Townsend, K. | Search bioRxiv

bioRxiv2019

Myeloid-secreted BDNF maintains innervation of inguinal adipose in male mice

BackgroundInnervation of adipose tissue is essential for the proper function of this critical metabolic organ. Numerous surgical and chemical denervation studies have demonstrated how maintenance of brain-adipose communication through both sympathetic efferent and sensory afferent nerves helps regulate adipocyte size, cell number, lipolysis, and browning of white adipose tissue. Neurotrophic factors are growth factors that promote neuron survival, regeneration and plasticity, including neurite outgrowth and synapse formation. Peripheral blood immune cells have been shown to be a source of neurotrophic factors in humans and mice. Although a number of immune cells reside in the adipose stromal vascular fraction (SVF), it has remained unclear what roles they play in adipose innervation. We previously demonstrated that adipose immune cells secrete brain derived neurotrophic factor (BDNF). MethodsWe now show that deletion of this neurotrophic factor from the myeloid lineage led to a genetic denervation of inguinal subcutaneous white adipose tissue (scWAT), thereby causing decreased energy expenditure, increased adipose mass, and a blunted UCP1 response to cold stimulation. ResultsWe and others have previously shown that noradrenergic stimulation via cold exposure increases adipose innervation in the inguinal depot. Here we have identified a subset of myeloid cells that home to scWAT upon cold exposure and are Ly6C + CCR2 + Cx3CR1 + monocytes/macrophages that express noradrenergic receptors and BDNF. ConclusionsWe propose that these cold induced neuroimmune cells (CINCs) are key players in maintaining adipose innervation as well as promoting adipose nerve remodeling under noradrenergic stimulation, such as cold exposure.

neuroscience↗

bioRxiv2019

GEOlimma: Differential Expression Analysis and Feature Selection Using Pre-Existing Microarray Data

BackgroundDifferential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.\n\nResultsIn this study, we propose a novel differential expression and feature selection method--GEOlimma--which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.\n\nConclusionsOur results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.

bioinformatics↗