A Scalable Permutation Approach Reveals Replication and Preservation Patterns of Network Modules in Large Datasets.

Network modules-topologically distinct groups of edges and nodes-that are preserved across datasets can reveal common features of organisms, tissues, cell types, and molecules. Many statistics to identify such modules have been developed, but testing their significance requires heuristics. Here, we demonstrate that current methods for assessing module preservation are systematically biased and produce skewed p values. We introduce NetRep, a rapid and computationally efficient method that uses a permutation approach to score module preservation without assuming data are normally distributed. NetRep produces unbiased p values and can distinguish between true and false positives during multiple hypothesis testing. We use NetRep to quantify preservation of gene coexpression modules across murine brain, liver, adipose, and muscle tissues. Complex patterns of multi-tissue preservation were revealed, including a liver-derived housekeeping module that displayed adipose- and muscle-specific association with body weight. Finally, we demonstrate the broader applicability of NetRep by quantifying preservation of bacterial networks in gut microbiota between men and women

An interaction map of circulating metabolites, immune gene networks, and their genetic regulation

Background: Immunometabolism plays a central role in many cardiometabolic diseases. However, a robust map of immune-related gene networks in circulating human cells, their interactions with metabolites, and their genetic control is still lacking. Here, we integrate blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts (total N = 2168), including a subset of individuals with matched multi-omic data at 7-year follow-up. Results: We identify topologically replicable gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules show complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, small molecules, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) reveal five modules with mQTLs that have both cis and trans effects. The strongest mQTL is in ARHGEF3 (rs1354034) and affects a module enriched for platelet function, independent of platelet counts. Modules of mast cell/basophil and neutrophil function show temporally stable metabolite associations over 7-year follow-up, providing evidence that these modules and their constituent gene products may play central roles in metabolic inflammation. Furthermore, the strongest mQTL in ARHGEF3 also displays clear temporal stability, supporting widespread trans effects at this locus. Conclusions: This study provides a detailed map of natural variation at the blood immunometabolic interface and its genetic basis, and may facilitate subsequent studies to explain inter-individual variation in cardiometabolic disease.Peer reviewe

PATHLOGIC-S: A scalable boolean framework for modelling cellular signalling

Curated databases of signal transduction have grown to describe several thousand reactions, and efficient use of these data requires the development of modelling tools to elucidate and explore system properties. We present PATHLOGIC-S, a Boolean specification for a signalling model, with its associated GPL-licensed implementation using integer programming techniques. The PATHLOGIC-S specification has been designed to function on current desktop workstations, and is capable of providing analyses on some of the largest currently available datasets through use of Boolean modelling techniques to generate predictions of stable and semi-stable network states from data in community file formats. PATHLOGIC-S also addresses major problems associated with the presence and modelling of inhibition in Boolean systems, and reduces logical incoherence due to common inhibitory mechanisms in signalling systems. We apply this approach to signal transduction networks including Reactome and two pathways from the Panther Pathways database, and present the results of computations on each along with a discussion of execution time. A software implementation of the framework and model is freely available under a GPL license

Analysis of Panther Pathways data.

<p>Output signals from the apoptosis and T-cell activation pathways described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041977#pone.0041977-Thomas1" target="_blank">[5]</a> are shown with the minimum number of input signals required for activation, and the number of (non-trivial) input combinations that give rise to the given output. Certain outputs are created in the curation process (eg, Bim) and thus lack results in the uncurated data.</p

The main window of PATHLOGIC-S provides problem instantiation options.

<p>Custom objectives are supported through an objective builder, along with several presets. Target specification and activity settings are through the table presented, which can be restricted to show only system inputs and system outputs. At present, PATHLOGIC-S offers two methods of curation - one at the point of execution through dialog boxes, and one prior to execution through Cytoscape. Once the user clicks OK, the software executes the simulation, producing as output either a tab-delimited summary data file or a series of GML files specifying visualizations of the solutions found.</p

An example of a PATHLOGIC-S formulation.

<p><b>a)</b> A pair of reactions from the androgen receptor pathway of the NCI-Nature Pathway Interaction Database <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041977#pone.0041977-Schaefer1" target="_blank">[6]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041977#pone.0041977-Chang1" target="_blank">[35]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041977#pone.0041977-Zhang1" target="_blank">[38]</a>. Inhibiting interactions are presented with a flat-ended arrow. <b>b)</b> Initial logical formulation. Conversion of upstream signals, catalysts, and activating signals takes place at this point. <b>c)</b> Logical statements with inhibition information added, prior to conversion to disjunctive form. <b>d)</b> Logical statements in disjunctive form prior to conversion to a system of linear constraints.</p

Data Properties and Statistics.

<p>The nature of the topology is best described by taking measurements of its representative graph (formulated as discussed in the Curation section of the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041977#s3" target="_blank">Methods</a>). Curation and SCC removal (by its nature) reduces connectivity within the graph, leading to the observed differences between the curated/uncurated pairs. Reactome, by contrast, is a much larger dataset with much greater complexity, as shown in number of connected components and the characteristic path length.</p

Logical loop structure, with an example from Reactome.

<p><b>a)</b> An example of a logical loop structure. There exist two satisfying assignments to this set of statements when J’ is set to 1 - one with all variables except I set to 1, and one with all variables set to 1. To exclude the former solution from the solution space, the statement describing the relationship represented as a dashed line is removed from the logical formulation. <b>b)</b> In one mechanism of ERK phosphorylation, MEK and ERK are activated by various upstream processes (inputs). These active signals then form a complex, with MEK acting as a catalytic subunit of the complex resulting in ERK phosphorylation. The complex dissociates post-phosphorylation to yield phosphorylated ERK and MEK. Conversion to the logical form of this reaction yields a set of logic statements with associated SCC that has topology similar to <b>a)</b>.</p

Synthetic Epigenetic Reprogramming of Mesenchymal to Epithelial States Using the CRISPR/dCas9 Platform in Triple Negative Breast Cancer

Altres ajuts: National Health and Medical Research Council (NHMRC) grant APP1147528; NHMRC grants APP1165208 and APP1187328; National Breast Cancer Foundation IIRS-22-044; Cancer Council New South Wales APP2013068; Cancer Council Western Australia APP2004608Epithelial-mesenchymal transition (EMT) is a reversible transcriptional program invoked by cancer cells to drive cancer progression. Transcription factor ZEB1 is a master regulator of EMT, driving disease recurrence in poor-outcome triple negative breast cancers (TNBCs). Here, this work silences ZEB1 in TNBC models by CRISPR/dCas9-mediated epigenetic editing, resulting in highly-specific and nearly complete suppression of ZEB1 in vivo, accompanied by long-lasting tumor inhibition. Integrated "omic" changes promoted by dCas9 linked to the KRAB domain (dCas9-KRAB) enabled the discovery of a ZEB1-dependent-signature of 26 genes differentially-expressed and -methylated, including the reactivation and enhanced chromatin accessibility in cell adhesion loci, outlining epigenetic reprogramming toward a more epithelial state. In the ZEB1 locus transcriptional silencing is associated with induction of locally-spread heterochromatin, significant changes in DNA methylation at specific CpGs, gain of H3K9me3, and a near complete erasure of H3K4me3 in the ZEB1 promoter. Epigenetic shifts induced by ZEB1-silencing are enriched in a subset of human breast tumors, illuminating a clinically-relevant hybrid-like state. Thus, the synthetic epi-silencing of ZEB1 induces stable "lock-in" epigenetic reprogramming of mesenchymal tumors associated with a distinct and stable epigenetic landscape. This work outlines epigenome-engineering approaches for reversing EMT and customizable precision molecular oncology approaches for targeting poor outcome breast cancers