Impact Mediated Loading Cytoplasmic Loading of Macromolecules into Adherent Cells

The advent of modern molecular biology, including the development of gene array technologies, has resulted in an explosion of information concerning the specific genes activated during normal cellular development, as well as those associated with a variety of pathological conditions. These techniques have served as a highly efficient, broacI.-based screening approach for those specific genes involved. in regulating normal cellular physiology and identifying candidate genes directly associated with the etiology of specific disease states. However, this approach provides information at the transcriptional' level only and does not necessarily indicate . that the gene in question is in fact translated i~to a protein, or whether or not post-translational modification of the protein occurs. The critical importance of post-translational modification (i.e. phosphorylation, glycosylation, sialyation, etc.) to protein function has been recognized with regard to a number of proteins involved in a variety of important disease states. For example, altered glycosylation of beta-amyloid precursor protein results in an increase in the amount of beta-amyloid peptide generated and hence secreted as insoluble extracellular amyloid deposits (Georgopoulou, McLaughlin et al. 2001; Walter, Fluhrer et al. 2001), a pathological hal1~nark of Alzheimer's disease. Abnormal phosphoryla~ion of synapsin I has been linked to alterations in synaptic vesicle trafficking leading to defective neurotransmission in Huntington's disease (Lievens, Woodman et al. 2002). Altered phosphorylation of the TAU protein involved in microtubule function has been linked to a number of neurodegenative diseases such as Alzheimer's disease (Billingsley and Kincaid 1997; Sanchez, Alvarez-T~llada et a1. 2001). Aberrant siaIyation of cell/I surface antigens has been detected in a number of different tumor cell types and has been linked to the acquisition of a neoplastic phenotype (Sell 1990), while improper' sia1yation of sodium channels in cardiac tissue has been linked to heart failure (Ufret-Vincenty, Baro et al. 2001; Fozzard and Kyle 2002)

Independent component analysis of Alzheimer's DNA microarray gene expression data

<p>Abstract</p> <p>Background</p> <p>Gene microarray technology is an effective tool to investigate the simultaneous activity of multiple cellular pathways from hundreds to thousands of genes. However, because data in the colossal amounts generated by DNA microarray technology are usually complex, noisy, high-dimensional, and often hindered by low statistical power, their exploitation is difficult. To overcome these problems, two kinds of unsupervised analysis methods for microarray data: principal component analysis (PCA) and independent component analysis (ICA) have been developed to accomplish the task. PCA projects the data into a new space spanned by the principal components that are mutually orthonormal to each other. The constraint of mutual orthogonality and second-order statistics technique within PCA algorithms, however, may not be applied to the biological systems studied. Extracting and characterizing the most informative features of the biological signals, however, require higher-order statistics.</p> <p>Results</p> <p>ICA is one of the unsupervised algorithms that can extract higher-order statistical structures from data and has been applied to DNA microarray gene expression data analysis. We performed FastICA method on DNA microarray gene expression data from Alzheimer's disease (AD) hippocampal tissue samples and consequential gene clustering. Experimental results showed that the ICA method can improve the clustering results of AD samples and identify significant genes. More than 50 significant genes with high expression levels in severe AD were extracted, representing immunity-related protein, metal-related protein, membrane protein, lipoprotein, neuropeptide, cytoskeleton protein, cellular binding protein, and ribosomal protein. Within the aforementioned categories, our method also found 37 significant genes with low expression levels. Moreover, it is worth noting that some oncogenes and phosphorylation-related proteins are expressed in low levels. In comparison to the PCA and support vector machine recursive feature elimination (SVM-RFE) methods, which are widely used in microarray data analysis, ICA can identify more AD-related genes. Furthermore, we have validated and identified many genes that are associated with AD pathogenesis.</p> <p>Conclusion</p> <p>We demonstrated that ICA exploits higher-order statistics to identify gene expression profiles as linear combinations of elementary expression patterns that lead to the construction of potential AD-related pathogenic pathways. Our computing results also validated that the ICA model outperformed PCA and the SVM-RFE method. This report shows that ICA as a microarray data analysis tool can help us to elucidate the molecular taxonomy of AD and other multifactorial and polygenic complex diseases.</p

Differential expression of exosomal microRNAs in prefrontal cortices of schizophrenia and bipolar disorder patients

Exosomes are cellular secretory vesicles containing microRNAs (miRNAs). Once secreted, exosomes are able to attach to recipient cells and release miRNAs potentially modulating the function of the recipient cell. We hypothesized that exosomal miRNA expression in brains of patients diagnosed with schizophrenia (SZ) and bipolar disorder (BD) might differ from controls, reflecting either disease-specific or common aberrations in SZ and BD patients. The sources of the analyzed samples included McLean 66 Cohort Collection (Harvard Brain Tissue Resource Center), BrainNet Europe II (BNE, a consortium of 18 brain banks across Europe) and Boston Medical Center (BMC). Exosomal miRNAs from frozen postmortem prefrontal cortices with well-preserved RNA were isolated and submitted to profiling by Luminex FLEXMAP 3D microfluidic device. Multiple statistical analyses of microarray data suggested that certain exosomal miRNAs were differentially expressed in SZ and BD subjects in comparison to controls. RT-PCR validation confirmed that two miRNAs, miR-497 in SZ samples and miR-29c in BD samples, have significantly increased expression when compared to control samples. These results warrant future studies to evaluate the potential of exosome-derived miRNAs to serve as biomarkers of SZ and BD

Exosomal cell-to-cell transmission of alpha synuclein oligomers

Background: Aggregation of alpha-synuclein (αsyn) and resulting cytotoxicity is a hallmark of sporadic and familial Parkinson’s disease (PD) as well as dementia with Lewy bodies, with recent evidence implicating oligomeric and pre-fibrillar forms of αsyn as the pathogenic species. Recent in vitro studies support the idea of transcellular spread of extracellular, secreted αsyn across membranes. The aim of this study is to characterize the transcellular spread of αsyn oligomers and determine their extracellular location. Results: Using a novel protein fragment complementation assay where αsyn is fused to non-bioluminescent amino-or carboxy-terminus fragments of humanized Gaussia Luciferase we demonstrate here that αsyn oligomers can be found in at least two extracellular fractions: either associated with exosomes or free. Exosome-associated αsyn oligomers are more likely to be taken up by recipient cells and can induce more toxicity compared to free αsyn oligomers. Specifically, we determine that αsyn oligomers are present on both the outside as well as inside of exosomes. Notably, the pathway of secretion of αsyn oligomers is strongly influenced by autophagic activity. Conclusions: Our data suggest that αsyn may be secreted via different secretory pathways. We hypothesize that exosome-mediated release of αsyn oligomers is a mechanism whereby cells clear toxic αsyn oligomers when autophagic mechanisms fail to be sufficient. Preventing the early events in αsyn exosomal release and uptake by inducing autophagy may be a novel approach to halt disease spreading in PD and other synucleinopathies

Eclipsing binaries in the open cluster Ruprecht 147. II: EPIC 219568666

We report our spectroscopic monitoring of the detached, grazing, and slightly eccentric 12 day double-lined eclipsing binary EPIC 219568666 in the old nearby open cluster Ruprecht 147. This is the second eclipsing system to be analyzed in this cluster, following our earlier study of EPIC 219394517. Our analysis of the radial velocities combined with the light curve from the K2 mission yields absolute masses and radii for EPIC 219568666 of M₁ = 1.121 ± 0.013 M☉ and R₁ = 1.1779 ± 0.0070 R☉ for the F8 primary and M₂ = 0.7334 ± 0.0050 M☉ and R₂ = 0.640 ± 0.017 R☉ for the faint secondary. Comparison with current stellar evolution models calculated for the known metallicity of the cluster points to a primary star that is oversized, as is often seen in active M dwarfs, but this seems rather unlikely for a star of its mass and with a low level of activity. Instead, we suspect a subtle bias in the radius ratio inferred from the photometry, despite our best efforts to avoid it, which may be related to the presence of spots on one or both stars. The radius sum for the binary, which bypasses this possible problem, indicates an age of 2.76 ± 0.61 Gyr, which is in good agreement with a similar estimate from the binary in our earlier study

Assessment of gene order computing methods for Alzheimer’s disease

This article was originally published by BMC Medical Genomics in 2013. doi:10.1186/1755-8794-6-S1-S8Background: Computational genomics of Alzheimer disease (AD), the most common form of senile dementia, is a nascent field in AD research. The field includes AD gene clustering by computing gene order which generates higher quality gene clustering patterns than most other clustering methods. However, there are few available gene order computing methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Further, their performance in gene order computation using AD microarray data is not known. We thus set forth to evaluate the performances of current gene order computing methods with different distance formulas, and to identify additional features associated with gene order computation. Methods: Using different distance formulas- Pearson distance and Euclidean distance, the squared Euclidean distance, and other conditions, gene orders were calculated by ACO and GA (including standard GA and improved GA) methods, respectively. The qualities of the gene orders were compared, and new features from the calculated gene orders were identified. Results: Compared to the GA methods tested in this study, ACO fits the AD microarray data the best when calculating gene order. In addition, the following features were revealed: different distance formulas generated a different quality of gene order, and the commonly used Pearson distance was not the best distance formula when used with both GA and ACO methods for AD microarray data. Conclusion: Compared with Pearson distance and Euclidean distance, the squared Euclidean distance generated the best quality gene order computed by GA and ACO methods.The work was supported by the BWH Radiology and MGH Psychiatry research funds (to X. Huang) and the Technology Innovation fund (No. 09zz028) of Key Developing Program from Education Department of Sichuan Province, ChinaPearson distanc

Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain

A key aspect of nearly all single-cell sequencing experiments is dissociation of intact tissues into single-cell suspensions. While many protocols have been optimized for optimal cell yield, they have often overlooked the effects that dissociation can have on ex vivo gene expression. Here, we demonstrate that use of enzymatic dissociation on brain tissue induces an aberrant ex vivo gene expression signature, most prominently in microglia, which is prevalent in published literature and can substantially confound downstream analyses. To address this issue, we present a rigorously validated protocol that preserves both in vivo transcriptional profiles and cell-type diversity and yield across tissue types and species. We also identify a similar signature in postmortem human brain single-nucleus RNA-sequencing datasets, and show that this signature is induced in freshly isolated human tissue by exposure to elevated temperatures ex vivo. Together, our results provide a methodological solution for preventing artifactual gene expression changes during fresh tissue digestion and a reference for future deeper analysis of the potential confounding states present in postmortem human samples