The Path to Preservation: Using Proteomics to Decipher the Fate of Diatom Proteins During Microbial Degradation

We drew upon recent advances in tandem mass spectrometry-based proteomic analyses in order to examine the proteins that remain after a diatom bloom enters the stationary phase, precipitates out of the photic zone, and is subjected to microbial degradation over a 23-d period within a controlled laboratory environment. Proteins were identified from tandem mass spectra searched against three different protein databases in order to track proteins from Thalassiosira pseudonana and any potential bacterial contributions. A rapid loss of diatom protein was observed over the incubation period; 75% of the proteins initially identified were not detected after 72 h of exposure to a microbial population. By the 23rd day, peptides identified with high confidence correlated with only four T. pseudonana proteins. Five factors may have influenced the preservation of diatom proteins: (1) protection within organelles or structures with multiple membranes, (2) the relative cellular abundance in the photosynthetic apparatus, (3) the number of transmembrane domains in the protein sequence, (4) the presence of glycan modification motifs, and (5) the capability of proteins or peptides to aggregate into supramolecules

WaveletQuant, an improved quantification software based on wavelet signal threshold de-noising for labeled quantitative proteomic analysis

<p>Abstract</p> <p>Background</p> <p>Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification.</p> <p>Results</p> <p>We developed a novel discrete wavelet transform (DWT) and a 'Spatial Adaptive Algorithm' to remove noise and to identify true peaks. We programmed and compiled WaveletQuant using Visual C++ 2005 Express Edition. We then incorporated the WaveletQuant program in the <b>Trans-Proteomic Pipeline (TPP)</b>, a commonly used open source proteomics analysis pipeline.</p> <p>Conclusions</p> <p>We showed that WaveletQuant was able to quantify more proteins and to quantify them more accurately than the ASAPRatio, a program that performs quantification in the TPP pipeline, first using known mixed ratios of yeast extracts and then using a data set from ovarian cancer cell lysates. The program and its documentation can be downloaded from our website at <url>http://systemsbiozju.org/data/WaveletQuant</url>.</p

An update on MRMAssayDB: a comprehensive resource for targeted proteomics assays in the community

Precise multiplexed quantification of proteins in biological samples can be achieved by targeted proteomics using multiple or parallel reaction monitoring (MRM/PRM). Combined with internal standards, the method achieves very good repeatability and reproducibility enabling excellent protein quantification and allowing longitudinal and cohort studies. A laborious part of performing such experiments lies in the preparation steps dedicated to the development and validation of individual protein assays. Several public repositories host information on targeted proteomics assays, including NCI's Clinical Proteomic Tumor Analysis Consortium assay portals, PeptideAtlas SRM Experiment Library, SRMAtlas, PanoramaWeb, and PeptideTracker, with all offering varying levels of details. We introduced MRMAssayDB in 2018 as an integrated resource for targeted proteomics assays. The Web-based application maps and links the assays from the repositories, includes comprehensive up-to-date protein and sequence annotations, and provides multiple visualization options on the peptide and protein level. We have extended MRMAssayDB with more assays and extensive annotations. Currently it contains >828 000 assays covering >51 000 proteins from 94 organisms, of which >17 000 proteins are present in >2400 biological pathways, and >48 000 mapping to >21 000 Gene Ontology terms. This is an increase of about four times the number of assays since introduction. We have expanded annotations of interaction, biological pathways, and disease associations. A newly added visualization module for coupled molecular structural annotation browsing allows the user to interactively examine peptide sequence and any known PTMs and disease mutations, and map all to available protein 3D structures. Because of its integrative approach, MRMAssayDB enables a holistic view of suitable proteotypic peptides and commonly used transitions in empirical data. Availability: http://mrmassaydb.proteincentre.com.Proteomic

Glycosylation characterization of therapeutic mAbs by top- and middle-down mass spectrometry

A reference monoclonal antibody IgG1 and a fusion IgG protein were analyzed by top- and middle-down mass spectrometry with multiple fragmentation techniques including electron transfer dissociation (ETD) and matrix-assisted laser desorption ionization in-source decay (MALDI-ISD) to investigate heterogeneity of glycosylated protein species. Specifically, glycan structure, sites, relative abundance levels, and termini structural conformation were investigated by use of Fourier transform ion cyclotron resonance (FT-ICR) or high performance liquid chromatography electrospray ionization (HPLC-ESI) linked to an Orbitrap. Incorporating a limited enzymatic digestion by immunoglobulin G-degrading enzyme Streptococcus pyogenes (IdeS) with MALDI-ISD analysis extended sequence coverage of the internal region of the proteins without pre-fractionation. The data in this article is associated with the research article published in Journal of Proteomics (Tran et al., 2015)

Precursor Ion Independent Algorithm for Top-Down Shotgun Proteomics

We present a precursor ion independent top-down algorithm (PIITA) for use in automated assignment of protein identifications from tandem mass spectra of whole proteins. To acquire the data, we utilize data-dependent acquisition to select protein precursor ions eluting from a C4-based HPLC column for collision induced dissociation in the linear ion trap of an LTQ-Orbitrap mass spectrometer. Gas-phase fractionation is used to increase the number of acquired tandem mass spectra, all of which are recorded in the Orbitrap mass analyzer. To identify proteins, the PIITA algorithm compares deconvoluted, deisotoped, observed tandem mass spectra to all possible theoretical tandem mass spectra for each protein in a genomic sequence database without regard for measured parent ion mass. Only after a protein is identified, is any difference in measured and theoretical precursor mass used to identify and locate post-translation modifications. We demonstrate the application of PIITA to data generated via our wet-lab approach on a Salmonella typhimurium outer membrane extract and compare these results to bottom-up analysis. From these data, we identify 154 proteins by top-down analysis, 73 of which were not identified in a parallel bottom-up analysis. We also identify 201 unique isoforms of these 154 proteins at a false discovery rate (FDR) of <1%

Deep-sea microbes as tools to refine the rules of innate immune pattern recognition.

The assumption of near-universal bacterial detection by pattern recognition receptors is a foundation of immunology. The limits of this pattern recognition concept, however, remain undefined. As a test of this hypothesis, we determined whether mammalian cells can recognize bacteria that they have never had the natural opportunity to encounter. These bacteria were cultivated from the deep Pacific Ocean, where the genus Moritella was identified as a common constituent of the culturable microbiota. Most deep-sea bacteria contained cell wall lipopolysaccharide (LPS) structures that were expected to be immunostimulatory, and some deep-sea bacteria activated inflammatory responses from mammalian LPS receptors. However, LPS receptors were unable to detect 80% of deep-sea bacteria examined, with LPS acyl chain length being identified as a potential determinant of immunosilence. The inability of immune receptors to detect most bacteria from a different ecosystem suggests that pattern recognition strategies may be defined locally, not globally.R01 AI093589 - NIAID NIH HHS; P30 DK034854 - NIDDK NIH HHS; U19 AI133524 - NIAID NIH HHS; R01 AI147314 - NIAID NIH HHS; R01 AI116550 - NIAID NIH HHS; R37 AI116550 - NIAID NIH HHS; R01 AI123820 - NIAID NIH HHSAccepted manuscrip

The human diabetes proteome project (HDPP): The 2014 update

Diabetes is an increasing worldwide problem leading to major associated health issues and increased health care costs. In 2012, 9.3% of the American population was affected by diabetes, according to the American Diabetes Association, with 1.7 million of new cases since during the year (www.diabetes.org). Proteome initiatives can provide a deeper understanding of the biology of this disease and help develop more effective treatments. The collaborative effort of the Human Diabetes Proteome Project (HDPP) brings together a wide variety of complementary resources to increase the existing knowledge about both type 1 and type 2 diabetes and their related complications. The goals are to identify proteins and protein isoforms associated with the pathology and to characterize underlying disease-related pathways and mechanisms. Moreover, a considerable effort is being made on data integration and network biology. Sharing these data with the scientific community will be an important part of the consortium. Here we report on: the content of the HDPP session held at the 12th HUPO meeting in Yokohama; recent achievements of the consortium; discussions of several HDPP workshops; as well as future HDPP directions as discussed at the 13th HUPO congress in Madrid, with a special attention given to the lists of prioritized, diabetes-related proteins and the proteomic means to study them.</p

Serum Proteomic Profiling to Identify Biomarkers of Premature Carotid Atherosclerosis

To evaluate the presence of serum protein biomarkers associated with the early phases of formation of carotid atherosclerotic plaques, label-free quantitative proteomics analyses were made for serum samples collected as part of The Cardiovascular Risk in Young Finns Study. Samples from subjects who had an asymptomatic carotid artery plaque detected by ultrasound examination (N = 43, Age = 30–45 years) were compared with plaque free controls (N = 43) (matched for age, sex, body weight and systolic blood pressure). Seven proteins (p </p