Deep phosphotyrosine characterisation of primary murine T cells using broad spectrum optimisation of selective triggering.

Sequencing the tyrosine phosphoproteome using MS-based proteomics is challenging due to the low abundance of tyrosine phosphorylation in cells, a challenge compounded in scarce samples like primary cells or clinical samples. The broad-spectrum optimisation of selective triggering (BOOST) method was recently developed to increase phosphotyrosine sequencing in low protein input samples by leveraging tandem mass tags (TMT), phosphotyrosine enrichment, and a phosphotyrosine-loaded carrier channel. Here, we demonstrate the viability of BOOST in T cell receptor (TCR)-stimulated primary murine T cells by benchmarking the accuracy and precision of the BOOST method and discerning significant alterations in the phosphoproteome associated with receptor stimulation. Using 1 mg of protein input (about 20 million cells) and BOOST, we identify and precisely quantify more than 2000 unique pY sites compared to about 300 unique pY sites in non-BOOST control samples. We show that although replicate variation increases when using the BOOST method, BOOST does not jeopardise quantitative precision or the ability to determine statistical significance for peptides measured in triplicate. Many pY previously uncharacterised sites on important T cell signalling proteins are quantified using BOOST, and we identify new TCR responsive pY sites observable only with BOOST. Finally, we determine that the phase-spectrum deconvolution method on Orbitrap instruments can impair pY quantitation in BOOST experiments.

Carrier-Guided Proteome Analysis in a High Protein Background: An Improved Approach to Host Cell Protein Identification.

Many shotgun proteomics experiments are negatively influenced by highly abundant proteins, such as those measuring residual host cell proteins (HCP) amidst highly abundant recombinant biotherapeutic or plasma proteins amidst albumin and immunoglobulins. While western blotting and ELISAs can reveal the presence of specific low abundance proteins from highly abundant background proteins, mass spectrometry approaches are required to define the low abundance protein composition in these scenarios. The challenge in detecting low abundance proteins in a high protein background by standard shotgun approaches is that spectra are often not triggered on their peptides in data dependent acquisition methods but rather on the highly abundant background peptides. Here, we use tandem mass tags (TMT) to introduce a carrier proteome approach to enhance the detection of proteins, such as from residual host cell proteomes amidst a highly abundant background. Using a mixture of bovine serum albumin (BSA) and E. coli as a mock high background/low abundance target protein formulation, we demonstrate proof-of-principle experiments allowing the improved detection of target proteins amidst a high protein background. While we observed significant coisolation interference, we mitigated it by using a spike-in interference detection TMT channel. Finally, we use the approach to identify 300 residual E. coli proteins from a protein A pulldown of a human IgG antibody, demonstrating that it may be applicable to analysis of HCPs in biotherapeutic protein formulations.

An Efficient, Amine-Specific, and Cost-Effective Method for TMT 6/11-plex Labeling Improves the Proteome Coverage, Quantitative Accuracy and Precision.

Tandem mass tags (TMT) are widely used in proteomics to simultaneously quantify multiple samples in a single experiment. The tags can be easily added to the primary amines of peptides/proteins through chemical reactions. In addition to amines, TMT reagents also partially react with the hydroxyl groups of serine, threonine, and tyrosine residues under alkaline conditions, which significantly compromises the analytical sensitivity and precision. Under alkaline conditions, reducing the TMT molar excess can partially mitigate overlabeling of histidine-free peptides, but has a limited effect on peptides containing histidine and hydroxyl groups. Here, we present a method under acidic conditions to suppress overlabeling while efficiently labeling amines, using only one-fifth of the TMT amount recommended by the manufacturer. In a deep-scale analysis of a yeast/human two-proteome sample, we systematically evaluated our method against the manufacturer's method and a previously reported TMT-reduced method. Our method reduced overlabeled peptides by 9-fold and 6-fold, respectively, resulting in the substantial enhancement in peptide/protein identification rates. More importantly, the quantitative accuracy and precision were improved as overlabeling was reduced, endowing our method with greater statistical power to detect 42% and 12% more statistically significant yeast proteins compared to the standard and TMT-reduced methods, respectively. Mass spectrometric data have been deposited in the ProteomeXchange Consortium via the iProX partner repository with the data set identifier PXD047052.

Boosting the Sensitivity of Quantitative Single-Cell Proteomics with Infrared-Tandem Mass Tags.

Single-cell proteomics is a powerful approach to precisely profile protein landscapes within individual cells toward a comprehensive understanding of proteomic functions and tissue and cellular states. The inherent challenges associated with limited starting material demand heightened analytical sensitivity. Just as advances in sample preparation maximize the amount of material that makes it from the cell to the mass spectrometer, we strive to maximize the number of ions that make it from ion source to the detector. In isobaric tagging experiments, limited reporter ion generation limits quantitative accuracy and precision. The combination of infrared photoactivation and ion parking circumvents the m/z dependence inherent in HCD, maximizing reporter generation and avoiding unintended degradation of TMT reporter molecules in infrared-tandem mass tags (IR-TMT). The method was applied to single-cell human proteomes using 18-plex TMTpro, resulting in 4-5-fold increases in reporter signal compared to conventional SPS-MS3 approaches. IR-TMT enables faster duty cycles, higher throughput, and increased peptide identification and quantification. Comparative experiments showcase 4-5-fold lower injection times for IR-TMT, providing superior sensitivity without compromising accuracy. In all, IR-TMT enhances the dynamic range of proteomic experiments and is compatible with gas-phase fractionation and real-time searching, promising increased gains in the study of cellular heterogeneity.

DiDBiT-TMT: A Novel Method to Quantify Changes in the Proteomic Landscape Induced by Neural Plasticity.

Direct detection of biotinylated proteins (DiDBiT) is a proteomic method that can enrich and detect newly synthesized proteins (NSPs) labeled with bio-orthogonal amino acids with 20-fold improved detectability compared to conventional methods. However, DiDBiT has currently been used to compare only two conditions per experiment. Here, we present DiDBiT-TMT, a method that can be used to quantify NSPs across many conditions and replicates in the same experiment by combining isobaric tandem mass tagging (TMT) with DiDBiT. We applied DiDBiT-TMT to brain slices to determine changes in the de novo proteome that occur after inducing chemical long-term potentiation (cLTP) or treatment with the neuromodulator norepinephrine. We successfully demonstrated DiDBiT-TMT's capacity to quantitatively compare up to 9 samples in parallel. We showed that there is a minimal overlap among NSPs that are differentially expressed in cLTP-treated organotypic brain slices, norepinephrine-treated organotypic brain slices, and organotypic slices undergoing combinatorial treatment with norepinephrine and cLTP. Our results point to the possible divergence of the molecular mechanisms underlying these treatments and showcase the applicability of DiDBiT-TMT for studying neurobiology.

Inserting Pre-analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics with Sample Multiplexing.

GoDig, a platform for targeted pathway proteomics without the need for manual assay scheduling or synthetic standards, is a powerful, flexible, and easy-to-use method that uses tandem mass tags to increase sample throughput up to 18-fold relative to label-free methods. Though the protein-level success rates of GoDig are high, the peptide-level success rates are more limited, hampering assays of harder-to-quantify proteins and site-specific phenomena. To guide the optimization of GoDig assays as well as improvements to the GoDig platform, we created GoDigViewer, a new stand-alone software that provides detailed visualizations of GoDig runs. GoDigViewer guided the implementation of "priming runs," an acquisition mode with significantly higher success rates. In this mode, two or more chromatographic priming runs are automatically performed to improve the accuracy and precision of target elution orders, followed by analytical runs which quantify targets. Using priming runs, success rates exceeded 97% for a list of 400 peptide targets and 95% for a list of 200 targets that are usually not quantified using untargeted mass spectrometry. We used priming runs to establish a quantitative assay of 125 macroautophagy proteins that had a >95% success rate and revealed differences in macroautophagy expression profiles across four human cell lines.

Differential proteins from EVs identification based on tandem mass tags analysis and effect of Treg-derived EVs on T-lymphocytes in COPD patients.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a widespread respiratory disease. This study examines extracellular vesicles (EVs) and proteins contained in EVs in COPD. METHODS: Blood samples were collected from 40 COPD patients and 10 health controls. Cytokines including IFN-γ, TNF-α, IL-1ß, IL-6, IL-8, and IL-17, were measured by ELISA. Small EVs samples were extracted from plasma and identified by transmission electron microscope (TEM), nanoparticle tracking analysis (NTA), and Western blot. Protein components contained in EVs were analyzed by Tandem Mass Tags (TMT) to identify differential proteins. Treg-derived EV was extracted and added to isolated CD8+, Treg, and Th17 subsets to assess its effect on T-lymphocytes. RESULTS: ELISA revealed higher levels of all cytokines and flow cytometry suggested a higher proportion of Treg and Th17 cells in COPD patients. After identification, TMT analysis identified 207 unique protein components, including five potential COPD biomarkers: BTRC, TRIM28, CD209, NCOA3, and SSR3. Flow cytometry revealed that Treg-derived EVs inhibited differentiation into CD8+, CD4+, and Th17 cells. CONCLUSION: The study shows that cytokines, T-lymphocyte subsets differences in COPD and Treg-derived EVs influence T-lymphocyte differentiation. Identified biomarkers may assist in understanding COPD pathogenesis, prognosis, and therapy. The study contributes to COPD biomarker research.

Quantitative urinary proteome analysis reveals potential biomarkers for disease activity of Behcet's disease uveitis.

PURPOSE: Behçet's disease-associated uveitis (BDU) is a severe, recurrent inflammatory condition affecting the eye and is part of a systemic vasculitis with unknown etiology, making biomarker discovery essential for disease management. In this study, we intend to investigate potential urinary biomarkers to monitor the disease activity of BDU. METHODS: Firstly, label-free data-dependent acquisition (DDA) and tandem mass tag (TMT)-labeled quantitative proteomics methods were used to profile the proteomes of urine from active and quiescent BDU patients, respectively. For further exploration, the remaining fifty urine samples were analyzed by a data-independent acquisition (DIA) quantitative proteomics method. RESULTS: Twenty-nine and 21 differential proteins were identified in the same urine from BDU patients by label-free DDA and TMT-labeled analyses, respectively. Seventy-nine differentially expressed proteins (DEPs) were significantly changed in other active BDU urine samples compared to those in quiescent BDU urine samples by IDA analysis. Gene Ontology (GO) and protein-protein interaction (PPI) analyses revealed that the DEPs were associated with multiple functions, including the immune and neutrophil activation responses. Finally, seven proteins were identified as candidate biomarkers for BDU monitoring and recurrence prediction, namely, CD38, KCRB, DPP4, FUCA2, MTPN, S100A8 and S100A9. CONCLUSIONS: Our results showed that urine can be a good source of biomarkers for BDU. These dysregulated proteins provide potential urinary biomarkers for BDU activity monitoring and provide valuable clues for the analysis of the pathogenic mechanisms of BDU.

Differential histone deacetylase inhibitor-induced perturbations of the global proteome landscape in the setting of high-grade serous ovarian cancer.

High-grade serous ovarian cancer (HGSOC) is the most lethal gynecologic malignancy in women. Its low survival rate is attributed to late detection, relapse, and drug resistance. The lack of effective second-line therapeutics remains a significant challenge. There is an opportunity to incorporate the use of histone deacetylase inhibitors (HDACi) into HGSOC treatment. However, the mechanism and efficacy of HDACi in the context of BRCA-1/2 mutation status is understudied. Therefore, we set out to elucidate how HDACi perturb the proteomic landscape within HGSOC cells. In this work, we used TMT labeling followed by data-dependent acquisition LC-MS/MS to quantitatively determine differences in the global proteomic landscape across HDACi-treated CAOV3, OVCAR3, and COV318 (BRCA-1/2 wildtype) HGSOC cells. We identified significant differences in the HDACi-induced perturbations of global protein regulation across CAOV3, OVCAR3, and COV318 cells. The HDACi Vorinostat and Romidepsin were identified as being the least and most effective in inhibiting HDAC activity across the three cell lines, respectively. Our results provide a justification for the further investigation of the functional mechanisms associated with the differential efficacy of FDA-approved HDACi within the context of HGSOC. This will enhance the efficacy of targeted HGSOC therapeutic treatment modalities that include HDACi.

Proteomics identifies differentially expressed proteins in glioblastoma U87 cells treated with hederagenin.

OBJECTIVE: We investigated differentially expressed proteins (DEPs) in human glioblastoma U87 cells after treatment with hederagenin as a therapeutic screening mechanism and provided a theoretical basis for hederagenin in treating glioblastoma. METHODS: The Cell Counting Kit 8 assay was used to analyze the inhibitory effect of hederagenin on the proliferation of U87 cells. Protein was identified by tandem mass tags and LC-MS/MS analysis techniques. Annotation of DEPs, Gene Ontology enrichment and function, and Kyoto Encyclopedia of Genes and Genomes pathways and domains were all examined by bioinformatics. According to the TMT results, hub protein was selected from DEPs for WB verification. RESULTS: Protein quantitative analysis found 6522 proteins in total. Compared with the control group, 43 DEPs (P < 0.05) were involved in the highly enriched signaling pathway in the hederagenin group, among which 20 proteins were upregulated, and 23 proteins were downregulated. These different proteins are mainly involved in the longness regulating pathway-WORM, the hedgehog signaling pathway, Staphylococcus aureus infection, complement, coagulation cascades, and mineral absorption. KIF7 and ATAD2B expression were significantly down-regulated and PHEX and TIMM9 expression were significantly upregulated, according to WB analysis, supporting the TMT findings. CONCLUSION: Hederagenin inhibition of GBM U87 cells may be related to KIF7, which is mainly involved in the hedgehog signaling pathway. Our findings lay a foundation for additional study of the therapeutic mechanism of hederagenin.

Proteomic Analysis of Protective Effects of Dl-3-n-Butylphthalide against mpp + -Induced Toxicity via downregulating P53 pathway in N2A Cells.

BACKGROUND: Dl-3-n-butylphthalide (NBP) is an important medial therapy for acute ischemic stroke in China. Recent studied have revealed that NBP not only rescued the loss of dopaminergic neurons in cellular and animal models of Parkinson's disease (PD), but also could improve motor symptoms in PD patients. However, the protective mechanism is not fully understood. P53 is a multifunctional protein implicated in numerous cellular processes, including apoptosis, DNA repair, mitochondrial functions, redox homeostasis, autophagy and protein aggregations. In PD, p53 integrated with various neurodegeneration-related signals inducing neuronal loss, indicating the suppression of P53 might be a promising target for PD treatment. Therefore, the purpose of the current study was to systemically screen new therapeutic targets of NBP in PD. METHOD: In our study, we constructed mpp + induced N2A cells to investigate the benefit effect of NBP in PD. MTT assay was performed to evaluate the cell viability; TMT-based LC-MS/MS was applied to determine the different expressed proteins (DEPs) of NBP pretreatment; online bioinformatics databases such as DAVID, STRING, and KEGG was used to construe the proteomic data. After further analyzed and visualized the protein-protein interactions (PPI) by Cytoscape, DEPs were verified by western blot. RESULT: A total of 5828 proteins were quantified in the comparative proteomics experiments and 417 proteins were considered as DEPs (fold change > 1.5 and p < 0.05). Among the 417 DEPs, 140 were upregulated and 277 were downregulated in mpp + -induced N2A cells with NBP pretreatment. KEGG pathway analysis indicated that lysosome, phagosome, apoptosis, endocytosis and ferroptosis are the mainly enriched pathways. By using MCL clustering in PPI analysis, 48 clusters were generated and the subsequent KEGG analysis of the top 3 clusters revealed that P53 signaling pathway was recognized as the dominant pathway for NBP treatment. CONCLUSION: NBP significantly relived mpp + -induced cell toxicity. The neuroprotective role of NBP was implicated with P53 signaling pathway in some extent. These findings will reinforce the understanding of the mechanism of NBP in PD and identify novel therapeutic targets.

Metabolite Cross-Feeding Promoting NADH Production and Electron Transfer during Efficient SMX Biodegradation by a Denitrifier and S. oneidensis MR-1 in the Presence of Nitrate.

Antibiotics often coexist with other pollutants (e.g., nitrate) in an aquatic environment, and their simultaneous biological removal has attracted widespread interest. We have found that sulfamethoxazole (SMX) and nitrate can be efficiently removed by the coculture of a model denitrifier (Paracoccus denitrificans, Pd) and Shewanella oneidensis MR-1 (So), and SMX degradation is affected by NADH production and electron transfer. In this paper, the mechanism of a coculture promoting NADH production and electron transfer was investigated by proteomic analysis and intermediate experiments. The results showed that glutamine and lactate produced by Pd were captured by So to synthesize thiamine and heme, and the released thiamine was taken up by Pd as a cofactor of pyruvate and ketoglutarate dehydrogenase, which were related to NADH generation. Additionally, Pd acquired heme, which facilitated electron transfer as heme, was the important composition of complex III and cytochrome c and the iron source of iron sulfur clusters, the key component of complex I in the electron transfer chain. Further investigation revealed that lactate and glutamine generated by Pd prompted So chemotactic moving toward Pd, which helped the two bacteria effectively obtain their required substances. Obviously, metabolite cross-feeding promoted NADH production and electron transfer, resulting in efficient SMX biodegradation by Pd and So in the presence of nitrate. Its feasibility was finally verified by the coculture of an activated sludge denitrifier and So.

Sensitive and Quantitative Detection of MHC-I Displayed Neoepitopes Using a Semiautomated Workflow and TOMAHAQ Mass Spectrometry.

Advances in several key technologies, including MHC peptidomics, have helped fuel our understanding of basic immune regulatory mechanisms and the identification of T cell receptor targets for the development of immunotherapeutics. Isolating and accurately quantifying MHC-bound peptides from cells and tissues enables characterization of dynamic changes in the ligandome due to cellular perturbations. However, the current multistep analytical process is challenging, and improvements in throughput and reproducibility would enable rapid characterization of multiple conditions in parallel. Here, we describe a robust and quantitative method whereby peptides derived from MHC-I complexes from a variety of cell lines, including challenging adherent lines such as MC38, can be enriched in a semiautomated fashion on reusable, dry-storage, customized antibody cartridges. Using this method, a researcher, with very little hands-on time and in a single day, can perform up to 96 simultaneous enrichments at a similar level of quality as a manual workflow. TOMAHAQ (Triggered by Offset, Multiplexed, Accurate-mass, High-resolution, and Absolute Quantification), a targeted mass spectrometry technique that combines sample multiplexing and high sensitivity, was employed to characterize neoepitopes displayed on MHC-I by tumor cells and to quantitatively assess the influence of neoantigen expression and induced degradation on neoepitope presentation. This unique combination of robust semiautomated MHC-I peptide isolation and high-throughput multiplexed targeted quantitation allows for both the routine analysis of >4000 unique MHC-I peptides from 250 million cells using nontargeted methods, as well as quantitative sensitivity down to the low amol/µl level using TOMAHAQ targeted MS.

Comparative Host Interactomes of the SARS-CoV-2 Nonstructural Protein 3 and Human Coronavirus Homologs.

Human coronaviruses have become an increasing threat to global health; three highly pathogenic strains have emerged since the early 2000s, including most recently SARS-CoV-2, the cause of COVID-19. A better understanding of the molecular mechanisms of coronavirus pathogenesis is needed, including how these highly virulent strains differ from those that cause milder, common-cold-like disease. While significant progress has been made in understanding how SARS-CoV-2 proteins interact with the host cell, nonstructural protein 3 (nsp3) has largely been omitted from the analyses. Nsp3 is a viral protease with important roles in viral protein biogenesis, replication complex formation, and modulation of host ubiquitinylation and ISGylation. Herein, we use affinity purification-mass spectrometry to study the host-viral protein-protein interactome of nsp3 from five coronavirus strains: pathogenic strains SARS-CoV-2, SARS-CoV, and MERS-CoV; and endemic common-cold strains hCoV-229E and hCoV-OC43. We divide each nsp3 into three fragments and use tandem mass tag technology to directly compare the interactors across the five strains for each fragment. We find that few interactors are common across all variants for a particular fragment, but we identify shared patterns between select variants, such as ribosomal proteins enriched in the N-terminal fragment (nsp3.1) data set for SARS-CoV-2 and SARS-CoV. We also identify unique biological processes enriched for individual homologs, for instance, nuclear protein import for the middle fragment of hCoV-229E, as well as ribosome biogenesis of the MERS nsp3.2 homolog. Lastly, we further investigate the interaction of the SARS-CoV-2 nsp3 N-terminal fragment with ATF6, a regulator of the unfolded protein response. We show that SARS-CoV-2 nsp3.1 directly binds to ATF6 and can suppress the ATF6 stress response. Characterizing the host interactions of nsp3 widens our understanding of how coronaviruses co-opt cellular pathways and presents new avenues for host-targeted antiviral therapeutics.

Thyroglobulin Interactome Profiling Defines Altered Proteostasis Topology Associated With Thyroid Dyshormonogenesis.

Thyroglobulin (Tg) is a secreted iodoglycoprotein serving as the precursor for triiodothyronine and thyroxine hormones. Many characterized Tg gene mutations produce secretion-defective variants resulting in congenital hypothyroidism. Tg processing and secretion is controlled by extensive interactions with chaperone, trafficking, and degradation factors comprising the secretory proteostasis network. While dependencies on individual proteostasis network components are known, the integration of proteostasis pathways mediating Tg protein quality control and the molecular basis of mutant Tg misprocessing remain poorly understood. We employ a multiplexed quantitative affinity purification-mass spectrometry approach to define the Tg proteostasis interactome and changes between WT and several congenital hypothyroidism variants. Mutant Tg processing is associated with common imbalances in proteostasis engagement including increased chaperoning, oxidative folding, and engagement by targeting factors for endoplasmic reticulum-associated degradation. Furthermore, we reveal mutation-specific changes in engagement with N-glycosylation components, suggesting distinct requirements for 1 Tg variant on dual engagement of both oligosaccharyltransferase complex isoforms for degradation. Modulating dysregulated proteostasis components and pathways may serve as a therapeutic strategy to restore Tg secretion and thyroid hormone biosynthesis.

Optimal analytical strategies for sensitive and quantitative phosphoproteomics using TMT-based multiplexing.

In large-scale quantitative mass spectrometry (MS)-based phosphoproteomics, isobaric labeling with tandem mass tags (TMTs) coupled with offline high-pH reversed-phase peptide chromatographic fractionation maximizes depth of coverage. To investigate to what extent limited sample amounts affect sensitivity and dynamic range of the analysis due to sample losses, we benchmarked TMT-based fractionation strategies against single-shot label-free quantification with spectral library-free data independent acquisition (LFQ-DIA), for different peptide input per sample. To systematically examine how peptide input amounts influence TMT-fractionation approaches in a phosphoproteomics workflow, we compared two different high-pH reversed-phase fractionation strategies, microflow (MF) and stage-tip fractionation (STF), while scaling the peptide input amount down from 12.5 to 1 µg per sample. Our results indicate that, for input amounts higher than 5 µg per sample, TMT labeling, followed by microflow fractionation (MF) and phospho-enrichment, achieves the deepest phosphoproteome coverage, even compared to single shot direct-DIA analysis. Conversely, STF of enriched phosphopeptides (STF) is optimal for lower amounts, below 5 µg/peptide per sample. As a result, we provide a decision tree to help phosphoproteomics users to choose the best workflow as a function of sample amount.

Retention Time Prediction for TMT-Labeled Peptides in Proteomic LC-MS Experiments.

We present the first detailed study of chromatographic behavior of peptides labeled with tandem mass tags (TMT and TMTpro) in 2D LC for proteomic applications. Carefully designed experimental procedures have permitted generating data sets of over 100,000 nonlabeled and TMT-labeled peptide pairs for the low pH RP in the second separation dimension and data sets of over 10,000 peptide pairs for high-pH RP, HILIC (amide and silica), and SCX separations in the first separation dimension. The average increase in peptide RPLC (0.1% formic acid) retention upon TMT labeling was found to be 3.3% acetonitrile (linear water/acetonitrile gradients), spanning a range of -4 to 10.3%. In addition to the bulk peptide properties such as length, hydrophobicity, and the number of labeled residues, we found several sequence-dependent features mostly associated with differences in N-terminal chemistry. The behavior of TMTpro-labeled peptides was found to be very similar except for a slightly higher hydrophobicity: an average retention shift of 3.7% acetonitrile. The respective versions of the sequence-specific retention calculator (SSRCalc) model have been developed to accommodate both TMT chemistries, showing identical prediction accuracy (R2 ∼ 0.98) for labeled and nonlabeled peptides. Higher retention for TMT-labeled peptides was observed for high-pH RP and HILIC separations, while SCX selectivity remained virtually unchanged.

In Vitro Kinase-to-Phosphosite Database (iKiP-DB) Predicts Kinase Activity in Phosphoproteomic Datasets.

Phosphoproteomics routinely quantifies changes in the levels of thousands of phosphorylation sites, but functional analysis of such data remains a major challenge. While databases like PhosphoSitePlus contain information about many phosphorylation sites, the vast majority of known sites is not assigned to any protein kinase. Assigning changes in the phosphoproteome to the activity of individual kinases therefore remains a key challenge. A recent large-scale study systematically identified in vitro substrates for most human protein kinases. Here, we reprocessed and filtered these data to generate an in vitro Kinase-to-Phosphosite database (iKiP-DB). We show that iKiP-DB can accurately predict changes in kinase activity in published phosphoproteomic data sets for both well-studied and poorly characterized kinases. We apply iKiP-DB to a newly generated phosphoproteomic analysis of SARS-CoV-2 infected human lung epithelial cells and provide evidence for coronavirus-induced changes in host cell kinase activity. In summary, we show that iKiP-DB is widely applicable to facilitate the functional analysis of phosphoproteomic data sets.

Temporal Quantitative Proteomics of mGluR-induced Protein Translation and Phosphorylation in Neurons.

At neuronal synapses, activation of group I metabotropic glutamate receptors (mGluR1/5) triggers a form of long-term depression (mGluR-LTD) that relies on new protein synthesis and the internalization of AMPA-type glutamate receptors. Dysregulation of these processes has been implicated in the development of mental disorders such as autism spectrum disorders and therefore merit a better understanding on a molecular level. Here, to study mGluR-induced signaling pathways, we integrated quantitative phosphoproteomics with the analyses of newly synthesized proteins via bio-orthogonal amino acids (azidohomoalanine) in a pulsed labeling strategy in cultured hippocampal neurons stimulated with DHPG, a specific agonist for group I mGluRs. We identified several kinases with important roles in DHPG-induced mGluR activation, which we confirmed using small molecule kinase inhibitors. Furthermore, changes in the AMPA receptor endocytosis pathway in both protein synthesis and protein phosphorylation were identified, whereby Intersectin-1 was validated as a novel player in this pathway. This study revealed several new insights into the molecular pathways downstream of group I mGluR activation in hippocampal neurons, and provides a rich resource for further analyses.

Categorization of Phosphorylation Site Behavior during the Diauxic Shift in Saccharomyces cerevisiae.

Protein phosphorylation has long been recognized as an essential regulator of protein activity, structure, complex formation, and subcellular localization among other cellular mechanisms. However, interpretation of the changes in protein phosphorylation is difficult. To address this difficulty, we measured protein and phosphorylation site changes across 11 points of a time course and developed a method for categorizing phosphorylation site behavior relative to protein level changes using the diauxic shift in yeast as a model and TMT11 sample multiplexing. We classified quantified proteins into behavioral categories that reflected differences in kinase activity, protein complex structure, and growth and metabolic pathway regulation across different phases of the diauxic shift. These data also provide a valuable resource for the study of fermentative versus respiratory growth and set a new benchmark for temporal quantitative proteomics and phosphoproteomics for the diauxic shift in Saccharomyces cerevisiae. Data are available via ProteomeXchange with identifier PXD022741.