Neuroprotective effect of Capsicum annuum var. abbreviatum against hydrogen peroxide-induced oxidative stress in HT22 hippocampus cells

ABSTRACT

Phenolic compounds isolated from pepper (Capsicum annum) have been demonstrated to have neuroprotective effects, whereas the physiological properties of Capsicum annuum var. abbreviatum (CAA) have not been studied. Thus, we investigate the chemical composition and neuroprotective activity of CAA extract (CAAE) in HT22 hippocampus cells against H₂O₂-induced neurotoxicity. CAAE treatment resulted in a significant protection of H₂O₂-exposed HT22, this protection ultimately occurred through an inhibition of MDA and ROS levels and an induction of SOD activity. Furthermore, CAAE treatment reduced H₂0₂-induced apoptosis though decreasing the expression of pro-apoptotic factors (Bax, cytochrome c, and cleaved caspases-3) while increasing the expression of the anti-apoptotic factors (Bcl-2), as well as the accumulation of nucleus-Nrf2-mediated HO-1 signaling. Interestingly, CAAE has a high concentration of unique phenolic compositions (chlrogenic acid, tangeretin, etc.) than other capsicum annum extracts. Altogether, these findings suggest that CAAE can be a useful natural resource for alleviating neurodegenerative diseases.     

Microsatellite markers from expressed sequence tags (ESTs) of seaweeds in differentiating various Gracilaria species

Gracilaria is a red seaweed that has been cultivated worldwide and is commercially used for food, fertilizers, animal fodder, and phycocolloids. However, the high morphological plasticity of seaweeds often leads to the misidentification in the traditional identification of Gracilaria species. Molecular markers are important especially in the correct identification of Gracilaria species with high economic value. Microsatellite markers were developed from the expressed sequence tags of seaweeds deposited at the National Center for Biotechnology Information database and used for differentiating Gracilaria changii collected at various localities and two other Gracilaria species. Out of 33 primer pairs, only one primer pair gave significant results that can distinguish between three different Gracilaria species as well as G. changii from various localities based on the variation in repeated nucleotides. The unweighted pair group method using arithmetic mean dendrogram analysis grouped Gracilaria species into five main clades: (a) G. changii from Batu Besar (Malacca), Sandakan (Sabah), Bintulu (Sarawak), Batu Tengah (Malacca), Gua Tanah (Malacca), Middle Banks (Penang), Sungai (Sg.) Merbok (Kedah), Teluk Pelandok (Negeri Sembilan), Pantai Dickson (Negeri Sembilan), Sg. Kong-Kong (Johore), and Sg. Pulai (Johore); (b) Gracilaria manilaensis from Cebu, Philippines; (c) G. changii from Morib (Selangor); (d) Gracilaria fisheri from Pattani, Thailand; and (e) G. changii from Pantai Dickson (Negeri Sembilan), Gua Tanah (Malacca), Sg. Merbok (Kedah), Sg. Kong-Kong (Johore), and Sg. Pulai (Johore). This result shows that this primer pair was able to distinguish between three different species, which are G. changii from Morib (Malaysia), G. fisheri from Pattani (Thailand), and G. manilaensis from Cebu (Philippines), and also between different genotypes of G. changii. This suggested that the simple sequence repeat primer we developed was suitable for differentiating between different Gracilaria species due to the polymorphisms caused by the variability in the number of tandem repeats.     

Pro-inflammatory cytokines modulate iron regulatory protein 1 expression and iron transportation through reactive oxygen/nitrogen species production in ventral mesencephalic neurons

Both inflammatory processes associated with microglia activation and abnormal iron deposit in dopaminergic neurons are involved in the pathogenesis of Parkinson's disease (PD). However, the relationship between neuroinflammation and iron accumulation was not fully elucidated. In the present study, we aimed to investigate whether the pro-inflammatory cytokines interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) released by microglia, could affect cellular iron transportation in primary cultured ventral mesencephalic (VM) neurons. The results showed that IL-1β or TNF-α treatment led to increased ferrous iron influx and decreased iron efflux in these cells, due to the upregulation of divalent metal transporter 1 with the iron response element (DMT1 + IRE) and downregulation of ferroportin1 (FPN1). Increased levels of iron regulatory protein 1 (IRP1), transferrin receptor 1 (TfR1) and hepcidin were also observed in IL-1β or TNF-α treated VM neurons. IRP1 upregulation could be fully abolished by co-administration of radical scavenger N-acetyl-l-cysteine and inducible NO synthetase inhibitor N_ω-nitro-l-arginine methyl ester hydrochloride. Further experiments demonstrated that IL-1β and TNF-α release was remarkably enhanced by iron load in activated microglia triggered by lipopolysaccharide or 1-methyl-4-phenylpyridinium (MPP⁺). In 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-intoxicated mice, salicylate application could not block DMT1 + IRE upregulation in dopaminergic neurons of substantia nigra. These results suggested that IL-1β and TNF-α released by microglia, especially under the condition of iron load, might contribute to iron accumulation in VM neurons by upregulating IRP1 and hepcidin levels through reactive oxygen/nitrogen species production. This might provide a new insight into unraveling that microglia might aggravate this iron mediated neuropathologies in PD.     

Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions

One key problem in computational neuroscience and neural engineering is the identification and modeling of functional connectivity in the brain using spike train data. To reduce model complexity, alleviate overfitting, and thus facilitate model interpretation, sparse representation and estimation of functional connectivity is needed. Sparsities include global sparsity, which captures the sparse connectivities between neurons, and local sparsity, which reflects the active temporal ranges of the input-output dynamical interactions. In this paper, we formulate a generalized functional additive model (GFAM) and develop the associated penalized likelihood estimation methods for such a modeling problem. A GFAM consists of a set of basis functions convolving the input signals, and a link function generating the firing probability of the output neuron from the summation of the convolutions weighted by the sought model coefficients. Model sparsities are achieved by using various penalized likelihood estimations and basis functions. Specifically, we introduce two variations of the GFAM using a global basis (e.g., Laguerre basis) and group LASSO estimation, and a local basis (e.g., B-spline basis) and group bridge estimation, respectively. We further develop an optimization method based on quadratic approximation of the likelihood function for the estimation of these models. Simulation and experimental results show that both group-LASSO-Laguerre and group-bridge-B-spline can capture faithfully the global sparsities, while the latter can replicate accurately and simultaneously both global and local sparsities. The sparse models outperform the full models estimated with the standard maximum likelihood method in out-of-sample predictions.     

Neural Response Phase Tracks How Listeners Learn New Acoustic Representations

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