CO2 with the measurements intervals of 5 min (from time 0 to 4.5 h) and 15 min (from 4.5 to 24 h).

#### Statistical Analysis

Alexa Fluor 488.

Non-linear regression analysis was done with the Origin 8.5 software (OriginLab). We applied user-defined function models. All regression models indicate the confidence coefficient χ 2 not worsen than 10−4 , and determination coefficient R 2 not less than 0.99. Statistical analysis was performed with the GraphPad Prism 6.0 software. Statistical significance was determined by one-way or two-way ANOVA with a Sidak's test multiple comparisons test. The data are presented as mean and standard deviation.

#### RESULTS

Immunohistochemical analysis of molecular markers expression in cells at different stages of neurogenesis (Pax6, Neurogenin 2, NeuroD1, NeuN) in the hippocampus revealed that their levels were dramatically affected in Aβ-treated rats and in the group of aging animals (**Figures 1A–D**). Noteworthy, EE did not affect the expression of the markers in the hippocampus of intact or sham-operated rats. EE demonstrated pronounced restorative effect on the impaired neurogenesis in the animals with AD model comparing with the elderly rats (**Figures 1E–H**). Among all the markers, most prominent changes were observed in the expression of NeuroD1 (reduction in all the tested animals subjected to EE).

Expression of Pax6 was efficiently restored by EE in all the groups tested (**Figure 2**). It is well-known that Pax6 is a transcription factor controlling proliferation of multipotent stem/progenitor cells in hippocampus and cortex being predominantly expressed by radial glia. High expression of Pax6 is usually followed by the elevated expression of Neurogenin 2 in amplifying unipotent progenitors, which is later replaced with the expression of NeuroD1 in neuronally committed cells of hippocampus where it is required for their survival (Roybon et al., 2009). At the final stage of neurogenesis, mature neurons in the granule zone of hippocampus are highly enriched in NeuN with the evident elevation in animals kept in EE, particularly in those having AD-type of neurodegeneration (**Figure 3**).

To analyze plausible mechanisms of the observed effect of EE at the initial stage of hippocampal adult neurogenesis, we further used the experimental model suitable for the assessing the kinetic parameters of cell proliferation in vitro. Evaluation of NS-forming capacity of hippocampal stem/progenitor cells is rather reliable tool to get the integral pattern of neurogenic cells proliferation and differentiation in the controlled conditions where cell cluster formation and development resemble the events occurring in vivo (Malik et al., 2015), particularly at the earliest steps of neurogenesis that were more sensitive to the action of EE in vivo (as shown above). We found that being cultured in vitro, adult hippocampal stem/progenitor cells produce NS whose appearance could be detected and analyzed with real-time impedance measurements. Recently, we have presented actual impedance traces for NS obtained from the animals with AD model kept under SC or EE (Kuvacheva et al., 2015). For analyzing the kinetic parameters of cell proliferation we applied several assumptions shown below.

Cell index (C) which is the impedance value corresponds to the number of adherent cells, therefore, cell sedimentation leads to the elevation of cell index. If the medium viscosity is not changed, then cell sedimentation to the microplate bottom

visualized using primary anti-NeuN antibody and secondary donkey anti-rabbit IgG conjugated with green fluorescent dye Alexa Fluor 488.

demonstrates constant velocity, and C is progressively rising at the initial phase of cell culture with the linear dependence on the time:

$$\mathbf{C} = \mathfrak{nt} + \mathbf{C}\_0.$$

Constant parameter η characterizes velocity of cell sedimentation, C0 is the cell index obtained for the adherent cells at the initial point of measurements. The described process corresponds to the phase A (attachment of cells to the plate). Let assume that cells might be at any phase of cell cycle at this period. Phase I1 of the impedance trace corresponds to the period after the complete cell adhesion (interphase of the cell cycle) when maximal value of cell index (Cmax) is not changed. It is well-known that just before mitosis cell adhesion is weakened, and flattened cells acquire round shape (VanHook, 2014), therefore formation of NS leads to decrease of C value. This process corresponds to the mitotic phase M1. Phase I2 (interphase) demonstrates stable C value established just before entering mitosis in the 2nd generation of cells. This period is followed by reducing cell adhesion in the 2nd cell generation which corresponds to the mitotic phase M2 (**Figure 4A**).

Thus, we postulate that in the given experimental conditions: (i) cell proliferation is synchronized; (ii) mitosis-associated loss of cell adhesion leads to C decline; (iii) two generations of cells are present in the system during 24 h of cell culture on microelectrode-covered microplates.

To exclude the impact of cell sedimentation velocity on C kinetics, lets adopt the cell index value as C=Cmax at the initial phase A. We may use the parameter shown below to take into account all the changes in C caused by the decreasing number of adherent cells:

$$\mathbf{C}\_{\rm N} = \mathbf{C}\_{\rm max} - \mathbf{C}$$

Normalization of cell index changes (cN) allows considering some deviations in cell concentrations in the microplate wells:

$$\text{c}\_{\text{N}} = \frac{\text{C}\_{\text{N}}}{\text{C}\_{\text{N}\,\text{max}}}$$

Typical curve for cN kinetics is shown in **Figure 4B**.

In order to analyze the observed changes in cN, we may apply a lag model developed for explaining the proliferative kinetics of cell population with the synchronized growth (Baker et al., 1998). For this purpose we must use bell-shaped distribution of cells along the different phases of cell cycle. Let's propose that the probability of cell detachment out of microelectrode caused by alterations in adhesion mechanisms would have Boltzmann's distribution of time, whereas normalized changes in cN value could be covered by the following equation:

$$\mathbf{c}\_{\mathrm{N}} = \mathbf{c}\_{0} + \frac{\mathbf{A}\_{1}}{1 + \exp\left(\frac{\mathbf{t}\_{\mathrm{C}} - \mathbf{t}}{\mathbf{t}\_{\mathrm{M}}}\right)},$$

where c0 – value of normalized changes of cell index at the initial point of measurements t = 0, A1 – amplitude of normalized changes of cell index at the point of completing the 1st cycle of proliferation, tc – duration of cell cycle, tM – duration of phase M1 (**Figure 2B**), presumably, corresponding to the duration of mitotic phase in the synchronously proliferating cell population.

The equation given above does not account for any time delay between the cell cycle initiation and beginning of cell index changes. To do this we will introduce another parameter – time-lag tL, and we will consider a shift from the half-time of mitotic phase duration in the previous cell division (before the

incubation is shown as t, hours. (A) A representative curve of cell index kinetics (indicated as C, arbitrary units) reflects real impedance trace obtained from the xCELLigence system during 24 h of cell culture. Cell index kinetics corresponds to changes in the number of attached cells during cell cycle progression. A – attachment phase, I1,2 – interphase of 1st or 2nd cell cycle (interphasic cells attach to the microelectrode-covered plate), M1,2 – mitosis in the 1st or 2nd cell cycle (mitotic cells detach from the microelectrode-covered plate). Corresponding changes in the cell culture are shown schematically at the top of the figure. (B) Normalized and approximated cell index (indicated as cn, relative units) was calculated from the real cell index C as shown in Section "Results," and relates to the number of detached cells. Solid curve corresponds to the best regression model.

measurements). Then, the equation covering cell index changes due to 1st mitosis would be as follows:

$$\mathbf{c}\_{\mathrm{N}} = \mathbf{c}\_{0} + \frac{\mathbf{A}\_{1}}{1 + \exp\left(\frac{\mathbf{t}\_{\mathrm{C}} - \mathbf{t}\_{\mathrm{L}} - \mathbf{0}.5\mathbf{t}\_{\mathrm{M}} - \mathbf{t}}{\mathbf{t}\_{\mathrm{M}}}\right)}$$

Second mitosis will be accounted by introducing the second summand:

$$\mathbf{c}\_{\mathrm{N}} = \mathbf{c}\_{0} + \frac{\mathbf{A}\_{\mathrm{I}}}{1 + \exp\left(\frac{\mathbf{t}\_{\mathrm{C}} - \mathbf{t}\_{\mathrm{L}} - 0.5\mathbf{t}\_{\mathrm{M}} - \mathbf{t}}{\mathbf{t}\_{\mathrm{M}}}\right)} + \frac{\mathbf{A}\_{\mathrm{2}}}{1 + \exp\left(\frac{2^{\ast}\mathbf{t}\_{\mathrm{C}} - \mathbf{t}\_{\mathrm{L}} - 0.5\mathbf{t}\_{\mathrm{M}} - \mathbf{t}}{\mathbf{t}\_{\mathrm{M}}}\right)}$$

We have used this equation for non-linear regression analysis of experimental data. **Figure 4B** shows regression solid curve corresponding to the applied model for the cells isolated from young adult rats exposed to EE.

At the first round of regression analysis, we determined the mean value of cell cycle duration tC with kinetic curves for the normalized changes of cell index for the groups of cells with the well-identified phase of 2nd division. This group included data obtained from EE-treated rats as well as from aged rats housed in SC. Then, the obtained value of cell cycle duration for all the cell types was as follows:

#### tC = 10.8 ± 2.5 hr.

At the second round of regression analysis, we fixed the cell cycle duration tC, and values of tM have been estimated for all the cell types. The data obtained for the duration of "mitotic phase" tM are presented in **Table 1**.

Supposing tM duration as a time of cell cycle "mismatch" in the synchronously proliferating population, we may assume that when the number of cell generations is Ng = tC tM full

tM – duration of mitotic phase M.

fnagi-09-00245 July 24, 2017 Time: 12:57 # 8

Ng – number of cell generations produced before full desynchronization of cell growth.

Nc – maximal cluster size obtained before full desynchronization of cell growth.

K – coefficient of cluster size enlargement due to effect of EE.

desynchronization would happen, thereby affecting cells microenvironment and their proliferative capacity. Under the given experimental conditions, desynchronization is most obvious in the cell culture obtained from the group of elderly rats.

Supposing doubling of cell number in every next cell generation, we may adopt that maximal cluster size (number of cells in an aggregate) which might be formed before full desynchronization of cell cycle in the culture is:

$$\mathsf{N}\_{\mathsf{C}} = 2^{\mathsf{N}\_{\theta}}$$

Therefore, maximal cluster size Nc should be interpreted as neurosphere-forming capacity. It is clearly shown (**Table 1**) that it is dramatically reduced in the elderly rats and – even more visibly – in the rats with AD-type of neurodegeneration. Since NS-forming ability marks the general neurogenic potential (or neurogenic reserve) of cells involved into adult hippocampal neurogenesis, we may conclude that Aβ treatment of young adult rats produces more prominent suppression of neurogenesis than healthy aging. However, the restorative potential of EE might be easily calculated using coefficient K describing changes in Nc:

$$\text{K} = \frac{\text{N}\_{\text{C}}(\text{EE})}{\text{N}\_{\text{C}}(\text{SC})}$$

As might be expected from our in vivo experiments, EE restored Nc more efficiently in young adult animals (fourfold greater in the control group comparing to the AD model group) but not in the elderly rats (no positive effect of EE at all). Thus, 60 days in the conditions of EE produced no any preventive effect on the aging-associated suppression of hippocampal neurogenesis in vitro, being, however, relatively effective in the Alzheimer's type of neurodegeneration. This finding well-corresponds to the number of NeuN+ mature neurons in the dentate gyrus in rats kept in SC or EE (**Figure 1H**) where prominent restoration of mature neurons number was evident in the group of amyloid-treated rats but not in the group of aged animals.

#### DISCUSSION

Our findings clearly indicate that EE restores neurogenesis affected by the toxic action of Aβ or by aging at the earliest stage of niche cell development (before neuronal fate specification) as was evident both in vivo and in vitro. In contrast to aging brain, amyloid-affected brain was much more susceptible to the action of EE on the neurogenic capacity of cells. These findings confirm our hypothesis that EE may have different efficacy in AD-type of neurodegeneration and healthy aging brain in the context of neurogenesis improvement.

In accordance to previously published data (Hattiangady and Shetty, 2008) we found that expression of Pax6 within the neurogenic niche was not dramatically decreased in aging animals, but demonstrated prominent decline in AD model. However, the number of NeuN-immunopositive mature neurons was reduced both in healthy aging brain and in AD-affected brain in a similar manner. Therefore, it was not surprising that the earliest stages of neurogenesis both in vivo and in vitro were more sensitive to the action of EE. Particularly, Pax6 expression was greatly improved by EE exposure in vivo in Aβ-treated or aging rats, and the expression pattern of the same marker confirmed the highest neurogenic reserve in young adult rats exposed to long-lasting EE.

Unexpectedly, EE promoted obvious reduction in NeuroD1 expression in the hippocampus in AD group or aged group, whereas in the group of young adult animals no significant changes in NeuroD1 expression have been observed upon action of EE. It is known that NeuroD1 controls survival and maturation of neurons born in the adult brain (Gao et al., 2009) being under the control of Wnt-regulated activity of Sox2 and HDAC1 in NSCs (Chen and Do, 2012). This transcription factor may also direct reprogramming of glial cells into functional neurons in vivo, particularly, in AD brain (Guo et al., 2014). NeuroD1 overexpression could reduce functional deficits in newly-formed hippocampal neurons in the experimental model of AD (Richetin et al., 2015). In the aging brain, NeuroD expression declines in the hippocampal neurogenic niche (Uittenbogaard and Chiaramello, 2000). Thus, taking into the consideration the role of NeuroD1 in controlling cell fate and differentiation in hippocampal neurogenesis, one may assume that EE-induced reduction of NeuroD1 expression in Aβ-treated or aged rats could relate to some kind of preservation of neurogenic cells in the nondifferentiated state, particularly, in the group of aging rats where elevation of NeuN+ neurons number was less evident than in the group with AD model. Since such effect on NeuroD1 expression was not found in young adult animals, changes in NeuroD1 controled cell differentiation might be considered as a "marker" of EE action in Alzheimer-type neurodegeneration or in healthy brain aging.

It should be noted that EE-mediated decline in NeuroD1 expression is in the contrast to the majority of reports on EEinduced expression of main neurogenesis markers, including NeuroD (Terada et al., 2008). However, similar effect was reported in Manuel et al. (2015) where it was attributed to the inhibition of avoidance behavior in fish subjected to EE. Thus, we may conclude that EE-induced reduction in NeuroD1

hippocampal expression in Aβ-treated or aging rats could reflect some adaptive behavioral reactions needed in further investigation.

We also should point out that housing in standard conditions revealed that NeuroD1 expression in healthy aging brain was well-preserved comparing with Aβ-affected brain. In EEexposed rats, NeuroD1 expression underwent dramatic decline, particularly in the group of aging animals. Different sensitivity of NeuroD1-expressing cells to the action of EE in aging and AD is consistent with the observed effect of EE on the number of NeuN-immunopositive hippocampal mature neurons: stimulatory action of EE was considerably stronger in Aβ-treated rats.

In sum, EE efficiently improves neurogenesis impaired in the Alzheimer-type neurodegeneration (at least in the AD model used in our study) but produces relatively weak effect on neurogenesis in physiologically aging brain. In both cases, earliest stages of neurogenesis (stimuli-induced proliferation of stem cells and progenitors) in vivo represent the periods with higher sensitivity to the action of EE.

The essential question now is whether this effect might be reproduced and quantitatively explained in vitro? Analysis of neurogenesis in vivo is commonly complemented with in vitro studies. Along this way, establishment of NS culture in vitro is very useful for assessing proliferative and differentiating properties of stem cells and their progeny, however, quantitative insight into the process of NS development and evolution is highly required (Pacey et al., 2006). Characteristic behavior of NSCs/NPCs in NS culture could not be easy interpreted with simple observations and routine statistics. This problem is further complicated when a number of kinetic data appears in the conditions enabling action of various factors in vivo (before cell isolation) or in vitro (during cell clusters expansion and development). Therefore, we applied original algorithm to produce comparative analysis of EE effects on the neurogenic reserve of NSCs/NPCs obtained from all the tested groups.

We found that mathematical modeling allowed analyzing the kinetics of cell proliferation in vitro with high accuracy and in a good correspondence to the data obtained in vivo. Moreover, we were able to get the integral parameter (Nc) reflecting neurogenic reserve of stem/progenitor hippocampal cells affected by different factors (aging, amyloid treatment, EE) in vivo. We demonstrated that NS-forming capacity of brain stem and progenitor cells was positively affected by EE in young adult animals, aging animals, but not in elderly rats. The observed difference in the effects of EE on aging- or AD-associated impairment of neurospheres generation in vitro might be linked to the very recently reported age-related alterations in the duration of cell cycle (particularly, abnormal extension of G1 phase of the cell cycle not associated with a differentiation commitment) in the hippocampal stem cells resulting in neurogenesis decline (Daynac et al., 2016). A rough analogy might be found with G1-lengthening and alterations in G1/S transition in embryonic stem cells leading to imbalance between self-renewal and differentiation (Coronado et al., 2013). Therefore, the observed prevalence of desynchronization-related changes in the neurosphere-forming capacity in the group of elderly rats is very likely caused by the analogous mechanism of cell cycle impairment. Moreover, such alterations in the cell cycle progression that are evident in neurogenic cells derived from the aged brain might contribute to the relative inefficacy of EE in the context of neurogenesis recovery in aging.

In contrast to physiological aging, Aβ is able to increase NSC activity at the earliest steps of AD pathogenesis followed by later decline (Heo et al., 2007; Diaz-Moreno et al., 2013). Furthermore, EE was reported to increase Aβ accumulation in the transgenic AD model mice due to extensive synaptic activity (Jankowsky et al., 2003). Here, we will note that neurospheresgenerating capacity of hippocampal stem/progenitor cells might be enhanced by neuronal excitation due to recruitment of latent stem cells into adult neurogenesis (Walker et al., 2008). That is why Aβ-induced alterations in the adult neurogenesis seem to be more profound but at the same time more sensitive to the restorative capacity of EE-provided neuronal excitation than in the healthy aging brain. Thus, it is reasonable to suggest that longlasting exposure to EE preceding Aβ neurotoxic action results in excitation-mediated mobilization of latent stem cells and partial restoration of neurosphere-forming capacity in vitro. However, this effect is less prominent in Aβ-treated rats than in intact young adult animals. In the group of elderly rats, alterations in the duration of cell cycle diminish EE-induced changes in the stem/progenitor cells pool.

Previously, several attempts have been done to get a mathematical model of neurogenesis (Ashbourn et al., 2012; Ziebell et al., 2014). In this study we propose a mathematical model describing early processes in NSCs/NPCs development in vitro. Our time-lag model is based on several assumptions and simplifications but proves our data obtained in vivo and provides quantitative parameter (Nc) which is suitable for comparative analysis of effects produced by different factors (i.e., age, housing conditions, neurotoxic stimuli). Thus, combination of in vivo and in vitro approaches with mathematical modeling reveals some important characteristics of EE action on hippocampal neurogenesis in healthy aging brain or in Alzheimer-type neurodegeneration. It is tempting to speculate that the same approach might be further applied for assessing neurogenesistargeting activity of drug-candidates in preclinical studies.

There is a growing interest to the restoration of impaired neurogenesis and prevention of neurological deficits in physiological aging (Marr et al., 2010; Mattson, 2015), but differential sensitivity of aging brain and Alzheimer's brain to the conditions of EE should be carefully taken into the consideration. Coming back to our initial hypothesis, we may conclude that: (i) early stages of adult hippocampal neurogenesis are more sensitive to the action of EE in the experimental model of Alzheimer-type neurodegeneration or in aging; (ii) EE differently affects neurogenesis in physiological aging and in experimental AD, particularly, Aβ-affected brain is more susceptible to the action of EE comparing with healthy aging brain; (iii) different outcomes of EE action on NSCs/NPCs in AD or in healthy aging brain can be reliably detected with NS-forming assay in vitro and time-lag model of synchronized growth.

Thus, it is clear that EE-based neuroprotective strategies effective in the amyloid-affected brain could not be directly extrapolated to neurodegenerative processes associated with physiological brain aging. Therefore, application of EE strategy aimed to enhance adult neurogenesis should be considered as a personalized and pathogenetically based preventive or therapeutic intervention.

#### ETHICS STATEMENT

fnagi-09-00245 July 24, 2017 Time: 12:57 # 10

All animal experiments were performed in accordance with the principles of humanity set out in the Directive of the European Community (2010/63/EU). Protocols were reviewed and approved by the Local ethics committee of the Krasnoyarsk State Medical University named after Prof. V.F. Voino-Yasenetsky (35/2011).

#### REFERENCES

#### AUTHOR CONTRIBUTIONS

VS, YK, and AS conceived and designed the research. All authors performed experiments. VS, YK, NK, AM, EK, and KS analyzed data. VS, YK, OL, and EP prepared figures. VS, YK, YU, EP, and AS prepared the initial draft; VS, YK, OL, and AS revised the manuscript. All authors reviewed the final manuscript and approved its publication.

#### ACKNOWLEDGMENT

The study is supported by the grant given by the President of Russian Federation for the Leading Scientific Teams (N 10241.2016.7).

5XFAD Mice. Mol. Neurobiol. doi: 10.1007/s12035-016-0167-x [Epub ahead of print].

expression in the hippocampus. Neural Regen. Res. 7, 1797–1804. doi: 10.3969/ j.issn.1673-5374.2012.23.006

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Salmin, Komleva, Kuvacheva, Morgun, Khilazheva, Lopatina, Pozhilenkova, Shapovalov, Uspenskaya and Salmina. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Analyzing the Behavior of Neuronal Pathways in Alzheimer's Disease Using Petri Net Modeling Approach

Javaria Ashraf 1 , Jamil Ahmad1 \*, Amjad Ali 2 and Zaheer Ul-Haq3

 *Research Center for Modeling and Simulation, National University of Sciences and Technology, Islamabad, Pakistan, Atta-Ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan, Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical Sciences, University of Karachi, Karachi, Pakistan*

Alzheimer's Disease (AD) is the most common neuro-degenerative disorder in the elderly that leads to dementia. The hallmark of AD is senile lesions made by abnormal aggregation of amyloid beta in extracellular space of brain. One of the challenges in AD treatment is to better understand the mechanism of action of key proteins and their related pathways involved in neuronal cell death in order to identify adequate therapeutic targets. This study focuses on the phenomenon of aggregation of amyloid beta into plaques by considering the signal transduction pathways of Calpain-Calpastatin (CAST) regulation system and Amyloid Precursor Protein (APP) processing pathways along with Ca2+ channels. These pathways are modeled and analyzed individually as well as collectively through Stochastic Petri Nets for comprehensive analysis and thorough understating of AD. The model predicts that the deregulation of Calpain activity, disruption of Calcium homeostasis, inhibition of CAST and elevation of abnormal APP processing are key cytotoxic events resulting in an early AD onset and progression. Interestingly, the model also reveals that plaques accumulation start early (at the age of 40) in life but symptoms appear late. These results suggest that the process of neuro-degeneration can be slowed down or paused by slowing down the degradation rate of Calpain-CAST Complex. In the light of this study, the suggestive therapeutic strategy might be the prevention of the degradation of Calpain-CAST complexes and the inhibition of Calpain for the treatment of neurodegenerative diseases such as AD.

#### Edited by:

*Athanasios Alexiou, Novel Global Community Educational Foundation (NGCEF), Hebersham, Australia*

#### Reviewed by:

*Faez Iqbal Khan, Rhodes University, South Africa Hong Qing, Beijing Institute of Technology, China*

#### \*Correspondence:

*Jamil Ahmad*

Received: *11 December 2017* Accepted: *30 April 2018* Published: *23 May 2018*

<EMAIL_ADDRESS>
#### Citation:

*Ashraf J, Ahmad J, Ali A and Ul-Haq Z (2018) Analyzing the Behavior of Neuronal Pathways in Alzheimer's Disease Using Petri Net Modeling Approach. Front. Neuroinform. 12:26. doi: 10.3389/fninf.2018.00026*

Keywords: Calpain, CAST, calcium, PKC, APP, Stochastic petri net, Alzheimer disease, Amyloid beta

### 1. INTRODUCTION

Alzheimer's disease (AD) is a neurodegenerative disorder which has impacted nearly 44 million1 people around the world and this number is still increasing. AD is the leading cause of dementia in the old age (Ashford, 2004). Unfortunately, it is diagnosed only in one out of four people living with the disease1 . Clinical characterization of AD includes memory loss and cognitive impairment which further lead to damaged behavioral activities and render a person completely dependent

1http://www.alzheimers.net/resources/alzheimers-statistics/

on an external aid (Budson and Price, 2005). AD establishes over time with the appearance of pathological emblems which are senile plaques and neurofibrillary tangles. These lesions comprise of extracellular deposits of Amyloid beta (Aβ) (Selkoe, 2000; Golde, 2005; Tam and Pasternak, 2012) and intracellular selfgathered clumps of tau proteins (Lee et al., 2001), respectively. Aβ is a 40–42 amino-acids long peptide which is formed after the proteolytic cleavage of Amyloid Precursor Protein (APP) (Selkoe, 2000; Golde, 2005; Tam and Pasternak, 2012). Previous studies have shown that Aβ monomers are initially non-toxic but their conversion to oligomers makes them toxic (Volles and Lansbury, 2002; Walsh and Selkoe, 2004). Eventually, the abnormal accumulation of oligomers form plaques (Walsh et al., 2002) that deposit into neuronal Endoplasmic Reticulum (ER) (Cuello, 2005) and in extracellular space (Trojanowski and Lee, 2000; Walsh et al., 2000). Aggregation of senile plaques and neurofibrillary tangles cause neuronal cell death and synaptic failure (Tiraboschi et al., 2000; Selkoe, 2002). During the last two decades, several lines of studies have pointed toward the imbalance between Aβ production and its clearance plays a central role in pathogenesis of AD. Since 1992, this hypothesis has earned acquiescence (Hardy and Higgins, 1992) and is known as "**Amyloid cascade hypothesis (ACH)**". It suggests that Aβ and processing of APP are crucial in neuro-degeneration. In AD,

aggregation of Aβ is the first step leading toward the formation of senile plaques (Hardy and Selkoe, 2002; Vassar, 2005). APP is a type1 trans-membrane protein produced in ER (Greenfield et al., 1999; Roussel et al., 2013). In neurons, production and metabolism of APP occurs rapidly which makes it a crucial element in neuro-pathogenesis (Lee et al., 2008). The main APP proteolytic processing steps occur at the cell surface and Trans-Golgi networks (TGNs). Proteolysis of APP can occur through the so-called non-amyloidogenic and amyloidogenic Pathways (**Figure 1**). The first step of non-amyloidogenic pathway is carried out by the enzyme alpha (α)-secretase that breaks down APP into soluble Amyloid precursor protein alpha (sAPPα) and alpha C-terminal fragment (αCTF / CTF83). The catalysis by αsecretase is imperative as it cuts APP within Aβ domain which blocks Aβ formation (Lichtenthaler, 2011). This initial step can also be driven by the beta (β)-secretase / β-site APP-cleaving enzyme (BACE), a transmembrane aspartyl protease (Vassar et al., 1999; Haass, 2004) (**Figure 1**), which constitute amyloidogenic pathway. BACE is a crucial enzyme, that acts as a rate limiting protein in Aβ generation. It breaks down the APP into soluble Amyloid precursor protein beta (sAPPβ) and beta C-terminal fragments (βCTF / CTF99) (Cai et al., 2001). The CTFs are intermediate products of the first step in both pathways which remain attached to the membrane and they are further cleaved by

FIGURE 1 | APP and processing products: APP is synthesized in the ER and then transported to the trans-Golgi-network (TGN) where it is cleaved by secretases. In non-amyloidogenic pathway (left), cleavage of APP by α-secretase results in the generation of sAPPα and C-terminal fragments CTF83 which is further cleaved by γ -secretase into p3 and AICD. Proteolysis by α-secretase prevents *A*β production as the cleavage site in APP is within the *A*β domain. In amyloidogenic pathway (right), APP is cleaved into sAPPβ and CTF99 by β−secretase / BACE activity. Furthermore, CTF99 breaks down into AICD and *A*β by γ -secretase activity. *A*β fragments oligomerize and fibrillize into plaques.

gamma (γ )-secretase (Zhang et al., 2011). In non-amyliodogenic pathway, the fragment α-CTF is cut down by γ -secretase into p38 and the Amyloid Precursor Protein Intracellular Cytoplasmic / C-terminal Domain (AICD). While in amyloidogenic pathway, γ -secretase degrades the βCTF into Aβ and AICD (O'Brien and Wong, 2011) (**Figure 1**).

The Biological Regulatory Networks (BRN) of APP processing, depicted in **Figure 2**, is also built from **Figure 1**. APP processing depends on sequential cleavage by three secretases (α/β-secretase and γ -secretase). In normal conditions, α-secretase residing at the plasma membrane is constitutively active for APP coming to the cell surface and thus favoring non-amyloidogenic pathway (De Strooper and Annaert, 2000). Though there is an interesting fact about APP proteolysis that none of the secretases show special substrate specificity toward APP. There are several transmembrane proteins such as cell surface receptors and ligand, growth factors and cytokines besides APP which undergo ectodomain shedding by enzymes with α-secretase activity (Annaert and Saftig, 2009). In the same manner, BACE shows low affinity toward APP and it is not its exclusive physiological substrate (DeStrooper et al., 2006; Hu et al., 2006). Many observations highlight that in healthy cells APP is frequently processed through non-amyloidogenic pathway to resist amyloid generation while it is altered in pathological conditions (De Strooper and Annaert, 2000). Abnormal processing of APP is stated to be the first and fundamental step in plaques formation in AD pathogenesis (Jonsson et al., 2012). In neuropathological conditions, BACE affinity toward APP increases two folds which leads to enhanced Aβ production (Yang et al., 2003; Li and Südhof, 2004). Recent studies on transgenic mice model have shown that BACE activity is modulated by Calpain activation in AD pathology (Liang et al., 2010). Calpain-Calpastatin system also plays a key role in neurodegeneration. Transgenic mice models have shown that over expression of APP, increased production of Aβ, inhibition of Calpastatin (CAST) and activation of Calpain increase neuronal degeneration in AD (Higuchi et al., 2012).

Calpains are protein clan of cysteine/ thiol proteases and their activity depends on Ca2+ concentration (Ferreira, 2012). The most studied Calpains, mu(µ)-Calpain (Calpain1) and m-Calpain (Calpain2) are present abundantly in neurons, central nervous system (CNS) and glial cells. Though their distribution differs, Calpain1 is ubiquitous and expressed more in neurons while Calpain2 is present in glial cells (Ono and Sorimachi, 2012; Santos et al., 2012). Calpain1 requires micro-molar concentration of Ca 2+ (10–50µM), while Calpain2 is activated by mili-molar concentration of Ca2+ (250–350µM) in vitro (Goll et al., 2003; Ryu and Nakazawa, 2014). Ca2+ plays important role in ensuring the cell's vital functions. In addition to calcium, Calpain is tightly regulated in the cell by CAST which is also ubiquitous and solely

a specific endogenous inhibitor for both Calpains (Melloni et al., 2006).

CAST is reported as an explicit suicide substrate for Calpain (Yang et al., 2013). The proportion of CAST in a cell is normally larger than Calpain, its ratio with location is crucial in controlling the extent of activation of Calpain within a cell (Todd et al., 2003). CAST interacts with Calpain at different stages i.e., first it constrains Calpain at the membrane where pro-Calpain is attached then it interacts with active Calpain inside cytosol (Hanna et al., 2008). CAST forms a reversible complex with Calpain at both the sites. At membrane, the reversible complex breaks down when Ca2+ influx increases to release Calpain. Inside cytosol, Calpain undergoes autolysis to attain active

conformation. In response, CAST changes its cellular distribution to make itself widely available in the cytoplasm to counter active Calpain (Todd et al., 2003). Both active Calpain and CAST rejoin in a reversible complex to resist persistent activity of Calpain (De Tullio et al., 1999). Active Calpain modulates CAST by slowly digesting it into small inactive fragments which results in plethora of Calpain in cell leading to pathological condition (Averna et al., 2001b; Tompa et al., 2002) (**Figure 3**). It has been reported that in AD CAST becomes depleted from different regions of the brain as compared to healthy aged brain (Rao et al., 2008). It has also been observed that by controlling Calpain, CAST is indirectly preventing cell membrane damages induced by high Ca2+ and Aβ peptide (Vaisid et al., 2008).

carried out by energy (*ATP*) dependent channels such as plasma membrane calcium ATPase (*PMCA*), sodium-potassium ATPase (*NKA*) and sodium-calcium exchanger (*NCX*) channels. Calcium homeostasis influences Calpain-CAST system. At membrane, Calpain is bound to CAST to form *mComplex* at low *Ca*2+ level. At high *Ca*2+ concentration, Calpain is released into cytoplasm and autolysed to active form *ACalp* that again forms complex with CAST (*cComplex*). Gradually the complex breaks down and releases *ACalp* which enhances *Plaque* accumulation and *LTP* events.

CAST pool is regulated by reversible phosphorylation via PKC, which is a Ca2+-activated phospholipid dependent kinase. Moreover, it is de-phosphorylated by protein phosphatases (ppase) (Melloni et al., 2006). Phosphorylation control CAST inhibitory efficiency in brain (Averna et al., 2001a) to regulate its availability for calpain inhibition. Reversible protein phosphorylation regulates many neuronal functions and is important for neuronal signal transduction (Wu and Lynch, 2006). Inactive PKC is converted to Ca2+-bound activated form in the presence of diacylglycerol (DAG) which in turn is activated by receptor based hydrolysis of phosphoinositides 3 (IP3) (Courjaret et al., 2003). The N-terminal region of CAST which is responsible for the function of the protein has a site for phosphorylation by PKC. CAST is phosphorylated by PKC to decrease its inhibitory efficiency toward calpain (Averna et al., 2001a) (**Figure 3**). It has been observed that PKC also regulates APP processing by activating α-secretase (Rossner et al., 2001; Racchi et al., 2003), it promotes non-amylodogenic pathway over β-secretase (Lanni et al., 2004). In vivo studies show that in the presence of PKC, secretion of sAPPα increases and Aβ secretion declines (Chen and Fernandez, 2004). Other studies about AD found that PKC has substantial role in AD pathology (Etcheberrigaray et al., 2004; Alkon et al., 2007). Active Calpain also interacts with PKC and converts it into constitutive active enzyme (Yamakawa et al., 2001; Goll et al., 2003). Calpain1 directly starts depletion of PKC from cell by converting it into protein kinase M (PKM) (Yamakawa et al., 2001; Liu et al., 2008). The whole mechanism is also depicted in the form of Calpain-CAST system BRN in **Figure 4**.

The dysregulation of Calcium homeostasis contributes in aging and neurodegeneration (Mattson, 2004; Smith et al., 2005; Stutzmann, 2005). A tremendous deal of work by calcium is tightly regulated in time, space and intensity by intracellular stores, influx and efflux channels (Stutzmann, 2005). At resting stage, extracellular Ca2+ concentration ranges from 1.5 to 2.0 mM (Orrenius et al., 2003). While magnitude of Ca2+ inside a cell is very low (between 50–100/ 50–300 nM) (LaFerla, 2002; Orrenius et al., 2003) and after activation it can rise to several micromoles. On contrary, inside ER, the level of Ca2+ is in the range 100-500µM (LaFerla, 2002) which is approximately 1000 times higher than cytosol concentration at the resting phase. Persistent alteration of Ca2+ homeostasis affects production and digestion of pathological proteins such as Calpain, Aβ and tau protein. Dysregulation of cellular Ca2+ level is an early and main feature of AD (Mattson et al., 2000; LaFerla, 2002; Small, 2009).

Cytosolic Ca 2+ is maintained at very low level as compared to extracellular space through several homeostatic mechanisms, working both temporally and spatially (**Figure 3**). These equilibrating apparatuses include voltage-operated channels (VOCs) and receptor operated channels (ROCs) for Ca2+ inclusion, Ca2+ storage in organelles e.g., ER (Wojda et al., 2008) and Ca2+ extrusion to extracellular space. Different ATPdependent membrane pumps such as plasma membrane calcium ATPase channel (PMCA) and sodium-calcium exchanger (NCX) which are dependent on sodium-potassium ATPase (NKA) (Wojda et al., 2008; Brittain et al., 2012) are used for Ca2+ efflux. In different physiological processes, elevation of Ca2+ is necessary to switch-on respective proteins. Ca2+ inclusion is

administered by several routes such as N-methyl-D-aspartate receptor (NMDAR), an imperative type of ROCs, which switch into open conformation after binding of endogenous glutamate (glu) as ligand. Another important influx gateway is voltage gated Ca2+ channel (VGCC) which is in closed conformation when neuronal membrane is polarized (Schmolesky et al., 2002; Cain and Snutch, 2011). The VGCC adopts open conformation as plasma membrane depolarizes due to Ca2+/ sodium (Na+) influx through ROCs or ion channels (Weber, 2012). Ca2+ influx also increases from intracellular stores in ER through storeoperated channels. There are two calcium channels in ER which are IP3-sensitive and ryanodine (RyRs)-sensitive Ca2+ stores (Berridge, 2009). IP3 driven release of Ca2+ starts by binding of G-protein coupled receptor (GPCR) on plasma membrane

which induces Phospholipase C (PLC) mediated cleavage of phosphatidylinositol-4,5-bisphosphate (PIP2) on cell membrane into DAG and IP3. IP3 binds to its receptor on ER membrane and stimulate Ca2+ release into the cytoplasm (Berridge, 2009; Krebs et al., 2015). Furthermore, depletion of ER stores mediate influx of extracellular Ca2+ through store-operated channels (SOCs) (Emptage et al., 2001; Weber, 2012). The mechanism for lowering Ca2+ from cell is controlled by PMCA and NCX. Both PMCA and NCX are energy dependent while, NCX is also Na+ gradient dependent (Wojda et al., 2008). The BRN of Calcium channels, **Figure 4**, is also helpful in understanding the mechanism underlying the Ca2+ homeostasis.

To comprehend the above mentioned neuronal pathways, models are constructed to understand their dynamics. Stochastic

approaches describe the randomness of biological system accurately as compared to ordinary differential equations. In BRNs, the activation or inhibition processes take place with random time delays, therefore, stochastic modeling frameworks are more suitable for their modeling. Petri nets provide complementary approach for both qualitative and quantitative modeling and simulation of the dynamical behavior of large systems in an intuitive way (Mounts and Liebman, 1997; Tsavachidou and Liebman, 2002; Tareen and Ahmad, 2015). The study (Tsavachidou and Liebman, 2002) shows that the Petri net models predict the experimental findings which support

the soundness of these models. Stochastic petri nets (SPNs) have emerged as a promising tool for modeling and analyzing BRNs in the field of molecular biology (Goss and Peccoud, 1998). The dynamic behaviors of a variety of BRNs have been studied using stochastic simulations (Mura and Csikász-Nagy, 2008; Lamprecht et al., 2011; Castaldi et al., 2012; Marwan et al., 2012).

In this study, we have modeled and analyzed the neuronal physiological system constituting Ca2+ channels maintaining homeostasis, CAST regulating Calpain system and APP processing pathways separately and collectively at molecular

FIGURE 7 | (A) An Unmarked Petri net model of Receptor Ligand binding. *RL\_Complex* is formed when *Receptor* is in open conformation (*Open\_R*) and *Ligand* is activated into *Active\_L*. (B), A marked PN model has tokens which represent initial marking of the Receptor Ligand binding model.

FIGURE 8 | (A) A Timed Petri net model of Receptor Ligand Binding. Initially, the *Receptor* is in close conformation and the *Ligand* is inactive. After enabling of respective transition (*Opening* and *Activation*), *Receptor* adopts open conformation (*Open\_R*) and *Ligand* is activated into *Active\_L* respectively. The transition *Opening* and *Activation* have an associated time delay *d*1. The transitions are fired when the associated time delay is elapsed. *RL\_Complex* is formed when *Open\_R*, *Active\_L* are present and time *d*2 is elapsed. (B,C) are Specified and Usual Stochastic Petri Nets respectively. The TPN can be converted into SPN when the deterministic firing function *d* changes into a random variable.

level using SPNs to understand the AD progression mechanism. Particularly, we have analyzed neuronal patho-physiological dynamic behaviors causing the development of hallmark lesions in brain to answer many question e.g., how dysregulation of Ca2+ triggers AD? When CAST, the sole inhibitor of Calpain, depletes from the brain cells? how Aβ production increases? and when the accumulation of plaques start? The answers to these questions lie in the modeling of the combined BRN **Figure 6**. The model predicts that Calpain is the main cause of dysregulation, which start with the rise in Ca2+ levels in the cytosol. Calpain activates different pathways through which Aβ production and accumulation increases. Plaques start building with time at the age of forty and older. Plaques first enter lag phase and then into rapid growth phase. Calpain slowly degrades CAST which depletes from the cell and eventually neuronal degradation

progresses. These results suggest that patho-physiological events such as dysregulation of Ca2+ homeostasis, Calpain hyperactivation, CAST degradation and abnormal digestion of APP, all are inter-connected and a cumulative study of these processes through SPN was needed.

### 2. METHODOLOGY

Petri nets (PNs) have three components namely places, transitions and edges. Place and transition are collectively known as nodes/ vertices of a PN. An arc or edge joins nodes such as a place (pre-place) to a transition or a transition to a place (post-place) to make a bipartite graph (Petri, 1966). Edges are directed and have weights associated with them. In biological systems, such as BRNs, places (◦) represent biological

entities e.g., protein or their complexes, gene, mRNA, ions, metabolites and cell or cellular components while transitions () represent biochemical reactions e.g., association, activation, decomposition, inhibition, phosphorylation, dephosphorylation and translocation (Tareen and Ahmad, 2015). The weights of edges represent the stoichiometry of reactions. The weight can be one (1) or greater than one (Blätke et al., 2011; Liu et al., 2016). Following are the different types of edges.

The following definitions are originally given in David and Alla (2010).

**Definition 1** (Unmarked Petri Net). An unmarked Petri Net (PN) is a five-tuple P= hP, T , E, pre, posti where:

A simple unmarked PN is given in **Figure 7A**, to model a receptor-ligand association. A receptor is in closed conformation and ligand is in in-active form. The receptor must undergoes open conformation and then the ligand activates to form an association (RL\_Complex) with receptor. The places in a PN may have tokens (black dots or positive real numbers) which represent marking of the places. In a marked PN, places are initially assigned tokens.

**Definition 2** (Marked Petri Net). A Marked Petri Net (MPN) is a tuple MP= hP, mˇ0i where:

The transition associated to a marked place is said to be enabled and it will be fired when the corresponding pre-places have

*The transitions (t) from (*Figure 9*) are listed with their rates. The transitions are adjusted to rates which can reproduce the physiological working of calcium channels.*

tokens equal or greater than the weight of the associated arc. Receptor opening requires a Receptor and ligand activation depends on the presence of an inactive Ligand. An active ligand and an opened receptor form a Receptor-Ligand Complex (**Figure 7B**).

**Definition 3** (Timed Petri Net). A Timed Petri Net (TPN) is a tuple TP= hMP, ˇfni where:

A TPN associates a time delay di to a transition Ti . The transition Ti is said to be enabled when it has sufficient tokens in its pre-places and it is fired when its deterministic delay time is elapsed (**Figure 8**). Stochastic Petri Net (SPN) are used when time delays are random variables. SPN (**Figure 8C**) (Marsan et al., 1994; Heiner et al., 2008) are explicitly derived from TPN (**Figure 8A**).

**Definition 4** (Stochastic Petri Net). A marked stochastic petri net is a pair SPN = hMP, Ratei where:

The random variables di have assigned negative exponential probability distribution function (PDF) ˇfn(t),

$$\check{\mathfrak{fin}}(t) = \Pr[\,d\_i \le t] = 1 - e^{-\mu\_i t} \,\, where\_r$$

$$\Pr[\,d\_i \le t + dt \mid d\_i > t] = \mu\_i \cdot dt$$

The marking of mˇt of SPN is a homogenous Markovanian Chain (HMC) process, which is a class of stochastic processes simply built from the reachability graph of a qualitative PN by

assigning transition rates to edges between all the states (Heiner et al., 2008). Thus an HMC can be associated with every SPN. Gillespie was the first one who designed special case of Petri nets for reaction networks and called them SPNs (Gillespie, 1976). Snoopy (Marwan et al., 2012) tool is used to design, animate and simulate SPNs of neurodegeneration related BRNs. This tool has been extensively used to model a variety of systems such as software systems, biological systems and production systems. The supplementary file S1 contains a general Enzyme-Substrate BRN that is modeled and simulated through the Petri net modeling tool Snoopy. This file helps to explain the working of the tool (Snoopy).

### 3. RESULTS

In this study, stochastic modeling and analysis of the neuronal pathways involved in neuro-degradation in AD was carried out. The SPN models of the three main neuronal physiological pathways: Calcium Influx Efflux channels (**Figure 5**), Calpain-CAST regulatory system (**Figure 4**) and APP digesting pathways (**Figure 2**). The SPN model of the crosstalk of these three pathways is also presented.

#### 3.1. SPN of Calcium Influx Efflux Channels

The BRN of Calcium influx efflux channels in **Figure 5** is translated into a SPN model as shown in **Figure 9**. The influx

channels comprise of receptor based and voltage gated channels (NMDR, VGCC1 / VGCC2). In addition, the GPCR channel regulates intracellular trafficking of Ca2+ ions and stimulation of

**PKC** signaling pathway. The energy dependent efflux channels (e.g., PMCA and Na-K ATPase) work efficiently in a sync with influx channels to maintain equilibrium between in-out flow of

FIGURE 12 | Calpain activation and regulation: In (A), CLP is bound to CAST in the form of mC at membrane. As the Ca\_In increases and Ca\_Out decreases the complex mC breaks down. In an elaborate view (B), CLP is produced by the break down of the mC which is again controlled by the formation of cC. In (C), mC is formed, maintained and eventually degraded into CAST and Calpain. Both Calpain and CAST form complex cC which decreases the availability of free CLP. *CLP* binding to cC slowly degrades the CAST which is represented as dCT. (D) Both complexes mC and cC are formed while cC is more stable than mC, as rate of degradation of mC is higher than (C).

Ca2+ ions in neurons. Ca2+ ions move from **Ca\_In** place (green) to **Ca\_Out** place (blue) and vice versa. The places in green colors and transitions (yellow) constitute influx channels. The places in light blue colors and transitions in grey colors make efflux channels. Initially, **NMDR\_0** place represents that receptor is in close conformation that binds with **glu** to form the complex represented by **NMDR\_glu\_0** place. This complex adapts open conformation represented by the place **NMDR\_glu\_1** and then further triggers the opening of VGCC channel represented by the place **VGCC\_c1** to facilitate Ca2+ ions influx. Excess Ca2+ ions are deposited into the place **Ca\_ER** through store-operated channel. The place **GProtein\_i** represents inactive GPCR, that

change into place **GProtein\_act** by a complex of GPCR and ligand represented by places **GPCR\_Lig** (complex), **GPCR** (receptor) and **ligand** respectively. **GProtein\_act** activates PLC represented by place **PLC\_act**, which mediate cleavage of **PIP2** place into **DAG** and **IP3**. The place **DAG** activates **PKC** by adding Ca2+ ion in it. At the surface of ER, IP3 binds with its receptor IP3R, represented by places **IP3** and **IP3R**, which induces Ca2+ ions flow into the place **Ca\_ER**. At the same time, Ca outflow is maintained by PMCA, NCX and Na-K ATPase. PMCA channel represented by place **PMCA\_1** requires ATP (place **ATP**) to extrude one Ca2+ ion into extracellular space. The place **NCX** representing NCX pump ensures extrusion of

Ashraf et al. Analyzing the Behavior of Neuronal Pathways in Alzheimer's

TABLE 2 | The transition rates of the SPN model of Calpain-CAST regulatory system.

*The transitions (t and c) from (*Figure 11*) are listed with their rates. The transitions are adjusted to rates which can reproduce the physiological working of Calpain-CAST regulatory system.*

one Ca2+ ion by adding three Na ions into the cytoplasm represented by place **Na\_In**. The place **Na\_K\_1** represents active (i.e., depends on ATP) Na-K ATPase pumping that pumps Na out of the neuron (place **Na\_out**) and K into the neuron (place **K\_In**). The place **VGCC\_c2** representing Ca inflow is also dependent on place **NCX**, which depolarizes the membrane. The SPN model is simulated after applying and adjusting the rates of transitions in order to reproduce physiological working of the pathway. The transitions are labeled by t, transition from **t1**–**t22** are associated with influx channels. **t1**–**t10** are linked to GPCR channel which help in storage of excess Ca2+ ions into place **Ca\_ER**. **t11** takes Ca2+ ions from **Ca\_ER** into cytoplasm (place **Ca\_In**). Remaining **t12**–**t22** make NMDR and VGCC channels functional. The efflux channels, consisting of NCX, NKA channels and PMCA are connected with transitions **t23**– **t29**. The transitions **t30** and **t31** are involved in activation of PKC which is dependent on calcium. The rates of transitions are shown in parameter in **Table 1**. The simulation results have shown that there is an equilibrium behavior in all channels, which is required to maintain homeostasis in calcium flow. In **Figure 10A**, a stable oscillating behavior can be observed among the places **Ca\_out**, **Ca\_ER** and **Ca\_In**. Concentration of Ca2+ ions in **Ca\_out** place is higher while it is lower in place **Ca\_In**. Na and K ions, (**Figures 10B,C**) are also oscillating properly, contributing to Ca2+ homeostasis and generating nerve impulses through polarization and depolarization. Ca2+ homeostasis has pivotal role in cell physiological working. Dysregulation of Ca2+ homeostasis will affect the regulation of most of the proteins, enzymes and genes which will have deleterious effects on the physiological processes.

# 3.2. SPN of Calpain-CAST Regulatory

dormant form of the cytosol; it activates into PKC to convert mCT and cCT into iCT. Phophotases (pp) convert the iCT into CT. PKC in the presence of

System The model in **Figure 11** represents Calpain-CAST regulatory

*CLP* is degraded into pkm.

system as given in **Figure 4**. In SPN model, due to lower Ca2+ level in cytosol, Calpain is in dormant form which is represented by place **pCalp**. It then attaches to CAST located on membrane (place **mCT**) to form a reversible complex of CAST and Calpain (place **mC**). Calpain is activated when cytosolic Ca2+ rises to specific concentration (Pal et al., 2001; Ryu and Nakazawa, 2014). Lower level of cytosolic Ca2+ represented by place **Ca\_In** facilitates (place **mC**), membrane bound inactive reversible Calpain-CAST complex. After the concentration of **Ca\_In** rises or crosses its threshold, then the short-lived **mC** is disintegrated into places **CLP** and **cCT**. The place **CLP** shows that Calpain moves to transmembrane and then converts into active form in the presence of Ca2+ ions and its autolysis. Now place **CLP** shows that Calpain is activated that is free to translocate to cytosol. The place **cCT** represents that CAST can again hinder over-activation of **CLP** by forming a reversible complex, represented by place **cC**. The place **cC**, splits into places **CLP** and **dCT**. In nature, active Calpain breaks down the bounded CAST into small subunits (Rao et al., 2008). The place **CLP** shows that it is active and which can be involved in various cellular processes. The place **CLP** can cleave place **P35** into p25 which is involved in hyperactivation of cyclin dependent kinase 5 (cdk5) (Kusakawa et al., 2000; Lee et al., 2000). The transitions of this system are labeled with c. The transition **c1** forms complex, place **mC** from **pCalp** and **mCT** at low Ca2+ concentration. The **c2** breaks complex and activates **CLP** when level of Ca2+ rises into cytoplasm. The

transition **c4** forms complex place **cC** by binding **CT** and **CLP** in cytoplasm and **c5**, at a very low rate (**Table 2**) slowly degrades the CAST into **dCT** and makes the **CLP** free. Transitions **c6**, **c12** and **c13** are linked to **CLP** mediated **P35** pathway. **CLP** also degrades **PKC** into **pkm** through **c7**. Remaining transitions **c3**, **c8**, **c9**, **c10**, **c11** and **c14** are associated to CAST regulation through **PKC** and phosphotases **pp**. The **Table 2** lists all reaction rates of associated transitions which are obtained after fine tuning. In **Figure 12A**, the simulation shows that when Ca influx, **Ca\_In**, is lower than its threshold then **pCalp** is bounded into complex of Calpain-CAST (**mC**) at membrane. When **Ca\_In** crosses its threshold, the peak of **mC** runs down and **CLP** rises representing release and activation of Calpain, (**Figure 12B**). These results are according to experimental findings (De Tullio et al., 1999; Todd et al., 2003) (**Figures 12A,B**). To regulate **CLP** activation, **cC** appears but gradually the complex peak falls down showing that the **cC** is broken down into active **CLP** and **dCT**. The results in **Figures 12C,D** show that as **CLP** is rising, both the complexes (**mC** and **cC**) are gradually degrading causing depletion of CAST from the cytosol (Averna et al., 2001b; Tompa et al., 2002). In **Figure 13**, regulation of **PKC**, CAST and phophatases (represented as **pp**) can be observed. **iPKC** converted into **PKC**

TABLE 3 | The transition rates of the SPN model of APP processing pathways.

*The transitions from* Figure 14 *are listed with their rates. The transitions are adjusted to rates which can reproduce the physiological working of amyloidogenic and nonamyloidogenic pathways.*

in the presence of Ca2+, which further converts into **pkm** by the action of **CLP**. As shown in the graph of **Figure 13**, **CT** is regulating through means of reversible phosphorylation and dephosphorylation expedited by **PKC** and **pp**, respectively.

#### 3.3. APP Processing Pathways

The SPN model of APP processing as shown in **Figure 14** is built by combining both amyliodogenic and non-amyliodogenic pathways (**Figure 2**). APP (place **aAPP**) is catalyzed by αsecretase (place **ALPHA**) which produces CTF83 and sAPPα (places **CTF83** and **sAPPa**), whereas digestion of APP (**bAPP**) by β-secretase (place **BACE**) yields CTF99 and sAPPβ, (represented by places **CTF99** and **sAPPb** respectively). Further processing is carried out by enzyme γ -secretase (place **GAMA**) which converts CTF83 into p3 and AICD, while it converts CTF99 into **A**β and **AICD**. Aβ accumulate into **Oligomer** and finally into **Plaq**. The transitions **a**, **a1** and **g\_a** constitute non-amyliodogenic pathway, while transitions **b**, **b1** and **g\_b** are linked to amyloidogenic pathway. The parameters are set according to **Table 3**, in which the rate of transition of α-secretase (place **ALPHA**) mediated APP processing (**aAPP**) is higher than BACE (**BACE**) driven APP digestion (**bAPP**) **(**µ**= 0.5, 0.3 respectively)**. The rates of plaques formation and accumulation (transitions: **p1** and **p2**) are also adjusted according to the observations in literature which predict low Aβ burden in normal healthy brain (Mawuenyega et al., 2010; Rodrigue et al., 2012).

The simulations in **Figure 15A** shows that all the three enzymes (**ALPHA**, **BACE**, and **GAMA**) are available for the proteolysis of APP (in the cell to digest the around-theclock production of) **APP**. The **Figure 15B** shows that the product **sAPPa** of non-amyloidogenic processing pathway is in higher concentration than the product **sAPPb** of amyloidogenic pathway. Production rate of **sAPPa** is higher than **sAPPb**. The graph in **Figure 16A** shows that in the healthy brain plaques are produced and accumulated in a linear fashion. Aβ burdens in the form of plaques at elder age i.e., approximately after 60 unit to 100 unit time. In **Figure 16B**, the linear behavior of plaques change to an exponential growth due to the fast accumulation

FIGURE 15 | APP processing in physiological conditions: In (A), substrate *aAPP* binds with alpha secretase (ALPHA) and bAPP binds with BACE. All the three secretases ALPHA ,BACE , and GAMA are oscillating. In (B), concentration of sAPPa is higher than the concentration of sAPPb which depicts healthy cell physiology.

quantity to form Oligo which gradually accumulates into Plaq.

rate with passage of time which depicts the lag phase (no plaques appearance) upto 80 unit time and after it evolves into growth phase (evolution).

## 3.4. Cross Talk of Calcium Channels, APP and Calpain-CAST Regulatory Pathways

The **Figure 17** represents the SPN model of the crosstalk network in **Figure 6**. The connection that joins the pathways is established through places **CLP**, **P35** and **BACE**. Active Calpain enhances βsecretase mediated APP cleavage twice than normal (Kusakawa et al., 2000; Liang et al., 2010) which initiates early production and accumulation of plaques (Jack et al., 2010; Braak et al., 2011). As a counter action, PKC **PKC** plays its role and enhances the activity of enzyme α-secretase (**ALPHA**) (Skovronsky et al., 2000). In the next connection, Calpain (**CLP**) hinders the functioning of **PKC** by degrading it into pkm (place **pkm**). Aβ

oligomers represented by place **Oligomer** forms pores into the membrane which instantaneously increases influx of Ca2+ ions. The place **Amyloidbeta** inhibits the transition **Energy** which indirectly hinders Ca2+ extrusion into extracellular space. The new transitions in the crosstalk network are labeled with k. The transition **k1** connects the place **PKC** with **ALPHA** while **P35** is

connected to **Plaq** through transitions**c13**, **k2**, **k4**, **k5**, and **k6**. The transition **k3** joins **Oligomer** with **Ca\_In**. The reaction rates of all the transitions are listed in **Table 4**.

The results in **Figure 18** show that **CLP** and **P35** are accumulating which can directly affect the plaques accumulation process by increase in their deposition rate, as compared to

*All the transitions from* Figure 17 *are listed with their corresponding rate values. As the SPN is a combination of previous three models, it has only three new connections with transition labeled k. All the transitions are adjusted to rates which can reproduce the patho-physiological condition of the brain.*

results in section 3.3 where accumulation of **Plaq** is minimal (**Figure 16A**). It can also be observed that **mC** and **cC** start declining at later stages which indicates depletion of CAST (**Figure 18A**).

In **Figure 18B**, the concentration of **sAPPb** is higher than **sAPPa** protein, which is contrary to the **Figure 15B**. Hyper activity of **CLP** influences more than one processes in the pathological network by enhancing **BACE** activity which increases the growth of **sAPPb**. The decrease in **PKC** concentration and calcium dysregulation (**Ca\_In** rises gradually, **Ca\_Out** and **Ca\_ER** both lower down) are also due to over-activation of **CLP** (**Figure 18C**). Instability in Calcium equilibrium in neurons appear after 60 unit time which also act as a signal indicating the development of AD. Additionally, degradation of **PKC** into **pkm** by the action of **CLP** also have bidirectional effect on the system. First the availability of active **CT** in the cell raises and then **ALPHA** secretase driven APP processing slows down (**Figure 18B**). **Figure 18D** represents the translocation and regulation of CAST (**cC** and**cC**) in the brain cell.

#### 3.5. Therapeutic Intervention

The parameter **Table 5** shows that by changing the rate of reaction of two crucial transitions i.e., **c2** and **c5** (representing degradation rate of Calpain-CAST complex at membrane and cytosol, respectively) would provide effective strategy to control or stop the development of AD and other related neurological disorders. After applying in silico intervention, it can be observed that as the rate of transition **c2** is decreased the neurological network move toward stability due to basal or low activity of **CLP** (**Figure 19A**). Further, the rate of **c2** is set to zero which indicates that **mC** is made unbreakable or stable which implies that there would be no production of **CLP** (**Figure 19B**). In **Figure 19A**, both the complexes (**mC** and **cC**) are maintained for longer time duration which result in low production of **CLP**, **A**β and **Plaq**. In **Figure 19B**, there is neither the production of **CLP** nor the formation of **cC** due to long life of **mC** complex occur and there is negligible production of **A**β. The complex (**mC**) also ensures low level of **sAPPb** and high concentration of **sAPPa** and as a result there is minor accumulation of **Plaq** in the neurons.

The stability of **mC** have positive effects in maintaining the calcium homeostasis which regains its equilibrium accompanied by substantial concentration of **PKC** and no production of **P35 Figure 20A**. The smaller rate of **c5** also favors stable condition of system and can delay the production of plaques. **Figures 19C,D** show that as the rate of transition of c5 decreases the system maintains a stable state due to prolonged life of **cC** which causes basal level production of **CLP** and eventually guard against rapid **Plaq** accumulation in brains. As shown in **Figure 19D**, **cC** helps in maintaining a balanced level of **CAST**, **sAPPa** and **sAPPb** and it lowers down the concentration and accumulation of **A**β and **Plaq**. Also it aids in maintaining homeostasis of other proteins and channels into the cell such as **PKC**, **pkm**, **P35** and calcium homeostasis (**Figure 20B**). Therefore, these complexes are very critical targets since they regulate homeostasis of many crucial proteins.

### 4. DISCUSSION

This study contributes in achieving long haul goal of AD research i.e., understanding and manipulating pathological conditions which include BACE and Calpain over-activation, Calcium dysregulation, CAST depletion and abnormal production of Aβ. In this study, neuronal pathways comprising Calpain-CAST system, APP processing pathways and Calcium channels were first modeled individually and then their cross-talk was also modeled. The simulation results of these models predicted a more clearer picture of AD pathogenisis also, on the basis of observations interventions were introduced to linger on the AD pathogenesis.

In a healthy physiological model, rate of Ca2+ ions inflow should be lower than rate of Ca2+ ions outflow with uniform oscillation within the safest level. Moreover, the concentration of Ca2+ ions in intracellular store ( **Ca\_ER**) is greater than (**Ca\_In**) (**Figures 10B,C**) (Korol et al., 2008; Gutierrez-Merino et al., 2014). An equilibrated flow is also observed in other important ions channels such as NCX and NKA, which mediate influx and efflux of Na and K ions. Balance in their flow is obligatory for the adequate efflux of Ca2+ ions for maintaining homeostasis (Gutierrez-Merino et al., 2014). Another important physiological pathway, Calpain-CAST regulatory system also depends on Ca2+. The calcium influx and efflux channels are required for the activation and regulation of Calpain.

The simulation results in section 3.2, show that when concentartion of Ca2+ is lower inside the cytosol then Calpain is bounded in the complex of Calpain-CAST at membrane.

FIGURE 18 | In (A), high concentration of CLP causes gradual degradation of cC and further increases the activation of P35 which then activates rapid production of Plaq leading to the onset of AD. In (B), the production of sAPPa is lower than the production sAPPb and AICD production is higher than both sAPPa and sAPPb. (C) shows slow dysregulation of calcium homeostasis by increase of concentration of Ca\_In from Ca\_Out and Ca\_ER. (D) shows conversion of iCT into active form then translocation to membrane mCT and cytosol cCT. Furthermore, cCT binds with CLP to form cC. CLP gradually degrades CAST into dCT.

*Changing the rate of crucial transitions c2 and c5 to study their effects on crosstalk network model* Figure 17*.*

As concentartion of Ca2+ rises, the complex degrades and active Calpain is produced (De Tullio et al., 1999; Todd et al., 2003). To regulate Calpain's activation, CAST appears to form Calpian-CAST complex in cytosol, but gradually its

peak falls down which shows release of active Calpain and degradation of CAST. The results in **Figures 12C,D** show that as **CLP** rises, both the complexes (**mC** and **cC**) gradually degrade which causes depletion of CAST (Averna et al., 2001b; Tompa et al., 2002). PKC also has important role in building a feedback mechanism for Calpain by phosphorylating CAST into inactive form (Averna et al., 2001a). Calpain is also regulating PKC by cleaving it into pkm (Goll et al., 2003) which is persistently active catalytic fragment and quickly disappears (Yamakawa et al., 2001; Liu et al., 2008). PKC also has important roles in the system, it is favoring α-secretase mediated APP cleavage (Rossner et al., 2001; Racchi et al., 2003) and is also involved in inactivation of CAST through phosphorylation

FIGURE 19 | A mild cognitive impairment behavior in (A,C). CLP is in loosely controlled by mC and cC (*c2* = 0.05 and *c5* = 0.005). There is low level of Plaq and significant level of Aβ. By changing the rate of *c2* = *0* (B) there is no conversion of pro-Calpain into CLP due to long life of mC. Production of sAPPb and Aβ are also decreased sufficiently and it lingers on the process of accumulation of Plaq. In (D), *c5* is set to zero which controls level of CLP after its activation and saves CAST from depletion into dCT. The production of sAPPa is higher than production of sAPPb in (B,D).

(Olariu et al., 2002). The inactive CAST regains its active conformation in the presence of phosphatases (Averna et al., 2001a).

Simulation results of the model APP processing pathways show that in a healthy brain all three enzymes of this pathway are available in the cell to digest the around-the-clock production of APP with α-secretase having high affinity as compared to BACE (De Strooper and Annaert, 2000). Consequently, production rate of sAPPα is higher than sAPPβ (**Figure 15B**) which is a depiction of normal physiological behavior (De Strooper and Annaert, 2000; Lichtenthaler, 2011). Studies showed that in healthy brain, plaques produce and accumulate in a linear fashion which is also

observed in our studies. Moreover, Aβ burdens in the form of plaques at elder age. This linear behavior of plaques accumulation can adopt exponential growth due to the high accumulation rate with passage of time. In our results the formation of plaques first enter the lag phase (no plaques appearance) upto 80 unit time and then evolve into growth phase (evolution), which is also experimentally reported (Friedrich et al., 2010; Jack et al., 2010; Sperling et al., 2011).

In neuro-pathological model, simulation revealed that the intermediate product sAPPβ of amyloidogenic pathway rises in the AD brain. Higher concentration of sAPPβ can lead to early occurrence of AD (at age of 40 and onward) due

to rapid deposition of the plaques in brain of AD patients (Freer et al., 2016). Higher level of sAPPβ also indicate the enhanced activity of BACE which is triggered by over-activation of Calpain. Enhanced activation of Calpain disturbs more than one processes in the pathological network i.e., increased BACE activity, decreased PKC functioning and calcium dysregulation. Initially CAST and its complex with pro-Calpain are in high quantity to control Calpain but with the elevation of calcium influx, the complex starts to degrade. The active Calpain accumulates in cytosol which is then controlled by cytosolic CAST by forming complex with it. After sometime Calpain degrades the CAST in the complex to release itself into the cytosol and eventually CAST and complex deplete from the cell (Higuchi et al., 2012). Depletion of CAST destroys the cell normal functioning and then nervous system deteriorates (Rao et al., 2008, 2014; Kurbatskaya et al., 2016).

In (B), there is less production of P35 and pkm.

After analyzing the neuropathological network, the vague picture of AD development becomes more clear. It provide useful observations such as loss of calcium homeostasis occur after 60 unit time due to intra-neuronal ATP depletion caused by elevation of Calpain (Lipton, 1999; Kurbatskaya et al., 2016) and formation of extracellular ion pores formed by Aβ oligomers (Small et al., 2009). Ca2+ disruption increases Calpain concentration in neurons which further elevates the BACE activity toward APP (Liang et al., 2010; Chami and Checler, 2012; Song et al., 2015) via cdk5 dependent pathway as a result of Aβ and sAPPβ levels in neurons built up (Sennvik et al., 2004). In normal healthy brain, the mean burden in the form of plaques are low and it increase in linear fashion from 60 to 90 unit time (Rodrigue et al., 2012; Freer et al., 2016; Kurbatskaya et al., 2016). It is noteworthy that both amyloidogenic and non-amyloidogenic pathways are enabled in an healthy individual but plaques in

the brain of AD patient grow rapidly due to increased Calpain mediated cleavage of APP through BACE (Mawuenyega et al., 2010). It can be inferred that all the neuro-pathological events are inter-related where Calpain and its complexes are playing crucial role. Calpain can be called as bone of contention and the complexes are the defenders. This neuropathological network provides valuable knowledge about interventions and offers new therapeutic targets. To stop the Calpain destructive effects, it may be effective strategy to slightly modify the natural process. In the work of Emmaneul and coworkers on cardiovascular remodeling, the Calpain-CAST system has emerged as new effective strategy to prevent angiotension (Letavernier et al., 2008). In our study, Calpain-CAST complexes have also proved to be effective therapeutic targets for delaying the process of neuronal degradation which can save the brains from AD.

### 5. CONCLUSION

In AD, Aβ has central role as they accumulate into hallmark lesions i.e., plaques. Aβ generates from APP cleavage through amyloidogenic pathway. Progression of the corresponding processing pathway increases due to over-activation of Caplain and depletion of its sole inhibitor CAST. Calpain hyper activation depends on high Calcium concentration in the cytosol. Calpain triggers the production of P35 and the degradation of CAST and PKC. In-Addition, it also triggers the imbalance of calcium homeostasis. All these observations were incorporated into a Stochastic PN models. We gained insight into the mechanism of the AD progression and were able to derive some useful inferences. The first important inference is that under hyperactivation of Calpain, calcium homeostasis dysregulates and CAST starts to degrade gradually. APP processing enzyme

(BACE) increases two folds which starts producing Aβ that slowly accumulates into plaques in AD brains at early age and lesions appear later. The second important inference is about the most crucial protein Calpain that influences many important proteins of neuronal network such as CAST, PKC, P35, and BACE. Furthermore, these proteins together dysregulate calcium homeostasis. Calpain regulation through CAST plays important role in keeping cells safe from neurodegradation. CAST encounters Calpain at two locations in the cell. Initially at membrane, CAST forms complex with inactive Calpain which is short lived and dissociates as the calcium influx increases. In the cytoplasm, active Calpain-CAST complex is long-standing which keeps the Calpain concentration in control but it also degraded slowly by the action of Calpain. From our study, Calpain and Calpain-CAST complexes have emerged as the potential therapeutic targets for the treatment of neurodegenerative pathologies. The pathway modeling of these networks have predicted that we can introduce delays in the production and accumulation of plaques by targeting the Calpain-CAST complexes. The production of Calpain should be kept in safe levels to avoid its hyper activity. It can be achieved implicitly

#### REFERENCES

by enhancing activity of Calpain-CAST complexes. The more durable are the complexes, the lesser would be the accumulation of plaques. Another useful strategy can be the designing of inhibitors against the active Calpain using in silico methods and in vitro experiments. By considering these effective interventions we can increase the chances of healthy life expectancy and can save many lives and families from the adversity of AD.

### AUTHOR CONTRIBUTIONS

JAs and JAh conceived research idea, designed and performed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper. AA and ZU-H analyzed the data, prepared tables and/or figures, wrote the paper, reviewed drafts of the paper.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fninf. 2018.00026/full#supplementary-material

detected in sporadic Alzheimer disease. Nat. Med. 9, 3–4. doi: 10.1038/ nm0103-3

Zhang, Y.-w., Thompson, R., Zhang, H., and Xu, H. (2011). App processing in alzheimer's disease. Mol. Brain 4:3. doi: 10.1186/1756-6606-4-3

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Ashraf, Ahmad, Ali and Ul-Haq. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Streptozotocin Impairs Proliferation and Differentiation of Adult Hippocampal Neural Stem Cells in Vitro-Correlation With Alterations in the Expression of Proteins Associated With the Insulin System

Ping Sun1,2 , Gabriela Ortega2 , Yan Tan3 , Qian Hua3 , Peter F. Riederer 2 , Jürgen Deckert 2 and Angelika G. Schmitt-Böhrer 2 \*

Mohammad Amjad Kamal, King Fahad Medical Research Center, King Abdulaziz University, Saudi Arabia

#### Reviewed by:

Olivier Thibault, University of Kentucky, United States Muddanna Sakkattu Rao, Department of Anatomy, Faculty of Medicine, Kuwait University, Kuwait

#### \*Correspondence:

Angelika G. Schmitt-Böhrer<EMAIL_ADDRESS>
Received: 25 February 2018 Accepted: 30 April 2018 Published: 18 May 2018

#### Citation:

Sun P, Ortega G, Tan Y, Hua Q, Riederer PF, Deckert J and Schmitt-Böhrer AG (2018) Streptozotocin Impairs Proliferation and Differentiation of Adult Hippocampal Neural Stem Cells in Vitro-Correlation With Alterations in the Expression of Proteins Associated With the Insulin System. Front. Aging Neurosci. 10:145. doi: 10.3389/fnagi.2018.00145

1Key Laboratory of Molecular Target & Clinical Pharmacology, School of Pharmaceutical Science & The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, China, 2Center of Mental Health, Department of Psychiatry, Psychosomatics, and Psychotherapy, University Hospital of Würzburg, Würzburg, Germany, 3School of Preclinical Medicine, Beijing University of Chinese Medicine, Beijing, China Edited by:

> Rats intracerebroventricularily (icv) treated with streptozotocin (STZ), shown to generate an insulin resistant brain state, were used as an animal model for the sporadic form of Alzheimer's disease (sAD). Previously, we showed in an in vivo study that 3 months after STZ icv treatment hippocampal adult neurogenesis (AN) is impaired. In the present study, we examined the effects of STZ on isolated adult hippocampal neural stem cells (NSCs) using an in vitro approach. We revealed that 2.5 mM STZ inhibits the proliferation of NSCs as indicated by reduced number and size of neurospheres as well as by less BrdU-immunoreactive NSCs. Double immunofluorescence stainings of NSCs already being triggered to start with their differentiation showed that STZ primarily impairs the generation of new neurons, but not of astrocytes. For revealing mechanisms possibly involved in mediating STZ effects we analyzed expression levels of insulin/glucose system-related molecules such as the glucose transporter (GLUT) 1 and 3, the insulin receptor (IR) and the insulin-like growth factor (IGF) 1 receptor. Applying quantitative Real time-PCR (qRT-PCR) and immunofluorescence stainings we showed that STZ exerts its strongest effects on GLUT3 expression, as GLUT3 mRNA levels were found to be reduced in NSCs, and less GLUT3-immunoreactive NSCs as well as differentiating cells were detected after STZ treatment. These findings suggest that cultured NSCs are a good model for developing new strategies to treat nerve cell loss in AD and other degenerative disorders.

> Keywords: streptozotocin, neural stem cells, proliferation, differentiation, insulin-like growth factor 1 receptor, insulin receptor, glucose transporter, Alzheimer's disease

## INTRODUCTION

Streptozotocin (STZ) is a glucosamine derivative of nitrosourea produced by Streptomyces achromogens and toxic to the insulinproducing β cells of the pancreas in mammals (Eileen Dolan, 1997). It is used in medicine for treating certain cancers of the Islets of Langerhans (Murray-Lyon et al., 1968; Brentjens and Saltz, 2001) and also used in research to produce animal models for type 1 diabetes mellitus (T1DM) via high dose intraperitoneal (i.p.) injections (Like and Rossini, 1976) and type 2 diabetes mellitus (T2DM) with low doses i.p. injections (Reaven and Ho, 1991; Wang and Gleichmann, 1998; Yuan et al., 2016).   With moderate to low dosage administration, STZ causes insulin resistance by decreased autophosphorylation of the insulin receptor (IR; Kadowaki et al., 1984; Blondel and Portha, 1989).

IR can be activated by insulin-like growth factor (IGF)- 1 and -2 besides insulin (Ward and Lawrence, 2009) and plays a key role in the regulation of glucose homeostasis. Besides the insulin-sensitive glucose transporter (GLUT)4, which is shown to be primarily expressed by cerebellar neurons, the insulin-independent GLUT1 is the main transporter responsible for glucose transport across the blood-brain barrier into the brain and into astrocytes (Leybaert et al., 2007) and GLUT3 is responsible for glucose transport into neurons (for review, see Simpson et al., 2007). Reduced IR signaling may finally results in insulin resistance, accompanied by impaired ability to maintain cell glucose and energy homeostasis (Draznin, 2006; Moloney et al., 2010; Talbot et al., 2012; Talbot, 2014). Insulin resistance in non-neural tissues can be considered as the reason for many peripheral metabolic disorders such as T2DM (Reaven, 1988). Insulin resistance in the brain may trigger pathophysiological key events of neurodegenerative disorders such as Alzheimer's disease (AD) and therefore can be linked to these disorders (Correia et al., 2011; Talbot et al., 2012).

Individuals with diabetes have higher risk for developing dementia syndromes (Ott et al., 1999; Roriz-Filho et al., 2009; Riederer et al., 2017). Epidemiologic studies revealed a significant association between T2DM and AD, and that metabolic dysfunctions like hyperglycemia and hyperinsulinemia and/or hypoinsulinemia are closely correlated with AD pathophysiology (Ryan and Geckle, 2000; Allen et al., 2004; Matsuzaki et al., 2010; Ou et al., 2018). Therefore, disturbances of brain glucose uptake, glucose tolerance and glucose utilization and impairment of the insulin/IR signaling cascade are thought to be key targets for the neuropathology of the sporadic form of AD (sAD) which covers >95% of AD patients (Grünblatt et al., 2007; de la Monte, 2009; Salkovic-Petrisic et al., 2009; Talbot et al., 2012).

STZ intracerebroventricularily (STZ icv) treated rats, which develop an insulin resistant brain state shortly after treatment, have been suggested to act as an animal model for sAD (Grünblatt et al., 2007; de la Monte, 2009; Salkovic-Petrisic et al., 2009). In this animal model, cognitive function was shown to be impaired already a few weeks after STZ icv treatment (Mayer et al., 1990; Blokland and Jolles, 1993) paralleled by cholinergic deficits (Hellweg et al., 1992), oxidative stress (Sharma and Gupta, 2001), neuronal loss (Shoham et al., 2003), amyloid angiopathy (Salkovic-Petrisic et al., 2011), increasing tau protein (Salkovic-Petrisic et al., 2006) as well as insulin signaling pathway damage (Salkovic-Petrisic et al., 2006). Brain glucose metabolism has been found to be dramatically harassed in this animal model including decreased glucose utilization (Duelli et al., 1994) and reduced glycolytic key enzymes activity (Plaschke and Hoyer, 1993), diminished adenosine triphosphate (ATP) and creatine phosphate (Lannert and Hoyer, 1998). The neuron-specific GLUT3 has been found to be significantly reduced in this rat model (Salkovic-Petrisic et al., 2013).