Fast and accurate database searches with MS-GF+Percolator

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

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.

Delineating functional and molecular impact of ex vivo sample handling in precision medicine

Consistent handling of samples is crucial for achieving reproducible molecular and functional testing results in translational research. Here, we used 229 acute myeloid leukemia (AML) patient samples to assess the impact of sample handling on high-throughput functional drug testing, mass spectrometry-based proteomics, and flow cytometry. Our data revealed novel and previously described changes in cell phenotype and drug response dependent on sample biobanking. Specifically, myeloid cells with a CD117 (c-KIT) positive phenotype decreased after biobanking, potentially distorting cell population representations and affecting drugs targeting these cells. Additionally, highly granular AML cell numbers decreased after freezing. Secondly, protein expression levels, as well as sensitivity to drugs targeting cell proliferation, metabolism, tyrosine kinases (e.g., JAK, KIT, FLT3), and BH3 mimetics were notably affected by biobanking. Moreover, drug response profiles of paired fresh and frozen samples showed that freezing samples can lead to systematic errors in drug sensitivity scores. While a high correlation between fresh and frozen for the entire drug library was observed, freezing cells had a considerable impact at an individual level, which could influence outcomes in translational studies. Our study highlights conditions where standardization is needed to improve reproducibility, and where validation of data generated from biobanked cohorts may be particularly important.

AttnPep: A Self-Attention-Based Deep Learning Method for Peptide Identification in Shotgun Proteomics

In shotgun proteomics, the proteome search engine analyzes mass spectra obtained by experiments, and then a peptide-spectra match (PSM) is reported for each spectrum. However, most of the PSMs identified are incorrect, and therefore various postprocessing software have been developed for reranking the peptide identifications. Yet these methods suffer from issues such as dependency on distribution, reliance on shallow models, and limited effectiveness. In this work, we propose AttnPep, a deep learning model for rescoring PSM scores that utilizes the Self-Attention module. This module helps the neural network focus on features relevant to the classification of PSMs and ignore irrelevant features. This allows AttnPep to analyze the output of different search engines and improve PSM discrimination accuracy. We considered a PSM to be correct if it achieves a q-value <0.01 and compared AttnPep with existing mainstream software PeptideProphet, Percolator, and proteoTorch. The results indicated that AttnPep found an average increase in correct PSMs of 9.29% relative to the other methods. Additionally, AttnPep was able to better distinguish between correct and incorrect PSMs and found more synthetic peptides in the complex SWATH data set.

Remodeling of the human skeletal muscle proteome found after long-term endurance training but not after strength training

Exercise training has tremendous systemic tissue-specific health benefits, but the molecular adaptations to long-term exercise training are not completely understood. We investigated the skeletal muscle proteome of highly endurance-trained, strength-trained, and untrained individuals and performed exercise- and sex-specific analyses. Of the 6,000+ proteins identified, >650 were differentially expressed in endurance-trained individuals compared with controls. Strikingly, 92% of the shared proteins with higher expression in both the male and female endurance groups were known mitochondrial. In contrast to the findings in endurance-trained individuals, minimal differences were found in strength-trained individuals and between females and males. Lastly, a co-expression network and comparative literature analysis revealed key proteins and pathways related to the health benefits of exercise, which were primarily related to differences in mitochondrial proteins. This network is available as an interactive database resource where investigators can correlate clinical data with global gene and protein expression data for hypothesis generation.

Small proteins in bacteria - Big challenges in prediction and identification

Proteins with up to 100 amino acids have been largely overlooked due to the challenges associated with predicting and identifying them using traditional methods. Recent advances in bioinformatics and machine learning, DNA sequencing, RNA and Ribo-seq technologies, and mass spectrometry (MS) have greatly facilitated the detection and characterisation of these elusive proteins in recent years. This has revealed their crucial role in various cellular processes including regulation, signalling and transport, as toxins and as folding helpers for protein complexes. Consequently, the systematic identification and characterisation of these proteins in bacteria have emerged as a prominent field of interest within the microbial research community. This review provides an overview of different strategies for predicting and identifying these proteins on a large scale, leveraging the power of these advanced technologies. Furthermore, the review offers insights into the future developments that may be expected in this field.

Comprehensive proteomics and meta-analysis of COVID-19 host response

COVID-19 is characterised by systemic immunological perturbations in the human body, which can lead to multi-organ damage. Many of these processes are considered to be mediated by the blood. Therefore, to better understand the systemic host response to SARS-CoV-2 infection, we performed systematic analyses of the circulating, soluble proteins in the blood through global proteomics by mass-spectrometry (MS) proteomics. Here, we show that a large part of the soluble blood proteome is altered in COVID-19, among them elevated levels of interferon-induced and proteasomal proteins. Some proteins that have alternating levels in human cells after a SARS-CoV-2 infection in vitro and in different organs of COVID-19 patients are deregulated in the blood, suggesting shared infection-related changes.The availability of different public proteomic resources on soluble blood proteome alterations leaves uncertainty about the change of a given protein during COVID-19. Hence, we performed a systematic review and meta-analysis of MS global proteomics studies of soluble blood proteomes, including up to 1706 individuals (1039 COVID-19 patients), to provide concluding estimates for the alteration of 1517 soluble blood proteins in COVID-19. Finally, based on the meta-analysis we developed CoViMAPP, an open-access resource for effect sizes of alterations and diagnostic potential of soluble blood proteins in COVID-19, which is publicly available for the research, clinical, and academic community.

Proteomic analysis reveals microvesicles containing NAMPT as mediators of radioresistance in glioma

Tumor-initiating cells contained within the aggressive brain tumor glioma (glioma stem cells, GSCs) promote radioresistance and disease recurrence. However, mechanisms of resistance are not well understood. Herein, we show that the proteome-level regulation occurring upon radiation treatment of several patient-derived GSC lines predicts their resistance status, whereas glioma transcriptional subtypes do not. We identify a mechanism of radioresistance mediated by the transfer of the metabolic enzyme NAMPT to radiosensitive cells through microvesicles (NAMPT-high MVs) shed by resistant GSCs. NAMPT-high MVs rescue the proliferation of radiosensitive GSCs and fibroblasts upon irradiation, and upon treatment with a radiomimetic drug or low serum, and increase intracellular NAD(H) levels. Finally, we show that the presence of NAMPT within the MVs and its enzymatic activity in recipient cells are necessary to mediate these effects. Collectively, we demonstrate that the proteome of GSCs provides unique information as it predicts the ability of glioma to resist radiation treatment. Furthermore, we establish NAMPT transfer via MVs as a mechanism for rescuing the proliferation of radiosensitive cells upon irradiation.

Comparing plastid proteomes points towards a higher plastidial redox turnover in vascular tissues than in mesophyll cells

Plastids are complex organelles that vary in size and function depending on the cell type. Accordingly, they can be referred to as amyloplasts, chloroplasts, chromoplasts, etioplasts, proplasts to only cite a few denominations. Over the past decades, methods based on density gradients and differential centrifugations have been extensively used for the purification of plastids. However, these methods need large amounts of starting material, and hardly provide a tissue-specific resolution. Here, we applied our IPTACT (Isolation of Plastids TAgged in specific Cell Types) method, which involves the biotinylation of plastids in vivo using one-shot transgenic lines expressing the TOC64 gene coupled with a biotin ligase receptor particle and the BirA biotin ligase, to isolate plastids from mesophyll and companion cells of Arabidopsis thaliana using tissue specific pCAB3 and pSUC2 promoters, respectively. Subsequently, a proteome profiling was performed, and allowed the identification of 1672 proteins, among which 1342 were predicted plastidial, and 705 were fully confirmed according to SUBA5. Interestingly, although 92% of plastidial proteins were equally distributed between the two tissues, we observed an accumulation of proteins associated with jasmonic acid biosynthesis, plastoglobuli (e.g. NDC1, VTE1, PGL34, ABC1K1) and cyclic electron flow in plastids originating from vascular tissues. Besides demonstrating the technical feasibility of isolating plastids in a tissue-specific manner, our work provides strong evidence that plastids from vascular tissue have a higher redox turnover to ensure optimal functioning, notably under high solute strength as encountered in vascular cells.

Transcriptional drifts associated with environmental changes in endothelial cells

Environmental cues, such as physical forces and heterotypic cell interactions play a critical role in cell function, yet their collective contributions to transcriptional changes are unclear. Focusing on human endothelial cells, we performed broad individual sample analysis to identify transcriptional drifts associated with environmental changes that were independent of genetic background. Global gene expression profiling by RNA sequencing and protein expression by liquid chromatography-mass spectrometry directed proteomics distinguished endothelial cells in vivo from genetically matched culture (in vitro) samples. Over 43% of the transcriptome was significantly changed by the in vitro environment. Subjecting cultured cells to long-term shear stress significantly rescued the expression of approximately 17% of genes. Inclusion of heterotypic interactions by co-culture of endothelial cells with smooth muscle cells normalized approximately 9% of the original in vivo signature. We also identified novel flow dependent genes, as well as genes that necessitate heterotypic cell interactions to mimic the in vivo transcriptome. Our findings highlight specific genes and pathways that rely on contextual information for adequate expression from those that are agnostic of such environmental cues.

Deep thermal profiling for detection of functional proteoform groups

The complexity of the functional proteome extends considerably beyond the coding genome, resulting in millions of proteoforms. Investigation of proteoforms and their functional roles is important to understand cellular physiology and its deregulation in diseases but challenging to perform systematically. Here we applied thermal proteome profiling with deep peptide coverage to detect functional proteoform groups in acute lymphoblastic leukemia cell lines with different cytogenetic aberrations. We detected 15,846 proteoforms, capturing differently spliced, cleaved and post-translationally modified proteins expressed from 9,290 genes. We identified differential co-aggregation of proteoform pairs and established links to disease biology. Moreover, we systematically made use of measured biophysical proteoform states to find specific biomarkers of drug sensitivity. Our approach, thus, provides a powerful and unique tool for systematic detection and functional annotation of proteoform groups.

Semisupervised Machine Learning for Sensitive Open Modification Spectral Library Searching

A key analysis task in mass spectrometry proteomics is matching the acquired tandem mass spectra to their originating peptides by sequence database searching or spectral library searching. Machine learning is an increasingly popular postprocessing approach to maximize the number of confident spectrum identifications that can be obtained at a given false discovery rate threshold. Here, we have integrated semisupervised machine learning in the ANN-SoLo tool, an efficient spectral library search engine that is optimized for open modification searching to identify peptides with any type of post-translational modification. We show that machine learning rescoring boosts the number of spectra that can be identified for both standard searching and open searching, and we provide insights into relevant spectrum characteristics harnessed by the machine learning model. The semisupervised machine learning functionality has now been fully integrated into ANN-SoLo, which is available as open source under the permissive Apache 2.0 license on GitHub at https://github.com/bittremieux/ANN-SoLo.

Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly

Monoclonal antibodies are biotechnologically produced proteins with various applications in research, therapeutics and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody-antigen interactions and ensuring their affinity and specificity. De novo protein sequencing based on mass spectrometry is a valuable method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. In this study, we evaluated six recently developed de novo peptide sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, PointNovo and Casanovo), which were not specifically designed for antibody data. We validated their ability to identify and assemble antibody sequences on three multi-enzymatic data sets. The deep learning-based tools Casanovo and PointNovo showed an increased peptide recall across different enzymes and data sets compared with spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra and peptides of various lengths. We achieved a sequence coverage of 97.69-99.53% on the light chains of three different antibody data sets using the de Bruijn assembler ALPS and the predictions from Casanovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies and hybrid approaches such as homology search to achieve high accuracy on long protein sequences.

MS2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates

Immunopeptidomics aims to identify major histocompatibility complex (MHC)-presented peptides on almost all cells that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the nontryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS²PIP and retention time predictions by DeepLC have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS²PIP was tailored toward tryptic peptides, we have here retrained MS²PIP to include nontryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides but also yield further improvements for tryptic peptides. We show that the integration of new MS²PIP models, DeepLC, and Percolator in one software package, MS²Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS²Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Altogether, MS²Rescore thus allows substantially improved identification of novel epitopes from existing immunopeptidomics workflows.

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MS²Rescore significantly boosts immunopeptide identification rates

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Data-driven post-processing allows for a ten-fold increase in specificity

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MS²PIP and DeepLC predictors are integrated with Percolator post-processing

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MS²Rescore accepts identification results from MaxQuant, PEAKS, MS-GF+ and X!Tandem

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MS²Rescore shows great promise to extend current neo- and xeno-epitope landscapes

SubCellBarCode: integrated workflow for robust spatial proteomics by mass spectrometry

The molecular functions of a protein are defined by its inherent properties in relation to its environment and interaction network. Within a cell, this environment and network are defined by the subcellular location of the protein. Consequently, it is crucial to know the localization of a protein to fully understand its functions. Recently, we have developed a mass spectrometry- (MS) and bioinformatics-based pipeline to generate a proteome-wide resource for protein subcellular localization across multiple human cancer cell lines ( www.subcellbarcode.org ). Here, we present a detailed wet-lab protocol spanning from subcellular fractionation to MS-sample preparation and analysis. A key feature of this protocol is that it includes all generated cell fractions without discarding any material during the fractionation process. We also describe the subsequent quantitative MS-data analysis, machine learning-based classification, differential localization analysis and visualization of the output. For broad applicability, we evaluated the pipeline by using MS data generated by two different peptide pre-fractionation approaches, namely high-resolution isoelectric focusing and high-pH reverse-phase fractionation, as well as direct analysis without pre-fractionation by using long-gradient liquid chromatography-MS. Moreover, an R package covering the dry-lab part of the method was developed and made available through Bioconductor. The method is straightforward and robust, and the entire protocol, from cell harvest to classification output, can be performed within 1-2 weeks. The protocol enables accurate classification of proteins to 15 compartments and 4 neighborhoods, visualization of the output data and differential localization analysis including treatment-induced protein relocalization, condition-dependent localization or cell type-specific localization. The SubCellBarCode package is freely available at https://bioconductor.org/packages/devel/bioc/html/SubCellBarCode.html .

DeepSCP: utilizing deep learning to boost single-cell proteome coverage

Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target–decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.

Proteome Analysis of Pancreatic Tumors Implicates Extracellular Matrix in Patient Outcome

Pancreatic cancer remains a disease with unmet clinical needs and inadequate diagnostic, prognostic, and predictive biomarkers. In-depth characterization of the disease proteome is limited. This study thus aims to define and describe protein networks underlying pancreatic cancer and identify protein centric subtypes with clinical relevance. Mass spectrometry-based proteomics was used to identify and quantify the proteome in tumor tissue, tumor-adjacent tissue, and patient-derived xenografts (PDX)-derived cell lines from patients with pancreatic cancer, and tissues from patients with chronic pancreatitis. We identified, quantified, and characterized 11,634 proteins from 72 pancreatic tissue samples. Network focused analysis of the proteomics data led to identification of a tumor epithelium-specific module and an extracellular matrix (ECM)-associated module that discriminated pancreatic tumor tissue from both tumor adjacent tissue and pancreatitis tissue. On the basis of the ECM module, we defined an ECM-high and an ECM-low subgroup, where the ECM-high subgroup was associated with poor prognosis (median survival months: 15.3 vs. 22.9 months; log-rank test, P = 0.02). The ECM-high tumors were characterized by elevated epithelial-mesenchymal transition and glycolytic activities, and low oxidative phosphorylation, E2F, and DNA repair pathway activities. This study offers novel insights into the protein network underlying pancreatic cancer opening up for proteome precision medicine development.

Significance

Pancreatic cancer lacks reliable biomarkers for prognostication and treatment of patients. We analyzed the proteome of pancreatic tumors, nonmalignant tissues of the pancreas and PDX-derived cell lines, and identified proteins that discriminate between patients with good and poor survival. The proteomics data also unraveled potential novel drug targets.

Sensitive and Specific Spectral Library Searching with CompOmics Spectral Library Searching Tool and Percolator

Maintaining high sensitivity while limiting false positives is a key challenge in peptide identification from mass spectrometry data. Here, we investigate the effects of integrating the machine learning-based postprocessor Percolator into our spectral library searching tool COSS (CompOmics Spectral library Searching tool). To evaluate the effects of this postprocessing, we have used 40 data sets from 2 different projects and have searched these against the NIST and MassIVE spectral libraries. The searching is carried out using 2 spectral library search tools, COSS and MSPepSearch with and without Percolator postprocessing, and using sequence database search engine MS-GF+ as a baseline comparator. The addition of the Percolator rescoring step to COSS is effective and results in a substantial improvement in sensitivity and specificity of the identifications. COSS is freely available as open source under the permissive Apache2 license, and binaries and source code are found at https://github.com/compomics/COSS.

Integrative multi-omics and drug response profiling of childhood acute lymphoblastic leukemia cell lines

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Although standard-of-care chemotherapeutics are sufficient for most ALL cases, there are subsets of patients with poor response who relapse in disease. The biology underlying differences between subtypes and their response to therapy has only partially been explained by genetic and transcriptomic profiling. Here, we perform comprehensive multi-omic analyses of 49 readily available childhood ALL cell lines, using proteomics, transcriptomics, and pharmacoproteomic characterization. We connect the molecular phenotypes with drug responses to 528 oncology drugs, identifying drug correlations as well as lineage-dependent correlations. We also identify the diacylglycerol-analog bryostatin-1 as a therapeutic candidate in the MEF2D-HNRNPUL1 fusion high-risk subtype, for which this drug activates pro-apoptotic ERK signaling associated with molecular mediators of pre-B cell negative selection. Our data is the foundation for the interactive online Functional Omics Resource of ALL (FORALL) with navigable proteomics, transcriptomics, and drug sensitivity profiles at https://proteomics.se/forall.

Childhood acute lymphoblastic leukemia is characterised by a range of genetic aberrations. Here, the authors use multi-omics profiling of ALL cell lines to connect molecular phenotypes and drug responses to provide an interactive resource of drug sensitivity.

TIDD: tool-independent and data-dependent machine learning for peptide identification

Background

In shotgun proteomics, database search engines have been developed to assign peptides to tandem mass (MS/MS) spectra and at the same time post-processing (or rescoring) approaches over the search results have been proposed to increase the number of confident peptide identifications. The most popular post-processing approaches such as Percolator and PeptideProphet have improved rates of peptide identifications by combining multiple scores from database search engines while applying machine learning techniques. Existing post-processing approaches, however, are limited when dealing with results from new search engines because their features for machine learning must be optimized specifically for each search engine.

Results

We propose a universal post-processing tool, called TIDD, which supports confident peptide identifications regardless of the search engine adopted. TIDD can work for any (including newly developed) search engines because it calculates universal features that assess peptide-spectrum match quality while it allows additional features provided by search engines (or users) as well. Even though it relies on universal features independent of search tools, TIDD showed similar or better performance than Percolator in terms of peptide identification. TIDD identified 10.23–38.95% more PSMs than target-decoy estimation for MSFragger, which is not supported by Percolator. TIDD offers an easy-to-use simple graphical user interface for user convenience.

Conclusions

TIDD successfully eliminated the requirement for an optimal feature engineering per database search tool, and thus, can be applied directly to any database search results including newly developed ones.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04640-y.

MS-Decipher: a user-friendly proteome database search software with an emphasis on deciphering the spectra of O-linked glycopeptides

MOTIVATION

The interpretation of mass spectrometry (MS) data is a crucial step in proteomics analysis, and the identification of post-translational modifications (PTMs) is vital for the understanding of the regulation mechanism of the living system. Among various PTMs, glycosylation is one of the most diverse ones. Though many search engines have been developed to decipher proteomic data, some of them are difficult to operate and have poor performance on glycoproteomic datasets compared to advanced glycoproteomic software.

RESULTS

To simplify the analysis of proteomic datasets, especially O-glycoproteomic datasets, here, we present a user-friendly proteomic database search platform, MS-Decipher, for the identification of peptides from MS data. Two scoring schemes can be chosen for peptide-spectra matching. It was found that MS-Decipher had the same sensitivity and confidence in peptide identification compared to traditional database searching software. In addition, a special search mode, O-Search, is integrated into MS-Decipher to identify O-glycopeptides for O-glycoproteomic analysis. Compared with Mascot, MetaMorpheus and MSFragger, MS-Decipher can obtain about 139.9%, 48.8% and 6.9% more O-glycopeptide-spectrum matches. A useful tool is provided in MS-Decipher for the visualization of O-glycopeptide-spectra matches. MS-Decipher has a user-friendly graphical user interface, making it easier to operate. Several file formats are available in the searching and validation steps. MS-Decipher is implemented with Java, and can be used cross-platform.

AVAILABILITY AND IMPLEMENTATION

MS-Decipher is freely available at https://github.com/DICP-1809/MS-Decipher for academic use. For detailed implementation steps, please see the user guide.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Protein allocation and utilization in the versatile chemolithoautotroph Cupriavidus necator

Bacteria must balance the different needs for substrate assimilation, growth functions, and resilience in order to thrive in their environment. Of all cellular macromolecules, the bacterial proteome is by far the most important resource and its size is limited. Here, we investigated how the highly versatile 'knallgas' bacterium Cupriavidus necator reallocates protein resources when grown on different limiting substrates and with different growth rates. We determined protein quantity by mass spectrometry and estimated enzyme utilization by resource balance analysis modeling. We found that C. necator invests a large fraction of its proteome in functions that are hardly utilized. Of the enzymes that are utilized, many are present in excess abundance. One prominent example is the strong expression of CBB cycle genes such as Rubisco during growth on fructose. Modeling and mutant competition experiments suggest that CO₂-reassimilation through Rubisco does not provide a fitness benefit for heterotrophic growth, but is rather an investment in readiness for autotrophy.

Perspectives on automated composition of workflows in the life sciences

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the "big picture" of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

Artificial intelligence for proteomics and biomarker discovery

There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have now taken hold in all omics disciplines and increasingly call for multi-omics integration as well as data interpretation by artificial intelligence technologies. Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone. This will dramatically improve the quality and reliability of analytical workflows because experimental results should agree with predictions in a multi-dimensional data landscape. Machine learning has also become central to biomarker discovery from proteomics data, which now starts to outperform existing best-in-class assays. Finally, we discuss model transparency and explainability and data privacy that are required to deploy MS-based biomarkers in clinical settings.

Human Multisubunit E3 Ubiquitin Ligase Required for Heterotrimeric G-Protein β-Subunit Ubiquitination and Downstream Signaling

G-protein-coupled receptors (GPCRs) initiate intracellular signaling events through heterotrimeric G-protein α-subunits (Gα) and the βγ-subunit dimer (Gβγ). In this study, we utilized mass spectrometry to identify novel regulators of Gβγ signaling in human cells. This prompted our characterization of KCTD2 and KCTD5, two related potassium channel tetramerization domain (KCTD) proteins that specifically recognize Gβγ. We demonstrated that these KCTD proteins are substrate adaptors for a multisubunit CUL3-RING ubiquitin ligase, in which a KCTD2-KCTD5 hetero-oligomer associates with CUL3 through KCTD5 subunits and recruits Gβγ through both KCTD proteins in response to G-protein activation. These KCTD proteins promote monoubiquitination of lysine-23 within Gβ_1/2 in vitro and in HEK-293 cells. Depletion of these adaptors from cancer cell lines sharply impairs downstream signaling. Together, our studies suggest that a KCTD2-KCTD5-CUL3-RING E3 ligase recruits Gβγ in response to signaling, monoubiquitinates lysine-23 within Gβ_1/2, and regulates Gβγ effectors to modulate downstream signal transduction.

Evaluation of Spin Columns for Human Plasma Depletion to Facilitate MS-Based Proteomics Analysis of Plasma

High abundant protein depletion is a common strategy applied to increase analytical depth in global plasma proteomics experiment setups. The standard strategies for depletion of the highest abundant proteins currently rely on multiple-use HPLC columns or multiple-use spin columns. Here we evaluate the performance of single-use spin columns for plasma depletion and show that the single-use spin reduces handling time by allowing parallelization and is easily adapted to a nonspecialized lab environment without reducing the high plasma proteome coverage and reproducibility. In addition, we evaluate the effect of viral heat inactivation on the plasma proteome, an additional step in the plasma preparation workflow that allows the sample preparation of SARS-Cov2-infected samples to be performed in a BSL3 laboratory, and report the advantage of performing the heat inactivation postdepletion. We further show the possibility of expanding the use of the depletion column cross-species to macaque plasma samples. In conclusion, we report that single-use spin columns for high abundant protein depletion meet the requirements for reproducibly in in-depth plasma proteomics and can be applied on a common animal model while also reducing the sample handling time.

Thermal proteome profiling identifies PIP4K2A and ZADH2 as off-targets of Polo-like kinase 1 inhibitor volasertib

Polo-like kinase 1 (PLK1) is an important cell cycle kinase and an attractive target for anticancer treatments. An ATP-competitive small molecular PLK1 inhibitor, volasertib, has reached phase III in clinical trials in patients with refractory acute myeloid leukemia as a combination treatment with cytarabine. However, severe side effects limited its use. The origin of the side effects is unclear and might be due to insufficient specificity of the drug. Thus, identifying potential off-targets to volasertib is important for future clinical trials and for the development of more specific drugs. In this study, we used thermal proteome profiling (TPP) to identify proteome-wide targets of volasertib. Apart from PLK1 and proteins regulated by PLK1, we identified about 200 potential volasertib off-targets. Comparison of this result with the mass-spectrometry analysis of volasertib-treated cells showed that phosphatidylinositol phosphate and prostaglandin metabolism pathways are affected by volasertib. We confirmed that PIP4K2A and ZADH2-marker proteins for these pathways-are, indeed, stabilized by volasertib. PIP4K2A, however, was not affected by another PLK1 inhibitor onvansertib, suggesting that PIP4K2A is a true off-target of volasertib. Inhibition of these proteins is known to impact both the immune response and fatty acid metabolism and could explain some of the side effects seen in volasertib-treated patients.

Slow Protein Turnover Explains Limited Protein-Level Response to Diurnal Transcriptional Oscillations in Cyanobacteria

Metabolically engineered cyanobacteria have the potential to mitigate anthropogenic CO₂ emissions by converting CO₂ into renewable fuels and chemicals. Yet, better understanding of metabolic regulation in cyanobacteria is required to develop more productive strains that can make industrial scale-up economically feasible. The aim of this study was to find the cause for the previously reported inconsistency between oscillating transcription and constant protein levels under day-night growth conditions. To determine whether translational regulation counteracts transcriptional changes, Synechocystis sp. PCC 6803 was cultivated in an artificial day-night setting and the level of transcription, translation and protein was measured across the genome at different time points using mRNA sequencing, ribosome profiling and quantitative proteomics. Furthermore, the effect of protein turnover on the amplitude of protein oscillations was investigated through in silico simulations using a protein mass balance model. Our experimental analysis revealed that protein oscillations were not dampened by translational regulation, as evidenced by high correlation between translational and transcriptional oscillations (r = 0.88) and unchanged protein levels. Instead, model simulations showed that these observations can be attributed to a slow protein turnover, which reduces the effect of protein synthesis oscillations on the protein level. In conclusion, these results suggest that cyanobacteria have evolved to govern diurnal metabolic shifts through allosteric regulatory mechanisms in order to avoid the energy burden of replacing the proteome on a daily basis. Identification and manipulation of such mechanisms could be part of a metabolic engineering strategy for overproduction of chemicals.

Patient Acceptance of Routine Serial Postoperative Endoscopy Following Low Anterior Resection (LAR) and Its Ability to Detect Biomarkers in Anastomotic Lavage Fluid

BACKGROUND

Various reports have now established that postoperative endoscopy to examine and intervene in the process of anastomotic healing is both feasible and safe. Here we present our preliminary experience with serial postoperative endoscopy to determine its feasibility, patient acceptance and the ability to obtain and the utility of perianastomotic material for molecular analysis.

METHODS

Patients undergoing LAR with ileostomy for rectal cancer were recruited for study to undergo routine serial endoscopic surveillance (SES) at three time points during the course of LAR: intraoperatively, before discharge (postoperative day 3-7) and at follow-up (postoperative day 10-28). At each endoscopy, images were captured, anastomotic tissues were lavaged and lavage fluid was retrieved. Fluid samples were analyzed using proteomics, zymography, ELISA and bacteria via 16S rRNA gene amplicon sequencing and culture of collagenolytic strains.

RESULTS

SES is feasible and acceptable to this limited set of patients following LAR. Biologic analysis of perianastomotic fluids was able to detect the presence of proteins, microbiota and inflammatory mediators previously identified at anastomotic sites in animals with pathologic healing.

CONCLUSION

SES can be implemented in patients undergoing LAR with a high degree of patient compliance and capture of biologic information and imaging. Application of this approach has the potential to uncover, for the first time, the natural history of normal versus pathologic anastomotic healing in patients undergoing anastomotic surgery.

RGS10 physically and functionally interacts with STIM2 and requires store-operated calcium entry to regulate pro-inflammatory gene expression in microglia

Chronic activation of microglia is a driving factor in the progression of neuroinflammatory diseases, and mechanisms that regulate microglial inflammatory signaling are potential targets for novel therapeutics. Regulator of G protein Signaling 10 is the most abundant RGS protein in microglia, where it suppresses inflammatory gene expression and reduces microglia-mediated neurotoxicity. In particular, microglial RGS10 downregulates the expression of pro-inflammatory mediators including cyclooxygenase 2 (COX-2) following stimulation with lipopolysaccharide (LPS). However, the mechanism by which RGS10 affects inflammatory signaling is unknown and is independent of its canonical G protein targeted mechanism. Here, we sought to identify non-canonical RGS10 interacting partners that mediate its anti-inflammatory mechanism. Through RGS10 co-immunoprecipitation coupled with mass spectrometry, we identified STIM2, an endoplasmic reticulum (ER) localized calcium sensor and a component of the store-operated calcium entry (SOCE) machinery, as a novel RGS10 interacting protein in microglia. Direct immunoprecipitation experiments confirmed RGS10-STIM2 interaction in multiple microglia and macrophage cell lines, as well as in primary cells, with no interaction observed with the homologue STIM1. We further determined that STIM2, Orai channels, and the calcium-dependent phosphatase calcineurin are essential for LPS-induced COX-2 production in microglia, and this pathway is required for the inhibitory effect of RGS10 on COX-2. Additionally, our data demonstrated that RGS10 suppresses SOCE triggered by ER calcium depletion and that ER calcium depletion, which induces SOCE, amplifies pro-inflammatory genes. In addition to COX-2, we also show that RGS10 suppresses the expression of pro-inflammatory cytokines in microglia in response to thrombin and LPS stimulation, and all of these effects require SOCE. Collectively, the physical and functional links between RGS10 and STIM2 suggest a complex regulatory network connecting RGS10, SOCE, and pro-inflammatory gene expression in microglia, with broad implications in the pathogenesis and treatment of chronic neuroinflammation.

Wnt-inducible Lrp6-APEX2 interacting proteins identify ESCRT machinery and Trk-fused gene as components of the Wnt signaling pathway

The canonical Wnt pathway serves as a hub connecting diverse cellular processes, including β-catenin signaling, differentiation, growth, protein stability, macropinocytosis, and nutrient acquisition in lysosomes. We have proposed that sequestration of β-catenin destruction complex components in multivesicular bodies (MVBs) is required for sustained canonical Wnt signaling. In this study, we investigated the events that follow activation of the canonical Wnt receptor Lrp6 using an APEX2-mediated proximity labeling approach. The Wnt co-receptor Lrp6 was fused to APEX2 and used to biotinylate targets that are recruited near the receptor during Wnt signaling at different time periods. Lrp6 proximity targets were identified by mass spectrometry, and revealed that many endosomal proteins interacted with Lrp6 within 5 min of Wnt3a treatment. Interestingly, we found that Trk-fused gene (TFG), previously known to regulate the cell secretory pathway and to be rearranged in thyroid and lung cancers, was strongly enriched in the proximity of Lrp6. TFG depletion with siRNA, or knock-out with CRISPR/Cas9, significantly reduced Wnt/β-catenin signaling in cell culture. In vivo, studies in the Xenopus system showed that TFG is required for endogenous Wnt-dependent embryonic patterning. The results suggest that the multivesicular endosomal machinery and the novel player TFG have important roles in Wnt signaling.

Lost and Found: Re-searching and Re-scoring Proteomics Data Aids Genome Annotation and Improves Proteome Coverage

Delineation of open reading frames (ORFs) causes persistent inconsistencies in prokaryote genome annotation. We demonstrate that by advanced (re)analysis of omics data, a higher proteome coverage and sensitive detection of unannotated ORFs can be achieved, which can be exploited for conditional bacterial genome (re)annotation, which is especially relevant in view of annotating the wealth of sequenced prokaryotic genomes obtained in recent years.

Prokaryotic genome annotation is heavily dependent on automated gene annotation pipelines that are prone to propagate errors and underestimate genome complexity. We describe an optimized proteogenomic workflow that uses ribosome profiling (ribo-seq) and proteomic data for Salmonella enterica serovar Typhimurium to identify unannotated proteins or alternative protein forms. This data analysis encompasses the searching of cofragmenting peptides and postprocessing with extended peptide-to-spectrum quality features, including comparison to predicted fragment ion intensities. When this strategy is applied, an enhanced proteome depth is achieved, as well as greater confidence for unannotated peptide hits. We demonstrate the general applicability of our pipeline by reanalyzing public Deinococcus radiodurans data sets. Taken together, our results show that systematic reanalysis using available prokaryotic (proteome) data sets holds great promise to assist in experimentally based genome annotation.

IMPORTANCE Delineation of open reading frames (ORFs) causes persistent inconsistencies in prokaryote genome annotation. We demonstrate that by advanced (re)analysis of omics data, a higher proteome coverage and sensitive detection of unannotated ORFs can be achieved, which can be exploited for conditional bacterial genome (re)annotation, which is especially relevant in view of annotating the wealth of sequenced prokaryotic genomes obtained in recent years.

Rapid and Easy Enrichment Strategy for Naturally Acetylated N Termini Based on LysN Digestion and Amine-Reactive Resin Capture

Protein N-terminal acetylation (N^α-acetylation) is one of the most common modifications in both eukaryotes and prokaryotes. Although studies have shown that N^α-acetylation plays important roles in protein assembly, stability, and location, the physiological role has not been fully elucidated. Therefore, a robust and large-scale analytical method is important for a better understanding of N^α-acetylation. Here, an enrichment strategy was presented based on LysN digestion and amine-reactive resin capture to study naturally acetylated protein N termini. Since LysN protease cleaves at the amino-terminus of the lysine residue, all resulting peptides except naturally acetylated N-terminal peptides contain free amino groups and can be removed by coupling with AminoLink Resin. Therefore, the naturally acetylated N-terminal peptides were left in solution and enriched for further liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The method was very simple and fast, which contained no additional chemical derivatization except protein reduction and alkylation necessarily needed in bottom-up proteomics. It could be used to study acetylated N termini from complex biological samples without bias toward different peptides with various physicochemical properties. The enrichment specificity was above 99% when it was applied in HeLa cell lysates. Neo-N termini generated by endogenous degradation could be directly distinguished without the use of stable-isotope labeling because no chemical derivatization was introduced in this method. Furthermore, this method was highly complementary to the traditional analytical methods for protein N termini based on trypsin only with ArgC-like activity. Therefore, the described method was beneficial to naturally acetylated protein N termini profiling.

The Proteome of the Dentate Terminal Zone of the Perforant Path Indicates Presynaptic Impairment in Alzheimer Disease

Synaptic dysfunction is an early pathogenic event in Alzheimer disease (AD) that contributes to network disturbances and cognitive decline. Some synapses are more vulnerable than others, including the synapses of the perforant path, which provides the main excitatory input to the hippocampus. To elucidate the molecular mechanisms underlying the dysfunction of these synapses, we performed an explorative proteomic study of the dentate terminal zone of the perforant path. The outer two-thirds of the molecular layer of the dentate gyrus, where the perforant path synapses are located, was microdissected from five subjects with AD and five controls. The microdissected tissues were dissolved and digested by trypsin. Peptides from each sample were labeled with different isobaric tags, pooled together and pre-fractionated into 72 fractions by high-resolution isoelectric focusing. Each fraction was then analyzed by liquid chromatography-mass spectrometry. We quantified the relative expression levels of 7322 proteins, whereof 724 showed significantly altered levels in AD. Our comprehensive data analysis using enrichment and pathway analyses strongly indicated that presynaptic signaling, such as exocytosis and synaptic vesicle cycle processes, is severely disturbed in this area in AD, whereas postsynaptic proteins remained unchanged. Among the significantly altered proteins, we selected three of the most downregulated synaptic proteins; complexin-1, complexin-2 and synaptogyrin-1, for further validation, using a new cohort consisting of six AD and eight control cases. Semi-quantitative analysis of immunohistochemical staining confirmed decreased levels of complexin-1, complexin-2 and synaptogyrin-1 in the outer two-thirds of the molecular layer of the dentate gyrus in AD. Our in-depth proteomic analysis provides extensive knowledge on the potential molecular mechanism underlying synaptic dysfunction related to AD and supports that presynaptic alterations are more important than postsynaptic changes in early stages of the disease. The specific synaptic proteins identified could potentially be targeted to halt synaptic dysfunction in AD.

Assessment of the Utility of the Oral Fluid and Plasma Proteomes for Hydrocodone Exposure

INTRODUCTION

Non-medical use and abuse of prescription opioids is a growing problem in both the civilian and military communities, with minimal technologies for detecting hydrocodone use. This study explored the proteomic changes that occur in the oral fluid and blood plasma following controlled hydrocodone administration in 20 subjects.

METHODS

The global proteomic profile was determined for samples taken at four time points per subject: pre-exposure and 4, 6, or 168 hours post-exposure. The oral fluid samples analyzed herein provided greater differentiation between baseline and response time points than was observed with blood plasma, at least partially due to significant person-to-person relative variability in the plasma proteome.

RESULTS

A total of 399 proteins were identified from oral fluid samples, and the abundance of 118 of those proteins was determined to be significantly different upon metabolism of hydrocodone (4 and 6 hour time points) as compared to baseline levels in the oral fluid (pre-dose and 168 hours).

CONCLUSIONS

We present an assessment of the oral fluid and plasma proteome following hydrocodone administration, which demonstrates the potential of oral fluid as a noninvasive sample that may reveal features of hydrocodone in opioid use, and with additional study, may be useful for other opioids and in settings of misuse.

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.

Detection and verification of 2.3 million cancer mutations in NCI60 cancer cell lines with a cloud search engine

Today we have unprecedented access to human genomic and proteomic data that appear to be rapidly approaching our current understanding of comprehensive coverage. Combining genomic information with shotgun proteomics remains challenging due to the large increase in proteomics search space. However, making this connection between genomic and proteomic information is critical for cancer studies to vaccine development. Furthermore, as we progress towards personalized medicine, it will be essential for proteomics analysis to identify individual mutations and variants in order to fully understand protein networks and to develop personalized therapies. While these advantages are well-established, only a few studies have demonstrated the successful integration of proteomic data with large genomic input. We present and examine the abilities of Bolt, a new cloud-based proteomics search engine to search for the presence of over 2.3 million known cancer mutations in a matter of minutes while still performing a standard proteomics search that includes 31 post translational modifications. We use previously published proteomics data sets and identify mutations that are verified using genomic studies as well as previous proteomics efforts. Our results also emphasize the need to search for mutations in a comprehensive manner while still searching for both common and rare PTMs. SIGNIFICANCE: We present and examine the abilities of Bolt, a new cloud-based proteomics search engine to search for the presence of over 2.3 million known cancer mutations in a matter of minutes while still performing a standard proteomics search that includes 31 post translational modifications. No other proteomics search software can do so.

An Oxygen-Dependent Interaction between FBXL5 and the CIA-Targeting Complex Regulates Iron Homeostasis

The iron-sensing protein FBXL5 is the substrate adaptor for a SKP1-CUL1-RBX1 E3 ubiquitin ligase complex that regulates the degradation of iron regulatory proteins (IRPs). Here, we describe a mechanism of FBXL5 regulation involving its interaction with the cytosolic Fe-S cluster assembly (CIA) targeting complex composed of MMS19, FAM96B, and CIAO1. We demonstrate that the CIA-targeting complex promotes the ability of FBXL5 to degrade IRPs. In addition, the FBXL5-CIA-targeting complex interaction is regulated by oxygen (O₂) tension displaying a robust association in 21% O₂ that is severely diminished in 1% O₂ and contributes to O₂-dependent regulation of IRP degradation. Together, these data identify a novel oxygen-dependent signaling axis that links IRP-dependent iron homeostasis with the Fe-S cluster assembly machinery.

Morph-specific protein patterns in the femoral gland secretions of a colour polymorphic lizard

Colour polymorphism occurs when two or more genetically-based colour morphs permanently coexist within an interbreeding population. Colouration is usually associated to other life-history traits (ecological, physiological, behavioural, reproductive …) of the bearer, thus being the phenotypic marker of such set of genetic features. This visual badge may be used to inform conspecifics and to drive those decision making processes which may contribute maintaining colour polymorphism under sexual selection context. The importance of such information suggests that other communication modalities should be recruited to ensure its transfer in case visual cues were insufficient. Here, for the first time, we investigated the potential role of proteins from femoral gland secretions in signalling colour morph in a polymorphic lizard. As proteins are thought to convey identity-related information, they represent the ideal cues to build up the chemical modality used to badge colour morphs. We found strong evidence for the occurrence of morph-specific protein profiles in the three main colour-morphs of the common wall lizard, which showed both qualitative and quantitative differences in protein expression. As lizards are able to detect proteins by tongue-flicking and vomeronasal organ, this result support the hypothesis that colour polymorphic lizards may use a multimodal signal to inform about colour-morph.

Breast cancer quantitative proteome and proteogenomic landscape

In the preceding decades, molecular characterization has revolutionized breast cancer (BC) research and therapeutic approaches. Presented herein, an unbiased analysis of breast tumor proteomes, inclusive of 9995 proteins quantified across all tumors, for the first time recapitulates BC subtypes. Additionally, poor-prognosis basal-like and luminal B tumors are further subdivided by immune component infiltration, suggesting the current classification is incomplete. Proteome-based networks distinguish functional protein modules for breast tumor groups, with co-expression of EGFR and MET marking ductal carcinoma in situ regions of normal-like tumors and lending to a more accurate classification of this poorly defined subtype. Genes included within prognostic mRNA panels have significantly higher than average mRNA-protein correlations, and gene copy number alterations are dampened at the protein-level; underscoring the value of proteome quantification for prognostication and phenotypic classification. Furthermore, protein products mapping to non-coding genomic regions are identified; highlighting a potential new class of tumor-specific immunotherapeutic targets.

Gene expression profiles can classify breast cancer into five clinically relevant subtypes. Here, the authors perform an in-depth quantitative profiling of the proteome of 45 breast tumors, and show they can recapitulate the transcriptome-based classifications and identify many potentially antigenic tumour-specific peptides.

Proteogenomics and Hi-C reveal transcriptional dysregulation in high hyperdiploid childhood acute lymphoblastic leukemia

Hyperdiploidy, i.e. gain of whole chromosomes, is one of the most common genetic features of childhood acute lymphoblastic leukemia (ALL), but its pathogenetic impact is poorly understood. Here, we report a proteogenomic analysis on matched datasets from genomic profiling, RNA-sequencing, and mass spectrometry-based analysis of >8,000 genes and proteins as well as Hi-C of primary patient samples from hyperdiploid and ETV6/RUNX1-positive pediatric ALL. We show that CTCF and cohesin, which are master regulators of chromatin architecture, display low expression in hyperdiploid ALL. In line with this, a general genome-wide dysregulation of gene expression in relation to topologically associating domain (TAD) borders were seen in the hyperdiploid group. Furthermore, Hi-C of a limited number of hyperdiploid childhood ALL cases revealed that 2/4 cases displayed a clear loss of TAD boundary strength and 3/4 showed reduced insulation at TAD borders, with putative leukemogenic effects.

Comprehensive Glycoproteomic Analysis of Chinese Hamster Ovary Cells

The Chinese hamster ovary (CHO) cell line is a major expression system for the production of therapeutic proteins, the majority of which are glycoproteins, such as antibodies and erythropoietin (EPO). The characterization glycosylation profile of therapeutic proteins produced from engineered CHO cells and therapeutic functions, as well as side effects, are critical to understand the important roles of glycosylation. In this study, a large scale glycoproteomic workflow was established and applied to CHO-K1 cells expressing EPO. The workflow includes enrichment of intact glycopeptides from CHO-K1 cell lysate and medium using hydrophilic enrichment, fractionation of the obtained intact glycopeptides (IGPs) by basic reversed phase liquid chromatography (bRPLC), analyzing the glycopeptides using LC-MS/MS, and annotating the results by GPQuest 2.0. A total of 10 338 N-linked glycosite-containing IGPs were identified, representing 1162 unique glycosites in 530 glycoproteins, including 71 unique atypical N-linked IGPs on 18 atypical N-glycosylation sequons with an overrepresentation of the N-X-C motifs. Moreover, we compared the glycoproteins from CHO cell lysate with those from medium using the in-depth N-linked glycoproteome data. The obtained large scale glycoproteomic data from intact N-linked glycopeptides in this study is complementary to the genomic, proteomic, and N-linked glycomic data previously reported for CHO cells. Our method has the potential to monitor the production of recombinant therapeutic glycoproteins.

SEPROGADIC - serum protein-based gastric cancer prediction model for prognosis and selection of proper adjuvant therapy

Gastric cancer (GC) patients usually receive surgical treatment. Postoperative therapeutic options such as anticancer adjuvant therapies (AT) based on prognostic prediction models would provide patient-specific treatment to decrease postsurgical morbidity and mortality rates. Relevant prognostic factors in resected GC patient’s serum may improve therapeutic measures in a non-invasive manner. In order to develop a GC prognostic model, we designed a retrospective study. In this study, serum samples were collected from 227 patients at a 4-week recovery period after D2 lymph node dissection, and 103 cancer-related serum proteins were analyzed by multiple reaction monitoring mass spectrometry. Using the quantitative values of the serum proteins, we developed SEPROGADIC (SErum PROtein-based GAstric cancer preDICtor) prognostic model consisting of 6 to 14 serum proteins depending on detailed purposes of the model, prognosis prediction and proper AT selection. SEPROGADIC could clearly classify patients with good or bad prognosis at each TNM stage (1b, 2, 3 and 4) and identify a patient subgroup who would benefit from CCRT (combined chemoradiation therapy) rather than CTX (chemotherapy), or vice versa. Our study demonstrated that serum proteins could serve as prognostic factors along with clinical stage information in patients with resected gastric cancer, thus allowing patient-tailored postsurgical treatment.

Sipros Ensemble improves database searching and filtering for complex metaproteomics

Motivation

Complex microbial communities can be characterized by metagenomics and metaproteomics. However, metagenome assemblies often generate enormous, and yet incomplete, protein databases, which undermines the identification of peptides and proteins in metaproteomics. This challenge calls for increased discrimination of true identifications from false identifications by database searching and filtering algorithms in metaproteomics.

Results

Sipros Ensemble was developed here for metaproteomics using an ensemble approach. Three diverse scoring functions from MyriMatch, Comet and the original Sipros were incorporated within a single database searching engine. Supervised classification with logistic regression was used to filter database searching results. Benchmarking with soil and marine microbial communities demonstrated a higher number of peptide and protein identifications by Sipros Ensemble than MyriMatch/Percolator, Comet/Percolator, MS-GF+/Percolator, Comet & MyriMatch/iProphet and Comet & MyriMatch & MS-GF+/iProphet. Sipros Ensemble was computationally efficient and scalable on supercomputers.

Availability and implementation

Freely available under the GNU GPL license at http://sipros.omicsbio.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Metabolic reprogramming of acute lymphoblastic leukemia cells in response to glucocorticoid treatment

Glucocorticoids (GCs) are metabolic hormones with immunosuppressive effects that have proven effective drugs against childhood acute lymphoblastic leukemia (ALL). Yet, the role of metabolic reprogramming in GC-induced ALL cell death is poorly understood. GCs efficiently block glucose uptake and metabolism in ALL cells, but this does not fully explain the observed induction of autophagy and cell death. Here, we have performed parallel time-course proteomics, metabolomics, and isotope-tracing studies to examine in detail the metabolic effects of GCs on ALL cells. We observed metabolic events associated with growth arrest, autophagy, and catabolism prior to onset of apoptosis: nucleotide de novo synthesis was reduced, while certain nucleobases accumulated; polyamine synthesis was inhibited; and phosphatidylcholine synthesis was induced. GCs suppressed not only glycolysis but also entry of both glucose and glutamine into the TCA cycle. In contrast, expression of glutamine-ammonia ligase (GLUL) and cellular glutamine content was robustly increased by GC treatment, suggesting induction of glutamine synthesis, similar to nutrient-starved muscle. Modulating medium glutamine and dimethyl-α-ketoglutarate (dm-αkg) to favor glutamine synthesis reduced autophagosome content of ALL cells, and dm-αkg also rescued cell viability. These data suggest that glutamine synthesis affects autophagy and possibly onset of cell death in response to GCs, which should be further explored to understand mechanism of action and possible sources of resistance.

An antisense RNA capable of modulating the expression of the tumor suppressor microRNA-34a

The microRNA-34a is a well-studied tumor suppressor microRNA (miRNA) and a direct downstream target of TP53 with roles in several pathways associated with oncogenesis, such as proliferation, cellular growth, and differentiation. Due to its broad tumor suppressive activity, it is not surprising that miR34a expression is altered in a wide variety of solid tumors and hematological malignancies. However, the mechanisms by which miR34a is regulated in these cancers is largely unknown. In this study, we find that a long noncoding RNA transcribed antisense to the miR34a host gene, is critical for miR34a expression and mediation of its cellular functions in multiple types of human cancer. We name this long noncoding RNA lncTAM34a, and characterize its ability to facilitate miR34a expression under different types of cellular stress in both TP53-deficient and wild-type settings.

Extracellular nanovesicles released from the commensal yeast Malassezia sympodialis are enriched in allergens and interact with cells in human skin

Malassezia sympodialis is a dominant commensal fungi in the human skin mycobiome but is also associated with common skin disorders including atopic eczema (AE). M. sympodialis releases extracellular vesicles, designated MalaEx, which are carriers of small RNAs and allergens, and they can induce inflammatory cytokine responses. Here we explored how MalaEx are involved in host-microbe interactions by comparing protein content of MalaEx with that of the parental yeast cells, and by investigating interactions of MalaEx with cells in the skin. Cryo-electron tomography revealed a heterogeneous population of MalaEx. iTRAQ based quantitative proteomics identified in total 2439 proteins in all replicates of which 110 were enriched in MalaEx compared to the yeast cells. Among the MalaEx enriched proteins were two of the M. sympodialis allergens, Mala s 1 and s 7. Functional experiments indicated an active binding and internalization of MalaEx into human keratinocytes and monocytes, and MalaEx were found in close proximity of the nuclei using super-resolution fluorescence 3D-SIM imaging. Our results provides new insights into host-microbe interactions, supporting that MalaEx may have a role in the sensitization and maintenance of inflammation in AE by containing enriched amounts of allergens and with their ability to interact with skin cells.

A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics

Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary between targets and decoys. To improve analysis time for large-scale data sets, we update Percolator's SVM learning engine through software and algorithmic optimizations rather than heuristic approaches that necessitate the careful study of their impact on learned parameters across different search settings and data sets. We show that by optimizing Percolator's original learning algorithm, l2-SVM-MFN, large-scale SVM learning requires nearly only a third of the original runtime. Furthermore, we show that by employing the widely used Trust Region Newton (TRON) algorithm instead of l2-SVM-MFN, large-scale Percolator SVM learning is reduced to nearly only a fifth of the original runtime. Importantly, these speedups only affect the speed at which Percolator converges to a global solution and do not alter recalibration performance. The upgraded versions of both l2-SVM-MFN and TRON are optimized within the Percolator codebase for multithreaded and single-thread use and are available under Apache license at bitbucket.org/jthalloran/percolator_upgrade .

Uncertainty estimation of predictions of peptides' chromatographic retention times in shotgun proteomics

Motivation

Liquid chromatography is frequently used as a means to reduce the complexity of peptide-mixtures in shotgun proteomics. For such systems, the time when a peptide is released from a chromatography column and registered in the mass spectrometer is referred to as the peptide's retention time . Using heuristics or machine learning techniques, previous studies have demonstrated that it is possible to predict the retention time of a peptide from its amino acid sequence. In this paper, we are applying Gaussian Process Regression to the feature representation of a previously described predictor E lude . Using this framework, we demonstrate that it is possible to estimate the uncertainty of the prediction made by the model. Here we show how this uncertainty relates to the actual error of the prediction.

Results

In our experiments, we observe a strong correlation between the estimated uncertainty provided by Gaussian Process Regression and the actual prediction error. This relation provides us with new means for assessment of the predictions. We demonstrate how a subset of the peptides can be selected with lower prediction error compared to the whole set. We also demonstrate how such predicted standard deviations can be used for designing adaptive windowing strategies.

Contact

lukas.kall@scilifelab.se.

Availability and Implementation

Our software and the data used in our experiments is publicly available and can be downloaded from https://github.com/statisticalbiotechnology/GPTime .