α-Synuclein interacts with lipoproteins in plasma

Parkinson’s disease (PD) is an age-related neurodegenerative disorder characterized by dopaminergic neural cell death in the substantia nigra of the brain and α-synuclein (α-syn) accumulation in Lewy bodies. α-Syn can be detected in blood and is a potential biomarker for PD. It has been shown recently that α-syn can pass through the blood-brain barrier (BBB), but the mechanism is not yet understood. We hypothesized that α-syn could interact with lipoproteins, and in association with these particles, could pass through the BBB. Here, we show that apoE, apoJ, and apoA1, but not apoB, were co-immunocaptured along with α-syn from human blood plasma, suggesting that α-syn is associated with high density lipoproteins (HDL). This association was also supported by experiments involving western blotting of plasma fractions separated by gel filtration,which revealed that α-syn was found in fractions identified as HDL. Interestingly, we could also detect α-syn and ApoJ in the intermediate fraction between HDL and LDL, referred to as lipoprotein (a) (Lp(a)), which has an important role in cholesterol metabolism. Overall, the results provide best support for the hypothesis that α-syn interacts with HDL, and this has potential implications for transport of α-syn from the brain to peripheral blood, across the BBB

Additional file 3 of SW1PerS: Sliding windows and 1-persistence scoring; discovering periodicity in gene expression time series data

Top genes. This zip file contains three pdf files, associated to each one of the 3 biological data sets studied in this paper. Each file shows the full ordered list, sparkLines included, of genes in the top 10 % of rankings according to SW1PerS and that are not present in the top 10 % of the other algorithms

Additional file 1 of SW1PerS: Sliding windows and 1-persistence scoring; discovering periodicity in gene expression time series data

Supplements. The supplements file contains detailed information on several points discussed in this paper. In particular: 1. The mathematics behind the SW1PerS algorithm, 2. A detailed description of the fast 1-Persistent Homology algorithm, 3. Generating functions for the synthetic data, 4. All ROC plots from the synthetic data analysis, 5. All score distributions from the synthetic data analysis, 6. Histograms of score distributions for permutation test,7. Details regarding the availability and processing of the biological data, 8. Gene lists from ChIP-chip and ChIP-seq data, 9. The method used for filtering noise using replicates and 10. GO Enrichment analysis

Reconciling conflicting models for global control of cell-cycle transcription

<p>Models for the control of global cell-cycle transcription have advanced from a CDK-APC/C oscillator, a transcription factor (TF) network, to coupled CDK-APC/C and TF networks. Nonetheless, current models were challenged by a recent study that concluded that the cell-cycle transcriptional program is primarily controlled by a CDK-APC/C oscillator in budding yeast. Here we report an analysis of the transcriptome dynamics in cyclin mutant cells that were not queried in the previous study. We find that B-cyclin oscillation is not essential for control of phase-specific transcription. Using a mathematical model, we demonstrate that the function of network TFs can be retained in the face of significant reductions in transcript levels. Finally, we show that cells arrested at mitotic exit with non-oscillating levels of B-cyclins continue to cycle transcriptionally. Taken together, these findings support a critical role of a TF network and a requirement for CDK activities that need not be periodic.</p

Additional file 20 of The Local Edge Machine: inference of dynamic models of gene regulation

Figure: Comparison of ODE systems inferred by LEM to networks in silico 3 and in silico 8. We used the networks in silico 3 (a) and in silico 8 (d) to generate data, which was then input to LEM. The most likely regulation of each node (as given by LEM output) was combined to form networks (b,e) with corresponding systems of ODEs. Simulation of the systems of ODEs inferred by LEM was then compared to the original data (c,f). (EPS 1045 kb

Additional file 3 of The Local Edge Machine: inference of dynamic models of gene regulation

Table: Evidence for regulatory interactions in yeast cell-cycle networks 1–5. Each row corresponds to a regulatory interaction (edge), where an upstream regulator acts on a target gene. p values from four high-throughput chromatin immunoprecipitation (ChIP) studies are shown to provide evidence (when available) for a regulator transcription factor (TF) binding to a target promoter [11, 50, 51]. ChIP p values were combined using Fisher’s method [52]. Combined p values less than 0.001 were considered high-confidence evidence for a given edge (shown in bold red). Where available, edges are supported by additional literature references (see Additional file 8). In the absence of ChIP data, literature evidence was used to determine edges. Evidence for many edges provided here is also documented in the YEASTRACT database [42]. The direction of each interaction (activation, repression, or N/A unknown) is derived from the YEASTRACT database, literature evidence, and/or biological priors about gene function (see Additional file 1). (XLSX 75 kb

Additional file 12 of The Local Edge Machine: inference of dynamic models of gene regulation

Figure: Network diagram of yeast cell-cycle 5. All transcription factor (TF) nodes from [2, 3] and expanded components of TF complexes were included in this network model (with the exception of ASH1, a daughter-specific repressor). Genes were generally ordered by the timing of peak expression and spatially optimized for network visualization. Pointed arrows represent activation, and blunted arrows represent repression of target gene expression. All regulatory interactions are supported by literature evidence (see Additional file 5). (EPS 379 kb