Ciproxifan, an H~3~ Receptor Antagonist, Improves Learning and Memory in the APP Mouse Model of Alzheimer's Disease

Mice that express the mutant form of the human amyloid precursor gene associated with early-onset familial Alzheimer's disease demonstrate memory deficits and amyloid plaques. We show here that ciproxifan, a prototypical antagonist of H~3~-type histamine receptors, alleviates two types of learning and memory impairments in such mice. These data support the idea that modulation of H~3~ receptors represents a viable therapeutic strategy in the treatment of Alzheimer's disease

Accelerating the Translation of Nanomaterials in Biomedicine

Due to their size and tailorable physicochemical properties, nanomaterials are an emerging class of structures utilized in biomedical applications. There are now many prominent examples of nanomaterials being used to improve human health, in areas ranging from imaging and diagnostics to therapeutics and regenerative medicine. An overview of these examples reveals several common areas of synergy and future challenges. This Nano Focus discusses the current status and future potential of promising nanomaterials and their translation from the laboratory to the clinic, by highlighting a handful of successful examples

A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis

Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns

Host galaxy identification for supernova surveys

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope (LSST), which will discover SNe by the thousands. Spectroscopic resources are limited, so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate "hostless" SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey

CD115+ monocytes protect microbially experienced mice against E. coli-induced sepsis

Summary: Uropathogenic E. coli (UPEC) is a primary organism responsible for urinary tract infections and a common cause of sepsis. Microbially experienced laboratory mice, generated by cohousing with pet store mice, exhibit increased morbidity and mortality to polymicrobial sepsis or lipopolysaccharide challenge. By contrast, cohoused mice display significant resistance, compared with specific pathogen-free mice, to a monomicrobial sepsis model using UPEC. CD115+ monocytes mediate protection in the cohoused mice, as depletion of these cells leads to increased mortality and UPEC pathogen burden. Further study of the cohoused mice reveals increased TNF-α production by monocytes, a skewing toward Ly6ChiCD115+ “classical” monocytes, and enhanced egress of Ly6ChiCD115+ monocytes from the bone marrow. Analysis of cohoused bone marrow also finds increased frequency and number of myeloid multipotent progenitor cells. These results show that a history of microbial exposure impacts innate immunity in mice, which can have important implications for the preclinical study of sepsis

CD115+ monocytes protect microbially experienced mice against E. coli-induced sepsis

Uropathogenic E. coli (UPEC) is a primary organism responsible for urinary tract infections and a common cause of sepsis. Microbially experienced laboratory mice, generated by cohousing with pet store mice, exhibit increased morbidity and mortality to polymicrobial sepsis or lipopolysaccharide challenge. By contrast, cohoused mice display significant resistance, compared with specific pathogen-free mice, to a monomicrobial sepsis model using UPEC. CD115+ monocytes mediate protection in the cohoused mice, as depletion of these cells leads to increased mortality and UPEC pathogen burden. Further study of the cohoused mice reveals increased TNF-α production by monocytes, a skewing toward Ly6ChiCD115+ “classical” monocytes, and enhanced egress of Ly6ChiCD115+ monocytes from the bone marrow. Analysis of cohoused bone marrow also finds increased frequency and number of myeloid multipotent progenitor cells. These results show that a history of microbial exposure impacts innate immunity in mice, which can have important implications for the preclinical study of sepsis