Accentuate the Negative:Proteome Comparisons Using the Negative Proteome Database

The availability of complete genome sequence information for diverse organisms including model genetic organisms has ushered in a new era of protein sequence comparisons making it possible to search for commonalities among entire proteomes using the Basic Local Alignment Search Tool (BLAST). Although the identification and analysis of proteins shared by humans and model organisms has proven an invaluable tool to understanding gene function, the sets of proteins unique to a given model organism's proteome have remained largely unexplored. We have constructed a searchable database that allows biologists to identify proteins unique to a given proteome. The Negative Proteome Database (NPD) is populated with pair-wise protein sequence comparisons between each of the following proteomes: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Dictyostelium discoideum, Chlamydomonus reinhardti, Escherichia coli K12, Arabidopsis thaliana and Methanoscarcina acetivorans. Our analysis of negative proteome datasets using the NPD has thus far revealed 107 proteins in humans that may be involved in motile cilia function, 1628 potential pesticide target proteins in flies, 659 proteins shared by flies and humans that are not represented in the less neurologically complex worm proteome, and 180 nuclear encoded human disease associated proteins that are absent from the fly proteome. The NPD is the only online resource where users can quickly perform complex negative and positive comparisons of model organism proteomes. We anticipate that the NPD and the adaptable algorithm which can readily be used to duplicate this analysis on custom sets of proteomes will be an invaluable tool in the investigation of organism specific protein sets.     

Accentuate the negative: proteome comparisons using the negative proteome database

The availability of complete genome sequence information for diverse organisms including model genetic organisms has ushered in a new era of protein sequence comparisons making it possible to search for commonalities among entire proteomes using the Basic Local Alignment Search Tool (BLAST). Although the identification and analysis of proteins shared by humans and model organisms has proven an invaluable tool to understanding gene function, the sets of proteins unique to a given model organism's proteome have remained largely unexplored. We have constructed a searchable database that allows biologists to identify proteins unique to a given proteome. The Negative Proteome Database (NPD) is populated with pair-wise protein sequence comparisons between each of the following proteomes: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Dictyostelium discoideum, Chlamydomonus reinhardti, Escherichia coli K12, Arabidopsis thaliana and Methanoscarcina acetivorans. Our analysis of negative proteome datasets using the NPD has thus far revealed 107 proteins in humans that may be involved in motile cilia function, 1628 potential pesticide target proteins in flies, 659 proteins shared by flies and humans that are not represented in the less neurologically complex worm proteome, and 180 nuclear encoded human disease associated proteins that are absent from the fly proteome. The NPD is the only online resource where users can quickly perform complex negative and positive comparisons of model organism proteomes. We anticipate that the NPD and the adaptable algorithm which can readily be used to duplicate this analysis on custom sets of proteomes will be an invaluable tool in the investigation of organism specific protein sets.     

In Silico Characterization of Proteins: UniProt,InterPro and Integr8

Nucleic acid sequences from genome sequencing projects are submitted as raw data, from which biologists attempt to elucidate the function of the predicted gene products. The protein sequences are stored in public databases, such as the UniProt Knowledgebase (UniProtKB), where curators try to add predicted and experimental functional information. Protein function prediction can be done using sequence similarity searches, but an alternative approach is to use protein signatures, which classify proteins into families and domains. The major protein signature databases are available through the integrated InterPro database, which provides a classification of UniProtKB sequences. As well as characterization of proteins through protein families, many researchers are interested in analyzing the complete set of proteins from a genome (i.e. the proteome), and there are databases and resources that provide non-redundant proteome sets and analyses of proteins from organisms with completely sequenced genomes. This article reviews the tools and resources available on the web for single and large-scale protein characterization and whole proteome analysis.     

Comprehensive Map of the Artemisia annua Proteome and Quantification of Differential Protein Expression in Chemotypes Producing High versus Low Content of Artemisinin

Artemisia annua is well known for biosynthesizing the antimalarial drug artemisinin. Here, a global proteomic profiling of A. annua is conducted with identification of a total of 13 403 proteins based on the genome sequence annotation database. Furthermore, a spectral library is generated to perform quantitative proteomic analysis using data independent acquisition mass spectrometry. Specifically, proteins between two chemotypes that produce high (HAP) and low (LAP) artemisinin content, respectively, are comprehensively quantified and compared. 182 proteins are identified with abundance significantly different between these two chemotypes means after the statistic use the p‐value and fold change it is found 182 proteins can reach the demand conditions which represent the expression are significantly different between the high artemisnin content plants (HAPs) and the low artemisnin content plants (LAPs). Data are available via ProteomeXchange with identifier PXD015547. Overall, this current study globally identifies the proteome of A. annua and quantitatively compares the targeted sub‐proteomes between the two cultivars of HAP and LAP, providing systematic information on metabolic pathways of A. annua.     

Integration of the barley genetic and seed proteome maps for chromosome 1H, 2H, 3H, 5H and 7H

Two-dimensional gel electrophoresis was used to screen spring barley cultivars for differences in seed protein profiles. In parallel, 72 microsatellite (simple sequence repeat (SSR)) markers and 11 malting quality parameters were analysed for each cultivar. Over 60 protein spots displayed cultivar variation, including peroxidases, serpins and proteins with unknown functions. Cultivars were clustered based on the spot variation matrix. Cultivars with superior malting quality grouped together, indicating malting quality to be more closely correlated with seed proteomes than with SSR profiles. Mass spectrometry showed that some spot variations were caused by amino acid differences encoded by single nucleotide polymorphisms (SNPs). Coding SNPs were validated by mass spectrometry, expressed sequence tag and 2D gel data. Coding SNPs can alter function of affected proteins and may thus represent a link between cultivar traits, proteome and genome. Proteome analysis of doubled haploid lines derived from a cross between a malting (Scarlett) and a feed cultivar (Meltan) enabled genetic localisation of protein phenotypes represented by 48 spot variations, involving e.g. peroxidases, serpins, α-amylase/trypsin inhibitors, peroxiredoxin and a small heat shock protein, in relation to markers on the chromosome map. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Understanding aerobic/anaerobic metabolism in Caldibacillus debilis through a comparison with model organisms

Caldibacillus debilis GB1 is a facultative anaerobe isolated from a thermophilic aero-tolerant cellulolytic enrichment culture. There is a lack of representative proteomes of facultative anaerobic thermophilic Bacillaceae, exploring aerobic/anaerobic expression. The C. debilis GB1 genome was sequenced and annotated, and the proteome characterized under aerobic and anaerobic conditions while grown on cellobiose. The draft sequence of C. debilis GB1 contains a 3,340,752 bp chromosome and a 5,386 bp plasmid distributed over 49 contigs. Two-dimensional liquid chromatography mass spectrometry/mass spectrometry was used with Isobaric Tags for Relative and Absolute Quantification (iTRAQ) to compare protein expression profiles, focusing on energy production and conversion pathways. Under aerobic conditions, proteins in glycolysis and pyruvate fermentation pathways were down-regulated. Simultaneously, proteins within the tricarboxylic acid cycle, pyruvate dehydrogenase, the electron transport chain, and oxygen scavenging pathways showed increased amounts. Under anaerobic conditions, protein levels in fermentation pathways were consistent with the generated end-products: formate, acetate, ethanol, lactate, and CO₂. Under aerobic conditions CO₂ and acetate production was consistent with incomplete respiration. Through a direct comparison with gene expression profiles from Escherichia coli, we show that global regulation of core metabolism pathways is similar in thermophilic and mesophilic facultative anaerobes of the Phylum Proteobacteria and Firmicutes.     

PCAS – a precomputed proteome annotation database resource

Background  

Many model proteomes or "complete" sets of proteins of given organisms are now publicly available. Much effort has been invested in computational annotation of those "draft" proteomes. Motif or domain based algorithms play a pivotal role in functional classification of proteins. Employing most available computational algorithms, mainly motif or domain recognition algorithms, we set up to develop an online proteome annotation system with integrated proteome annotation data to complement existing resources.     

SECRETOOL: integrated secretome analysis tool for fungi

The secretome (full set of secreted proteins) has been studied in multiple fungal genomes to elucidate the potential role of those protein collections involved in a number of metabolic processes from host infection to wood degradation. Being aminoacid composition a key factor to recognize secretory proteins, SECRETOOL comprises a group of web tools that enable secretome predictions out of aminoacid sequence files, up to complete fungal proteomes, in one step. SECRETOOL is freely available on the web at http://genomics.cicbiogune.es/SECRETOOL/Secretool.php.     

A comprehensive analysis of Trypanosoma brucei mitochondrial proteome

The composition of the large, single, mitochondrion (mt) of Trypanosoma brucei was characterized by MS (2‐D LC‐MS/MS and gel‐LC‐MS/MS) analyses. A total of 2897 proteins representing a substantial proportion of procyclic form cellular proteome were identified, which confirmed the validity of the vast majority of gene predictions. The data also showed that the genes annotated as hypothetical (species specific) were overpredicted and that virtually all genes annotated as hypothetical, unlikely are not expressed. By comparing the MS data with genome sequence, 40 genes were identified that were not previously predicted. The data are placed in a publicly available web‐based database (www.TrypsProteome.org). The total mitochondrial proteome is estimated at 1008 proteins, with 401, 196, and 283 assigned to the mt with high, moderate, and lower confidence, respectively. The remaining mitochondrial proteins were estimated by statistical methods although individual assignments could not be made. The identified proteins have predicted roles in macromolecular, metabolic, energy generating, and transport processes providing a comprehensive profile of the protein content and function of the T. brucei mt.     

Proteomics reveals the effects of sustained weight loss on the human plasma proteome

Sustained weight loss is a preferred intervention in a wide range of metabolic conditions, but the effects on an individual's health state remain ill‐defined. Here, we investigate the plasma proteomes of a cohort of 43 obese individuals that had undergone 8 weeks of 12% body weight loss followed by a year of weight maintenance. Using mass spectrometry‐based plasma proteome profiling, we measured 1,294 plasma proteomes. Longitudinal monitoring of the cohort revealed individual‐specific protein levels with wide‐ranging effects of losing weight on the plasma proteome reflected in 93 significantly affected proteins. The adipocyte‐secreted SERPINF1 and apolipoprotein APOF1 were most significantly regulated with fold changes of ?16% and +37%, respectively (P < 10^?13), and the entire apolipoprotein family showed characteristic differential regulation. Clinical laboratory parameters are reflected in the plasma proteome, and eight plasma proteins correlated better with insulin resistance than the known marker adiponectin. Nearly all study participants benefited from weight loss regarding a ten‐protein inflammation panel defined from the proteomics data. We conclude that plasma proteome profiling broadly evaluates and monitors intervention in metabolic diseases.     

Mapping the proteome of Leishmania Viannia parasites using two-dimensional polyacrylamide gel electrophoresis and associated technologies

In this study we have demonstrated the potential of two-dimensional electrophoresis (2DE)-based technologies as tools for characterization of the Leishmania proteome (the expressed protein complement of the genome). Standardized neutral range (pH 5-7) proteome maps of Leishmania (Viannia) guyanensis and Leishmania (Viannia) panamensis promastigotes were reproducibly generated by 2DE of soluble parasite extracts, which were prepared using lysis buffer containing urea and nonidet P-40 detergent. The Coomassie blue and silver nitrate staining systems both yielded good resolution and representation of protein spots, enabling the detection of approximately 800 and 1,500 distinct proteins, respectively. Several reference protein spots common to the proteomes of all parasite species/strains studied were isolated and identified by peptide mass spectrometry (LC-ES-MS/MS), and bioinformatics approaches as members of the heat shock protein family, ribosomal protein S12, kinetoplast membrane protein 11 and a hypothetical Leishmania-specific 13 kDa protein of unknown function. Immunoblotting of Leishmania protein maps using a monoclonal antibody resulted in the specific detection of the 81.4 kDa and 77.5 kDa subunits of paraflagellar rod proteins 1 and 2, respectively. Moreover, differences in protein expression profiles between distinct parasite clones were reproducibly detected through comparative proteome analyses of paired maps using image analysis software. These data illustrate the resolving power of 2DE-based proteome analysis. The production and basic characterization of good quality Leishmania proteome maps provides an essential first step towards comparative protein expression studies aimed at identifying the molecular determinants of parasite drug resistance and virulence, as well as discovering new drug and vaccine targets.     

Improvements in proteomic metrics of low abundance proteins through proteome equalization using ProteoMiner prior to MudPIT

Ideally, shotgun proteomics would facilitate the identification of an entire proteome with 100% protein sequence coverage. In reality, the large dynamic range and complexity of cellular proteomes results in oversampling of abundant proteins, while peptides from low abundance proteins are undersampled or remain undetected. We tested the proteome equalization technology, ProteoMiner, in conjunction with Multidimensional Protein Identification Technology (MudPIT) to determine how the equalization of protein dynamic range could improve shotgun proteomics methods for the analysis of cellular proteomes. Our results suggest low abundance protein identifications were improved by two mechanisms: (1) depletion of high abundance proteins freed ion trap sampling space usually occupied by high abundance peptides and (2) enrichment of low abundance proteins increased the probability of sampling their corresponding more abundant peptides. Both mechanisms also contributed to dramatic increases in the quantity of peptides identified and the quality of MS/MS spectra acquired due to increases in precursor intensity of peptides from low abundance proteins. From our large data set of identified proteins, we categorized the dominant physicochemical factors that facilitate proteome equalization with a hexapeptide library. These results illustrate that equalization of the dynamic range of the cellular proteome is a promising methodology to improve low abundance protein identification confidence, reproducibility, and sequence coverage in shotgun proteomics experiments, opening a new avenue of research for improving proteome coverage.     

Quantitative proteome analysis using isotope-coded affinity tags and mass spectrometry

A main objective of proteomics research is to systematically identify and quantify proteins in a given proteome (cells, subcellular fractions, protein complexes, tissues or body fluids). Protein labeling with isotope-coded affinity tags (ICAT) followed by tandem mass spectrometry allows sequence identification and accurate quantification of proteins in complex mixtures, and has been applied to the analysis of global protein expression changes, protein changes in subcellular fractions, components of protein complexes, protein secretion and body fluids. This protocol describes protein-sample labeling with ICAT reagents, chromatographic fractionation of the ICAT-labeled tryptic peptides, and protein identification and quantification using tandem mass spectrometry. The method is suitable for both large-scale analysis of complex samples including whole proteomes and small-scale analysis of subproteomes, and allows quantitative analysis of proteins, including those that are difficult to analyze by gel-based proteomics technology.     

Proteome analysis of the rice etioplast: metabolic and regulatory networks and novel protein functions

We report an extensive proteome analysis of rice etioplasts, which were highly purified from dark-grown leaves by a novel protocol using Nycodenz density gradient centrifugation. Comparative protein profiling of different cell compartments from leaf tissue demonstrated the purity of the etioplast preparation by the absence of diagnostic marker proteins of other cell compartments. Systematic analysis of the etioplast proteome identified 240 unique proteins that provide new insights into heterotrophic plant metabolism and control of gene expression. They include several new proteins that were not previously known to localize to plastids. The etioplast proteins were compared with proteomes from Arabidopsis chloroplasts and plastid from tobacco Bright Yellow 2 cells. Together with computational structure analyses of proteins without functional annotations, this comparative proteome analysis revealed novel etioplast-specific proteins. These include components of the plastid gene expression machinery such as two RNA helicases, an RNase II-like hydrolytic exonuclease, and a site 2 protease-like metalloprotease all of which were not known previously to localize to the plastid and are indicative for so far unknown regulatory mechanisms of plastid gene expression. All etioplast protein identifications and related data were integrated into a data base that is freely available upon request.     

A comparative proteomics resource: proteins of <Emphasis Type="Italic">Arabidopsis thaliana</Emphasis>

Using an integrative genome annotation pipeline (iGAP) for proteome-wide protein structure and functional domain assignment, we analyzed all the proteins of Arabidopsis thaliana. Three-dimensional structures at the level of the domain are assigned by fold recognition and threading based on a novel fold library that extends common domain classifications. iGAP is being applied to proteins from all available proteomes as part of a comparative proteomics resource. The database is accessible from the web.     

Proteomic analysis of the extremely thermoacidophilic archaeon Picrophilus torridus at pH and temperature values close to its growth limit

The thermoacidophilic archaeon Picrophilus torridus belongs to the Thermoplasmatales order and is the most acidophilic organism known to date, growing under extremely acidic conditions around pH 0 (pH(opt) 1) and simultaneously at high temperatures up to 65°C. Some genome features that may be responsible for survival under these harsh conditions have been concluded from the analysis of its 1.55 megabase genome sequence. A proteomic map was generated for P. torridus cells grown to the mid-exponential phase. The soluble fraction of the cells was separated by isoelectric focusing in the pH ranges 4-7 and 3-10, followed by a two dimension (2D) on SDS-PAGE gels. A total of 717 Coomassie collodial-stained protein spots from both pH ranges (pH 4-7 and 3-10) were excised and subjected to LC-MS/MS, leading to the identification of 665 soluble protein spots. Most of the enzymes of the central carbon metabolism were identified on the 2D gels, corroborating biochemically the metabolic pathways predicted from the P. torridus genome sequence. The 2D master gels elaborated in this study represent useful tools for physiological studies of this thermoacidophilic organism. Based on quantitative 2D gel electrophoresis, a proteome study was performed to find pH- or temperature-dependent differences in the proteome composition under changing growth conditions. The proteome expression patterns at two different temperatures (50 and 70°C) and two different pH conditions (pH 0.5 and 1.8) were compared. Several proteins were up-regulated under most stress stimuli tested, pointing to general roles in coping with stress.