PPARα and PPARγ activation is associated with pleural mesothelioma invasion but therapeutic inhibition is ineffective

Mesothelioma is a cancer that typically originates in the pleura of the lungs. It rapidly invades the surrounding tissues, causing pain and shortness of breath. We compared cell lines injected either subcutaneously or intrapleurally and found that only the latter resulted in invasive and rapid growth. Pleural tumors displayed a transcriptional signature consistent with increased activity of nuclear receptors PPARα and PPARγ and with an increased abundance of endogenous PPAR-activating ligands. We found that chemical probe GW6471 is a potent, dual PPARα/γ antagonist with anti-invasive and anti-proliferative activity in vitro. However, administration of GW6471 at doses that provided sustained plasma exposure levels sufficient for inhibition of PPARα/γ transcriptional activity did not result in significant anti-mesothelioma activity in mice. Lastly, we demonstrate that the in vitro anti-tumor effect of GW6471 is off-target. We conclude that dual PPARα/γ antagonism alone is not a viable treatment modality for mesothelioma

Dissecting the role of G-protein signalling in primary metabolism in the wheat pathogen Stagonospora nodorum

Mutants of the wheat pathogenic fungus Stagonospora nodorum lacking G-protein subunits display a variety of phenotypes including melanization defects, primary metabolic changes and a decreased ability to sporulate. To better understand the causes of these phenotypes, Stagonospora nodorum strains lacking a Gα, Gβ or Gγ subunit were compared to a wild-type strain using metabolomics. Agar plate growth at 22 °C revealed a number of fundamental metabolic changes and highlighted the influential role of these proteins in glucose utilization. A further characterization of the mutants was undertaken during prolonged storage at 4 °C, conditions known to induce sporulation in these sporulation-deficient signalling mutants. The abundance of several compounds positively correlated with the onset of sporulation including the dissacharide trehalose, the tryptophan degradation product tryptamine and the secondary metabolite alternariol; metabolites all previously associated with sporulation. Several other compounds decreased or were absent during sporulation. The levels of one such compound (Unknown_35.27_2194_319) decreased from being one of the more abundant compounds to absence during pycnidial maturation. This study has shed light on the role of G-protein subunits in primary metabolism during vegetative growth and exploited the cold-induced sporulation phenomenon in these mutants to identify some key metabolic changes that occur during asexual reproduction

Matrix-assisted laser desorption ionization mass spectrometry imaging by freeze-spot deposition of the matrix

Imaging mass spectrometry has emerged as a powerful metabolite measurement approach to capture the spatial dimension of metabolite distribution in a biological sample. In matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI), deposition of the chemical-matrix onto the sample serves to simultaneously extract biomolecules to the sample surface and concurrently render the sample amenable to MALDI. However, matrix application may mobilize sample metabolites and will dictate the efficiency of matrix crystallization, together limiting the lateral resolution which may be optimally achieved by MSI. Here, we describe a matrix application technique, herein referred to as the freeze-spot method, conceived as a low-cost preparative approach requiring minimal amounts of chemical matrix while maintaining the spatial dimension of sample metabolites for MALDI-MSI. Matrix deposition was achieved by pipette spot application of the matrix-solubilized within a solvent solution with a freezing point above that of a chilled sample stage to which the sample section is mounted. The matrix solution freezes on contact with the sample and the solvent is removed by sublimation, leaving a fine crystalline matrix on the sample surface. Freeze-spotting is quick to perform, found particularly useful for MALDI-MSI of small sample sections, and well suited to efficient and cost-effective method development pipelines, while capable of maintaining the lateral resolution required by MSI

Untargeted metabolomics of human plasma reveal lipid markers unique to chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis

Chronic obstructive pulmonary disease (COPD) is characterised by airway inflammation and progressive airflow limitation, whereas idiopathic pulmonary fibrosis (IPF) is characterised by a restrictive pattern due to fibrosis and impaired gas exchange. We undertook metabolomic analysis of blood samples in IPF, COPD and healthy controls (HC) to determine differences in circulating molecules and identify novel pathogenic pathways. An untargeted metabolomics using an ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometer (UHPLC-QTOF-MS) was performed to profile plasma of patients with COPD (n = 21), and IPF (n = 24) in comparison to plasma from healthy controls (HC; n = 20). The most significant features were identified using multiple database matching. One-way ANOVA and variable importance in projection (VIP) scores were also used to highlight metabolites that influence the specific disease groups. Non-polar metabolites such as fatty acids (FA) and membrane lipids were well resolved and a total of 4805 features were identified. The most prominent metabolite composition differences in lipid mediators identified at ∼2–3 fold higher in both diseases compared to HC were palmitoleic acid, oleic acid and linoleic acid; and dihydrotestosterone was lower in both diseases. We demonstrated that COPD and IPF were characterised by systemic changes in lipid constituents such as essential FA sampled from circulating plasma

Dataset 7.8 Skin matrix.isoforms.TPM.not_cross_norm.TMM_info

Dataset 7 contains the raw gene/transcript counts data for each frog tissue sample (Experiment B, n = 61) resulting from allocation of sequence reads to genes from the transcriptome assemblies. These data are contained in Dataset 7, as 12 individual tab-separated text files (.txt) [Data Citation 1]. The four files for each tissue type (skin, liver and spleen) represent raw (1) counts, (2) TMM.EXPR files, (3) TPM.not_cross_norm files, and (4) normalisation information (as per the standard output from RSEM). The first row of these files (1-3 above) represents frog ID, and the first element of all subsequent rows represents transcript/isoform ID

Dataset 5.5 Spleen-filtered-condensed

Dataset 5 consists of de novo assembled transcriptomes for each tissue type from frogs in Experiment B (n = 61). These data are contained in Dataset 5, as six individual .fasta text files, including both nucleotide sequence assembly files and amino acid sequence assembly files (translated using TransDecoder) for each of the three tissue types (skin, liver and spleen) [Data Citation 1]. Descriptions of the sequence identifier lines are contained in the associated readme file

Data from: Survival, gene and metabolite responses of Litoria verreauxii alpina frogs to fungal disease chytridiomycosis

PLEASE NOTE, THESE DATA ARE ALSO REFERRED TO IN ANOTHER PUBLICATION. PLEASE SEE http://dx.doi.org/10.1111/mec.14493. The fungal skin disease chytridiomycosis has caused the devastating decline and extinction of hundreds of amphibian species globally, yet the potential for evolving resistance, and the underlying pathophysiological mechanisms remain poorly understood. We exposed 406 naïve, captive-raised alpine tree frogs (Litoria verreauxii alpina) from multiple populations (one evolutionarily naïve to chytridiomycosis) to the aetiological agent Batrachochytrium dendrobatidis in two concurrent and controlled infection experiments. We investigated (A) survival outcomes and clinical pathogen burdens between populations and clutches, and (B) individual host tissue responses to chytridiomycosis. Here we present multiple interrelated datasets associated with these exposure experiments, including animal signalment, survival and pathogen burden of 355 animals from Experiment A, and the following datasets related to 61 animals from Experiment B: animal signalment and pathogen burden; raw RNA-Seq reads from skin, liver and spleen tissues; de novo assembled transcriptomes for each tissue type; raw gene expression data; annotation data for each gene; and raw metabolite expression data from skin and liver tissues. These data provide an extensive baseline for future analyses

Effect of Deferoxamine on Post-Transfusion Iron, Inflammation, and In Vitro Microbial Growth in a Canine Hemorrhagic Shock Model: A Randomized Controlled Blinded Pilot Study

Red blood cell (RBC) transfusion is associated with recipient inflammation and infection, which may be triggered by excessive circulating iron. Iron chelation following transfusion may reduce these risks. The aim of this study was to evaluate the effect of deferoxamine on circulating iron and inflammation biomarkers over time and in vitro growth of Escherichia coli (E. coli) following RBC transfusion in dogs with atraumatic hemorrhage. Anesthetized dogs were subject to atraumatic hemorrhage and transfusion of RBCs, then randomized to receive either deferoxamine or saline placebo of equivalent volume (n = 10 per group) in a blinded fashion. Blood was sampled before hemorrhage and then 2, 4, and 6 h later. Following hemorrhage and RBC transfusion, free iron increased in all dogs over time (both p \u3c 0.001). Inflammation biomarkers interleukin-6 (IL6), CXC motif chemokine-8 (CXCL8), interleukin-10 (IL10), and keratinocyte-derived chemokine (KC) increased in all dogs over time (all p \u3c 0.001). Logarithmic growth of E. coli clones within blood collected 6 h post-transfusion was not different between groups. Only total iron-binding capacity was different between groups over time, being significantly increased in the deferoxamine group at 2 and 4 h post-transfusion (both p \u3c 0.001). In summary, while free iron and inflammation biomarkers increased post-RBC transfusion, deferoxamine administration did not impact circulating free iron, inflammation biomarkers, or in vitro growth of E. coli when compared with placebo

Effect of Deferoxamine on Post-Transfusion Iron, Inflammation, and In Vitro Microbial Growth in a Canine Hemorrhagic Shock Model: A Randomized Controlled Blinded Pilot Study

Red blood cell (RBC) transfusion is associated with recipient inflammation and infection, which may be triggered by excessive circulating iron. Iron chelation following transfusion may reduce these risks. The aim of this study was to evaluate the effect of deferoxamine on circulating iron and inflammation biomarkers over time and in vitro growth of Escherichia coli (E. coli) following RBC transfusion in dogs with atraumatic hemorrhage. Anesthetized dogs were subject to atraumatic hemorrhage and transfusion of RBCs, then randomized to receive either deferoxamine or saline placebo of equivalent volume (n = 10 per group) in a blinded fashion. Blood was sampled before hemorrhage and then 2, 4, and 6 h later. Following hemorrhage and RBC transfusion, free iron increased in all dogs over time (both p < 0.001). Inflammation biomarkers interleukin-6 (IL6), CXC motif chemokine-8 (CXCL8), interleukin-10 (IL10), and keratinocyte-derived chemokine (KC) increased in all dogs over time (all p < 0.001). Logarithmic growth of E. coli clones within blood collected 6 h post-transfusion was not different between groups. Only total iron-binding capacity was different between groups over time, being significantly increased in the deferoxamine group at 2 and 4 h post-transfusion (both p < 0.001). In summary, while free iron and inflammation biomarkers increased post-RBC transfusion, deferoxamine administration did not impact circulating free iron, inflammation biomarkers, or in vitro growth of E. coli when compared with placebo

Dataset 7.3 Liver matrix.isoforms.TPM.not_cross_norm

Dataset 7 contains the raw gene/transcript counts data for each frog tissue sample (Experiment B, n = 61) resulting from allocation of sequence reads to genes from the transcriptome assemblies. These data are contained in Dataset 7, as 12 individual tab-separated text files (.txt) [Data Citation 1]. The four files for each tissue type (skin, liver and spleen) represent raw (1) counts, (2) TMM.EXPR files, (3) TPM.not_cross_norm files, and (4) normalisation information (as per the standard output from RSEM). The first row of these files (1-3 above) represents frog ID, and the first element of all subsequent rows represents transcript/isoform ID