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1.
J Proteome Res ; 17(11): 3914-3922, 2018 11 02.
Article in English | MEDLINE | ID: mdl-30300549

ABSTRACT

Human tissues are known to exhibit interindividual variability, but a deeper understanding of the different factors affecting protein expression is necessary to further apply this knowledge. Our goal was to explore the proteomic variability between individuals as well as between healthy and diseased samples, and to test the efficacy of machine learning classifiers. In order to investigate whether disparate proteomics data sets may be combined, we performed a retrospective analysis of proteomics data from 9 different human tissues. These data sets represent several different sample prep methods, mass spectrometry instruments, and tissue health. Using these data, we examined interindividual and intertissue variability in peptide expression, and analyzed the methods required to build accurate tissue classifiers. We also evaluated the limits of tissue classification by downsampling the peptide data to simulate situations where less data is available, such as clinical biopsies, laser capture microdissection or potentially single-cell proteomics. Our findings reveal the strong potential for utilizing proteomics data to build robust tissue classifiers, which has many prospective clinical applications for evaluating the applicability of model clinical systems.


Subject(s)
Biological Variation, Individual , Data Mining/statistics & numerical data , Gene Expression Regulation , Peptides/chemistry , Proteins/genetics , Proteomics/methods , Amino Acid Sequence , Biopsy , Cell Line , Female , Gene Expression Profiling , Humans , Laser Capture Microdissection , Liver/chemistry , Machine Learning , Male , Monocytes/chemistry , Organ Specificity , Ovary/chemistry , Pancreas/chemistry , Peptides/isolation & purification , Peptides/metabolism , Proteins/metabolism , Retrospective Studies , Single-Cell Analysis , Substantia Nigra/chemistry , Temporal Lobe/chemistry
2.
Sci Rep ; 7: 44296, 2017 03 13.
Article in English | MEDLINE | ID: mdl-28287158

ABSTRACT

Compartmentalization of metabolic pathways to particular organelles is a hallmark of eukaryotic cells. Knowledge of the development of organelles and attendant pathways under different metabolic states has been advanced by live cell imaging and organelle specific analysis. Nevertheless, relatively few studies have addressed the cellular localization of pathways for synthesis of fungal secondary metabolites, despite their importance as bioactive compounds with significance to medicine and agriculture. When triggered to produce sesquiterpene (trichothecene) mycotoxins, the endoplasmic reticulum (ER) of the phytopathogenic fungus Fusarium graminearum is reorganized both in vitro and in planta. Trichothecene biosynthetic enzymes accumulate in organized smooth ER with pronounced expansion at perinuclear- and peripheral positions. Fluorescence tagged trichothecene biosynthetic proteins co-localize with the modified ER as confirmed by co-fluorescence and co-purification with known ER proteins. We hypothesize that changes to the fungal ER represent a conserved process in specialized eukaryotic cells such as in mammalian hepatocytes and B-cells.


Subject(s)
Endoplasmic Reticulum/metabolism , Fusarium/metabolism , Mycotoxins/biosynthesis , Trichothecenes/biosynthesis , Biosynthetic Pathways/genetics , Endoplasmic Reticulum/ultrastructure , Fungal Proteins/genetics , Fungal Proteins/metabolism , Fusarium/genetics , Fusarium/physiology , Luminescent Proteins/genetics , Luminescent Proteins/metabolism , Microscopy, Electron, Transmission , Microscopy, Fluorescence , Plant Diseases/microbiology , Secondary Metabolism/genetics , Triticum/microbiology
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