Multiple Reaction Monitoring-Mass Spectrometry Enables Robust Quantitation of Plasma Proteins Regardless of Whole Blood Processing Delays That May Occur in the Clinic

Plasma is an important biofluid for clinical research and diagnostics. In the clinic, unpredictable delays—from minutes to hours—between blood collection and plasma generation are often unavoidable. These delays can potentially lead to protein degradation and modification and might considerably affect intact protein measurement methods such as sandwich enzyme-linked immunosorbent assays that bind proteins on two epitopes to increase specificity, thus requiring largely intact protein structures. Here, we investigated, using multiple reaction monitoring mass spectrometry (MRM-MS), how delays in plasma processing affect peptide-centric “bottom-up” proteomics. We used validated assays for proteotypic peptide surrogates of 270 human proteins to analyze plasma generated after whole blood had been kept at room temperature from 0 to 40 h to mimic delays that occur in the clinic. Moreover, we evaluated the impact of different plasma-thawing conditions on MRM-based plasma protein quantitation. We demonstrate that >90% of protein concentration measurements were unaffected by the thawing procedure and by up to 40-h delayed plasma generation, reflected by relative standard deviations (RSDs) of <30%. Of the 159 MRM assays that yielded quantitative results in 60% of the measured time points, 139 enabled a stable protein quantitation (RSD <20%), 14 showed a slight variation (RSD 20–30%), and 6 appeared unstable/irreproducible (RSD > 30%). These results demonstrate the high robustness and thus the potential for MRM-based plasma-protein quantitation to be used in a clinical setting. In contrast to enzyme-linked immunosorbent assay, peptide-based MRM assays do not require intact three-dimensional protein structures for an accurate and precise quantitation of protein concentrations in the original sample.     

P300 Interacted With N-Myc and Regulated Its Protein Stability via Altering Its Post-Translational Modifications in Neuroblastoma

MYCN amplification is an independent risk factor for poor prognosis in neuroblastoma (NB), but its protein product cannot be directly targeted because of protein structure. Thus, this study aimed to explore novel ways to indirectly target N-Myc by regulating its post-translational modifications (PTMs) and therefore protein stability. N-Myc coimmunoprecipitation combined with HPLC–MS/MS identified 16 PTM residues and 114 potential N-Myc-interacting proteins. Notably, both acetylation and ubiquitination were identified on lysine 199 of N-Myc. We then discovered that p300, which can interact with N-Myc, modulated the protein stability of N-Myc in MYCN-amplified NB cell lines and simultaneously regulated the acetylation level and ubiquitination level on lysine-199 of N-Myc protein in vitro. Furthermore, p300 correlated with poor prognosis in NB patients. Taken together, p300 can be considered as a potential therapeutic target to treat MYCN-amplified NB patients, and other identified PTMs and interacting proteins also provide potential targets for further study.     

Recent advancements in mass spectrometry–based tools to investigate newly synthesized proteins

Tight regulation of protein translation drives the proteome to undergo changes under influence of extracellular or intracellular signals. Despite mass spectrometry–based proteomics being an excellent method to study differences in protein abundance in complex proteomes, analyzing minute or rapid changes in protein synthesis and abundance remains challenging. Therefore, several dedicated techniques to directly detect and quantify newly synthesized proteins have been developed, notably puromycin-based, bio-orthogonal noncanonical amino acid tagging–based, and stable isotope labeling by amino acids in cell culture–based methods, combined with mass spectrometry. These techniques have enabled the investigation of perturbations, stress, or stimuli on protein synthesis. Improvements of these methods are still necessary to overcome various remaining limitations. Recent improvements include enhanced enrichment approaches and combinations with various stable isotope labeling techniques, which allow for more accurate analysis and comparison between conditions on shorter timeframes and in more challenging systems. Here, we aim to review the current state in this field.     

Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.     

Reanalysis of ProteomicsDB Using an Accurate,Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups

Estimating false discovery rates (FDRs) of protein identification continues to be an important topic in mass spectrometry–based proteomics, particularly when analyzing very large datasets. One performant method for this purpose is the Picked Protein FDR approach which is based on a target-decoy competition strategy on the protein level that ensures that FDRs scale to large datasets. Here, we present an extension to this method that can also deal with protein groups, that is, proteins that share common peptides such as protein isoforms of the same gene. To obtain well-calibrated FDR estimates that preserve protein identification sensitivity, we introduce two novel ideas. First, the picked group target-decoy and second, the rescued subset grouping strategies. Using entrapment searches and simulated data for validation, we demonstrate that the new Picked Protein Group FDR method produces accurate protein group-level FDR estimates regardless of the size of the data set. The validation analysis also uncovered that applying the commonly used Occam’s razor principle leads to anticonservative FDR estimates for large datasets. This is not the case for the Picked Protein Group FDR method. Reanalysis of deep proteomes of 29 human tissues showed that the new method identified up to 4% more protein groups than MaxQuant. Applying the method to the reanalysis of the entire human section of ProteomicsDB led to the identification of 18,000 protein groups at 1% protein group-level FDR. The analysis also showed that about 1250 genes were represented by ≥2 identified protein groups. To make the method accessible to the proteomics community, we provide a software tool including a graphical user interface that enables merging results from multiple MaxQuant searches into a single list of identified and quantified protein groups.     

Adapting protein sequences for optimized therapeutic efficacy

Therapeutic proteins alleviate disease pathology by supplementing missing or defective native proteins, sequestering superfluous proteins, or by acting through designed non-natural mechanisms. Although therapeutic proteins often have the same amino acid sequence as their native counterpart, their maturation paths from expression to the site of physiological activity are inherently different, and optimizing protein sequences for properties that 100s of millions of years of evolution did not need to address presents an opportunity to develop better biological treatments. Because therapeutic proteins are inherently non-natural entities, optimization for their desired function should be considered analogous to that of small molecule drug candidates, which are optimized through expansive combinatorial variation by the medicinal chemist. Here, we review recent successes and challenges of protein engineering for optimized therapeutic efficacy.     

Deconvoluting the biology and druggability of protein lipidation using chemical proteomics

Lipids are indispensable cellular building blocks, and their post-translational attachment to proteins makes them important regulators of many biological processes. Dysfunction of protein lipidation is also implicated in many pathological states, yet its systematic analysis presents significant challenges. Thanks to innovations in chemical proteomics, lipidation can now be readily studied by metabolic tagging using functionalized lipid analogs, enabling global profiling of lipidated substrates using mass spectrometry. This has spearheaded the first deconvolution of their full scope in a range of contexts, from cells to pathogens and multicellular organisms. Protein N-myristoylation, S-acylation, and S-prenylation are the most well-studied lipid post-translational modifications because of their extensive contribution to the regulation of diverse cellular processes. In this review, we focus on recent advances in the study of these post-translational modifications, with an emphasis on how novel mass spectrometry methods have elucidated their roles in fundamental biological processes.     

Native Size-Exclusion Chromatography–Based Mass Spectrometry Reveals New Components of the Early Heat Shock Protein 90 Inhibition Response Among Limited Global Changes

The molecular chaperone heat shock protein 90 (HSP90) works in concert with co-chaperones to stabilize its client proteins, which include multiple drivers of oncogenesis and malignant progression. Pharmacologic inhibitors of HSP90 have been observed to exert a wide range of effects on the proteome, including depletion of client proteins, induction of heat shock proteins, dissociation of co-chaperones from HSP90, disruption of client protein signaling networks, and recruitment of the protein ubiquitylation and degradation machinery—suggesting widespread remodeling of cellular protein complexes. However, proteomics studies to date have focused on inhibitor-induced changes in total protein levels, often overlooking protein complex alterations. Here, we use size-exclusion chromatography in combination with mass spectrometry (SEC-MS) to characterize the early changes in native protein complexes following treatment with the HSP90 inhibitor tanespimycin (17-AAG) for 8 h in the HT29 colon adenocarcinoma cell line. After confirming the signature cellular response to HSP90 inhibition (e.g., induction of heat shock proteins, decreased total levels of client proteins), we were surprised to find only modest perturbations to the global distribution of protein elution profiles in inhibitor-treated HT29 cells at this relatively early time-point. Similarly, co-chaperones that co-eluted with HSP90 displayed no clear difference between control and treated conditions. However, two distinct analysis strategies identified multiple inhibitor-induced changes, including known and unknown components of the HSP90-dependent proteome. We validate two of these—the actin-binding protein Anillin and the mitochondrial isocitrate dehydrogenase 3 complex—as novel HSP90 inhibitor-modulated proteins. We present this dataset as a resource for the HSP90, proteostasis, and cancer communities (https://www.bioinformatics.babraham.ac.uk/shiny/HSP90/SEC-MS/), laying the groundwork for future mechanistic and therapeutic studies related to HSP90 pharmacology. Data are available via ProteomeXchange with identifier PXD033459.     

Simple But Efficacious Enrichment of Integral Membrane Proteins and Their Interactions for In-Depth Membrane Proteomics

Membrane proteins play essential roles in various cellular processes, such as nutrient transport, bioenergetic processes, cell adhesion, and signal transduction. Proteomics is one of the key approaches to exploring membrane proteins comprehensively. Bottom–up proteomics using LC–MS/MS has been widely used in membrane proteomics. However, the low abundance and hydrophobic features of membrane proteins, especially integral membrane proteins, make it difficult to handle the proteins and are the bottleneck for identification by LC–MS/MS. Herein, to improve the identification and quantification of membrane proteins, we have stepwisely evaluated methods of membrane enrichment for the sample preparation. The enrichment methods of membranes consisted of precipitation by ultracentrifugation and treatment by urea or alkaline solutions. The best enrichment method in the study, washing with urea after isolation of the membranes, resulted in the identification of almost twice as many membrane proteins compared with samples without the enrichment. Notably, the method significantly enhances the identified numbers of multispanning transmembrane proteins, such as solute carrier transporters, ABC transporters, and G-protein–coupled receptors, by almost sixfold. Using this method, we revealed the profiles of amino acid transport systems with the validation by functional assays and found more protein–protein interactions, including membrane protein complexes and clusters. Our protocol uses standard procedures in biochemistry, but the method was efficient for the in-depth analysis of membrane proteome in a wide range of samples.     

SP3-Enabled Rapid and High Coverage Chemoproteomic Identification of Cell-State–Dependent Redox-Sensitive Cysteines

Proteinaceous cysteine residues act as privileged sensors of oxidative stress. As reactive oxygen and nitrogen species have been implicated in numerous pathophysiological processes, deciphering which cysteines are sensitive to oxidative modification and the specific nature of these modifications is essential to understanding protein and cellular function in health and disease. While established mass spectrometry-based proteomic platforms have improved our understanding of the redox proteome, the widespread adoption of these methods is often hindered by complex sample preparation workflows, prohibitive cost of isotopic labeling reagents, and requirements for custom data analysis workflows. Here, we present the SP3-Rox redox proteomics method that combines tailored low cost isotopically labeled capture reagents with SP3 sample cleanup to achieve high throughput and high coverage proteome-wide identification of redox-sensitive cysteines. By implementing a customized workflow in the free FragPipe computational pipeline, we achieve accurate MS1-based quantitation, including for peptides containing multiple cysteine residues. Application of the SP3-Rox method to cellular proteomes identified cysteines sensitive to the oxidative stressor GSNO and cysteine oxidation state changes that occur during T cell activation.     

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry–based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.     

Uncovering post-translational modification-associated protein–protein interactions

In living systems, the chemical space and functional repertoire of proteins are dramatically expanded through the post-translational modification (PTM) of various amino acid residues. These modifications frequently trigger unique protein–protein interactions (PPIs) – for example with reader proteins that directly bind the modified amino acid residue – which leads to downstream functional outcomes. The modification of a protein can also perturb its PPI network indirectly, for example, through altering its conformation or subcellular localization. Uncovering the network of unique PTM-triggered PPIs is essential to fully understand the roles of an ever-expanding list of PTMs in our biology. In this review, we discuss established strategies and current challenges associated with this endeavor.     

Differential Proteome and Interactome Analysis Reveal the Basis of Pleiotropy Associated With the Histidine Methyltransferase Hpm1p

The methylation of histidine is a post-translational modification whose function is poorly understood. Methyltransferase histidine protein methyltransferase 1 (Hpm1p) monomethylates H243 in the ribosomal protein Rpl3p and represents the only known histidine methyltransferase in Saccharomyces cerevisiae. Interestingly, the hpm1 deletion strain is highly pleiotropic, with many extraribosomal phenotypes including improved growth rates in alternative carbon sources. Here, we investigate how the loss of histidine methyltransferase Hpm1p results in diverse phenotypes, through use of targeted mass spectrometry (MS), growth assays, quantitative proteomics, and differential crosslinking MS. We confirmed the localization and stoichiometry of the H243 methylation site, found unreported sensitivities of Δhpm1 yeast to nonribosomal stressors, and identified differentially abundant proteins upon hpm1 knockout with clear links to the coordination of sugar metabolism. We adapted the emerging technique of quantitative large-scale stable isotope labeling of amino acids in cell culture crosslinking MS for yeast, which resulted in the identification of 1267 unique in vivo lysine–lysine crosslinks. By reproducibly monitoring over 350 of these in WT and Δhpm1, we detected changes to protein structure or protein–protein interactions in the ribosome, membrane proteins, chromatin, and mitochondria. Importantly, these occurred independently of changes in protein abundance and could explain a number of phenotypes of Δhpm1, not addressed by expression analysis. Further to this, some phenotypes were predicted solely from changes in protein structure or interactions and could be validated by orthogonal techniques. Taken together, these studies reveal a broad role for Hpm1p in yeast and illustrate how crosslinking MS will be an essential tool for understanding complex phenotypes.