Development of a neuroprotective potential algorithm for medicinal plants
Graphical abstract
Introduction
Alzheimer's disease (AD) is a common neurodegenerative disorder characterized by the progression of cognitive decline leading to severe dementia (Buckner et al., 2005). The accumulation of senile plaques and neurofibrillary tangles in cerebral cortex and hippocampus are two major pathological hallmarks of AD (Ittner and Götz, 2011). However, due to the complexity of the disease, the precise factors which trigger the development of AD remains unknown (Alzheimer's Association, 2015). Moreover, given the unclear etiology of AD, current therapeutic approaches focus mainly on symptom management but no treatment is available to alter or reverse the course of the disease (Alzheimer's Association, 2015, Citron, 2010). Although the pathogenesis of AD is still under investigation, increasing evidence suggest that AD is a multifactorial disease which develops as a result of several risk contributors instead of a single cause alone (Norton et al., 2014, Reitz and Mayeux, 2014).
Oxidative stress and the production of reactive oxygen species (ROS) have been implicated in the pathogenesis of AD and are believed to be leading causative factors for neuronal cell dysfunction and cell death (Lin and Beal, 2006, Smith et al., 2000). It has been demonstrated that the products of protein oxidation and lipid peroxidation are elevated in AD patients (Christen, 2000). In addition, in the AD brain, the activities of antioxidant enzymes are altered, accompanied with a decline in the expression of these antioxidant enzymes (Christen, 2000, Smith et al., 2000). Given the established links between oxidative stress and AD, antioxidants, including those from natural products, are extensively studied for their neuroprotective abilities and constitute dietary intervention strategies for AD prevention and treatment (Alzheimer's Association, 2015, Choi et al., 2012, Praticò, 2008).
Apart from oxidative stress, carbonyl stress and the formation of advanced glycation end-products (AGEs) resulting from protein glycation are also believed to be vital contributors to AD (Srikanth et al., 2011, Vicente Miranda and Outeiro, 2010). Glycation is one type of post-translational modification of proteins, resulting in the formation of AGEs both intracellularly and extracellularly. Glycation and AGEs formation are associated with AD due to several reasons. First, AGEs bind to the transmembrane receptor, RAGE (receptor for AGEs), upregulate RAGE expression, and activate RAGE-mediated neuronal dysfunction and neuron damages (Srikanth et al., 2011). Second, RAGE mediates the transportation of beta amyloid (Aβ) across the blood brain barrier (BBB) (Donahue et al., 2006). Therefore, the activation of RAGE by AGE can cause Aβ accumulation in the brain. Third, during the course of glycation and AGE formation, ROS and reactive carbonyl species (RCS) are generated as by-products which, in turn, promote AGE formation and cause neurotoxicity (Ahmed et al., 2005, Münch et al., 2012, Picklo et al., 2002). Consequently, all of the factors involved in this positive feedback loop including AGEs, RCS, and ROS are considered to be promising targets for AD prevention and treatment.
Another common target for AD therapy is the Aβ peptide which consists of 40–42 amino acids and is generated from the cleavage of the Aβ precursor protein. Aβ is the major component of senile plaques and neurofibrillary tangles, two pathological hallmarks of AD (Buckner et al., 2005, Palop and Mucke, 2010). In AD patients, elevated Aβ levels were observed in both cerebrospinal fluid and blood (Mawuenyega et al., 2010). In addition, certain forms of Aβ, including fibrillated Aβ and glycated Aβ (Aβ-AGEs), have been shown to be neurotoxic (Butterfield, 2002; Li et al., 2013). Fibrillated Aβ can induce neurotoxicity by enhancing neuronal oxidative stress and neuroinflammation (Butterfield, 2002). Aβ-AGEs can induce intracellular oxidative stress and inflammation by activating RAGE and upregulating RAGE expression in neuronal cells (Li et al., 2013). Therefore, considerable research efforts have been directed to finding inhibitors which may prevent or reverse the formation of Aβ fibrils and Aβ-AGEs. For example, aminoguanidine (AG), a synthetic glycation inhibitor, can reduce glycated Aβ formation, attenuate RAGE upregulation, and restore the cognitive deficit in AD animal models (Li et al., 2013). However, AG failed in human clinical trials due to severe side effects (Thornalley, 2003) leading to the search for non-toxic alternatives including medicinal plants and their derived natural products and botanical extracts (Solanki et al., 2016, Venigalla et al., 2016).
In addition to oxidative stress, glycation, and Aβ formation, neuroinflammation is another pivotal factor implicated in the development of neurodegenerative diseases with increased inflammation observed in AD (Eikelenboom et al., 2002). In addition, inflammatory stress leads to the activation of microglia cells, the immune cells in the central nervous system, which release nitric oxide species (NOS) including nitrates and nitrites. These NOS are neurotoxic and cause massive neuronal death further exacerbating neurodegenerative diseases (Eikelenboom et al., 2002, Eikelenboom et al., 2006).
For centuries, traditional systems of medicines such as Ayurveda [from India, a country which has one of the lowest incidences of AD worldwide (Chandra et al., 2001, Vas et al., 2001)] and traditional Chinese medicine (TCM) (Steele et al., 2013), have used medicinal plants to treat several ailments including neurodegenerative diseases. While neurochemical/biological studies have been conducted on some these traditional medicinal plants, there is a lack of systematic procedures and algorithms to help guide the selection and evaluation of the most promising candidates for further AD-based research using animal models. This is urgently needed given the large variety of medicinal plant species (and combinations thereof) used worldwide in the traditional systems of medicines of various cultures. Furthermore, although medicinal plants are consumed as foods, herbs, spices, beverages, and botanical extracts, their underlying mechanisms of neuroprotective effects remain unclear. Therefore, given all of the aforementioned factors, herein, we utilized a panel of bioassays including total polyphenol contents, antioxidant capacities, anti-glycation effects, carbonyl scavenging abilities, anti-Aβ fibrillation, acetylcholinesterase (AChE) inhibition, and anti-neuroinflammatory activities to develop a Neuroprotective Potential Algorithm (NPA) to aid in the evaluation and selection of promising medicinal plant candidates for future AD based research using pre-clinical animal models (see Fig. 1). We selected twenty-three commercially available and chemically characterized medicinal plant extracts (see Table 1), commonly consumed as foods (in India, and elsewhere), and used in Ayurveda, to develop the NPA.
Section snippets
Chemicals
The herbal extracts are botanically authenticated and chemically standardized GRAS (generally regarded as safe) extracts which are commercially available for human consumption and sourced from a single reputable natural products supplier, namely, Verdure Sciences (Noblesville, IN, USA), to ensure access to validated and consistent samples. The Latin binomial and common names, as well as the traditional uses, of the twenty-three medicinal plant species are provided in Table 1 and additional
Total polyphenol content and antioxidant capacities
The herbal extracts were first evaluated for their total polyphenol contents and antioxidant activities (DPPH and FRAP assays) since several natural plant antioxidants, for example, resveratrol (RESV; from grapes) and curcumin (from the Indian turmeric Curcuma longa spice) have been reported to show promising neuroprotective effects against AD (Li et al., 2012, Lim et al., 2001). As shown in Table 1, the twenty-three medicinal plant extracts were evaluated for their phenolic content by the
Conclusion
In summary, using bioassays with established links between AD and oxidative stress, carbonyl stress, glycation, Aβ fibrillation, Aβ-AGE formation, AChE inhibition, and neuroinflammation, a Neuroprotective Potential Algorithm (NPA) was developed as part of a strategy to help guide the selection and evaluation of medicinal plant candidates for their neuroprotective potential. From the current study, four extracts identified with a cumulative neuroprotective potential index score ≥60 were
Conflict of interest
The authors declare no conflicts of interest.
Acknowledgments
HM was supported by a scholarship from the Omar Magnate Family Foundation. The herbal extracts were kindly provided by Verdure Sciences (Noblesville, IN, USA) courtesy of Mr. Ajay Patel. Spectrophotometric data were acquired from instruments in the RI-INBRE core facility located at the University of Rhode Island (Kingston, RI, USA) supported by grant # 5P20GM103430-13 from the National Institute of General Medical Sciences of the National Institutes of Health.
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These authors contributed equally to this work.