• 2018-07
  • 2018-10
  • 2018-11
  • Then all calculations were performed using R


    Then, all calculations were performed using R v.3.1.1 (R Development Core Team, 2011; [1].
    Experimental design, materials and methods In metabolic profiling, there is no single platform or method to analyze the entire metabolome of a biological sample, mainly due to the wide concentration range of the metabolites coupled to their extensive chemical diversity [2,3]. The current study used multiple UPLC-MS platforms, which were optimized for extensive coverage of the serum metabolome. Metabolite extraction was accomplished by fractionating the samples into pools of species with similar physicochemical properties, using appropriate combinations of organic solvents [4]. Then, three separate UPLC-MS based platforms were used. Briefly, UPLC-single quadrupole-MS amino inhibitor of catalase analysis system was combined with two separate UPLC-time-of-flight-MS based platforms analyzing methanol and chloroform/methanol extracts. Identified ion features in the methanol extract platform included non-esterified fatty acids, oxidized fatty acids, acyl carnitines, N-acyl ethanolamines, bile acids, steroids, monoacylglycerophospholipids, and monoetherglycerophospholipids. The chloroform/methanol extract platform provided coverage over glycerolipids, sphingolipids, diacylglycerophospholipids, acyl-ether-glycerophospholipids, cholesteryl esters, and primary fatty acid amides. Data pre-processing, data pre-treatment and data processing steps have been widely described [5]. A schematic flowchart of this metabolic profiling workflow is shown in Fig. 1.
    Statistical analysis of anthropometric, analytical and hematological parameters A heatmap for the correlation between age and the anthropometric, analytical and hematological parameters is included in Fig. 2. Variations in age and gender of each variable were evaluated by a two-way ANOVA (Table 1). The analysis per variable was completed with a boxplot and a table indicating the mean value and standard deviation per group. Those results are presented in Supplementary Material 1.
    Statistical analysis and visualization The advantages of using both univariate and multivariate approaches in data mining have been recently reviewed [6]. Both approaches are complementary and their results do not necessarily coincide. Following the advice to combine the use of both univariate and multivariate approaches, we have developed a web application. This is expected to help with the visualization and interpretation of the data analyses.
    MANOVA A multivariate analysis of variance (MANOVA) was one of the multivariate models selected to decipher an aging metabolic signature [5]. This model was considered for studying age as a categorical variable, establishing the groups according to the age of the volunteers.
    Linear analysis A linear least-squares regression analysis was the second multivariate model selected. In this case, age was considered as a continuous variable [5]. Previous to model construction, a random division of samples into estimation (80% of the volunteers) and validation (20%) data set was performed. Possible overfitting of the model was assessed by comparison of the residuals of both data sets. Complete information about residuals evaluation is available in Supplementary Material 6.
    Session info Information on R Session and packages versions that were used in this work:
    Value of the data
    Data, experimental design, materials and methods Fungal secretome were extracted and processed according to our earlier protocol [2]. The cultivation of A. fumigatus LF9 under different carbon sources such as glucose, cellulose, xylan; this study quantified diverse group of hemicellulases including endo-1,4-beta-xylanase, beta-xylosidase, alpha-1,2-mannosidase, alpha-l-arabinofuranosidase, arabinanase, beta-galactosidase, acetyl xylan esterase, rhamnogalacturonan acetylesterase, and many more as listed in Supplementary Table S1. Enzymes like laccase, isoamyl alcohol oxidase, glutathione reductase, oxidoreductases etc. involved in lignin degradation were also identified and quantified. Their comparative abundances under different carbon sources were listed in Supplementary Table S2. In addition to cellulases, hemicellulases and lignin degrading enzymes, this study identified several peptidases and proteases in the secretome (Supplementary Table S2). The proteins with unknown function (hypothetical proteins) were also expressed and their comparative abundances were listed in Supplementary Table S3. The cellulolytic activity of the isolated strain was further confirmed by using zymographic analysis. The band showing cellulolytic activity was cut, digested and analyzed by LC–MS/MS. The proteins identified in the band are listed in Supplementary Table S4. The data analysis revealed deamidation of key cellulose hydrolyzing enzymes, hemicellulases and several other proteins. The spectrums showing peptide sequences, fragmentation pattern and sites of modification are provided as a supplementary material (spectrum deamidation).