Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes

Turnover atlas of proteome and phosphoproteome across mouse tissues and brain regions

Understanding how proteins in different mammalian tissues are regulated is central to biology. Protein abundance, turnover, and post-translational modifications such as phosphorylation are key factors that determine tissue-specific proteome properties. However, these properties are challenging to study across tissues and remain poorly understood. Here, we present Turnover-PPT, a comprehensive resource mapping the abundance and lifetime of 11,000 proteins and 40,000 phosphosites in eight mouse tissues and various brain regions using advanced proteomics and stable isotope labeling. We reveal tissue-specific short- and long-lived proteins, strong correlations between interacting protein lifetimes, and distinct impacts of phosphorylation on protein turnover. Notably, we discover a remarkable pattern of turnover changes for peroxisome proteins in specific tissues and that phosphorylation regulates the stability of neurodegeneration-related proteins, such as Tau and α-synuclein. Thus, Turnover-PPT provides fundamental insights into protein stability, tissue dynamic proteotypes, and functional protein phosphorylation and is accessible via an interactive web-based portal at https://yslproteomics.shinyapps.io/tissuePPT.

GABAA receptor π forms channels that stimulate ERK through a G-protein-dependent pathway

The rare γ-aminobutyric acid type-A receptor (GABAAR) subunit π (GABRP) is highly expressed in certain cancers, where it stimulates growth through extracellular-regulated kinase (ERK) signaling by an uncharacterized pathway. To elucidate GABRP's signaling mechanism, we determined cryoelectron microscopy (cryo-EM) structures of GABRP embedded in native nanodiscs, both in the presence and absence of GABA. Structurally, GABRP homopentamers closely resemble heteropentameric GABAAR anion channels, transitioning from a closed "resting" state to an open "active" state upon GABA binding. However, functional assays reveal that GABRP responds more like a type-B metabotropic receptor. At physiological concentrations of GABA, chloride flux is not detected. Rather, GABRP activates a G-protein-coupled pathway leading to ERK signaling. Ionotropic activity is only triggered at supraphysiological GABA concentrations, effectively decoupling it from GABRP's signaling functions. These findings provide a structural and functional blueprint for GABRP, opening new avenues for targeted inhibition of GABA growth signals in GABRP-positive cancers.

An Extensive Atlas of Proteome and Phosphoproteome Turnover Across Mouse Tissues and Brain Regions

Understanding how proteins in different mammalian tissues are regulated is central to biology. Protein abundance, turnover, and post-translational modifications like phosphorylation, are key factors that determine tissue-specific proteome properties. However, these properties are challenging to study across tissues and remain poorly understood. Here, we present Turnover-PPT, a comprehensive resource mapping the abundance and lifetime of 11,000 proteins and 40,000 phosphosites across eight mouse tissues and various brain regions, using advanced proteomics and stable isotope labeling. We revealed tissue-specific short- and long-lived proteins, strong correlations between interacting protein lifetimes, and distinct impacts of phosphorylation on protein turnover. Notably, we discovered that peroxisomes are regulated by protein turnover across tissues, and that phosphorylation regulates the stability of neurodegeneration-related proteins, such as Tau and α-synuclein. Thus, Turnover-PPT provides new fundamental insights into protein stability, tissue dynamic proteotypes, and the role of protein phosphorylation, and is accessible via an interactive web-based portal at https://yslproteomics.shinyapps.io/tissuePPT.

Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

High-Throughput Proteomics and Phosphoproteomics of Rat Tissues Using Microflow Zeno SWATH

High-throughput tissue proteomics has great potential in the advancement of precision medicine. Here, we investigated the combined sensitivity of trap-elute microflow liquid chromatography with a ZenoTOF for DIA proteomics and phosphoproteomics. Method optimization was conducted on HEK293T cell lines to determine the optimal variable window size, MS2 accumulation time and gradient length. The ZenoTOF 7600 was then compared to the previous generation TripleTOF 6600 using eight rat organs, finding up to 23% more proteins using a fifth of the sample load and a third of the instrument time. Spectral reference libraries generated from Zeno SWATH data in FragPipe (MSFragger-DIA/DIA-NN) contained 4 times more fragment ions than the DIA-NN only library and quantified more proteins. Replicate single-shot phosphopeptide enrichments of 50-100 μg of rat tryptic peptide were analyzed by microflow HPLC using Zeno SWATH without fractionation. Using Spectronaut we quantified a shallow phosphoproteome containing 1000-3000 phosphoprecursors per organ. Promisingly, clear hierarchical clustering of organs was observed with high Pearson correlation coefficients >0.95 between replicate enrichments and median CV of 20%. The combined sensitivity of microflow HPLC with Zeno SWATH allows for the high-throughput quantitation of an extensive proteome and shallow phosphoproteome from small tissue samples.

EGFR targeting PhosTACs as a dual inhibitory approach reveals differential downstream signaling

We recently developed a heterobifunctional approach [phosphorylation targeting chimeras (PhosTACs)] to achieve the targeted protein dephosphorylation (TPDephos). Here, we envisioned combining the inhibitory effects of receptor tyrosine kinase inhibitors (RTKIs) and the active dephosphorylation by phosphatases to achieve dual inhibition of kinases. We report an example of tyrosine phosphatase-based TPDephos and the effective epidermal growth factor receptor (EGFR) tyrosine dephosphorylation. We also used phosphoproteomic approaches to study the signaling transductions affected by PhosTAC-related molecules at the proteome-wide level. This work demonstrated the differential signaling pathways inhibited by PhosTAC compared with the TKI, gefitinib. Moreover, a covalent PhosTAC selective for mutated EGFR was developed and showed its inhibitory potential for dysregulated EGFR. Last, EGFR PhosTACs, consistent with EGFR dephosphorylation profiles, induced apoptosis and inhibited cancer cell viability during prolonged PhosTAC treatment. PhosTACs showcased their potential of modulating RTKs activity, expanding the scope of bifunctional molecule utility.

The CUL5 E3 ligase complex negatively regulates central signaling pathways in CD8+ T cells

CD8+ T cells play an important role in anti-tumor immunity. Better understanding of their regulation could advance cancer immunotherapies. Here we identify, via stepwise CRISPR-based screening, that CUL5 is a negative regulator of the core signaling pathways of CD8+ T cells. Knocking out CUL5 in mouse CD8+ T cells significantly improves their tumor growth inhibiting ability, with significant proteomic alterations that broadly enhance TCR and cytokine signaling and their effector functions. Chemical inhibition of neddylation required by CUL5 activation, also enhances CD8 effector activities with CUL5 validated as a major target. Mechanistically, CUL5, which is upregulated by TCR stimulation, interacts with the SOCS-box-containing protein PCMTD2 and inhibits TCR and IL2 signaling. Additionally, CTLA4 is markedly upregulated by CUL5 knockout, and its inactivation further enhances the anti-tumor effect of CUL5 KO. These results together reveal a negative regulatory mechanism for CD8+ T cells and have strong translational implications in cancer immunotherapy.

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

A major challenge in mass spectrometry-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. Machine learning techniques are promising approaches for leveraging large-scale phosphoproteomics data to computationally predict substrates of kinases. However, the small number of experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together limit their applicability and utility. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, and both traditional and deep learning models, we first demonstrate that a 'pseudo-positive' learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data resampling-based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model ('SnapKin') by incorporating the above two learning strategies into a 'snapshot' ensemble learning algorithm. We propose SnapKin, an ensemble deep learning method, for predicting substrates of kinases from large-scale phosphoproteomics data. We demonstrate that SnapKin consistently outperforms existing methods in kinase-substrate prediction. SnapKin is freely available at https://github.com/PYangLab/SnapKin.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

MSBooster: improving peptide identification rates using deep learning-based features

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

An optogenetic-phosphoproteomic study reveals dynamic Akt1 signaling profiles in endothelial cells

The serine/threonine kinase AKT is a central node in cell signaling. While aberrant AKT activation underlies the development of a variety of human diseases, how different patterns of AKT-dependent phosphorylation dictate downstream signaling and phenotypic outcomes remains largely enigmatic. Herein, we perform a systems-level analysis that integrates methodological advances in optogenetics, mass spectrometry-based phosphoproteomics, and bioinformatics to elucidate how different intensity, duration, and pattern of Akt1 stimulation lead to distinct temporal phosphorylation profiles in vascular endothelial cells. Through the analysis of ~35,000 phosphorylation sites across multiple conditions precisely controlled by light stimulation, we identify a series of signaling circuits activated downstream of Akt1 and interrogate how Akt1 signaling integrates with growth factor signaling in endothelial cells. Furthermore, our results categorize kinase substrates that are preferably activated by oscillating, transient, and sustained Akt1 signals. We validate a list of phosphorylation sites that covaried with Akt1 phosphorylation across experimental conditions as potential Akt1 substrates. Our resulting dataset provides a rich resource for future studies on AKT signaling and dynamics.

Integrated proteomic and phosphoproteomic data-independent acquisition data evaluate the personalized drug responses of primary and metastatic tumors in colorectal cancer

Mass spectrometry (MS)-based proteomics and phosphoproteomics are powerful methods to study the biological mechanisms, diagnostic biomarkers, prognostic analysis, and drug therapy of tumors. Data-independent acquisition (DIA) mode is considered to perform better than data-dependent acquisition (DDA) mode in terms of quantitative reproducibility, specificity, accuracy, and identification of low-abundance proteins. Mini patient derived xenograft (MiniPDX) model is an effective model to assess the response to antineoplastic drugs in vivo and is helpful for the precise treatment of cancer patients. Kinases are favorable spots for tumor-targeted drugs, and their functional completion relies on signaling pathways through phosphorylating downstream substrates. Kinase-phosphorylation networks or edge interactions are considered more credible and permanent for characterizing complex diseases. Here, we provide a workflow for personalized drug response assessment in primary and metastatic colorectal cancer (CRC) tumors using DIA proteomic data, DIA phosphoproteomic data, and MiniPDX models. Three kinase inhibitors, afatinib, gefitinib, and regorafenib, are tested pharmacologically. The process mainly includes the following steps: clinical tissue collection, sample preparation, hybrid spectral libraries establishment, MS data acquisition, kinase-substrate network construction, in vivo drug test, and elastic regression modeling. Our protocol gives a more direct data basis for individual drug responses, and will improve the selection of treatment strategies for patients without the druggable mutation.

A basic phosphoproteomic-DIA workflow integrating precise quantification of phosphosites in systems biology

Phosphorylation is one of the most important post-translational modifications (PTMs) of proteins, governing critical protein functions. Most human proteins have been shown to undergo phosphorylation, and phosphoproteomic studies have been widely applied due to recent advancements in high-resolution mass spectrometry technology. Although the experimental workflow for phosphoproteomics has been well-established, it would be useful to optimize and summarize a detailed, feasible protocol that combines phosphoproteomics and data-independent acquisition (DIA), along with follow-up data analysis procedures due to the recent instrumental and bioinformatic advances in measuring and understanding tens of thousands of site-specific phosphorylation events in a single experiment. Here, we describe an optimized Phos-DIA protocol, from sample preparation to bioinformatic analysis, along with practical considerations and experimental configurations for each step. The protocol is designed to be robust and applicable for both small-scale phosphoproteomic analysis and large-scale quantification of hundreds of samples for studies in systems biology and systems medicine.

Proteome and phosphoproteome analysis of 2,4-epibrassinolide-mediated cold stress response in cucumber seedlings

The 2, 4-epibrassinolide (EBR) significantly increased plants cold tolerance. However, mechanisms of EBR in regulating cold tolerance in phosphoproteome and proteome levels have not been reported. The mechanism of EBR regulating cold response in cucumber was studied by multiple omics analysis. In this study, phosphoproteome analysis showed that cucumber responded to cold stress through multi-site serine phosphorylation, while EBR further upregulated single-site phosphorylation for most of cold-responsive phosphoproteins. Association analysis of the proteome and phosphoproteome revealed that EBR reprogrammed proteins in response to cold stress by negatively regulating protein phosphorylation and protein content, and phosphorylation negatively regulated protein content in cucumber. Further functional enrichment analysis of proteome and phosphoproteome showed that cucumber mainly upregulated phosphoproteins related to spliceosome, nucleotide binding and photosynthetic pathways in response to cold stress. However, different from the EBR regulation in omics level, hypergeometric analysis showed that EBR further upregulated 16 cold-up-responsive phosphoproteins participated photosynthetic and nucleotide binding pathways in response to cold stress, suggested their important function in cold tolerance. Analysis of cold-responsive transcription factors (TFs) by correlation between proteome and phosphoproteome showed that cucumber regulated eight class TFs may through protein phosphorylation under cold stress. Further combined with cold-related transcriptome found that cucumber phosphorylated eight class TFs, and mainly through targeting major hormone signal genes by bZIP TFs in response to cold stress, while EBR further increased these bZIP TFs (CsABI5.2 and CsABI5.5) phosphorylation level. In conclusion, the EBR mediated schematic of molecule response mechanisms in cucumber under cold stress was proposed.

Targeted Dephosphorylation of Tau by Phosphorylation Targeting Chimeras (PhosTACs) as a Therapeutic Modality

Microtubule-associated protein tau is essential for microtubule assembly and stabilization. Hyperphosphorylation of the microtubule-associated protein tau plays an important pathological role in the development of Alzheimer's disease and other tauopathies. In vivo studies using kinase inhibitors suggest that reducing tau phosphorylation levels has therapeutic potential; however, such approaches showed limited benefits. We sought to further develop our phosphorylation targeting chimera (PhosTAC) technology to specifically induce tau dephosphorylation. Herein, we use small molecule-based PhosTACs to recruit tau to PP2A, a native tau phosphatase. PhosTACs induced the formation of a stable ternary complex, leading to rapid, efficient, and sustained tau dephosphorylation, which also correlated with the enhanced downregulation of tau protein. Mass spectrometry data validated that PhosTACs downregulated multiple phosphorylation sites of tau. We believe that PhosTAC possesses several advantages over current strategies to modulate tau phosphorylation and represents a new avenue for disease-modifying therapies for tauopathies.

A dataset resource for clinically associated phosphosites in hepatocellular carcinoma

Phosphorylation is one of the most common post-translational modifications (PTMs) and is closely related to protein activity and function, playing a critical role during cancer development. Quantitative phosphoproteomic strategies have been widely used to study the underlying mechanisms of cancer progression or drug resistance. In this report, we analyzed the association of phosphosite levels originated from our previously reported proteogenomic study in hepatocellular carcinoma (HCC) with clinical parameters, including prognosis, recurrence, and Tumor-Node-Metastasis (TNM) stages. By using both the log-rank test and univariate Cox proportional hazards regression analysis, we found that the abundance levels of 1712 phosphosites were associated with prognosis and those of 393 phosphosites associated with recurrence. Besides, 692 phosphosites had different abundance levels among TNM stages (I, II, III+IV) by Analysis of Variance (ANOVA) test. Gene ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using proteins with these statistically significant phosphosites. In conclusion, we provided a dataset resource for clinically associated phosphosites in HCC, which may be beneficial to liver cancer related basic research.

Genomic and proteomic findings in early melanoma and opportunities for early diagnosis

Overdiagnosis of early melanoma is a significant problem. Due to subtle unique and overlapping clinical and histological criteria between pigmented lesions and the risk of mortality from melanoma, some benign pigmented lesions are diagnosed as melanoma. Although histopathology is the gold standard to diagnose melanoma, there is a demand to find alternatives that are more accurate and cost-effective. In the current "omics" era, there is gaining interest in biomarkers to help diagnose melanoma early and to further understand the mechanisms driving tumor progression. Genomic investigations have attempted to differentiate malignant melanoma from benign pigmented lesions. However, genetic biomarkers of early melanoma diagnosis have not yet proven their value in the clinical setting. Protein biomarkers may be more promising since they directly influence tissue phenotype, a result of by-products of genomic mutations, posttranslational modifications and environmental factors. Uncovering relevant protein biomarkers could increase confidence in their use as diagnostic signatures. Currently, proteomic investigations of melanoma progression from pigmented lesions are limited. Studies have previously characterised the melanoma proteome from cultured cell lines and clinical samples such as serum and tissue. This has been useful in understanding how melanoma progresses into metastasis and development of resistance to adjuvant therapies. Currently, most studies focus on metastatic melanoma to find potential drug therapy targets, prognostic factors and markers of resistance. This paper reviews recent advancements in the genomics and proteomic fields and reports potential avenues, which could help identify and differentiate melanoma from benign pigmented lesions and prevent the progression of melanoma.

Phosphoproteomic analysis of metformin signaling in colorectal cancer cells elucidates mechanism of action and potential therapeutic opportunities

BACKGROUND

The biguanide drug metformin is a safe and widely prescribed drug for type 2 diabetes. Interestingly, hundreds of clinical trials have been set to evaluate the potential role of metformin in the prevention and treatment of cancer including colorectal cancer (CRC). However, the "metformin signaling" remains controversial.

AIMS AND METHODS

To interrogate cell signaling induced by metformin in CRC and explore the druggability of the metformin-rewired phosphorylation network, we performed integrative analysis of phosphoproteomics, bioinformatics, and cell proliferation assays on a panel of 12 molecularly heterogeneous CRC cell lines. Using the high-resolute data-independent analysis mass spectrometry (DIA-MS), we monitored a total of 10,142 proteins and 56,080 phosphosites (P-sites) in CRC cells upon a short- and a long-term metformin treatment.

RESULTS AND CONCLUSIONS

We found that metformin tended to primarily remodel cell signaling in the long-term and only minimally regulated the total proteome expression levels. Strikingly, the phosphorylation signaling response to metformin was highly heterogeneous in the CRC panel, based on a network analysis inferring kinase/phosphatase activities and cell signaling reconstruction. A "MetScore" was determined to assign the metformin relevance of each P-site, revealing new and robust phosphorylation nodes and pathways in metformin signaling. Finally, we leveraged the metformin P-site signature to identify pharmacodynamic interactions and confirmed a number of candidate metformin-interacting drugs, including navitoclax, a BCL-2/BCL-xL inhibitor. Together, we provide a comprehensive phosphoproteomic resource to explore the metformin-induced cell signaling for potential cancer therapeutics. This resource can be accessed at https://yslproteomics.shinyapps.io/Metformin/.

Proteome and phosphoproteome profiling of non-small cell lung cancer cell line A549 treated with TRAIL

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is recognized for its promising therapeutic effects against cancer. However, mechanisms underlying the effect of TRAIL on protein expression, signal transduction, and apoptosis induction remain unclear. We surmised that a systematic analysis of the proteome and phosphoproteome associated with TRAIL signaling may help elucidate the mechanisms involved and facilitate the development of therapeutics. Therefore, we investigated the proteome and phosphoproteome of non-small cell lung cancer cell line A549 treated with TRAIL. Our results indicated that 126 proteins and 1684 phosphosites were markedly differentially expressed between the phosphate-buffered saline- and TRAIL-treated groups. The expression at protein and phosphosite levels were not completely consistent. Gene ontology functional analysis revealed that metal ion (zinc) binding was highly affected by TRAIL treatment. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that almost all pathways that involved differentially expressed phosphosites were associated with apoptosis. We also identified an important kinase, AKT1, and its series of substrates in TRAIL signaling. The results of this study may provide guidance for future research on tumor therapy using TRAIL.

Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover

The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulsed SILAC (pSILAC). Very recent studies including ours have integrated themeasurement of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics) with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased "hypothesis-free" analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during cell starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover.

Hippo Signaling Pathway Regulates Cancer Cell-Intrinsic MHC-II Expression

MHC-II is known to be mainly expressed on the surface of antigen-presenting cells. Evidence suggests MHC-II is also expressed by cancer cells and may be associated with better immunotherapy responses. However, the role and regulation of MHC-II in cancer cells remain unclear. In this study, we leveraged data mining and experimental validation to elucidate the regulation of MHC-II in cancer cells and its role in modulating the response to immunotherapy. We collated an extensive collection of omics data to examine cancer cell-intrinsic MHC-II expression and its association with immunotherapy outcomes. We then tested the functional relevance of cancer cell-intrinsic MHC-II expression using a syngeneic transplantation model. Finally, we performed data mining to identify pathways potentially involved in the regulation of MHC-II expression, and experimentally validated candidate regulators. Analyses of preimmunotherapy clinical samples in the CheckMate 064 trial revealed that cancer cell-intrinsic MHC-II protein was positively correlated with more favorable immunotherapy outcomes. Comprehensive meta-analyses of multiomics data from an exhaustive collection of data revealed that MHC-II is heterogeneously expressed in various solid tumors, and its expression is particularly high in melanoma. Using a syngeneic transplantation model, we further established that melanoma cells with high MHC-II responded better to anti-PD-1 treatment. Data mining followed by experimental validation revealed the Hippo signaling pathway as a potential regulator of melanoma MHC-II expression. In summary, we identified the Hippo signaling pathway as a novel regulator of cancer cell-intrinsic MHC-II expression. These findings suggest modulation of MHC-II in melanoma could potentially improve immunotherapy response.

Proteotype coevolution and quantitative diversity across 11 mammalian species

Evolutionary profiling has been largely limited to the nucleotide level. Using consistent proteomic methods, we quantified proteomic and phosphoproteomic layers in fibroblasts from 11 common mammalian species, with transcriptomes as reference. Covariation analysis indicates that transcript and protein expression levels and variabilities across mammals remarkably follow functional role, with extracellular matrix-associated expression being the most variable, demonstrating strong transcriptome-proteome coevolution. The biological variability of gene expression is universal at both interindividual and interspecies scales but to a different extent. RNA metabolic processes particularly show higher interspecies versus interindividual variation. Our results further indicate that while the ubiquitin-proteasome system is strongly conserved in mammals, lysosome-mediated protein degradation exhibits remarkable variation between mammalian lineages. In addition, the phosphosite profiles reveal a phosphorylation coevolution network independent of protein abundance.

A peptidoform based proteomic strategy for studying functions of post-translational modifications

Protein post-translational modifications (PTMs) generate an enormous, but as yet undetermined, expansion of the produced proteoforms. In this Viewpoint, we firstly reviewed the concepts of proteoform and peptidoform. We show that many of the current PTM biological investigation and annotation studies largely follow a PTM site-specific rather than proteoform-specific approach. We further illustrate a potentially useful matching strategy in which a particular "modified peptidoform" is matched to the corresponding "unmodified peptidoform" as a reference for the quantitative analysis between samples and conditions. We suggest this strategy has the potential to provide more directly relevant information to learn the PTM site-specific biological functions. Accordingly, we advocate for the wider use of the nomenclature "peptidoform" in future bottom-up proteomic studies.

Evolution of higher mesenchymal CD44 expression in the human lineage

CD44 is an extracellular matrix receptor implicated in cancer progression. CD44 increases the invasibility of skin (SF) and endometrial stromal fibroblasts (ESF) by cancer and trophoblast cells. We reasoned that the evolution of CD44 expression can affect both, the fetal–maternal interaction through CD44 in ESF as well as vulnerability to malignant cancer through expression in SF. We studied the evolution of CD44 expression in mammalian SF and ESF and demonstrate that in the human lineage evolved higher CD44 expression. Isoform expression in cattle and human is very similar suggesting that differences in invasibility are not due to the nature of expressed isoforms. We then asked whether the concerted gene expression increase in both cell types is due to shared regulatory mechanisms or due to cell type-specific factors. Reporter gene experiments with cells and cis-regulatory elements from human and cattle show that the difference of CD44 expression is due to cis effects as well as cell type-specific trans effects. These results suggest that the concerted expression increase is likely due to selection acting on both cell types because the evolutionary change in cell type-specific factors requires selection on cell type-specific functions. This scenario implies that the malignancy enhancing effects of elevated CD44 expression in humans likely evolved as a side-effect of positive selection on a yet unidentified other function of CD44. A possible candidate is the anti-fibrotic effect of CD44 but there are no reliable data showing that humans and primates are less fibrotic than other mammals.

Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells

INTRODUCTION

Many pathologies, including cancer, have been associated with aberrant phosphorylation-mediated signaling networks that drive altered cell proliferation, migration, metabolic regulation, and can lead to systemic inflammation. Phosphoproteomics, the large-scale analysis of protein phosphorylation sites, has emerged as a powerful tool to define signaling network regulation and dysregulation in normal and pathological conditions.

AREAS COVERED

We provide an overview of methodology for global phosphoproteomics as well as enrichment of specific subsets of the phosphoproteome, including phosphotyrosine and phospho-motif enrichment of kinase substrates. We review quantitative methods, advantages and limitations of different mass spectrometry acquisition formats, and computational approaches to extract biological insight from phosphoproteomics data. Throughout, we discuss various applications and their challenges in implementation.

EXPERT OPINION

Over the past 20 years the field of phosphoproteomics has advanced to enable deep biological and clinical insight through the quantitative analysis of signaling networks. Future areas of development include Clinical Laboratory Improvement Amendments (CLIA)-approved methods for analysis of clinical samples, continued improvements in sensitivity to enable analysis of small numbers of rare cells and tissue microarrays, and computational methods to integrate data resulting from multiple systems-level quantitative analytical methods.