Charge structure in volcanic plumes: a comparison of plume properties predicted by an integral plume model to observations of volcanic lightning during the 2010 eruption of Eyjafjallajökull, Iceland

Cancer is a heterogeneous disease with different combinations of genetic alterations driving its development in different individuals. We introduce CoMEt, an algorithm to identify combinations of alterations that exhibit a pattern of mutual exclusivity across individuals, often observed for alterations in the same pathway. CoMEt includes an exact statistical test for mutual exclusivity and techniques to perform simultaneous analysis of multiple sets of mutually exclusive and subtype-specific alterations. We demonstrate that CoMEt outperforms existing approaches on simulated and real data. We apply CoMEt to five different cancer types, identifying both known cancer genes and pathways, and novel putative cancer genes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0700-7) contains supplementary material, which is available to authorized users

Electrosynthesized poly(o-aminophenol) films as biomimetic coatings for dopamine detection on Pt substrates

Dopamine (DA) is a neurotransmitter, and its levels in the human body are associated with serious diseases. The need for a suitable detection method in medical practice has encouraged the development of electrochemical sensors that take advantage of DA electroactivity. Molecularly imprinted polymers (MIPs) are biomimetic materials able to selectively recognize target analytes. A novel MIP sensor for DA is proposed here based on a thin film of poly(o-aminophenol) electrosynthesized on bare Pt. A fast and easy method for executing the procedure for MIP deposition has been developed based on mild experimental conditions that are able to prevent electrode fouling from DA oxidation products. The MIP exhibited a limit of detection of 0.65 µM, and appreciable reproducibility and stability. The high recognition capability of poly(o-aminophenol) towards DA allowed for the achievement of notable selectivity: ascorbic acid, uric acid, serotonin, and tyramine did not interfere with DA detection, even at higher concentrations. The proposed sensor was successfully applied for DA detection in urine samples, showing good recovery

Mars Regolith Simulant Ameliorated by Compost as In Situ Cultivation Substrate Improves Lettuce Growth and Nutritional Aspects

Heavy payloads in future shuttle journeys to Mars present limiting factors, making self-sustenance essential for future colonies. Therefore, in situ resources utilization (ISRU) is the path to successful and feasible space voyages. This research frames the concept of planting leafy vegetables on Mars regolith simulant, ameliorating this substrate’s fertility by the addition of organic residues produced in situ. For this purpose, two butterhead lettuce (Lactuca sativa L. var. capitata) cultivars (green and red Salanova®) were chosen to be cultivated in four dierent mixtures of MMS-1 Mojave Mars simulant:compost (0:100, 30:70, 70:30 and 100:0; v:v) in a phytotron open gas exchange growth chamber. The impact of compost rate on both crop performance and the nutritive value of green- and red-pigmented cultivars was assessed. The 30:70 mixture proved to be optimal in terms of crop performance, photosynthetic activity, intrinsic water use eciency and quality traits of lettuce. In particular, red Salanova® showed the best performance in terms of these quality traits, registering 32% more phenolic content in comparison to 100% simulant. Nonetheless, the 70:30 mixture represents a more realistic scenario when taking into consideration the sustainable use of compost as a limited resource in space farming, while still accepting a slight significant decline in yield and quality in comparison to the 30:70 mixture

A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers.

Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as 'cancer hallmarks'. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody-related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer

Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations.

We present a novel method for the identification of sets of mutually exclusive gene alterations in a given set of genomic profiles. We scan the groups of genes with a common downstream effect on the signaling network, using a mutual exclusivity criterion that ensures that each gene in the group significantly contributes to the mutual exclusivity pattern. We test the method on all available TCGA cancer genomics datasets, and detect multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events

Comparison of computational methods for the identification of topologically associating domains.

Chromatin folding gives rise to structural elements among which are clusters of densely interacting DNA regions termed topologically associating domains (TADs). TADs have been characterized across multiple species, tissue types, and differentiation stages, sometimes in association with regulation of biological functions. The reliability and reproducibility of these findings are intrinsically related with the correct identification of these domains from high-throughput chromatin conformation capture (Hi-C) experiments. Here, we test and compare 22 computational methods to identify TADs across 20 different conditions. We find that TAD sizes and numbers vary significantly among callers and data resolutions, challenging the definition of an average TAD size, but strengthening the hypothesis that TADs are hierarchically organized domains, rather than disjoint structural elements. Performances of these methods differ based on data resolution and normalization strategy, but a core set of TAD callers consistently retrieve reproducible domains, even at low sequencing depths, that are enriched for TAD-associated biological features. This study provides a reference for the analysis of chromatin domains from Hi-C experiments and useful guidelines for choosing a suitable approach based on the experimental design, available data, and biological question of interest

Pan-cancer inference of intra-tumor heterogeneity reveals associations with different forms of genomic instability.

Genomic instability is a major driver of intra-tumor heterogeneity. However, unstable genomes often exhibit different molecular and clinical phenotypes, which are associated with distinct mutational processes. Here, we algorithmically inferred the clonal phylogenies of ~6,000 human tumors from 32 tumor types to explore how intra-tumor heterogeneity depends on different implementations of genomic instability. We found that extremely unstable tumors associated with DNA repair deficiencies or high chromosomal instability are not the most intrinsically heterogeneous. Conversely, intra-tumor heterogeneity is greatest in tumors exhibiting relatively high numbers of both mutations and copy number alterations, a feature often observed in cancers associated with exogenous mutagens. Independently of the type of instability, tumors with high number of clones invariably evolved through branching phylogenies that could be stratified based on the extent of clonal (early) and subclonal (late) instability. Interestingly, tumors with high number of subclonal mutations frequently exhibited chromosomal instability, TP53 mutations, and APOBEC-related mutational signatures. Vice versa, mutations of chromatin remodeling genes often characterized tumors with few subclonal but multiple clonal mutations. Understanding how intra-tumor heterogeneity depends on genomic instability is critical to identify markers predictive of the tumor complexity and envision therapeutic strategies able to exploit this association

Systematic inference and comparison of multi-scale chromatin sub-compartments connects spatial organization to cell phenotypes.

Chromatin compartmentalization reflects biological activity. However, inference of chromatin sub-compartments and compartment domains from chromosome conformation capture (Hi-C) experiments is limited by data resolution. As a result, these have been characterized only in a few cell types and systematic comparisons across multiple tissues and conditions are missing. Here, we present Calder, an algorithmic approach that enables the identification of multi-scale sub-compartments at variable data resolution. Calder allows to infer and compare chromatin sub-compartments and compartment domains in >100 cell lines. Our results reveal sub-compartments enriched for poised chromatin states and undergoing spatial repositioning during lineage differentiation and oncogenic transformation