MetaNetX/MNXref - reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks.

MetaNetX is a repository of genome-scale metabolic networks (GSMNs) and biochemical pathways from a number of major resources imported into a common namespace of chemical compounds, reactions, cellular compartments-namely MNXref-and proteins. The MetaNetX.org website (http://www.metanetx.org/) provides access to these integrated data as well as a variety of tools that allow users to import their own GSMNs, map them to the MNXref reconciliation, and manipulate, compare, analyze, simulate (using flux balance analysis) and export the resulting GSMNs. MNXref and MetaNetX are regularly updated and freely available

Assessment of seasonal winter temperature forecast errors in the regcm model over northern Vietnam

This study verified the seasonal six-month forecasts for winter temperatures for northern Vietnam in 1998–2018 using a regional climate model (RegCM4) with the boundary conditions of the climate forecast system Version 2 (CFSv2) from the National Centers for Environmental Prediction (NCEP). First, different physical schemes (land-surface process, cumulus, and radiation parameterizations) in RegCM4 were applied to generate 12 single forecasts. Second, the simple ensemble forecasts were generated through the combinations of those different physical formulations. Three subclimate regions (R1, R2, R3) of northern Vietnam were separately tested with surface observations and a reanalysis dataset (Japanese 55-year reanalysis (JRA55)). The highest sensitivity to the mean monthly temperature forecasts was shown by the land-surface parameterizations (the biosphere−atmosphere transfer scheme (BATS) and community land model version 4.5 (CLM)). The BATS forecast groups tended to provide forecasts with lower temperatures than the actual observations, while the CLM forecast groups tended to overestimate the temperatures. The forecast errors from single forecasts could be clearly reduced with ensemble mean forecasts, but ensemble spreads were less than those root-mean-square errors (RMSEs). This indicated that the ensemble forecast was underdispersed and that the direct forecast from RegCM4 needed more postprocessing

RNA Sequencing-Based Genome Reannotation of the Dermatophyte Arthroderma benhamiae and Characterization of Its Secretome and Whole Gene Expression Profile during Infection.

Dermatophytes are the most common agents of superficial mycoses in humans and animals. The aim of the present investigation was to systematically identify the extracellular, possibly secreted, proteins that are putative virulence factors and antigenic molecules of dermatophytes. A complete gene expression profile of Arthroderma benhamiae was obtained during infection of its natural host (guinea pig) using RNA sequencing (RNA-seq) technology. This profile was completed with those of the fungus cultivated in vitro in two media containing either keratin or soy meal protein as the sole source of nitrogen and in Sabouraud medium. More than 60% of transcripts deduced from RNA-seq data differ from those previously deposited for A. benhamiae. Using these RNA-seq data along with an automatic gene annotation procedure, followed by manual curation, we produced a new annotation of the A. benhamiae genome. This annotation comprised 7,405 coding sequences (CDSs), among which only 2,662 were identical to the currently available annotation, 383 were newly identified, and 15 secreted proteins were manually corrected. The expression profile of genes encoding proteins with a signal peptide in infected guinea pigs was found to be very different from that during in vitro growth when using keratin as the substrate. Especially, the sets of the 12 most highly expressed genes encoding proteases with a signal sequence had only the putative vacuolar aspartic protease gene PEP2 in common, during infection and in keratin medium. The most upregulated gene encoding a secreted protease during infection was that encoding subtilisin SUB6, which is a known major allergen in the related dermatophyte Trichophyton rubrum. IMPORTANCE Dermatophytoses (ringworm, jock itch, athlete's foot, and nail infections) are the most common fungal infections, but their virulence mechanisms are poorly understood. Combining transcriptomic data obtained from growth under various culture conditions with data obtained during infection led to a significantly improved genome annotation. About 65% of the protein-encoding genes predicted with our protocol did not match the existing annotation for A. benhamiae. Comparing gene expression during infection on guinea pigs with keratin degradation in vitro, which is supposed to mimic the host environment, revealed the critical importance of using real in vivo conditions for investigating virulence mechanisms. The analysis of genes expressed in vivo, encoding cell surface and secreted proteins, particularly proteases, led to the identification of new allergen and virulence factor candidates

An in vitro approach to understand contribution of kidney cells to human urinary extracellular vesicles

Extracellular vesicles (EV) are membranous particles secreted by all cells and found in body fluids. Established EV contents include a variety of RNA species, proteins, lipids and metabolites that are considered to reflect the physiological status of their parental cells. However, to date, little is known about cell-type enriched EV cargo in complex EV mixtures, especially in urine. To test whether EV secretion from distinct human kidney cells in culture differ and can recapitulate findings in normal urine, we comprehensively analysed EV components, (particularly miRNAs, long RNAs and protein) from conditionally immortalised human kidney cell lines (podocyte, glomerular endothelial, mesangial and proximal tubular cells) and compared to EV secreted in human urine. EV from cell culture media derived from immortalised kidney cells were isolated by hydrostatic filtration dialysis (HFD) and characterised by electron microscopy (EM), nanoparticle tracking analysis (NTA) and Western blotting (WB). RNA was isolated from EV and subjected to miRNA and RNA sequencing and proteins were profiled by tandem mass tag proteomics. Representative sets of EV miRNAs, RNAs and proteins were detected in each cell type and compared to human urinary EV isolates (uEV), EV cargo database, kidney biopsy bulk RNA sequencing and proteomics, and single-cell transcriptomics. This revealed that a high proportion of the in vitro EV signatures were also found in in vivo datasets. Thus, highlighting the robustness of our in vitro model and showing that this approach enables the dissection of cell type specific EV cargo in biofluids and the potential identification of cell-type specific EV biomarkers of kidney disease.Peer reviewe

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes:an IMI-RHAPSODY Study

Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised “bottom-up” approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.</p

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes:an IMI-RHAPSODY Study

Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised “bottom-up” approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.</p

Mobility of Cr, Pb, Cd, Cu and Zn in a loamy sand soil : a comparative study

Interest in soil contamination has been growing in recent years due to the ongoing degradation of soil environments. Therefore, the development of remediation techniques and the study of contaminant sorption and migration are areas of intense research. In this study, the authors sought to evaluate the scenario of co-contamination of a loamy sand soil by multiple heavy metals. To that end, the sorption and transport of five metals—Cr, Pb, Cd, Cu and Zn—was evaluated using representative samples of a soil from the north of Portugal. The tests were conducted in batch and continuous systems using single- and multiple-metal acid solutions to evaluate the effect of metal competition. In accordance with the type of assay—batch or continuous—Langmuir or Convection Dispersion Two-Site Nonequilibrium models were adjusted to explain the sorption/transport data. FTIR analyses were performed on the final samples of the continuous systems. Generally, the results revealed good fitting of the tested models for the metals in competitive and noncompetitive scenarios, with the exception of Zn that was originally present in soil samples at higher concentrations. As expected, the influence of competition was observed in both batch and continuous systems, but with different tendencies. The FTIR spectra also revealed a strong influence of clay minerals and organic matter on the sorption of the metals.The PhD grants of Bruna Fonseca and Hugo Figueiredo and the research grant of Joana Rodrigues were financially supported by Fundacao para a Ciencia e Tecnologia, Ministerio da Ciencia e Tecnologia, Portugal and Fundo Social Europeu (FSE)

Model-independent search for CP violation in D0→K−K+π−π+ and D0→π−π+π+π− decays

A search for CP violation in the phase-space structures of D0 and View the MathML source decays to the final states K−K+π−π+ and π−π+π+π− is presented. The search is carried out with a data set corresponding to an integrated luminosity of 1.0 fb−1 collected in 2011 by the LHCb experiment in pp collisions at a centre-of-mass energy of 7 TeV. For the K−K+π−π+ final state, the four-body phase space is divided into 32 bins, each bin with approximately 1800 decays. The p-value under the hypothesis of no CP violation is 9.1%, and in no bin is a CP asymmetry greater than 6.5% observed. The phase space of the π−π+π+π− final state is partitioned into 128 bins, each bin with approximately 2500 decays. The p-value under the hypothesis of no CP violation is 41%, and in no bin is a CP asymmetry greater than 5.5% observed. All results are consistent with the hypothesis of no CP violation at the current sensitivity

Search for CP violation in D+→K−K+π+D^{+} \to K^{-}K^{+}\pi^{+} decays

A model-independent search for direct CP violation in the Cabibbo suppressed decay $D^+ \to K^- K^+\pi^+$ in a sample of approximately 370,000 decays is carried out. The data were collected by the LHCb experiment in 2010 and correspond to an integrated luminosity of 35 pb$^{-1}$. The normalized Dalitz plot distributions for $D^+$ and $D^-$ are compared using four different binning schemes that are sensitive to different manifestations of CP violation. No evidence for CP asymmetry is found.Comment: 13 pages, 8 figures, submitted to Phys. Rev.

Measurement of charged particle multiplicities in pppp collisions at s=7{\sqrt{s} =7}TeV in the forward region

The charged particle production in proton-proton collisions is studied with the LHCb detector at a centre-of-mass energy of ${\sqrt{s} =7}$TeV in different intervals of pseudorapidity $\eta$. The charged particles are reconstructed close to the interaction region in the vertex detector, which provides high reconstruction efficiency in the $\eta$ ranges $-2.5<\eta<-2.0$ and $2.0<\eta<4.5$. The data were taken with a minimum bias trigger, only requiring one or more reconstructed tracks in the vertex detector. By selecting an event sample with at least one track with a transverse momentum greater than 1 GeV/c a hard QCD subsample is investigated. Several event generators are compared with the data; none are able to describe fully the multiplicity distributions or the charged particle density distribution as a function of $\eta$. In general, the models underestimate the charged particle production