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      Lipidomics reveals dramatic lipid compositional changes in the maturing postnatal lung

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          Abstract

          Lung immaturity is a major cause of morbidity and mortality in premature infants. Understanding the molecular mechanisms driving normal lung development could provide insights on how to ameliorate disrupted development. While transcriptomic and proteomic analyses of normal lung development have been previously reported, characterization of changes in the lipidome is lacking. Lipids play significant roles in the lung, such as dipalmitoylphosphatidylcholine in pulmonary surfactant; however, many of the roles of specific lipid species in normal lung development, as well as in disease states, are not well defined. In this study, we used liquid chromatography-mass spectrometry (LC-MS/MS) to investigate the murine lipidome during normal postnatal lung development. Lipidomics analysis of lungs from post-natal day 7, day 14 and 6–8 week mice (adult) identified 924 unique lipids across 21 lipid subclasses, with dramatic alterations in the lipidome across developmental stages. Our data confirmed previously recognized aspects of post-natal lung development and revealed several insights, including in sphingolipid-mediated apoptosis, inflammation and energy storage/usage. Complementary proteomics, metabolomics and chemical imaging corroborated these observations. This multi-omic view provides a unique resource and deeper insight into normal pulmonary development.

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          Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells.

          In this study, we evaluated a concatenated low pH (pH 3) and high pH (pH 10) reversed-phase liquid chromatography strategy as a first dimension for two-dimensional liquid chromatography tandem mass spectrometry ("shotgun") proteomic analysis of trypsin-digested human MCF10A cell sample. Compared with the more traditional strong cation exchange method, the use of concatenated high pH reversed-phase liquid chromatography as a first-dimension fractionation strategy resulted in 1.8- and 1.6-fold increases in the number of peptide and protein identifications (with two or more unique peptides), respectively. In addition to broader identifications, advantages of the concatenated high pH fractionation approach include improved protein sequence coverage, simplified sample processing, and reduced sample losses. The results demonstrate that the concatenated high pH reversed-phased strategy is an attractive alternative to strong cation exchange for two-dimensional shotgun proteomic analysis. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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            Temporal Proteome and Lipidome Profiles Reveal Hepatitis C Virus-Associated Reprogramming of Hepatocellular Metabolism and Bioenergetics

            Introduction Persistent infection with hepatitis C virus (HCV), a single-stranded positive RNA virus of the Flaviviridae family, is a major cause of liver disease and a global public health problem. Chronically infected individuals develop variable degrees of hepatic inflammation and fibrosis, and are at increased risk for developing cirrhosis and hepatocellular carcinoma [1]. Current therapy consists of a combination of drugs that target cellular functions, including pegylated-interferon to boost the interferon-mediated antiviral response, and ribavirin, a nucleoside analog that suppresses HCV replication by impairing guanine nucleotide biosynthesis [2]. Unfortunately, this treatment regimen has limited efficacy, especially for certain HCV genotypes and patient populations, and its poor tolerability often leads to discontinuation. All viruses rely on constituents of the host cell to provide the energy, macromolecules and structural organization necessary for their propagation. This dependence on host interactions has led to significant interest in better understanding those pathways/processes crucial to the viral life cycle, as these represent potential targets for new antiviral strategies [2]–[4]. HCV infection has long been associated with abnormalities in lipid metabolism, and lipids have been shown to play important roles in various aspects of the virus life cycle [2],[3],[5]. For example, the biosynthesis of cholesterol, fatty acids, and geranylgeranyl and sphingolipid species is key to HCV replication, presumably by promoting the formation of lipid rafts on which replicase complexes assemble [6]–[10]. The development of a cell culture system that supports not only HCV replication but also the production of infectious virus has revealed additional roles for lipid metabolism in viral particle assembly, secretion and infectivity. Lipid droplets have been shown to function in the assembly of infectious particles, and HCV production is further dependent on apolipoprotein B (apoB) expression and very low density lipoprotein (VLDL) assembly and secretion [11]–[13]. The association of HCV morphogenesis with VLDL production has led to the identification of new cellular targets (e.g. apoB, microsomal triglyceride transfer protein, and long chain acyl –coenzyme A synthetase 3) with the potential to limit both processes [12]–[15]. Lipidomic analyses of mature virions isolated from infected-cell culture supernatants suggest that the HCV membrane is enriched in cholesterol; modulation of the virion-associated cholesterol or sphingomyelin composition alters infectivity by inhibiting virus internalization [16]. Host cell lipid metabolism is therefore critical for multiple stages of the HCV life cycle, and represents an important area for the exploration of new antiviral reagents. Despite the demonstrated importance of lipid components, the extent to which HCV modulates global intracellular metabolism to create an environment for RNA replication and production of progeny particles is currently unknown. Here we use liquid chromatography-mass spectrometry (LC-MS) together with the AMT tag approach to identify alterations in the host cell proteome and lipidome occurring in response to in vitro infection of Huh-7.5 cells with a chimeric HCV genotype 2a virus, J6/JFH-1. Our data reveal a temporal sequence of modifications to the host proteome that were not predicted from our previous gene expression analyses [17], suggesting that HCV dramatically disrupts cellular metabolic homeostasis via post-transcriptional regulatory mechanisms. We also observe global changes in lipid abundance, which are predicted to impact the HCV life cycle and pathogenesis. We further describe a computational modeling approach, which uses these high-throughput datasets to infer regulatory and functional relationship networks that provide information about the systems-level role of key proteins and lipids important for HCV-associated metabolic reprogramming. Materials and Methods Generation of Cell Culture Virus and Experimental Infections Approximately 7×106 Huh-7.5 cells (human hepatoma cell line) were electroporated with 2 µg of in vitro transcribed RNA, representing the chimeric HCV genome J6/JFH-1 [18]. One source of virus used for the infection experiments consisted of a pool of adapted virions obtained from cell culture supernatants that had been passaged multiple times following electroporation (HCVcc ‘pool’; refer to [17]). Alternatively, a J6/JFH-1 chimeric genome containing twelve cell culture adaptive mutations (HCVcc ‘clone’), isolated from the HCVcc ‘pool’, was used to generate virus stocks directly from electroporated cell supernatants by collecting samples every 12 h over 2–5 days post-electroporation. Both sources of virus demonstrated enhanced viral kinetics (>10-fold viral titer compared to parent; manuscript in preparation, Rice CM) that was necessary to infect the large number of cells required for the proteomic analysis. Parallel electroporations were performed in the absence of HCV RNA to generate a control supernatant sample (‘mock’). In addition, the HCV stock was used to generate a UV-inactivated, non-infectious control (UV-HCVcc; refer to [17]). For infection experiments (n = 4), low passage Huh-7.5 cells were seeded at a density of ∼3×106 cells/p150 plate, to ensure that cells would not reach confluency by the time of harvest, and treated for ∼8–12 h with 20 ml of supernatant containing virus (HCVcc), UV-inactivated virus (UV-HCVcc), or conditioned media (mock, CM). Following initial exposure, the supernatant was replaced with fresh media and incubated until harvest at 24, 48, or 72 h post-infection. The multiplicity of infection (MOI) for the experiments was ∼1–2 and resulted in >50% of cells being infected by 24 hours after HCVcc exposure. Harvesting Cells After Virus Infection Following removal of supernatant, cells were washed once with PBS and then scraped from the plate(s) in ice cold PBS. Identically treated cells were pooled from replicate plates, if necessary, to obtain approximately 107 total cells per time point per condition. The cells were subsequently split into several fractions: 10% for RNA isolation for previously reported genomics analyses [17], 10% for lipidomic analysis, and 80% for peptide generation/proteomic analysis. Lipidomic Sample Preparation Cells were pelleted in a siliconized microfuge tube, followed by disruption with chloroform∶methanol (2∶1 ratio), and pre-chilled to −20°C. After several rounds of vortexing on ice, samples were centrifuged at 2,000 RPM for 10 minutes to separate a water-soluble fraction (upper layer) and a lipid soluble fraction (lower layer). Both fractions were dried in a speed vac and stored at −80°C until analysis. Proteomic Sample Preparation Cells were washed in 0.5 x PBS, pelleted, and stored at −80°C until sample preparation. Samples from all time points were prepared on the same day, initially by lysing the thawed cell pellets in hypotonic buffer (5 mM K3PO4) at room temperature. Samples were solubilized and denatured by adding trifluoroethanol (TFE) to achieve a final concentration of 50%, followed by sonication on ice and incubation at 60°C for 1 h along with additional sonication. An equal volume of 500 mM NH4HCO3 (pH 8.0) was added to adjust to pH 8.0, and 5 mM tributylphosphine (TBP) was then added for 1 h at 37°C to allow reduction of disulfide bonds. Following centrifugation, soluble material was transferred to a fresh tube and the volume of sample was reduced to ∼100 µl using a speed vac. Additional NH4HCO3 (50 mM) was then added to reduce the amount of TFE in the samples to 1.5-fold change, P-Value 1.5-fold change, P-Value<0.05) exhibiting statistically significant increases in abundance are highlighted in red and those exhibiting statistically significant decreases in abundance are highlighted in green. Functional categories represented include glycolysis, the pentose phosphate pathway, and the tricarboxylic acid (TCA) cycle whose constituents are listed in their order of appearance in the appropriate pathway. For all other functional categories typically representing a broader spectrum of activities including pyruvate metabolism, oxidative phosphorylation, glutamate metabolism, fatty acid oxidation, nucleotide biosynthesis and homeostasis, lipogenesis, chaperones, and NRF2 stress response the proteins have been arranged in order of the fold-change observed in the HCVcc sample at 24 h post-infection. (0.07 MB XLS) Click here for additional data file. Table S3 Select list of 272 lipid species exhibiting significant differences across conditions and time points (ANOVA P<0.05). Mass to charge ratio (M/Z), normalized elution time (NET), log 2 average abundances (mean) and standard deviations (sd) are presented for each condition (CM: conditioned media, UV-HCVcc: UV-inactivated chimeric HCV 2a, HCVcc: chimeric HCV 2a virus) and time point. Also reported are the overall P-values from ANOVA analysis performed on minimum observation data (a feature was required to be observed in at least two out of three conditions (CM, UV-HCVcc, HCVcc) and there must be duplicate measurements for the two out of three conditions). P-values listed as 0.000 are equivalent to p<0.001. NA indicates where missing values exist. Among the 272 features exhibiting statistically significant differences between treatment conditions and/or time points, 73 lipid species were identified by matching to a lipid AMT tag database or fragmentation information collected via targeted MS/MS analyses. Identity abbreviations were made for phoshatidylcholine (PC; O- fatty acid chain number means that an alkyl acyl linkage to the glycerol chain is present for the respective PC), sphingomyelin (SM), ceramide (Cer), triacylglycerol (TAG), and cholesterol ester (CE). The notation further indicates total number of carbons and double bonds however it does not discern redundancy associated with varying fatty acid composition for the same molecular weight. (0.07 MB XLS) Click here for additional data file. Table S4 Top 5% of the bottlenecks identified from the combined lipidomics, proteomics inferred and protein-protein interaction network. Accompanying notes about the known or implicated roles of the identified bottlenecks in HCV response is also included. (0.03 MB XLS) Click here for additional data file.
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              A novel informatics concept for high-throughput shotgun lipidomics based on the molecular fragmentation query language

              Shotgun lipidome profiling relies on direct mass spectrometric analysis of total lipid extracts from cells, tissues or organisms and is a powerful tool to elucidate the molecular composition of lipidomes. We present a novel informatics concept of the molecular fragmentation query language implemented within the LipidXplorer open source software kit that supports accurate quantification of individual species of any ionizable lipid class in shotgun spectra acquired on any mass spectrometry platform.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                01 February 2017
                2017
                : 7
                : 40555
                Affiliations
                [1 ]Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
                [2 ]Physical Sciences Division, Pacific Northwest National Laboratory , Richland, Washington, USA
                [3 ]Center for Lung Biology, University of Washington , Seattle, Washington, USA.
                [4 ]Texas Advanced Computing Center, University of Texas at Austin , Austin, TX 78758, USA.
                Author notes
                [*]

                These authors contributed equally to this work and considered co-first authors.

                Article
                srep40555
                10.1038/srep40555
                5286405
                28145528
                25b85fc8-8ae1-4159-8601-e2af6b30c603
                Copyright © 2017, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 29 July 2016
                : 01 December 2016
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