Antidepressant drugs modulate growth factors in cultured cells

Background Different classes of antidepressant drugs are used as a treatment for depression by activating the catecholinergic system. In addition, depression has been associated with decrease of growth factors, which causes insufficient axonal sprouting and reduced neuronal damage repair. In this study, antidepressant treatments are analyzed in a cell culture system, to study the modulation of growth factors. Results We quantified the transcription of several growth factors in three cell lines after application of antidepressant drugs by real time polymerase chain reaction. Antidepressant drugs counteracted against phorbolester-induced deregulation of growth factors in PMA-differentiated neuronal SY5Y cells. We also found indications in a pilot experiment that magnetic stimulation could possibly modify BDNF in the cell culture system. Conclusion The antidepressant effects antidepressant drugs might be explained by selective modulation of growth factors, which subsequently affects neuronal plasticity

CD133-Positive Membrane Particles in Cerebrospinal Fluid of Patients with Inflammatory and Degenerative Neurological Diseases

Background: Analysis of cerebrospinal fluid (CSF) is a frequently used diagnostic tool in a variety of neurological diseases. Recent studies suggested that investigating membrane particles enriched with the stem cell marker CD133 may offer new avenues for studying neurological disease. In this study, we evaluated the amount of membrane particle-associated CD133 in human CSF in neuroinflammatory and degenerative diseases. Methods: We compared the amount of membrane particle-associated CD133 in CSF samples collected from 45 patients with normal pressure hydrocephalus, parkinsonism, dementia, and cognitive impairment, chronic inflammatory diseases and 10 healthy adult individuals as controls. After ultracentrifugation of CSF, gel electrophoresis and immunoblotting using anti-CD133 monoclonal antibody 80B258 were performed. Antigen-antibody complexes were detected using chemiluminescence. Results: The amount of membrane particle-associated CD133 was significantly increased in patients with normal pressure hydrocephalus (p < 0.001), parkinsonism (p = 0.011) as well as in patients with chronic inflammatory disease (p = 0.008). Analysis of CSF of patients with dementia and cognitive impairment revealed no significant change compared with healthy individuals. Furthermore, subgroup analysis of patients with chronic inflammatory diseases demonstrated significantly elevated levels in individuals with relapsing-remitting multiple sclerosis (p = 0.023) and secondary progressive multiple sclerosis (SPMS; p = 0.010). Conclusion: Collectively, our study revealed elevated levels of membrane particle-associated CD133 in patients with normal pressure hydrocephalus, parkinsonism as well as relapsing-remitting and SPMS. Membrane glycoprotein CD133 may be of clinical value for several neurological diseases

A Specific Reduction in A beta(1-42) vs. a Universal Loss of A beta Peptides in CSF Differentiates Alzheimer's Disease From Meningitis and Multiple Sclerosis

A reduced concentration of A beta(1-42) in CSF is one of the established biomarkers of Alzheimer's disease Reduced CSF concentrations of A beta(1-42) have also been shown in multiple sclerosis, viral encephalitis and bacterial meningitis As neuroinflammation is one of the neuropathological hallmarks of Alzheimer's disease, an infectious origin of the disease has been proposed According to this hypothesis, amyloid pathology is a consequence of a microbial infection and the resulting immune defense Accordingly, changes in CSF levels of amyloid-beta peptides should be similar in AD and inflammatory brain diseases A beta(1-42) and A beta(1-40) levels were measured in cerebrospinal fluid by ELISA and Western blotting in 34 patients with bacterial meningitis (n = 9), multiple sclerosis (n = 5) or Alzheimer's disease (n = 9) and in suitable controls (n = 11) Reduced concentrations of A beta(1-42) were detected in patients with bacterial meningitis, multiple sclerosis and Alzheimer's disease However, due to a concurrent reduction in A beta(1-40) in multiple sclerosis and meningitis patients, the ratio of A beta(1-42)/A beta(1-40) was reduced only in the CSF of Alzheimer's disease patients Urea-SDS-PAGE followed by Western blotting revealed that all A beta peptide variants are reduced in bacterial meningitis, whereas in Alzheimer's disease, only A beta(1-42) is reduced These results have two implications First, they confirm the discriminatory diagnostic power of the A beta(1-42)/A beta(1-40) ratio Second, the differential pattern of A beta peptide reductions suggests that the amyloid pathology in meningitis and multiple sclerosis differs from that in AD and does not support the notion of AD as an infection-triggered immunopathology

Spot quantification in two dimensional gel electrophoresis image analysis: comparison of different approaches and presentation of a novel compound fitting algorithm

Background Various computer-based methods exist for the detection and quantification of protein spots in two dimensional gel electrophoresis images. Area-based methods are commonly used for spot quantification: an area is assigned to each spot and the sum of the pixel intensities in that area, the so-called volume, is used a measure for spot signal. Other methods use the optical density, i.e. the intensity of the most intense pixel of a spot, or calculate the volume from the parameters of a fitted function. Results In this study we compare the performance of different spot quantification methods using synthetic and real data. We propose a ready-to-use algorithm for spot detection and quantification that uses fitting of two dimensional Gaussian function curves for the extraction of data from two dimensional gel electrophoresis (2-DE) images. The algorithm implements fitting using logical compounds and is computationally efficient. The applicability of the compound fitting algorithm was evaluated for various simulated data and compared with other quantification approaches. We provide evidence that even if an incorrect bell-shaped function is used, the fitting method is superior to other approaches, especially when spots overlap. Finally, we validated the method with experimental data of urea-based 2-DE of Aβ peptides andre-analyzed published data sets. Our methods showed higher precision and accuracy than other approaches when applied to exposure time series and standard gels. Conclusion Compound fitting as a quantification method for 2-DE spots shows several advantages over other approaches and could be combined with various spot detection methods. The algorithm was scripted in MATLAB (Mathworks) and is available as a supplemental file

Antidepressant drugs modulate growth factors in cultured cells

© 2008 Henkel et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Characterization of the postsynaptic protein neurogranin in paired cerebrospinal fluid and plasma samples from Alzheimer's disease patients and healthy controls

Introduction: Synaptic dysfunction and degeneration are central events in Alzheimer's disease (AD) pathophysiology that are thought to occur early in disease progression. Synaptic pathology may be studied by examining protein biomarkers specific for different synaptic elements. We recently showed that the dendritic protein neurogranin (Ng), including the endogenous Ng peptide 48 to 76 (Ng(48-76)), is markedly increased in cerebrospinal fluid (CSF) in AD and that Ng48-76 is the dominant peptide in human brain tissue. The aim of this study was to characterize Ng in plasma and CSF using mass spectrometry and to investigate the performance of plasma Ng as an AD biomarker. Methods: Paired plasma and CSF samples from patients with AD (n = 25) and healthy controls (n = 20) were analyzed in parallel using an immunoassay developed in-house on the Meso Scale Discovery platform and hybrid immunoaffinity-mass spectrometry (HI-MS). A second plasma material from patients with AD (n = 13) and healthy controls (n = 17) was also analyzed with HI-MS. High-resolution mass spectrometry was used for identification of endogenous plasma Ng peptides. Results: Ng in human plasma is present as several endogenous peptides. Of the 16 endogenous Ng peptides identified, seven were unique for plasma and not detectable in CSF. However, Ng(48-76) was not present in plasma. CSF Ng was significantly increased in AD compared with controls (P < 0.0001), whereas the plasma Ng levels were similar between the groups in both studies. Plasma and CSF Ng levels showed no correlation. CSF Ng was stable during storage at -20 degrees C for up to 2 days, and no de novo generation of peptides were detected. Conclusions: For the first time, to our knowledge, we have identified several endogenous Ng peptides in human plasma. In agreement with previous studies, we show that CSF Ng is significantly increased in AD as compared with healthy controls. The origin of Ng in plasma and its possible use as a biomarker need to be further investigated. The results suggest that CSF Ng, in particular Ng(48-76), might reflect the neurodegenerative processes within the brain, indicating a role for Ng as a potential novel clinical biomarker for synaptic function in AD

Low amounts of bisecting glycans characterize cerebrospinal fluid-borne IgG

Immunoglobulin G (IgG) harbors a conserved N-glycosylation site which is important for its effector functions. Changes in glycosylation of IgG occur in many autoimmune diseases but also in physiological conditions. Therefore, the glycosylation pattern of serum IgG is well characterized. However, limited data is available on the glycosylation pattern of IgG in cerebrospinal fluid (CSF) compared to serum. Here, we report significantly reduced levels of bisected glycans in CSF IgG. Galactosylation and sialylation of IgG4 also differed significantly. Therefore, we propose a common mechanism mediating glycosylation changes of IgG at the transition from serum to CSF in steady state conditions

Spot quantification in two dimensional gel electrophoresis image analysis: comparison of different approaches and presentation of a novel compound fitting algorithm

Background Various computer-based methods exist for the detection and quantification of protein spots in two dimensional gel electrophoresis images. Area-based methods are commonly used for spot quantification: an area is assigned to each spot and the sum of the pixel intensities in that area, the so-called volume, is used a measure for spot signal. Other methods use the optical density, i.e. the intensity of the most intense pixel of a spot, or calculate the volume from the parameters of a fitted function. Results In this study we compare the performance of different spot quantification methods using synthetic and real data. We propose a ready-to-use algorithm for spot detection and quantification that uses fitting of two dimensional Gaussian function curves for the extraction of data from two dimensional gel electrophoresis (2-DE) images. The algorithm implements fitting using logical compounds and is computationally efficient. The applicability of the compound fitting algorithm was evaluated for various simulated data and compared with other quantification approaches. We provide evidence that even if an incorrect bell-shaped function is used, the fitting method is superior to other approaches, especially when spots overlap. Finally, we validated the method with experimental data of urea-based 2-DE of Aβ peptides andre-analyzed published data sets. Our methods showed higher precision and accuracy than other approaches when applied to exposure time series and standard gels. Conclusion Compound fitting as a quantification method for 2-DE spots shows several advantages over other approaches and could be combined with various spot detection methods. The algorithm was scripted in MATLAB (Mathworks) and is available as a supplemental file