- N-terminal proteomics assisted profiling of the unexplored translation initiation landscape in Arabidopsis thaliana. [Journal Article]
- MCMol Cell Proteomics 2017 Apr 21
- Proteogenomics is an emerging research field yet lacking a uniform method of analysis. Proteogenomic studies in which N-terminal proteomics and ribosome profiling are combined, suggest that a high nu...
Proteogenomics is an emerging research field yet lacking a uniform method of analysis. Proteogenomic studies in which N-terminal proteomics and ribosome profiling are combined, suggest that a high number of protein start sites are currently missing in genome annotations. We constructed a proteogenomic pipeline specific for the analysis of N-terminal proteomics data, with the aim of discovering novel translational start sites outside annotated protein coding regions. In summary, unidentified MS/MS spectra were matched to a specific N-terminal peptide library encompassing protein N-termini encoded in the Arabidopsis thaliana genome. After a stringent false discovery rate filtering, 157 protein N-termini compliant with N-terminal methionine excision specificity and indicative of translation initiation were found. These include N-terminal protein extensions and translation from transposable elements and pseudogenes. Gene prediction provided supporting protein-coding models for approximately half of the protein N-termini. Besides the prediction of functional domains (partially) contained within the newly predicted ORFs, further supporting evidence of translation was found in the recently released Araport11 genome re-annotation of Arabidopsis and computational translations of sequences stored in public repositories. Most interestingly, complementary evidence by ribosome profiling was found for 23 protein N-termini. Finally, by analyzing protein N-terminal peptides, an in silico analysis demonstrates the applicability of our N-terminal proteogenomics strategy in revealing protein-coding potential in species with well- and poorly-annotated genomes.
- Heralds of parallel MS: Data-independent acquisition surpassing sequential identification of data dependent acquisition in proteomics. [Journal Article]
- MCMol Cell Proteomics 2017 Apr 20
- Comprehensive, reproducible and precise analysis of large sample cohorts is one of the key objectives of quantitative proteomics. Here, we present an implementation of data-independent acquisition us...
Comprehensive, reproducible and precise analysis of large sample cohorts is one of the key objectives of quantitative proteomics. Here, we present an implementation of data-independent acquisition using its parallel acquisition nature that surpasses the serial limitation of data-dependent acquisition on a quadrupole ultra-high field Orbitrap mass spectrometer. In deep single shot data-independent acquisition, we quantified over 7,500 proteins of human cell lines and 9,000 proteins of mouse tissues, with fewer than 2.5% missing proteins and median coefficients of variation of down to 5% among replicates. In very complex mixtures, we could quantify more than 15,000 proteins, outperforming data-dependent acquisition methods by more than two-fold. Using this method, we investigated large-protein networks before and after the critical period for whisker experience-induced synaptic strength in the murine somatosensory cortex 1 barrel field. This work shows that parallel mass spectrometry enables proteome profiling for discovery with high coverage, reproducibility, precision and scalability.
- TCUP: Typing and characterization of bacteria using bottom-up tandem mass spectrometry proteomics. [Journal Article]
- MCMol Cell Proteomics 2017 Apr 18
- Methods for rapid and reliable microbial identification are essential in modern healthcare. The ability to detect and correctly identify pathogenic species and their resistance phenotype is necessary...
Methods for rapid and reliable microbial identification are essential in modern healthcare. The ability to detect and correctly identify pathogenic species and their resistance phenotype is necessary for accurate diagnosis and efficient treatment of infectious diseases. Bottom-up tandem mass spectrometry (MS) proteomics enables rapid characterization of large parts of the expressed genes of microorganisms. However, the generated data is highly fragmented, making down-stream analyses complex. Here we present TCUP, a new computational method for typing and characterizing bacteria using proteomics data from bottom-up tandem MS. TCUP compares the generated protein sequence data to reference databases and automatically finds peptides suitable for characterization of taxonomic composition and identification of expressed antimicrobial resistance genes. TCUP was evaluated using several clinically relevant bacterial species (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus pneumoniae, Moraxella catarrhalis, and Haemophilus influenzae), using both simulated data generated by in silico peptide digestion and experimental proteomics data generated by liquid chromatography-tandem mass spectrometry (MS/MS). The results showed that TCUP performs correct peptide classifications at rates between 90.3% and 98.5% at the species level. The method was also able to estimate the relative abundances of individual species in mixed cultures. Furthermore, TCUP could identify expressed beta-lactamases in an extended spectrum beta-lactamase-producing (ESBL) E. coli strain, even when the strain was cultivated in the absence of antibiotics. Finally, TCUP is computationally efficient, easy to integrate in existing bioinformatics workflows and freely available under an open source license for both Windows and Linux environments.
- Quantitative mass spectrometry analysis of PD-L1 protein expression, N-glycosylation and expression stoichiometry with PD-1 and PD-L2 in human melanoma. [Journal Article]
- MCMol Cell Proteomics 2017 Apr 17
- Quantitative assessment of key proteins that control the tumor-immune interface is one of the most formidable analytical challenges in immunotherapeutics. We developed a targeted mass spectrometry (M...
Quantitative assessment of key proteins that control the tumor-immune interface is one of the most formidable analytical challenges in immunotherapeutics. We developed a targeted mass spectrometry (MS) platform to quantify programmed cell death-1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and programmed cell death 1 ligand 2 (PD-L2) at fmol/microgram protein levels in formalin fixed, paraffin-embedded sections from 22 human melanomas. PD-L1 abundance ranged 50-fold, from approximately 0.03 to 1.5 fmol/microgram protein and the PRM data were largely concordant with total PD-L1-positive cell content, as analyzed by immunohistochemistry (IHC) with the E1L3N antibody. PD-1 was measured at levels up to 20-fold lower than PD-L1, but the abundances were not significantly correlated (r(2) = 0.062, p = 0.264). PD-1 abundance was weakly correlated (r(2) = 0.3057, p = 0.009) with the fraction of lymphocytes and histiocytes in sections. PD-L2 was measured from 0.03 to 1.90 fmol/microgram protein and the ratio of PD-L2 to PD-L1 abundance ranged from 0.03 to 2.58. In 10 samples, PD-L2 was present at more than half the level of PD-L1, which suggests that PD-L2, a higher affinity PD-1 ligand, is sufficiently abundant to contribute to T-cell downregulation. We also identified five branched mannose and N-acetylglucosamine glycans at PD-L1 position N192 in all 22 samples. Extent of PD-L1 glycan modification varied by approximately 10-fold and the melanoma with the highest PD-L1 protein abundance and most abundant glycan modification yielded a very low PD-L1 IHC estimate, thus suggesting that N-glycosylation may affect IHC measurement and PD-L1 function. Additional PRM analyses quantified immune checkpoint/co-regulator proteins LAG3, IDO1, TIM-3, VISTA and CD40, which all displayed distinct expression independent of PD-1, PD-L1 and PD-L2. Targeted MS can provide a next-generation analysis platform to advance cancer immuno-therapeutic research and diagnostics.
- The GDNF-responsive Phosphoprotein Landscape Identifies Raptor Phosphorylation Required for Spermatogonial Progenitor Cell Proliferation. [Journal Article]
- MCMol Cell Proteomics 2017 Apr 13
- Cytokine-dependent renewal of stem cells is a fundamental requisite for tissue homeostasis and regeneration. Spermatogonial progenitor cells (SPCs) including stem cells support life-long spermatogene...
Cytokine-dependent renewal of stem cells is a fundamental requisite for tissue homeostasis and regeneration. Spermatogonial progenitor cells (SPCs) including stem cells support life-long spermatogenesis and male fertility, but pivotal phosphorylation events that regulate fate decisions in SPCs remain unresolved. Here, we described a quantitative mass-spectrometry-based proteomic and phosphoproteomic analyses of SPCs following sustained stimulation with glial cell-derived neurotrophic factor (GDNF), an extrinsic factor supporting SPC proliferation. Stimulated SPCs contained 3382 identified phosphorylated proteins and 12141 phosphorylation sites. Of them, 325 differentially phosphorylated proteins and 570 phosphorylation sites triggered by GDNF were highly enriched for ERK1/2, GSK3, CDK1, and CDK5 phosphorylating motifs. We validated that inhibition of GDNF/ERK1/2-signalling impaired SPC proliferation and increased G2/M cell cycle arrest. Significantly, we found that proliferation of SPCs requires phosphorylation of the mTORC1 component Raptor at Ser863. Tissue-specific deletion of Raptor in mouse germline cells results in impaired spermatogenesis and progressive loss of spermatogonia, but in vitro increased phosphorylation of Raptor by raptor over-expression in SPCs induced a more rapidly growth of SPCs in culture. These findings implicate previously undescribed signalling networks in governing fate decision of SPCs, which is essential for the understanding of spermatogenesis and of potential consequences of pathogenic insult for male infertility.
- Dynamics of the interaction between cotton bollworm Helicoverpa armigera and nucleopolyhedrovirus as revealed by integrated transcriptomic and proteomic analyses. [Journal Article]
- MCMol Cell Proteomics 2017 Apr 12
- Over the past decades, Helicoverpa armigera nucleopolyhedrovirus (HearNPV) has been widely used for biocontrol of cotton bollworm, which is one of the most destructive pest insects in agriculture wor...
Over the past decades, Helicoverpa armigera nucleopolyhedrovirus (HearNPV) has been widely used for biocontrol of cotton bollworm, which is one of the most destructive pest insects in agriculture worldwide. However, the molecular mechanism underlying the interaction between HearNPV and host insects remains poorly understood. In this study, high throughput RNA-sequencing was integrated with label-free quantitative proteomics analysis to examine the dynamics of gene expression in the fat body of H. armigera larvae in response to challenge with HearNPV. RNA-sequencing-based transcriptomic analysis indicated that host gene expression was substantially altered, yielding 3,850 differentially expressed genes (DEGs), while no global transcriptional shut-off effects were observed in the fat body. Among the DEGs, 60 immunity-related genes were down-regulated after baculovirus infection, a finding that was consistent with the results of quantitative real-time RT-PCR (qRT-PCR). Gene ontology and functional classification demonstrated that the majority of down-regulated genes were enriched in gene cohorts involved in energy, carbohydrate, and amino acid metabolic pathways. Proteomics analysis identified differentially expressed proteins in the fat body, among which 76 were up-regulated, whereas 373 were significantly down-regulated upon infection. The down-regulated proteins are involved in metabolic pathways such as energy metabolism, carbohydrate metabolism (CM), and amino acid metabolism, in agreement with the RNA-seq data. Furthermore, correlation analysis suggested a strong association between the mRNA level and protein abundance in the H. armigera fat body. More importantly, the predicted gene interaction network indicated that a large subset of metabolic networks was significantly negatively regulated by viral infection, including CM-related enzymes such as aldolase, enolase, malate dehydrogenase, and triose-phosphate isomerase. Taken together, transcriptomic combined with proteomic data elucidated that baculovirus established systemic infection of host larvae, and manipulated the host mainly by suppressing the host immune response and down-regulating metabolism to allow viral self-replication and proliferation. Therefore, this study provided important insights into the mechanism of host-baculovirus interaction.
- Neprosin, a selective prolyl endoprotease for bottom-up proteomics and histone mapping. [Journal Article]
- MCMol Cell Proteomics 2017 Apr 12
- Trypsin dominates bottom-up proteomics, but there are reasons to consider alternative enzymes. Improving sequence coverage, exposing proteomic dark matter and clustering post-translational modificati...
Trypsin dominates bottom-up proteomics, but there are reasons to consider alternative enzymes. Improving sequence coverage, exposing proteomic dark matter and clustering post-translational modifications in different ways and with higher-order drive the pursuit of reagents complementary to trypsin. Additionally, enzymes that are easy to use and generate larger peptides that capitalize upon newer fragmentation technologies should have a place in proteomics. We expressed and characterized recombinant neprosin, a novel prolyl endoprotease of the DUF239 family, which preferentially cleaves C-terminal to proline residues under highly acidic conditions. Cleavage also occurs C-terminal to alanine with some frequency, but with an intriguingly high skipping rate. Digestion proceeds to a stable endpoint, resulting in an average peptide mass of 2521 u and a higher dependency upon ETD for peptide-spectrum matches. In contrast to most proline-cleaving enzymes, neprosin effectively degrades proteins of any size. For 1251 HeLa cell proteins identified in common using trypsin, Lys-C and neprosin, almost 50% of the neprosin sequence contribution is unique. The high average peptide mass coupled with cleavage at residues not usually modified provide new opportunities for profiling clusters of post-translational modifications. We show that neprosin is a useful reagent for reading epigenetic marks on histones. It generates peptide 1-38 of histone H3 and peptide 1-32 of histone H4 in a single digest, permitting the analysis of co-occurring post-translational modifications in these important N-terminal tails.
- A database-augmented, exosome-based mass spectrometry approach exemplarily identifies circulating claudin 3 as biomarker in prostate cancer. [Journal Article]
- MCMol Cell Proteomics 2017 Apr 09
- In prostate cancer and other malignancies sensitive and robust biomarkers are lacking or have relevant limitations. Prostate specific antigen (PSA), the only biomarker widely used in prostate cancer,...
In prostate cancer and other malignancies sensitive and robust biomarkers are lacking or have relevant limitations. Prostate specific antigen (PSA), the only biomarker widely used in prostate cancer, is suffering from low specificity. Exosomes offer new perspectives in the discovery of blood-based biomarkers. Here we present a proof-of principle study for a proteomics-based identification pipeline, implementing existing data sources, to exemplarily identify exosome-based biomarker candidates in prostate cancer. Exosomes from malignant PC3 and benign PNT1A cells and from FBS-containing medium were isolated using sequential ultracentrifugation. Exosome and control samples were analyzed on an LTQ-Orbitrap XL mass spectrometer. Proteomic data is available via ProteomeXchange with identifier PXD003651. We developed a scoring scheme to rank 64 proteins exclusively found in PC3 exosomes, integrating data from four public databases and published mass spectrometry datasets. Among the top candidates, we focused on the tight junction protein claudin 3. Retests under serum-free conditions using immunoblotting and immunogold labeling confirmed the presence of claudin 3 on PC3 exosomes. Claudin 3 levels were determined in the blood plasma of patients with localized (n=58; 42 with Gleason score 6-7, 16 with Gleason score ≥8) and metastatic prostate cancer (n=11) compared to patients with benign prostatic hyperplasia (n=15) and healthy individuals (n=15) using ELISA, without prior laborious exosome isolation. ANOVA showed different CLDN3 plasma levels in these groups (p=0.004). CLDN3 levels were higher in patients with Gleason ≥8 tumors compared to patients with benign prostatic hyperplasia (p=0.012) and Gleason 6-7 tumors (p=0.029). In patients with localized tumors CLDN3 levels predicted a Gleason score ≥ 8 (AUC=0.705; p=0.164) and did not correlate with serum PSA. By using the described workflow claudin 3 was identified and validated as a potential blood-based biomarker in prostate cancer. Furthermore this workflow could serve as a template to be used in other cancer entities.
- Development of large-scale cross-linking mass spectrometry. [Journal Article]
- MCMol Cell Proteomics 2017 Apr 07
- Cross-linking mass spectrometry (CLMS) provides distance constraints to study the structure of proteins, multiprotein complexes and protein-protein interactions which are critical for the understandi...
Cross-linking mass spectrometry (CLMS) provides distance constraints to study the structure of proteins, multiprotein complexes and protein-protein interactions which are critical for the understanding of protein function. CLMS is an attractive technology to bridge the gap between high-resolution structural biology techniques and proteomic-based interactome studies. However, as outlined in this review there are still several bottlenecks associated with CLMS which limit its application on a proteome-wide level. Specifically, there is an unmet need for comprehensive software that can reliably identify cross-linked peptides from large datasets. In this review we provide supporting information to reason that targeted proteomics of cross-links may provide the required sensitivity to reliably detect and quantify cross-linked peptides and that a reporter ion signature for cross-linked peptides may become a useful approach to increase confidence in the identification process of cross-linked peptides. In addition, the review summarizes the recent advances in CLMS workflows using the analysis of condensin complex in intact chromosomes as a model complex.
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- Protease-inhibitor interaction predictions: Lessons on the complexity of protein-protein interactions. [Journal Article]
- MCMol Cell Proteomics 2017 Apr 06
- Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited...
Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features. Thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of non-proteolytic and non-inhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task, and thereby highlight limitations of computational interaction prediction methods.