IPeak: An open source tool to combine results from multiple MS/MS search engines

On the use of tandem mass spectra acquired from samples of evolutionarily distant organisms to validate methods for false discovery rate estimation

Estimating the false discovery rate (FDR) of peptide identifications is a key step in proteomics data analysis, and many methods have been proposed for this purpose. Recently, an entrapment-inspired protocol to validate methods for FDR estimation appeared in articles showcasing new spectral library search tools. That validation approach involves generating incorrect spectral matches by searching spectra from evolutionarily distant organisms (entrapment queries) against the original target search space. Although this approach may appear similar to the solutions using entrapment databases, it represents a distinct conceptual framework whose correctness has not been verified yet. In this viewpoint, we first discussed the background of the entrapment-based validation protocols and then conducted a few simple computational experiments to verify the assumptions behind them. The results reveal that entrapment databases may, in some implementations, be a reasonable choice for validation, while the assumptions underpinning validation protocols based on entrapment queries are likely to be violated in practice. This article also highlights the need for well-designed frameworks for validating FDR estimation methods in proteomics.

The molecular response of Mytilus coruscus mantle to shell damage under acute acidified sea water revealed by iTRAQ based quantitative proteomic analysis

Mytilus coruscus is an economically important marine bivalve that lives in estuarine sea areas with seasonal coastal acidification and frequently suffers shell injury in the natural environment. However, the molecular responses and biochemical properties of Mytilus under these conditions are not fully understood. In the present study, we employed tandem mass spectrometry combined with isobaric tagging to identify differentially expressed proteins in the mantle tissue of M. coruscus under different short-term treatments, including shell-complete mussels raised in normal seawater (pH 8.1), shell-damaged mussels raised in normal seawater (pH 8.1), and acidified seawater (pH 7.4). A total of 2694 proteins were identified in the mantle, and analysis of their relative abundance from the three different treatments revealed alterations in the proteins involved in immune regulation, oxidation-reduction processes, protein folding and processing, energy provision, and cytoskeleton. The results obtained by quantitative proteomic analysis of the mantle allowed us to delineate the molecular strategies adopted by M. coruscus in the shell repair process in acidified environments, including an increase in proteins involved in oxidation-reduction processes, protein processing, and cell growth at the expense of proteins involved in immune capacity and energy metabolism. SIGNIFICANCE: The impact of global ocean acidification on calcifying organisms has become a major ecological and environmental problem in the world. Mytilus coruscus is an economically important marine bivalve living in estuary sea area with seasonal coastal acidification, and frequently suffering shell injury in natural environment. Molecular responses of M coruscus under the shell damage and acute acidification is still largely unknown. For this reason, iTRAQ based quantitative proteomic and histological analysis of the mantle from M. coruscus under shell damage and acute acidification were performed, for revealing the proteomic response and possible adaptation mechanism of Mytilus under combined shell damage and acidified sea water, and understanding how the mussel mantle implement a shell-repair process under acidified sea water. Our study provides important data for understanding the shell repair process and proteomic response of Mytilus under ocean acidification, and providing insights into potential adaptation of mussels to future global change.

Defence mechanisms of Pinctada fucata martensii to Vibrio parahaemolyticus infection: Insights from proteomics and metabolomics

Survival of pearl oysters is not only challenged by coastal pollution, but also pathogen infection that may eventually incur substantial economic losses in the pearl farming industry. Yet, whether pearl oysters can defend themselves against pathogen infection through molecular mechanisms remains largely unexplored. By using iTRAQ proteomic and metabolomic analyses, we analysed the proteins and metabolites in the serum of pearl oysters (Pinctada fucata martensii) when stimulated by pathogenic bacteria (Vibrio parahaemolyticus). Proteomic results found that a total of 2,242 proteins were identified in the experimental (i.e., Vibrio-stimulated) and control groups, where 166 of them were differentially expressed (120 upregulated and 46 downregulated in the experimental group). Regarding the immune response enrichment results, the pathway of signal transduction was significantly enriched, such as cytoskeleton and calcium signalling pathways. Proteins, including cathepsin L, heat shock protein 20, myosin and astacin-like protein, also contributed to the immune response of oysters. Pathogen stimulation also altered the metabolite profile of oysters, where 49 metabolites associated with metabolism of energy, fatty acids and amino acids were found. Integrated analysis suggests that the oysters could respond to pathogen infection by coordinating multiple cellular processes. Thus, the proteins and metabolites identified herein not only represent valuable genetic resources for developing molecular biomarkers and genetic breeding research, but also open new avenues for studies on the molecular defence mechanisms of pearl oysters to pathogen infection.

Metaexoproteomics Reveals Microbial Behavior in the Ocean's Interior

The proteins present in the extracellular environment of cells, named the "exoproteome," are critical for microbial survival, growth, and interaction with their surroundings. However, little is known about microbial exoproteomes in natural marine environments. Here, we used a metaproteomic approach to characterize the exoprotein profiles (10 kDa-0.2 μm) throughout a water column in the South China Sea. Viruses, together with Alpha- and Gammaproteobacteria were the predominant contributors. However, the exoprotein-producing microbial communities varied with depth: SAR11 in the shallow waters, Pseudomonadales and Nitrososphaeria in the mesopelagic layer, and Alteromonadales, Rhizobiales, and Betaproteobacteria in the bathypelagic layer. Besides viral and unknown proteins, diverse transporters contributed substantially to the exoproteomes and varied vertically in their microbial origins, but presented similar patterns in their predicted substrate identities throughout the water column. Other microbial metabolic processes subject to vertical zonation included proteolysis, the oxidation of ammonia, nitrite and carbon monoxide, C1 metabolism, and the degradation of sulfur-containing dissolved organic matter (DOM). Our metaexoproteomic study provides insights into the depth-variable trends in the in situ ecological traits of the marine microbial community hidden in the non-cellular world, including nutrient cycling, niche partitioning and DOM remineralization.

Effects of DISC1 on Alzheimer's disease cell models assessed by iTRAQ proteomics analysis

Alzheimer’s disease (AD) is a form of neurodegenerative disease in the elderly with no cure at present. In a previous study, we found that the scaffold protein, disrupted in Schizophrenia 1 (DISC1) is down-regulated in the AD brains, and ectopic expression of DISC1 can delay the progression of AD by protecting synaptic plasticity and down-regulating BACE1. However, the underlying mechanisms remain not to be elucidated. In the present study, we compared the proteomes of normal and DISC1^high AD cells expressing the amyloid precursor protein (APP) using isobaric tag for relative and absolute quantitation (iTRAQ) and mass spectrometry (MS). The differentially expressed proteins (DEPs) were identified, and the protein–protein interaction (PPI) network was constructed to identify the interacting partners of DISC1. Based on the interaction scores, NDE1, GRM3, PTGER3 and KATNA1 were identified as functionally or physically related to DISC1, and may therefore regulate AD development. The DEPs were functionally annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases with the DAVID software, and the Non-supervised Orthologous Groups (eggNOG) database was used to determine their evolutionary relationships. The DEPs were significantly enriched in microtubules and mitochondria-related pathways. Gene set enrichment analysis (GSEA) was performed to identify genes and pathways that are activated when DISC1 is overexpressed. Our findings provide novel insights into the regulatory mechanisms underlying DISC1 function in AD.

Bottom-up and top-down proteomic approaches for the identification, characterization, and quantification of the low molecular weight proteome with focus on short open reading frame-encoded peptides

The recent discovery of alternative open reading frames creates a need for suitable analytical approaches to verify their translation and to characterize the corresponding gene products at the molecular level. As the analysis of small proteins within a background proteome by means of classical bottom-up proteomics is challenging, method development for the analysis of small open reading frame encoded peptides (SEPs) have become a focal point for research. Here, we highlight bottom-up and top-down proteomics approaches established for the analysis of SEPs in both pro- and eukaryotes. Major steps of analysis, including sample preparation and (small) proteome isolation, separation and mass spectrometry, data interpretation and quality control, quantification, the analysis of post-translational modifications, and exploration of functional aspects of the SEPs by means of proteomics technologies are described. These methods do not exclusively cover the analytics of SEPs but simultaneously include the low molecular weight proteome, and moreover, can also be used for the proteome-wide analysis of proteolytic processing events.

Screening and identification of endometrial proteins as novel potential biomarkers for repeated implantation failure

Inadequate endometrial receptivity may be responsible for the low implantation rate of transferred embryos in in vitro fertilization (IVF) treatments. Patients with repeated implantation failure (RIF) impact the clinical pregnancy rate for IVF. We collected endometrial tissue during the implantation window of hysteroscopy biopsies from September 2016 to December 2019 and clinical data were collected simultaneously. Patients were divided into RIF and pregnant controls group according to pregnancy outcomes. A total of 82 differentially expressed endometrial proteins were identified, including 55 up-regulated proteins (>1.50-fold, P < 0.05) and 27 down-regulated proteins (<0.67-fold, P < 0.05) by iTRAQ labeling coupled with the 2D LC MS/MS technique in the RIF group. String analysis found interactions between these proteins which assembled in two bunches: ribosomal proteins and blood homeostasis proteins. The most significant enriched Gene Ontology terms were negative regulation of hydrolase activity, blood microparticle, and enzyme inhibitor activity. Our results emphasized the corticosteroid-binding globulin and fetuin-A as the specific proteins of endometrial receptivity by Western-blot. Our study provided experimental data to establish the objective indicator of endometrial receptivity, and also provided new insight into the pathogenesis of RIF.

Proteomic Analysis of the Alterations in Follicular Fluid Proteins During Oocyte Maturation in Humans

Many components in ovarian follicles (follicular fluid, cumulus cells, granular cells, etc.) dynamically change during folliculogenesis and play a positive or negative role in oocyte maturation. Infertile women who underwent intracytoplasmic sperm injection (ICSI) treatment in the reproductive medicine centre of Hangzhou Women's Hospital between October 2018 and October 2021 were included. The ovarian follicular fluid and cumulus cells of diminished ovarian response (DOR) patients and control subjects with medical records of clinical data were collected. In total, 31 differentially expressed proteins, including 10 upregulated proteins (>1.50-fold, P<0.05) and 21 downregulated proteins (<0.67-fold, P<0.05), were identified in mature vs. immature oocytes by iTRAQ labelling coupled with 2D LC-MS/MS. GO analysis revealed that 'cell population proliferation' was the most diverse enrichment trend between up/downregulated proteins, while phagosome process and the PI3K-Akt signaling pathway were the two most significant pathways revealed by KEGG enrichment classification. Human prostatic acid phosphatase (PAP, ACPP) and CD5 antigen-like (CD5L) were two proteins verified by ELISA to be differentially expressed between MII and Gv oocytes (P<0.0001 and P<0.0001, respectively). Further measurement found significantly lower level of ACPP in follicular fluids and cumulus cells of DOR patients (P=0.028 and P=0.004, respectively), as an indicator of oocyte quality. Otherwise, CD5L level is upregulated in follicular fluid of DOR patients (P<0.0001). Our study provided experimental data to establish the objective indicator of oocyte maturation in the microenvironment of ovarian follicles, and also provided new insight into the measurement of oocyte quality.

DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics

The identification of major histocompatibility complex (MHC)-binding peptides in mass spectrometry (MS)-based immunopeptideomics relies largely on database search engines developed for proteomics data analysis. However, because immunopeptidomics experiments do not involve enzymatic digestion at specific residues, an inflated search space leads to a high false positive rate and low sensitivity in peptide identification. In order to improve the sensitivity and reliability of peptide identification, a post-processing tool named DeepRescore is developed. DeepRescore combines peptide features derived from deep learning predictions, namely accurate retention timeand MS/MS spectra predictions, with previously used features to rescore peptide-spectrum matches. Using two public immunopeptidomics datasets, it is shown that rescoring by DeepRescore increases both the sensitivity and reliability of MHC-binding peptide and neoantigen identifications compared to existing methods. It is also shown that the performance improvement is, to a large extent, driven by the deep learning-derived features. DeepRescore is developed using NextFlow and Docker and is available at https://github.com/bzhanglab/DeepRescore.

Construction of a Conditionally Asporogenous Bacillus thuringiensis Recombinant Strain Overproducing Cry Protein by Deletion of the leuB Gene

One of the common shortcomings with Bacillus thuringiensis (Bt) biopesticides in field application is their instability under UV irradiation. In Bt, the leuB gene encodes the 3-isopropylmalate dehydrogenase. In addition to its role in leucine biosynthesis, LeuB would be likely recruited to catalyze the dehydrogenation of malate in the final step of tricarboxylic acid cycle during sporulation. In this study, we constructed a Bt recombinant strain in which the gene leuB was deleted by using the markerless gene deletion system. The ΔleuB mutant strain showed a conditionally asporogenous phenotype while overproducing insecticidal crystal proteins and retaining its insecticidal activity well in both fermentation and LB media. Furthermore, the metabolic regulation mechanisms of LeuB was elucidated by iTRAQ-based quantitative proteomics approach. Evidences from proteomics data suggested that the inhibited supply of pyruvate (carbon source) was an important factor related to the conditionally asporogenous feature of the mutant. Consistently, the mutant regained its ability to sporulate in LB medium by adding 1% glucose or 1% sodium pyruvate. Taken together, our study demonstrated that deletion of the leuB gene resulted in delayed or completely blocked mother cell lysis, allowing the crystals encapsulated within cells, which makes this recombinant strain a good candidate for developing Bt preparations with better UV-stability.

Integrated Analysis of Comparative Lipidomics and Proteomics Reveals the Dynamic Changes of Lipid Molecular Species in High-Oleic Acid Peanut Seed

Modern peanut contains fatty acid desaturase 2 (FAD2) mutation, which is capable of producing high oleic acid for human health. However, the dynamic changes of the lipidome regarding fad2 remain elusive in peanut seed. In the present study, 547 lipid features were identified in high- and normal-oleic peanut seeds by utilizing the mass spectrometric approach. The fad2-induced differently expressed lipids (DELs) were polarly distributed at early and maturation stages during high-oleic acid (OA) seed development. Subsequently, integration of previously published proteomic data and lipidomic data revealed that 21 proteins and 149 DELs were annotated into the triacylglycerol assembly map, of which nine enzymes and 31 lipid species shared similar variation tendencies. Additionally, the variation tendencies of 17 acyl fatty acids were described in a hypothetical biosynthetic pathway. Collectively, the understanding of the lipid composition correlated with fad2 established a foundation for future high-OA peanut breeding based on lipidomic data.

Speeding Up Percolator

The processing of peptide tandem mass spectrometry data involves matching observed spectra against a sequence database. The ranking and calibration of these peptide-spectrum matches can be improved substantially using a machine learning postprocessor. Here, we describe our efforts to speed up one widely used postprocessor, Percolator. The improved software is dramatically faster than the previous version of Percolator, even when using relatively few processors. We tested the new version of Percolator on a data set containing over 215 million spectra and recorded an overall reduction to 23% of the running time as compared to the unoptimized code. We also show that the memory footprint required by these speedups is modest relative to that of the original version of Percolator.

Proteomic characterization of the hepatopancreas in the Pacific white shrimp Litopenaeus vannamei under cold stress: Revealing the organism homeostasis mechanism

To understand the homeostasis mechanism of crustacean hepatopancreas to cold stress, iTRAQ proteomics based on the genome database of Litopenaeus vannamei (L. vannamei) was applied to investigate proteins changes and variety of the hepatopancreas during cold stress stage in this study. A total of 4062 distinct proteins were identified, 137 differentially expressed proteins (DEPs) including 62 differentially up-regulated proteins (DUPs) and 75 differentially down-regulated proteins (DDPs) were identified in G1 (18 °C) compared with CK (28 °C), 359 DEPs including 131 DUPs and 228 DDPs were identified in G2 (13 °C for 24 h) compared with CK. Based on bioinformatics analysis, the cold tolerance of L. vannamei might be related to energy metabolism such as amino acid, carbohydrate, lipid, and oxidative phosphorylation. Moreover, shrimp immunity was declined during cold stress stage. However, L. vannamei could cope with cold stress by enhancing the production of ATP and UFA. Notably, arginine kinase, heat shock proteins, and histones may act as positive regulators in L. vannamei under cold stress. Ten randomly selected proteins were used for validation using qRT-PCR and the expressions on the transcription level for most of the genes were similar to the results of iTRAQ. These results indicated that L. vannamei can maintain the organism homeostasis by a series of orderly regulatory process during cold stress. Furthermore, the results can provide guidance for shrimp farming.

Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities

We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.

Rapid evolution of protein diversity by de novo origination in Oryza

New protein-coding genes that arise de novo from non-coding DNA sequences contribute to protein diversity. However, de novo gene origination is challenging to study as it requires high-quality reference genomes for closely related species, evidence for ancestral non-coding sequences, and transcription and translation of the new genes. High-quality genomes of 13 closely related Oryza species provide unprecedented opportunities to understand de novo origination events. Here, we identify a large number of young de novo genes with discernible recent ancestral non-coding sequences and evidence of translation. Using pipelines examining the synteny relationship between genomes and reciprocal-best whole-genome alignments, we detected at least 175 de novo open reading frames in the focal species O. sativa subspecies japonica, which were all detected in RNA sequencing-based transcriptomes. Mass spectrometry-based targeted proteomics and ribosomal profiling show translational evidence for 57% of the de novo genes. In recent divergence of Oryza, an average of 51.5 de novo genes per million years were generated and retained. We observed evolutionary patterns in which excess indels and early transcription were favoured in origination with a stepwise formation of gene structure. These data reveal that de novo genes contribute to the rapid evolution of protein diversity under positive selection.

PepQuery enables fast, accurate, and convenient proteomic validation of novel genomic alterations

Massively parallel or second-generation sequencing-based genomic studies continuously identify new genomic alterations that may lead to novel protein sequences, which are attractive candidates for disease biomarkers and therapeutic targets after proteomic validation. Integrative proteogenomic methods have been developed to use mass spectrometry (MS)-based proteomics data for such validation. These methods replace the reference sequence database in proteomic database searching with a customized protein database that incorporates sample- or disease-specific sequences derived from DNA or RNA sequencing, thus enabling the identification of novel protein sequences. Although useful, this spectrum-centric approach requires a full evaluation of all possible spectrum-peptide pairs, which is time-consuming, error-prone, and difficult to apply. Here, we present PepQuery, a peptide-centric approach that focuses on only novel DNA or protein sequences of interest. PepQuery allows quick and easy proteomic validation of genomic alterations without customized database construction. We demonstrated the sensitivity and specificity of the approach in validating completely novel proteins, novel splice junctions, and single amino acid variants using simulations and experimental data. Notably, enabling unrestricted modification searching in PepQuery reduced false positives by up to 95%. We implemented PepQuery as both web-based and stand-alone applications. The web version provides direct access to more than half a billion MS/MS spectra from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and other cancer proteomic studies. The stand-alone version supports batch analysis and user-provided MS/MS data. PepQuery will increase the usage of proteogenomics beyond the proteomics community and will broaden the application of proteogenomics in personalized medicine.

PPIP: Automated Software for Identification of Bioactive Endogenous Peptides

Endogenous peptides play an important role in multiple biological processes in many species. Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is an important technique for detecting these peptides on a large scale. We present PPIP, which is a dedicated peptidogenomics software for identifying endogenous peptides based on peptidomics and RNA-Seq data. This software automates the de novo transcript assembly based on RNA-Seq data, construction of a protein reference database based on the de novo assembled transcripts, peptide identification, function analysis, and HTML-based report generation. Different function components are integrated using Docker technology. The Docker image of PPIP is available at https://hub.docker.com/r/shawndp/ppip , and the source code under GPL-3 license is available at https://github.com/Shawn-Xu/PPIP . A user manual of PPIP is available at https://shawn-xu.github.io/PPIP .

Quantitative Proteomic Analysis of Castor (Ricinus communis L.) Seeds During Early Imbibition Provided Novel Insights into Cold Stress Response

Early planting is one of the strategies used to increase grain yield in temperate regions. However, poor cold tolerance in castor inhibits seed germination, resulting in lower seedling emergence and biomass. Here, the elite castor variety Tongbi 5 was used to identify the differential abundance protein species (DAPS) between cold stress (4 °C) and control conditions (30 °C) imbibed seeds. As a result, 127 DAPS were identified according to isobaric tag for relative and absolute quantification (iTRAQ) strategy. These DAPS were mainly involved in carbohydrate and energy metabolism, translation and posttranslational modification, stress response, lipid transport and metabolism, and signal transduction. Enzyme-linked immunosorbent assays (ELISA) demonstrated that the quantitative proteomics data collected here were reliable. This study provided some invaluable insights into the cold stress responses of early imbibed castor seeds: (1) up-accumulation of all DAPS involved in translation might confer cold tolerance by promoting protein synthesis; (2) stress-related proteins probably protect the cell against damage caused by cold stress; (3) up-accumulation of key DAPS associated with fatty acid biosynthesis might facilitate resistance or adaptation of imbibed castor seeds to cold stress by the increased content of unsaturated fatty acid (UFA). The data has been deposited to the ProteomeXchange with identifier PXD010043.

Improvements to the Rice Genome Annotation Through Large-Scale Analysis of RNA-Seq and Proteomics Data Sets

Rice (Oryza sativa) is one of the most important worldwide crops. The genome has been available for over 10 years and has undergone several rounds of annotation. We created a comprehensive database of transcripts from 29 public RNA sequencing data sets, officially predicted genes from Ensembl plants, and common contaminants in which to search for protein-level evidence. We re-analyzed nine publicly accessible rice proteomics data sets. In total, we identified 420K peptide spectrum matches from 47K peptides and 8,187 protein groups. 4168 peptides were initially classed as putative novel peptides (not matching official genes). Following a strict filtration scheme to rule out other possible explanations, we discovered 1,584 high confidence novel peptides. The novel peptides were clustered into 692 genomic loci where our results suggest annotation improvements. 80% of the novel peptides had an ortholog match in the curated protein sequence set from at least one other plant species. For the peptides clustering in intergenic regions (and thus potentially new genes), 101 loci were identified, for which 43 had a high-confidence hit for a protein domain. Our results can be displayed as tracks on the Ensembl genome or other browsers supporting Track Hubs, to support re-annotation of the rice genome.

The Chinese Medicinal Formulation Guzhi Zengsheng Zhitongwan Modulates Chondrocyte Structure, Dynamics, and Metabolism by Controlling Multiple Functional Proteins

Traditional Chinese medicine is one of the oldest medical systems in the world and has its unique principles and theories in the prevention and treatment of human diseases, which are achieved through the interactions of different types of materia medica in the form of Chinese medicinal formulations. GZZSZTW, a classical and effective Chinese medicinal formulation, was designed and created by professor Bailing Liu who is the only national medical master professor in the clinical research field of traditional Chinese medicine and skeletal diseases. GZZSZTW has been widely used in clinical settings for several decades for the treatment of joint diseases. However, the underlying molecular mechanisms are still largely unknown. In the present study, we performed quantitative proteomic analysis to investigate the effects of GZZSZTW on mouse primary chondrocytes using state-of-the-art iTRAQ technology. We demonstrated that the Chinese medicinal formulation GZZSZTW modulates chondrocyte structure, dynamics, and metabolism by controlling multiple functional proteins that are involved in the cellular processes of DNA replication and transcription, protein synthesis and degradation, cytoskeleton dynamics, and signal transduction. Thus, this study has expanded the current knowledge of the molecular mechanism of GZZSZTW treatment on chondrocytes. It has also shed new light on possible strategies to further prevent and treat cartilage-related diseases using traditional Chinese medicinal formulations.

Reagents for Isobaric Labeling Peptides in Quantitative Proteomics

Currently, the commercial reagents for isobaric peptides labeling (TMT and iTRAQ) have some drawbacks, such as high cost in experiments, especially in quantitation for the modified peptides, and inconvenient handling for variable sizes of samples. Herein, we developed a set of 10-plex isobaric tags (IBT) with high stability and low cost. The labeled peptides were sensitively detected on Orbitrap Q Exactive MS with an MS2 resolution of 35 000 at 30% NCE, while the peptides were efficiently labeled over 97% by IBT at a ratio of 10:1 of reagent/peptide (w/w) in 200 mM TEAB buffer for 2 h. The IBT labeling was demonstrated with a wide dynamic range of 50-fold without obvious matrix effects on quantification. Importantly, there was little quantification bias found among the individual IBT tags, indicating that the peptides labeled by different tags were quantitatively comparable. The IBT 10-plex reagents were applied for dynamically monitoring the quantitative responses of phosphoproteome stimulated by EGF treatment in HeLa cells. In total, 5 361 unique phosphopeptides were identified, which reached a similar conclusion as others reported. The IBT reagents were therefore experimentally proven as a new type of reagents for isobaric peptides labeling and useful in a large quantity peptides of quantitative proteomics.

Integrated Proteomic and Transcriptomic Analysis Reveals Long Noncoding RNA HOX Transcript Antisense Intergenic RNA (HOTAIR) Promotes Hepatocellular Carcinoma Cell Proliferation by Regulating Opioid Growth Factor Receptor (OGFr)

Long noncoding RNA HOX transcript antisense RNA (HOTAIR) is involved in human tumorigenesis and is dysregulated in hepatocellular carcinoma (HCC). However, the molecular mechanisms underlying HOTAIR functions in HCC are largely unknown. Here, we employed an integrated transcriptomic and quantitative proteomic analysis to systematically explore the regulatory role of HOTAIR in HCC. A total of 673 transcripts and 293 proteins were found to be dysregulated after HOTAIR inhibition. Bioinformatics studies indicated that differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) are involved in many biological processes, especially cancer-related signaling pathways. A set of DEGs and DEPs were validated by quantitative RT-PCR, Western blot and parallel reaction monitoring (PRM) analysis, respectively. Further functional studies of the opioid growth factor receptor (OGFr), a negative biological regulator of cell proliferation in HCC, revealed that HOTAIR exerts its effects on cell proliferation, at least in part, through the regulation of OGFr expression. By correlating the omics data with functional studies, the current results provide novel insights into the functional mechanisms of HOTAIR in HCC cells.

Proteomic Cinderella: Customized analysis of bulky MS/MS data in one night

Proteomic challenges, stirred up by the advent of high-throughput technologies, produce large amount of MS data. Nowadays, the routine manual search does not satisfy the "speed" of modern science any longer. In our work, the necessity of single-thread analysis of bulky data emerged during interpretation of HepG2 proteome profiling results for proteoforms searching. We compared the contribution of each of the eight search engines (X!Tandem, MS-GF[Formula: see text], MS Amanda, MyriMatch, Comet, Tide, Andromeda, and OMSSA) integrated in an open-source graphical user interface SearchGUI ( http://searchgui.googlecode.com ) into total result of proteoforms identification and optimized set of engines working simultaneously. We also compared the results of our search combination with Mascot results using protein kit UPS2, containing 48 human proteins. We selected combination of X!Tandem, MS-GF[Formula: see text] and OMMSA as the most time-efficient and productive combination of search. We added homemade java-script to automatize pipeline from file picking to report generation. These settings resulted in rise of the efficiency of our customized pipeline unobtainable by manual scouting: the analysis of 192 files searched against human proteome (42153 entries) downloaded from UniProt took 11[Formula: see text]h.

Improvement of peptide identification with considering the abundance of mRNA and peptide

BACKGROUND

Tandem mass spectrometry (MS/MS) followed by database search is a main approach to identify peptides/proteins in proteomic studies. A lot of effort has been devoted to improve the identification accuracy and sensitivity for peptides/proteins, such as developing advanced algorithms and expanding protein databases.

RESULTS

Herein, we described a new strategy for enhancing the sensitivity of protein/peptide identification through combination of mRNA and peptide abundance in Percolator. In our strategy, a new workflow for peptide identification is established on the basis of the abundance of transcripts and potential novel transcripts derived from RNA-Seq and abundance of peptides towards the same life species. We demonstrate the utility of this strategy by two MS/MS datasets and the results indicate that about 5% ~ 8% improvement of peptide identification can be achieved with 1% FDR in peptide level by integrating the peptide abundance, the transcript abundance and potential novel transcripts from RNA-Seq data. Meanwhile, 181 and 154 novel peptides were identified in the two datasets, respectively.

CONCLUSIONS

We have demonstrated that this strategy could enable improvement of peptide/protein identification and discovery of novel peptides, as compared with the traditional search methods.

PGA: an R/Bioconductor package for identification of novel peptides using a customized database derived from RNA-Seq

Background

Peptide identification based upon mass spectrometry (MS) is generally achieved by comparison of the experimental mass spectra with the theoretically digested peptides derived from a reference protein database. Obviously, this strategy could not identify peptide and protein sequences that are absent from a reference database. A customized protein database on the basis of RNA-Seq data is thus proposed to assist with and improve the identification of novel peptides. Correspondingly, development of a comprehensive pipeline, which provides an end-to-end solution for novel peptide detection with the customized protein database, is necessary.

Results

A pipeline with an R package, assigned as a PGA utility, was developed that enables automated treatment to the tandem mass spectrometry (MS/MS) data acquired from different MS platforms and construction of customized protein databases based on RNA-Seq data with or without a reference genome guide. Hence, PGA can identify novel peptides and generate an HTML-based report with a visualized interface. On the basis of a published dataset, PGA was employed to identify peptides, resulting in 636 novel peptides, including 510 single amino acid polymorphism (SAP) peptides, 2 INDEL peptides, 49 splice junction peptides, and 75 novel transcript-derived peptides. The software is freely available from http://bioconductor.org/packages/PGA/, and the example reports are available at http://wenbostar.github.io/PGA/.

Conclusions

The pipeline of PGA, aimed at being platform-independent and easy-to-use, was successfully developed and shown to be capable of identifying novel peptides by searching the customized protein database derived from RNA-Seq data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1133-3) contains supplementary material, which is available to authorized users.

A year (2014-2015) of plants in Proteomics journal. Progress in wet and dry methodologies, moving from protein catalogs, and the view of classic plant biochemists

The present review is an update of the previous one published in Proteomics 2015 Reviews special issue [Jorrin-Novo, J. V. et al., Proteomics 2015, 15, 1089-1112] covering the July 2014-2015 period. It has been written on the bases of the publications that appeared in Proteomics journal during that period and the most relevant ones that have been published in other high-impact journals. Methodological advances and the contribution of the field to the knowledge of plant biology processes and its translation to agroforestry and environmental sectors will be discussed. This review has been organized in four blocks, with a starting general introduction (literature survey) followed by sections focusing on the methodology (in vitro, in vivo, wet, and dry), proteomics integration with other approaches (systems biology and proteogenomics), biological information, and knowledge (cell communication, receptors, and signaling), ending with a brief mention of some other biological and translational topics to which proteomics has made some contribution.

Ursgal, Universal Python Module Combining Common Bottom-Up Proteomics Tools for Large-Scale Analysis

Proteomics data integration has become a broad field with a variety of programs offering innovative algorithms to analyze increasing amounts of data. Unfortunately, this software diversity leads to many problems as soon as the data is analyzed using more than one algorithm for the same task. Although it was shown that the combination of multiple peptide identification algorithms yields more robust results, it is only recently that unified approaches are emerging; however, workflows that, for example, aim to optimize search parameters or that employ cascaded style searches can only be made accessible if data analysis becomes not only unified but also and most importantly scriptable. Here we introduce Ursgal, a Python interface to many commonly used bottom-up proteomics tools and to additional auxiliary programs. Complex workflows can thus be composed using the Python scripting language using a few lines of code. Ursgal is easily extensible, and we have made several database search engines (X!Tandem, OMSSA, MS-GF+, Myrimatch, MS Amanda), statistical postprocessing algorithms (qvality, Percolator), and one algorithm that combines statistically postprocessed outputs from multiple search engines ("combined FDR") accessible as an interface in Python. Furthermore, we have implemented a new algorithm ("combined PEP") that combines multiple search engines employing elements of "combined FDR", PeptideShaker, and Bayes' theorem.