Biofunctionalized magnetic nanoparticles for specifically detecting biomarkers of Alzheimer's disease in vitro.
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ABSTRACT:
SUBMITTER: Yang CC
PROVIDER: S-EPMC3369781 | biostudies-other | 2011 Sep
REPOSITORIES: biostudies-other
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Project description:Alzheimer's disease (AD) is the most common form of dementia. During the recent decade, nanotechnology has been widely considered, as a promising tool, for theranosis (diagnosis and therapy) of AD. Here we first discuss pathophysiology and characteristics of AD with a focus on the amyloid cascade hypothesis. Then magnetic nanoparticles (MNPs) and recent works on their applications in AD, focusing on the superparamagnetic iron oxide nanoparticles (SPIONs), are reviewed. Furthermore, the amyloid-nanoparticle interaction is highlighted, with the scope to be highly considered by the scientists aiming for diagnostics and/or treatment of AD employing nanoparticles. Furthermore, recent findings on the "ignored" parameters (e.g., effect of protein "corona" at the surface of nanoparticles on amyloid-β (Aβ) fibrillation process) are discussed.
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Project description:Reliable blood biomarkers for Alzheimer's disease (AD) have been lacking. The D-amino acids oxidase modulator (named pLG72) modulates glutamate N-methyl-D-aspartate receptor activity. The cystine/glutamate antiporter contains a SLC7A11 subunit, which mediates glutamate release. This study aimed to determine the accuracy of pLG72 protein and SLC7A11 mRNA in diagnosing AD. This study enrolled 130 healthy controls and 109 unmatched AD patients; among them, 40 controls and 70 patients were selected to match by age. We measured their pLG72 protein in plasma and SLC7A11 mRNA in white blood cells. AD patients had markedly higher pLG72 levels and SLC7A11 mRNA ΔCT values than healthy controls (in both unmatched and matched cohorts; all 4 P values <.001). The receiver operating characteristics analysis in the unmatched cohorts demonstrated that the pLG72 level had a high specificity (0.900) at the optimal cutoff value of 2.3285, the ΔCT of SLC7A11 mRNA displayed an excellent sensitivity (0.954) at the cutoff of 12.185, and the combined value of pLG72 and SLC7A11 ΔCT determined a favorable area under the curve (AUC) (0.882) at the cutoff of 21.721. The AUC of the combined value surpassed that of either biomarker. The specificity, sensitivity, and AUC of the matched cohort were like those of the unmatched cohort. The findings suggest that pLG72 protein and SLC7A11 mRNA can distinguish AD patients from healthy controls with excellent specificity and sensitivity, respectively. The combination of pLG72 and SLC7A11 yields better AUC than either, suggesting the superiority of simultaneously measuring both biomarkers in identifying AD patients.
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Project description:Alzheimer's disease (AD) is a neurodegenerative and progressive disease, which often causes irreversible damages to the cerebrum. The pathogenesis of AD is far from being fully understood, while there are some popular hypotheses. So far, the diagnosis of AD relies only on clinical screening in the form of imaging techniques or cerebrospinal fluid analysis, which may lead to inaccurate evaluation and then cause the delay of suitable treatments. While molecular biomarkers provide promising alternatives of establishing correct relationships between genotypes and phenotypes of clinical symptoms. In this paper, we propose a machine-learning-based method of identifying potential diagnostic biomarkers of AD based on gene coexpression network by integrating gene expression profiles in six brain regions. After building an integrated gene coexpression network of multiple brain regions, we decompose the differential network into some subnetwork modules. The module candidates from these coexpressed gene communities are then identified by screening their discriminative powers in control from disease samples. The potential biomarkers are then validated by multiple cross-validations and functional enrichment analyses. If the biomarkers successfully pass clinical significance tests, they can be used as a reference for clinical diagnosis after wet-experimental validations.
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Project description:Alzheimer's disease (AD) is a fatal neurodegenerative disorder that takes about a decade to develop, making early diagnosis possible. Clinically, the diagnosis of AD is complicated, costly, and inaccurate, so it is urgent to find specific biomarkers. Due to its multifactorial nature, a panel of biomarkers for the multiple pathologies of AD, such as cerebral amyloidogenesis, neuronal dysfunction, synapse loss, oxidative stress, and inflammation, are most promising for accurate diagnosis. Highly sensitive and high-throughput proteomic techniques can be applied to develop a panel of novel biomarkers for AD. In this review, we discuss the metabolism and diagnostic performance of the well-established core candidate cerebrospinal fluid (CSF) biomarkers (β-amyloid, total tau, and hyperphosphorylated tau). Meanwhile, novel promising CSF biomarkers, especially those identified by proteomics, updated in the last five years are also extensively discussed. Furthermore, we provide perspectives on how biomarker discovery for AD is evolving.
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Project description:Magnetic nanoparticles (MNPs) were synthesized using the colloidal co-precipitation method and further coated with silica using the Stöber process. These were functionalized with carboxylic and amine functionalities for further covalent immobilization of antibodies on these MNPs. The procedure for covalent immobilization of antibodies on MNPs was developed using 1-ethyl-3-(dimethylaminopropyl)carbodiimide (EDC) and N-hydroxysuccinimide (NHS). The evaluation of the efficiency of the coupling reaction was carried out by UV-vis spectrophotometry. The developed antibodies coupled to MNPs were tested for the pre-concentration of two biomarkers tumor necrosis factor alpha (TNF-α) and Interleukin-10 (IL-10). Both biomarkers were assessed in the matrix based on phosphate-buffered saline solution (PBS) and artificial saliva (AS) to carry out the demonstration of the format assay. Supernatants were used to determine the number of free biomarkers for both studies. Reduction of the nonspecific saliva protein adsorption on the surface of the complex antibodies-MNPs to levels low enough to allow the detection of biomarkers in complex media has been achieved.
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Project description:Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.
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Project description:BackgroundMemantine, drug approved for moderate to severe Alzheimer's disease, has not shown to be fully effective. In order to solve this issue, polylactic-co-glycolic (PLGA) nanoparticles could be a suitable solution to increase drug's action on the target site as well as decrease adverse effects. For these reason, Memantine was loaded in biodegradable PLGA nanoparticles, produced by double emulsion method and surface-coated with polyethylene glycol. MEM-PEG-PLGA nanoparticles (NPs) were aimed to target the blood-brain barrier (BBB) upon oral administration for the treatment of Alzheimer's disease.ResultsThe production parameters were optimized by design of experiments. MEM-PEG-PLGA NPs showed a mean particle size below 200 nm (152.6 ± 0.5 nm), monomodal size distribution (polydispersity index, PI < 0.1) and negative surface charge (- 22.4 mV). Physicochemical characterization of NPs confirmed that the crystalline drug was dispersed inside the PLGA matrix. MEM-PEG-PLGA NPs were found to be non-cytotoxic on brain cell lines (bEnd.3 and astrocytes). Memantine followed a slower release profile from the NPs against the free drug solution, allowing to reduce drug administration frequency in vivo. Nanoparticles were able to cross BBB both in vitro and in vivo. Behavioral tests carried out on transgenic APPswe/PS1dE9 mice demonstrated to enhance the benefit of decreasing memory impairment when using MEM-PEG-PLGA NPs in comparison to the free drug solution. Histological studies confirmed that MEM-PEG-PLGA NPs reduced β-amyloid plaques and the associated inflammation characteristic of Alzheimer's disease.ConclusionsMemantine NPs were suitable for Alzheimer's disease and more effective than the free drug.
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Project description:BackgroundPrevious studies suggest that Dickkopf-1 (DKK1), an inhibitor of Wnt signaling, plays a role in amyloid-induced toxicity and hence Alzheimer's disease (AD). However, the effect of DKK1 expression on protein expression, and whether such proteins are altered in disease, is unknown.ObjectiveWe aim to test whether DKK1 induced protein signature obtained in vitro were associated with markers of AD pathology as used in the amyloid/tau/neurodegeneration (ATN) framework as well as with clinical outcomes.MethodsWe first overexpressed DKK1 in HEK293A cells and quantified 1,128 proteins in cell lysates using aptamer capture arrays (SomaScan) to obtain a protein signature induced by DKK1. We then used the same assay to measure the DKK1-signature proteins in human plasma in two large cohorts, EMIF (n = 785) and ANM (n = 677).ResultsWe identified a 100-protein signature induced by DKK1 in vitro. Subsets of proteins, along with age and apolipoprotein E ɛ4 genotype distinguished amyloid pathology (A + T-N-, A+T+N-, A+T-N+, and A+T+N+) from no AD pathology (A-T-N-) with an area under the curve of 0.72, 0.81, 0.88, and 0.85, respectively. Furthermore, we found that some signature proteins (e.g., Complement C3 and albumin) were associated with cognitive score and AD diagnosis in both cohorts.ConclusionsOur results add further evidence for a role of DKK regulation of Wnt signaling in AD and suggest that DKK1 induced signature proteins obtained in vitro could reflect theATNframework as well as predict disease severity and progression in vivo.
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