The analysis of cell-free DNA (cfDNA) in plasma represents a rapidly advancing field in medicine. cfDNA consists predominantly of nucleosome-protected DNA shed into the bloodstream by cells undergoing apoptosis. We performed whole-genome sequencing of plasma DNA and identified two discrete regions at transcription start sites (TSSs) where nucleosome occupancy results in different read depth coverage patterns for expressed and silent genes. By employing machine learning for gene classification, we found that the plasma DNA read depth patterns from healthy donors reflected the expression signature of hematopoietic cells. In patients with cancer having metastatic disease, we were able to classify expressed cancer driver genes in regions with somatic copy number gains with high accuracy. We were able to determine the expressed isoform of genes with several TSSs, as confirmed by RNA-seq analysis of the matching primary tumor. Our analyses provide functional information about cells releasing their DNA into the circulation.

The most striking characteristic of CHO cells is their adaptability, which enables efficient production of proteins as well as growth under a variety of culture conditions, but also results in genomic and phenotypic instability. To investigate the relative contribution of genomic and epigenetic modifications towards phenotype evolution, comprehensive genome and epigenome data are presented for six related CHO cell lines, both in response to perturbations (different culture conditions and media as well as selection of a specific phenotype with increased transient productivity) and in steady state (prolonged time in culture under constant conditions). Clear transitions were observed in DNA‐methylation patterns upon each perturbation, while few changes occurred over time under constant conditions. Only minor DNA‐methylation changes were observed between exponential and stationary growth phase; however, throughout a batch culture the histone modification pattern underwent continuous adaptation. Variation in genome sequence between the six cell lines on the level of SNPs, InDels, and structural variants is high, both upon perturbation and under constant conditions over time. The here presented comprehensive resource may open the door to improved control and manipulation of gene expression during industrial bioprocesses based on epigenetic mechanisms. Biotechnol. Bioeng. 2016;113: 2241–2253. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

The innate immune response is highly conserved across all eukaryotes and has been studied in great detail in several model organisms. Hemocytes, the primary immune cell population in mosquitoes, are important components of the mosquito innate immune response, yet critical aspects of their biology have remained uncharacterized. Using a novel method of enrichment, we isolated phagocytic granulocytes and quantified their proteomes by mass spectrometry. The data demonstrate that phagocytosis, blood-feeding, and Plasmodium falciparum infection promote dramatic shifts in the proteomic profiles of An. gambiae granulocyte populations. Of interest, large numbers of immune proteins were induced in response to blood feeding alone, suggesting that granulocytes have an integral role in priming the mosquito immune system for pathogen challenge. In addition, we identify several granulocyte proteins with putative roles as membrane receptors, cell signaling, or immune components that when silenced, have either positive or negative effects on malaria parasite survival. Integrating existing hemocyte transcriptional profiles, we also compare differences in hemocyte transcript and protein expression to provide new insight into hemocyte gene regulation and discuss the potential that post-transcriptional regulation may be an important component of hemocyte gene expression. These data represent a significant advancement in mosquito hemocyte biology, providing the first comprehensive proteomic profiling of mosquito phagocytic granulocytes during homeostasis blood-feeding, and pathogen challenge. Together, these findings extend current knowledge to further illustrate the importance of hemocytes in shaping mosquito innate immunity and their principal role in defining malaria parasite survival in the mosquito host.

In models of cholestasis, autophagy is impaired at late levels, when autophagosomes and lysosomes would normally fuse.The impairment depends on bile acids and FXRdependent induction of Rubicon.OCA impairs autophagy, while UDCA is a potent activator of hepatic autophagy. Autophagy, and in particular Rubicon, represents a novel molecular drug target in cholestatic liver diseases.UDCA may already be used in liver diseases where induction of autophagy is warranted.

The Yeast Protein Localization database (YPL.db) contains information about the localization patterns of yeast proteins resulting from microscopic analyses. The data and parameters of the experiments to obtain the localization information, together with images from confocal or video microscopy, are stored in a relational database, building an archive of, and the documentation for, all experiments. The database can be queried based on gene name, protein localization, growth conditions and a number of additional parameters. All experiment parameters are selectable from predefined lists to ensure database integrity and conformity across different investigators. The database provides a structure reference resource to allow for better characterization of unknown or ambiguous localization patterns. Links to MIPS, YPD and SGD databases are provided to allow fast access to further information not contained in the localization database itself. YPL.db is available at http://ypl.tugraz.at.

Background: Epidermal hyperplasia represents a morphologic hallmark of psoriatic skin lesions. Langerhans cells (LCs) in the psoriatic epidermis engage with keratinocytes (KCs) in tight physical interactions; moreover, they induce T-cell-mediated immune responses critical to psoriasis. Objective: This study sought to improve the understanding of epidermal factors in psoriasis pathogenesis.Methods: BMP7-LCs versus TGF-b1-LCs were phenotypically characterized and their functional properties were analyzed using flow cytometry, cell kinetic studies, co-culture with CD4 T cells, and cytokine measurements. Furthermore, immunohistology of healthy and psoriatic skin was performed. Additionally, in vivo experiments with Jun f/f JunB f/f K5cre-ER T mice were carried out to assess the From a the

Metabolomics, the comprehensive study of the metabolome, and lipidomics—the large-scale study of pathways and networks of cellular lipids—are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods—including quantum chemistry and machine learning—and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them—especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.

Since the introduction of microarray technology, it has become the workhorse for mRNA expression profiling. Its application ranges from investigating gene function, regulation, and co-expression, to clinical use in diagnosis and prognosis. Over the last decade, a large number of microarray experiments have become available in public repositories often addressing similar or related hypotheses. The large compendia of gene expression data provide the opportunity to conduct meta-analyses by combining data from various independent but related studies. Such data integration has the potential to enhance the reliability and generalizability of the results of individual microarray studies.This chapter describes the meta-analysis process including objectives, data collection, annotation, analysis methods, and visualizations. For each step we present a selection of tools available and discuss associated problems and difficulties.