Autocrine Effects of Brain Endothelial Cell-Produced Human Apolipoprotein E on Metabolism and Inflammation in vitro

Reports of APOE4-associated neurovascular dysfunction during aging and in neurodegenerative disorders has led to ongoing research to identify underlying mechanisms. In this study, we focused on whether the APOE genotype of brain endothelial cells modulates their own phenotype. We utilized a modified primary mouse brain endothelial cell isolation protocol that enabled us to perform experiments without subculture. Through initial characterization we found, that compared to APOE3, APOE4 brain endothelial cells produce less apolipoprotein E (apoE) and have altered metabolic and inflammatory gene expression profiles. Further analysis revealed APOE4 brain endothelial cultures have higher preference for oxidative phosphorylation over glycolysis and, accordingly, higher markers of mitochondrial activity. Mitochondrial activity generates reactive oxygen species, and, with APOE4, there were higher mitochondrial superoxide levels, lower levels of antioxidants related to heme and glutathione and higher markers/outcomes of oxidative damage to proteins and lipids. In parallel, or resulting from reactive oxygen species, there was greater inflammation in APOE4 brain endothelial cells including higher chemokine levels and immune cell adhesion under basal conditions and after low-dose lipopolysaccharide (LPS) treatment. In addition, paracellular permeability was higher in APOE4 brain endothelial cells in basal conditions and after high-dose LPS treatment. Finally, we found that a nuclear receptor Rev-Erb agonist, SR9009, improved functional metabolic markers, lowered inflammation and modulated paracellular permeability at baseline and following LPS treatment in APOE4 brain endothelial cells. Together, our data suggest that autocrine signaling of apoE in brain endothelial cells represents a novel cellular mechanism for how APOE regulates neurovascular function

Regulated Capture of Vκ Gene Topologically Associating Domains by Transcription Factories

Expression of vast repertoires of antigen receptors by lymphocytes, with each cell expressing a single receptor, requires stochastic activation of individual variable (V) genes for transcription and recombination. How this occurs remains unknown. Using single-cell RNA sequencing (scRNA-seq) and allelic variation, we show that individual pre-B cells monoallelically transcribe divergent arrays of Vκ genes, thereby opening stochastic repertoires for subsequent Vκ-Jκ recombination. Transcription occurs upon translocation of Vκ genes to RNA polymerase II arrayed on the nuclear matrix in transcription factories. Transcription is anchored by CTCF-bound sites or E2A-loaded Vκ promotors and continues over large genomic distances delimited only by topological associating domains (TADs). Prior to their monoallelic activation, Vκ loci are transcriptionally repressed by cyclin D3, which prevents capture of Vκ gene containing TADs by transcription factories. Cyclin D3 also represses protocadherin, olfactory, and other monoallelically expressed genes, suggesting a widely deployed mechanism for coupling monoallelic gene activation with cell cycle exit

Improved Statistical Methods Enable Greater Sensitivity in Rhythm Detection for Genome-Wide Data

Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function. To this end, several analytic methods have been developed for detecting periodic patterns. We improve one such method, JTK_CYCLE, by explicitly calculating the null distribution such that it accounts for multiple hypothesis testing and by including non-sinusoidal reference waveforms. We term this method empirical JTK_CYCLE with asymmetry search, and we compare its performance to JTK_CYCLE with Bonferroni and Benjamini-Hochberg multiple hypothesis testing correction, as well as to five other methods: cyclohedron test, address reduction, stable persistence, ANOVA, and F24. We find that ANOVA, F24, and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate. Our analysis also provides insight into experimental design and we find that, for a fixed number of samples, better sensitivity and specificity are achieved with higher numbers of replicates than with higher sampling density. Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophila melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms. These include a wide range of oxidation reduction and metabolic genes, as well as genes with transcripts that have multiple splice forms.</p

Improved Statistical Methods Enable Greater Sensitivity in Rhythm Detection for Genome-Wide Data

<div><p>Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function. To this end, several analytic methods have been developed for detecting periodic patterns. We improve one such method, JTK_CYCLE, by explicitly calculating the null distribution such that it accounts for multiple hypothesis testing and by including non-sinusoidal reference waveforms. We term this method empirical JTK_CYCLE with asymmetry search, and we compare its performance to JTK_CYCLE with Bonferroni and Benjamini-Hochberg multiple hypothesis testing correction, as well as to five other methods: cyclohedron test, address reduction, stable persistence, ANOVA, and F24. We find that ANOVA, F24, and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate. Our analysis also provides insight into experimental design and we find that, for a fixed number of samples, better sensitivity and specificity are achieved with higher numbers of replicates than with higher sampling density. Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophila melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms. These include a wide range of oxidation reduction and metabolic genes, as well as genes with transcripts that have multiple splice forms.</p></div

Annotation terms identified by DAVID as enriched for rhythmic genes.

<p>Rhythmic genes shown are those that are identified with eJTK_aby4 with a Benjamini-Hochberg adjusted <i>p</i>-value less than 0.05. The terms shown are those identified by the DAVID web tool [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004094#pcbi.1004094.ref051" target="_blank">51</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004094#pcbi.1004094.ref052" target="_blank">52</a>] as enriched with a Benjamini-Hochberg adjusted <i>p</i>-value less than 0.05. (A) The individual annotation terms are shown with their adjusted <i>p</i>-values and phase distributions. The red numbers refer to the number of genes in that annotation term with that phase. The horizontal axis of A is the same as that of B. (B) Total phase distribution of the cycling genes. (C) Total asymmetry distribution of the cycling genes.</p

JTK_CYCLE compares all possible pair relations for a time series to those for a reference waveform.

<p>(A) JTK_CYCLE tests for pairwise agreement between a reference (blue) and a signal (cyan) time series. Three discordant pairwise relationships are indicated by red lines. (B) The previous implementation compared a time series to a set of phase-shifted cosines. (C) We add a set of asymmetric waveforms to the reference. An example is shown here with the same phases as in A.</p