Project description:The rapidly increasing number of engineered nanoparticles (NPs), and products containing NPs, raises concerns for human exposure and safety. With this increasing, and ever changing, catalogue of NPs it is becoming more difficult to adequately assess the toxic potential of new materials in a timely fashion. It is therefore important to develop methods which can provide high-throughput screening of biological responses. The use of omics technologies, including metabolomics, can play a vital role in this process by providing relatively fast, comprehensive, and cost-effective assessment of cellular responses. These techniques thus provide the opportunity to identify specific toxicity pathways and to generate hypotheses on how to reduce or abolish toxicity.We have used untargeted metabolome analysis to determine differentially expressed metabolites in human lung epithelial cells (A549) exposed to copper oxide nanoparticles (CuO NPs). Toxicity hypotheses were then generated based on the affected pathways, and critically tested using more conventional biochemical and cellular assays. CuO NPs induced regulation of metabolites involved in oxidative stress, hypertonic stress, and apoptosis. The involvement of oxidative stress was clarified more easily than apoptosis, which involved control experiments to confirm specific metabolites that could be used as standard markers for apoptosis; based on this we tentatively propose methylnicotinamide as a generic metabolic marker for apoptosis.Our findings are well aligned with the current literature on CuO NP toxicity. We thus believe that untargeted metabolomics profiling is a suitable tool for NP toxicity screening and hypothesis generation.
Project description:The thermoacidophilic red alga Galdieria sulphuraria has been optimizing a photosynthetic system for low-light conditions over billions of years, thriving in hot and acidic endolithic habitats. The growth of G. sulphuraria in the laboratory is very much dependent on light and substrate supply. Here, higher cell densities in G. sulphuraria under high-light conditions were obtained, although reductions in photosynthetic pigments were observed, which indicated this alga might be able to relieve the effects caused by photoinhibition. We further describe an extensive untargeted metabolomics study to reveal metabolic changes in autotrophic and mixotrophic G. sulphuraria grown under high and low light intensities. The up-modulation of bilayer lipids, that help generate better-ordered lipid domains (e.g., ergosterol) and keep optimal membrane thickness and fluidity, were observed under high-light exposure. Moreover, high-light conditions induced changes in amino acids, amines, and amide metabolism. Compared with the autotrophic algae, higher accumulations of osmoprotectant sugars and sugar alcohols were recorded in the mixotrophic G. sulphuraria. This response can be interpreted as a measure to cope with stress due to the high concentration of organic carbon sources. Our results indicate how G. sulphuraria can modulate its metabolome to maintain energetic balance and minimize harmful effects under changing environments.
Project description:MicroRNAs are important negative regulators of protein coding gene expression, and have been studied intensively over the last few years. To this purpose, different measurement platforms to determine their RNA abundance levels in biological samples have been developed. In this study, we have systematically compared 12 commercially available microRNA expression platforms by measuring an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples, and synthetic spikes from homologous microRNA family members. We developed novel quality metrics in order to objectively assess platform performance of very different technologies such as small RNA sequencing, RT-qPCR and (microarray) hybridization. We assessed reproducibility, sensitivity, quantitative performance, and specificity. The results indicate that each method has its strengths and weaknesses, which helps guiding informed selection of a quantitative microRNA gene expression platform in function of particular study goals.
Project description:Antarctic cryptoendolithic communities are self-supporting borderline ecosystems spreading across the extreme conditions of the Antarctic desert and represent the predominant life-form in the ice-free areas of McMurdo Dry Valleys, accounted as the closest terrestrial Martian analogue. Components of these communities are highly adapted extremophiles and extreme-tolerant microorganisms, among the most resistant known to date. Recently, studies investigated biodiversity and community composition in these ecosystems but the metabolic activity of the metacommunity has never been investigated. Using an untargeted metabolomics, we explored stress-response of communities spreading in two sites of the same location, subjected to increasing environmental pressure due to opposite sun exposure, accounted as main factor influencing the diversity and composition of these ecosystems. Overall, 331 altered metabolites (206 and 125 unique for north and south, respectively), distinguished the two differently exposed communities. We also selected 10 metabolites and performed two-stage Receiver Operating Characteristic (ROC) analysis to test them as potential biomarkers. We further focused on melanin and allantoin as protective substances; their concentration was highly different in the community in the shadow or in the sun. These results clearly indicate that opposite insolation selected organisms in the communities with different adaptation strategies in terms of key metabolites produced.
Project description:Prenatal stress through glucocorticoid (GC) exposure leads to an increased risk of developing diseases such as cardiovascular disease, metabolic syndrome and hypertension in adulthood. We have previously shown that administration of the synthetic glucocorticoid, dexamethasone (Dex), to pregnant Wistar-Kyoto dams produces offspring with elevated blood pressures and disrupted circadian rhythm signaling. Given the link between stress, circadian rhythms and metabolism, we performed an untargeted metabolomic screen on the livers of offspring to assess potential changes induced by prenatal Dex exposure. This metabolomic analysis highlighted 18 significantly dysregulated metabolites in females and 12 in males. Pathway analysis using MetaboAnalyst 4.0 highlighted key pathway-level metabolic differences: glycerophospholipid metabolism, purine metabolism and glutathione metabolism. Gene expression analysis revealed significant upregulation of several lipid metabolism genes in females while males showed no dysregulation. Triglyceride concentrations were also found to be significantly elevated in female offspring exposed to Dex in utero, which may contribute to lipid metabolism activation. This study is the first to conduct an untargeted metabolic profile of liver from GC exposed offspring. Corroborating metabolic, gene expression and lipid profiling results demonstrates significant sex-specific lipid metabolic differences underlying the programming of hepatic metabolism.
Project description:Oncogene-associated metabolic signatures in prostate cancer, identified by an integrative analysis of cultured cells and murine and human tumors, suggest that AKT activation results in a glycolytic phenotype whereas MYC induces aberrant lipid metabolism. Heterogeneity in human tumors makes this simplistic interpretation obtained from experimental models more challenging. Metabolic reprogramming as a function of distinct molecular aberrations has major diagnostic and therapeutic implications.
Project description:Direct-infusion shotgun proteome analysis (DI-SPA) increases throughput by removing liquid chromatography, but the resulting DIA spectra are highly multiplexed and difficult to interpret. This dataset was generated to evaluate ProjDIA, a computational workflow for direct-infusion DIA proteomics. ProjDIA uses fragment-bin projection scoring, confidence-guided two-pass mass-error recalibration, monotone q-value reporting, and semi-supervised rescoring to improve peptide and protein identification confidence. The dataset includes benchmarking experiments across MS2 resolution and sample load, replicate-series analyses, species-entrapment controls for external error assessment, and a three-species mixture for label-free quantification evaluation. The submission contains the raw mass spectrometry files together with processed search and result files used to support the analyses reported in the study.