Proteomes最新文献

The interaction between plants and pathogenic microorganisms is a multifaceted process mediated by both plant- and pathogen-derived molecules, including proteins, metabolites, and lipids. Large-scale proteome analysis can quantify the dynamics of proteins, biological pathways, and posttranslational modifications (PTMs) involved in the plant-pathogen interaction. Mass spectrometry (MS)-based proteomics has become the preferred method for characterizing proteins at the proteome and sub-proteome (e.g., the phosphoproteome) levels. MS-based proteomics can reveal changes in the quantitative state of a proteome and provide a foundation for understanding the mechanisms involved in plant-pathogen interactions. This review is intended as a primer for biologists that may be unfamiliar with the diverse range of methodology for MS-based shotgun proteomics, with a focus on techniques that have been used to investigate plant-pathogen interactions. We provide a summary of the essential steps required for shotgun proteomic studies of plants, pathogens and plant-pathogen interactions, including methods for protein digestion, identification, separation, and quantification. Finally, we discuss how protein PTMs may directly participate in the interaction between a pathogen and its host plant.

Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's disease. In this study, the tear proteome profile of patients with idiopathic PD (iPD, n = 24), carriers of the E46K-SNCA mutation (n = 3) and healthy control (CT, n = 27) subjects was analyzed to identify candidate biomarkers for the diagnosis of PD. An observational, prospective and case-control pilot study was carried out, analyzing the participants tear samples by nano-liquid chromatography-mass spectrometry (nLC-MS/MS) and assessing their neurological impairment. The proteomic data obtained are available at ProteomeXchange with identifier 10.6019/PXD028811. These analyses led to the identification of 560 tear proteins, some of which were deregulated in PD patients and that have been implicated in immune responses, inflammation, apoptosis, collagen degradation, protein synthesis, defense, lipid transport and altered lysosomal function. Of these proteins, six were related to neurodegenerative processes and showed a good capacity to classify patients and controls. These findings revealed that certain proteins were upregulated in the tears of PD patients, mainly proteins involved in lysosomal function. Thus, in this study, tear proteins were identified that are implicated in neurodegeneration and that may be related to an aggressive disease phenotype in PD patients.

Multiplexed proteomics using isobaric tagging allows for simultaneously comparing the proteomes of multiple samples. In this technique, digested peptides from each sample are labeled with a chemical tag prior to pooling sample for LC-MS/MS with nanoflow chromatography (NanoLC). The isobaric nature of the tag prevents deconvolution of samples until fragmentation liberates the isotopically labeled reporter ions. To ensure efficient peptide labeling, large concentrations of labeling reagents are included in the reagent kits to allow scientists to use high ratios of chemical label per peptide. The increasing speed and sensitivity of mass spectrometers has reduced the peptide concentration required for analysis, leading to most of the label or labeled sample to be discarded. In conjunction, improvements in the speed of sample loading, reliable pump pressure, and stable gradient construction of analytical flow HPLCs has continued to improve the sample delivery process to the mass spectrometer. In this study we describe a method for performing multiplexed proteomics without the use of NanoLC by using offline fractionation of labeled peptides followed by rapid "standard flow" HPLC gradient LC-MS/MS. Standard Flow Multiplexed Proteomics (SFloMPro) enables high coverage quantitative proteomics of up to 16 mammalian samples in about 24 h. In this study, we compare NanoLC and SFloMPro analysis of fractionated samples. Our results demonstrate that comparable data is obtained by injecting 20 µg of labeled peptides per fraction with SFloMPro, compared to 1 µg per fraction with NanoLC. We conclude that, for experiments where protein concentration is not strictly limited, SFloMPro is a competitive approach to traditional NanoLC workflows with improved up-time, reliability and at a lower relative cost per sample.

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the "gold standard" for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

De novo sequencing is indispensable for the analysis of proteins from organisms with unknown genomes, novel splice variants, and antibodies. However, despite a variety of methods developed to this end, distinguishing between the correct interpretation of a mass spectrum and a number of incorrect alternatives often remains a challenge. Tag convolution is computed for a set of peptide sequence tags of a fixed length k generated from the input tandem mass spectra and can be viewed as a generalization of the well-known spectral convolution. We demonstrate its utility for validating de novo peptide sequences by using a set of those generated by the algorithm PepNovo+ from high-resolution bottom-up data sets for carbonic anhydrase 2 and the Fab region of alemtuzumab and indicate its further potential applications.

Germinated paddy rice (GPR) could be a good alternative feed source for poultry with stocking density and heat stress problems. A total of 72 Hy-line Brown laying hens raised under low (LSD, 0.12 m²/bird) and high stocking densities (HSD, 0.06 m²/bird) were investigated. Three dietary GPR levels (0, 74 and 148 g/kg) were used. It was found that average daily feed intake, hen-day egg production, and egg mass significantly decreased in the HSD group. The levels of serum glucose (GLU), phosphorous (P), corticosterone (CORT), total Ig, lysozyme (LZY), and superoxide dismutase activities (SOD) in the HSD group were higher than those in the LSD group. Dietary GPR significantly affected GLU, P, alternative complement haemolytic 50 (ACH50), total Ig, and LZY. Moreover, CORT level significantly decreased in 74 and 148 g/kg dietary GPR groups, whereas SOD significantly increased only in the 148 g/kg dietary GPR group. Serum samples were analyzed using liquid chromatography-tandem mass spectrometry, and 8607 proteins were identified. Proteome analysis revealed 19 proteins which were enriched in different stocking densities and dietary GPR levels. Quantitative real-time reverse transcription-PCR technique was successfully used to verify the differentiated abundant protein profile changes. The proteins identified in this study could serve as appropriate biomarkers.