Establishing Personalized Blood Protein Reference Ranges Using Noninvasive Microsampling and Targeted Proteomics: Implications for Antidoping Strategies.

To prevent doping practices in sports, the World Anti-Doping Agency implemented the Athlete Biological Passport (ABP) program, monitoring biological variables over time to indirectly reveal the effects of doping rather than detect the doping substance or the method itself. In the context of this program, a highly multiplexed mass spectrometry-based proteomics assay for 319 peptides corresponding to 250 proteins was developed, including proteins associated with blood-doping practices. "Baseline" expression profiles of these potential biomarkers in capillary blood (dried blood spots (DBS)) were established using multiple reaction monitoring (MRM). Combining DBS microsampling with highly multiplexed MRM assays is the best-suited technology to enhance the effectiveness of the ABP program, as it represents a cost-effective and robust alternative analytical method with high specificity and selectivity of targets in the attomole range. DBS data were collected from 10 healthy athlete volunteers over a period of 140 days (28 time points per participant). These comprehensive findings provide a personalized targeted blood proteome "fingerprint" showcasing that the targeted proteome is unique to an individual and likely comparable to a DNA fingerprint. The results can serve as a baseline for future studies investigating doping-related perturbations.

Accelerating Proteomics Using Broad Specificity Proteases.

Trypsin is the gold-standard protease in bottom-up proteomics, but many sequence stretches of the proteome are inaccessible to trypsin and standard LC-MS approaches. Thus, multienzyme strategies are used to maximize sequence coverage in post-translational modification profiling. We present fast and robust SP3- and STRAP-based protocols for the broad-specificity proteases subtilisin, proteinase K, and thermolysin. All three enzymes are remarkably fast, producing near-complete digests in 1-5 min, and cost 200-1000× less than proteomics-grade trypsin. Using FragPipe resolved a major challenge by drastically reducing the duration of the required "unspecific" searches. In-depth analyses of proteinase K, subtilisin, and thermolysin Jurkat digests identified 7374, 8178, and 8753 unique proteins with average sequence coverages of 21, 29, and 37%, including 10,000s of amino acids not reported in PeptideAtlas' >2400 experiments. While we could not identify distinct cleavage patterns, machine learning could distinguish true protease products from random cleavages, potentially enabling the prediction of cleavage products. Finally, proteinase K, subtilisin, and thermolysin enabled label-free quantitation of 3111, 3659, and 4196 unique Jurkat proteins, which in our hands is comparable to trypsin. Our data demonstrate that broad-specificity proteases enable quantitative proteomics of uncharted areas of the proteome. Their fast kinetics may allow "on-the-fly" digestion of samples in the future.

The Deubiquitylase Otub1 Regulates the Chemotactic Response of Splenic B Cells by Modulating the Stability of the γ-Subunit Gng2.

The ubiquitin proteasome system performs the covalent attachment of lysine 48-linked polyubiquitin chains to substrate proteins, thereby targeting them for degradation, while deubiquitylating enzymes (DUBs) reverse this process. This posttranslational modification regulates key features both of innate and adaptative immunity, including antigen presentation, protein homeostasis and signal transduction. Here we show that loss of one of the most highly expressed DUBs, Otub1, results in changes in murine splenic B cell subsets, leading to a significant increase in marginal zone and transitional B cells and a concomitant decrease in follicular B cells. We demonstrate that Otub1 interacts with the γ-subunit of the heterotrimeric G protein, Gng2, and modulates its ubiquitylation status, thereby controlling Gng2 stability. Proximal mapping of Gng2 revealed an enrichment in partners associated with chemokine signaling, actin cytoskeleton and cell migration. In line with these findings, we show that Otub1-deficient B cells exhibit greater Ca2+ mobilization, F-actin polymerization and chemotactic responsiveness to Cxcl12, Cxcl13 and S1P in vitro, which manifests in vivo as altered localization of B cells within the spleen. Together, our data establishes Otub1 as a novel regulator of G-protein coupled receptor signaling in B cells, regulating their differentiation and positioning in the spleen.

Hyperinsulinemia acts via acinar insulin receptors to initiate pancreatic cancer by increasing digestive enzyme production and inflammation.

The rising pancreatic cancer incidence due to obesity and type 2 diabetes is closely tied to hyperinsulinemia, an independent cancer risk factor. Previous studies demonstrated reducing insulin production suppressed pancreatic intraepithelial neoplasia (PanIN) pre-cancerous lesions in Kras-mutant mice. However, the pathophysiological and molecular mechanisms remained unknown, and in particular it was unclear whether hyperinsulinemia affected PanIN precursor cells directly or indirectly. Here, we demonstrate that insulin receptors (Insr) in KrasG12D-expressing pancreatic acinar cells are dispensable for glucose homeostasis but necessary for hyperinsulinemia-driven PanIN formation in the context of diet-induced hyperinsulinemia and obesity. Mechanistically, this was attributed to amplified digestive enzyme protein translation, triggering of local inflammation, and PanIN metaplasia in vivo. In vitro, insulin dose-dependently increased acinar-to-ductal metaplasia formation in a trypsin- and Insr-dependent manner. Collectively, our data shed light on the mechanisms connecting obesity-driven hyperinsulinemia and pancreatic cancer development.

Compendium of Chromatographic Behavior of Post-translationally and Chemically Modified Peptides in Bottom-Up Proteomic Experiments.

We have systematically evaluated the chromatographic behavior of post-translationally/chemically modified peptides using data spanning over 70 of the most relevant modifications. These retention properties were measured for standard bottom-up proteomic settings (fully porous C18 separation media, 0.1% formic acid as ion-pairing modifier) using collections of modified/nonmodified peptide pairs. These pairs were generated by spontaneous degradation, chemical or enzymatic treatment, analysis of synthetic peptides, or the cotranslational incorporation of noncanonical proline analogues. In addition, these measurements were validated using external data acquired for synthetic peptides and enzymatically induced citrullination. Working in units of hydrophobicity index (HI, % ACN) and evaluating the average retention shifts (ΔHI) represent the simplest approach to describe the effect of modifications from a didactic point of view. Plotting HI values for modified (y-axis) vs nonmodified (x-axis) counterparts generates unique slope and intercept values for each modification defined by the chemistry of the modifying moiety: its hydrophobicity, size, pKa of ionizable groups, and position of the altered residue. These composition-dependent correlations can be used for coarse incorporation of PTMs into models for prediction of peptide retention. More accurate predictions would require the development of specific sequence-dependent algorithms to predict ΔHI values.

Improved Coverage of the N-Terminome by Combining ChaFRADIC with Alternative Proteases.

Many proteolytic cleavage events cannot be covered with conventional trypsin-based N-terminomics workflows. These typically involve the derivatization of protein N-termini and Lys residues as an initial step, such that trypsin will cleave C-terminal of arginine but not lysine residues (ArgC-like cleavage). From 20,422 reviewed human protein sequences in Uniprot, 3597 have known N-terminal signal peptides. An in silico ArgC-like digestion of the corresponding 3597 mature protein sequences reveals that-even for these well-known and well-studied proteolytic events-trypsin-based N-terminomics workflows may miss up to 50% of signaling cleavage events as the corresponding neo-N-terminal peptides will have an unfavorable length of <7 (875 peptides) or >30 (911 peptides) amino acids. In this chapter, we provide a protocol that can be applied to all kinds of samples to improve access to this "inaccessible" N-terminome, by making use of the alternative, broad-specificity protease subtilisin for fast and reproducible digestion of proteins.

FGF1 supports glycolytic metabolism through the estrogen receptor in endocrine-resistant and obesity-associated breast cancer.

BACKGROUND: Obesity increases breast cancer risk and breast cancer-specific mortality, particularly for people with estrogen receptor (ER)-positive tumors. Body mass index (BMI) is used to define obesity, but it may not be the best predictor of breast cancer risk or prognosis on an individual level. Adult weight gain is an independent indicator of breast cancer risk. Our previous work described a murine model of obesity, ER-positive breast cancer, and weight gain and identified fibroblast growth factor receptor (FGFR) as a potential driver of tumor progression. During adipose tissue expansion, the FGF1 ligand is produced by hypertrophic adipocytes as a stimulus to stromal preadipocytes that proliferate and differentiate to provide additional lipid storage capacity. In breast adipose tissue, FGF1 production may stimulate cancer cell proliferation and tumor progression. METHODS: We explored the effects of FGF1 on ER-positive endocrine-sensitive and resistant breast cancer and compared that to the effects of the canonical ER ligand, estradiol. We used untargeted proteomics, specific immunoblot assays, gene expression profiling, and functional metabolic assessments of breast cancer cells. The results were validated in tumors from obese mice and breast cancer datasets from women with obesity. RESULTS: FGF1 stimulated ER phosphorylation independently of estradiol in cells that grow in obese female mice after estrogen deprivation treatment. Phospho- and total proteomic, genomic, and functional analyses of endocrine-sensitive and resistant breast cancer cells show that FGF1 promoted a cellular phenotype characterized by glycolytic metabolism. In endocrine-sensitive but not endocrine-resistant breast cancer cells, mitochondrial metabolism was also regulated by FGF1. Comparison of gene expression profiles indicated that tumors from women with obesity shared hallmarks with endocrine-resistant breast cancer cells. CONCLUSIONS: Collectively, our data suggest that one mechanism by which obesity and weight gain promote breast cancer progression is through estrogen-independent ER activation and cancer cell metabolic reprogramming, partly driven by FGF/FGFR. The first-line treatment for many patients with ER-positive breast cancer is inhibition of estrogen synthesis using aromatase inhibitors. In women with obesity who are experiencing weight gain, locally produced FGF1 may activate ER to promote cancer cell metabolic reprogramming and tumor progression independently of estrogen.

Omics approaches for the assessment of biological responses to nanoparticles.

Nanotechnology has enabled the development of innovative therapeutics, diagnostics, and drug delivery systems. Nanoparticles (NPs) can influence gene expression, protein synthesis, cell cycle, metabolism, and other subcellular processes. While conventional methods have limitations in characterizing responses to NPs, omics approaches can analyze complete sets of molecular entities that change upon exposure to NPs. This review discusses key omics approaches, namely transcriptomics, proteomics, metabolomics, lipidomics and multi-omics, applied to the assessment of biological responses to NPs. Fundamental concepts and analytical methods used for each approach are presented, as well as good practices for omics experiments. Bioinformatics tools are essential to analyze, interpret and visualize large omics data, and to correlate observations in different molecular layers. The authors envision that conducting interdisciplinary multi-omics analyses in future nanomedicine studies will reveal integrated cell responses to NPs at different omics levels, and the incorporation of omics into the evaluation of targeted delivery, efficacy, and safety will improve the development of nanomedicine therapies.

Mapping Peptide-Protein Interactions by Amine-Reactive Cleavable Photoaffinity Reagents.

Photoaffinity labeling followed by tandem mass spectrometry is an often used strategy to identify protein targets of small-molecule drugs or drug candidates, which, under ideal conditions, enables the identification of the actual drug binding site. In the case of bioactive peptides, however, identifying the distinct binding site is hampered because of complex fragmentation patterns during tandem mass spectrometry. We here report the development and use of small cleavable photoaffinity reagents that allow functionalization of bioactive peptides for light-induced covalent binding to their protein targets. Upon cleavage of the covalently linked peptide drug, a chemical remnant of a defined mass remains on the bound amino acid, which is then used to unambiguously identify the drug binding site. Applying our approach to known peptide-drug/protein pairs with reported crystal structures, such as the calmodulin-melittin interaction, we were able to validate the identified binding sites based on structural models. Overall, our cleavable photoaffinity labeling strategy represents a powerful tool to enable the identification of protein targets and specific binding sites of a wide variety of bioactive peptides in the future.

Exploring the variable space of shallow machine learning models for reversed-phase retention time prediction.

Peptide retention time (RT) prediction algorithms are tools to study and identify the physicochemical properties that drive the peptide-sorbent interaction. Traditional RT algorithms use multiple linear regression with manually curated parameters to determine the degree of direct contribution for each parameter and improvements to RT prediction accuracies relied on superior feature engineering. Deep learning led to a significant increase in RT prediction accuracy and automated feature engineering via chaining multiple learning modules. However, the significance and the identity of these extracted variables are not well understood due to the inherent complexity when interpreting "relationships-of-relationships" found in deep learning variables. To achieve both accuracy and interpretability simultaneously, we isolated individual modules used in deep learning and the isolated modules are the shallow learners employed for RT prediction in this work. Using a shallow convolutional neural network (CNN) and gated recurrent unit (GRU), we find that the spatial features obtained via the CNN correlate with real-world physicochemical properties namely cross-collisional sections (CCS) and variations of assessable surface area (ASA). Furthermore, we determined that the discovered parameters are "micro-coefficients" that contribute to the "macro-coefficient" - hydrophobicity. Manually embedding CCS and the variations of ASA to the GRU model yielded an R2 = 0.981 using only 525 variables and can represent 88% of the ∼110,000 tryptic peptides used in our dataset. This work highlights the feature discovery process of our shallow learners can achieve beyond traditional RT models in performance and have better interpretability when compared with the deep learning RT algorithms found in the literature.

Discovery-Based Proteomics Identify Skeletal Muscle Mitochondrial Alterations as an Early Metabolic Defect in a Mouse Model of ß-Thalassemia.

Although metabolic complications are common in thalassemia patients, there is still an unmet need to better understand underlying mechanisms. We used unbiased global proteomics to reveal molecular differences between the th3/+ mouse model of thalassemia and wild-type control animals focusing on skeletal muscles at 8 weeks of age. Our data point toward a significantly impaired mitochondrial oxidative phosphorylation. Furthermore, we observed a shift from oxidative fibre types toward more glycolytic fibre types in these animals, which was further supported by larger fibre-type cross-sectional areas in the more oxidative type fibres (type I/type IIa/type IIax hybrid). We also observed an increase in capillary density in th3/+ mice, indicative of a compensatory response. Western blotting for mitochondrial oxidative phosphorylation complex proteins and PCR analysis of mitochondrial genes indicated reduced mitochondrial content in the skeletal muscle but not the hearts of th3/+ mice. The phenotypic manifestation of these alterations was a small but significant reduction in glucose handling capacity. Overall, this study identified many important alterations in the proteome of th3/+ mice, amongst which mitochondrial defects leading to skeletal muscle remodelling and metabolic dysfunction were paramount.

Offline Peptide Fractionation and Parallel Reaction Monitoring MS for the Quantitation of Low-Abundance Plasma Proteins.

Mass spectrometry (MS)-based protein quantitation is an attractive means for research and diagnostics due to its high specificity, precision, sensitivity, versatility, and the ability to develop multiplexed assays for the "absolute" quantitation of virtually any protein target. However, due to the large dynamic range of protein concentrations in blood, high abundance proteins in blood plasma hinder the detectability and quantification of lower-abundance proteins which are often relevant in the context of different diseases. Here we outline a streamlined method involving offline high-pH reversed-phase fractionation of human plasma samples followed by the quantitative analysis of specific fractions using nanoLC-parallel reaction monitoring (PRM) on a Q Exactive Plus mass spectrometer for peptide detection and quantitation with increased sensitivity. Because we use a set of synthetic peptide standards, we can more efficiently determine the precise retention times of the target peptides in the first-dimensional separation and specifically collect eluting fractions of interest for the subsequent targeted MS quantitation, making the analysis faster and easier. An eight-point standard curve was generated by serial dilution of a mixture of previously validated unlabeled ("light") synthetic peptides of interest at known concentrations. The corresponding heavy stable-isotope-labeled standard (SIS) analogues were used as normalizers to account for losses during sample processing and analysis. Using this method, we were able to improve the sensitivity of plasma protein quantitation by up to 50-fold compared to using nanoLC-PRM alone.

Inhibition of the MNK1/2-eIF4E Axis Augments Palbociclib-Mediated Antitumor Activity in Melanoma and Breast Cancer.

Aberrant cell-cycle progression is characteristic of melanoma, and CDK4/6 inhibitors, such as palbociclib, are currently being tested for efficacy in this disease. Despite the promising nature of CDK4/6 inhibitors, their use as single agents in melanoma has shown limited clinical benefit. Herein, we discovered that treatment of tumor cells with palbociclib induces the phosphorylation of the mRNA translation initiation factor eIF4E. When phosphorylated, eIF4E specifically engenders the translation of mRNAs that code for proteins involved in cell survival. We hypothesized that cancer cells treated with palbociclib use upregulated phosphorylated eIF4E (phospho-eIF4E) to escape the antitumor benefits of this drug. Indeed, we found that pharmacologic or genetic disruption of MNK1/2 activity, the only known kinases for eIF4E, enhanced the ability of palbociclib to decrease clonogenic outgrowth. Moreover, a quantitative proteomics analysis of melanoma cells treated with combined MNK1/2 and CDK4/6 inhibitors showed downregulation of proteins with critical roles in cell-cycle progression and mitosis, including AURKB, TPX2, and survivin. We also observed that palbociclib-resistant breast cancer cells have higher basal levels of phospho-eIF4E, and that treatment with MNK1/2 inhibitors sensitized these palbociclib-resistant cells to CDK4/6 inhibition. In vivo we demonstrate that the combination of MNK1/2 and CDK4/6 inhibition significantly increases the overall survival of mice compared with either monotherapy. Overall, our data support MNK1/2 inhibitors as promising drugs to potentiate the antineoplastic effects of palbociclib and overcome therapy-resistant disease.

Molecular mechanisms in chloroquine-exposed muscle cells elucidated by combined proteomic and microscopic studies.

OBJECTIVES: Chloroquine (CQ) is an antimalarial drug with a growing number of applications as recently demonstrated in attempts to treat Covid-19. For decades, it has been well known that skeletal and cardiac muscle cells might display vulnerability against CQ exposure resulting in the clinical manifestation of a CQ-induced myopathy. In line with the known effect of CQ on inhibition of the lysosomal function and thus cellular protein clearance, the build-up of autophagic vacuoles along with protein aggregates is a histological hallmark of the disease. Given that protein targets of the perturbed proteostasis are still not fully discovered, we applied different proteomic and immunological-based studies to improve the current understanding of the biochemical nature of CQ-myopathy. METHODS: To gain a comprehensive understanding of the molecular pathogenesis of this acquired myopathy and to define proteins targets as well as pathophysiological processes beyond impaired proteolysis, utilising CQ-treated C2C12 cells and muscle biopsies derived from CQ-myopathy patients, we performed different proteomic approaches and Coherent Anti-Stokes Raman Scattering (CARS) microscopy, in addition to immunohistochemical studies. RESULTS: Our combined studies confirmed an impact of CQ-exposure on proper protein processing/folding and clearance, highlighted changes in the interactome of p62, a known aggregation marker and hereby identified the Rett syndrome protein MeCP2 as being affected. Moreover, our approach revealed-among others-a vulnerability of the extracellular matrix, cytoskeleton and lipid homeostasis. CONCLUSION: We demonstrated that CQ exposure (secondarily) impacts biological processes beyond lysosomal function and linked a variety of proteins with known roles in the manifestation of other neuromuscular diseases.

Multi-omics approaches to study platelet mechanisms.

Platelets are small anucleate cell fragments (2-4 µm in diameter) in the blood, which play an essential role in thrombosis and hemostasis. Genetic or acquired platelet dysfunctions are linked to bleeding, increased risk of thromboembolic events and cardiovascular diseases. Advanced proteomic approaches may pave the way to a better understanding of the roles of platelets in hemostasis, and pathophysiological processes such as inflammation, metastatic spread and thrombosis. Further insights into the molecular biology of platelets are crucial to aid drug development and identify diagnostic markers of platelet activation. Platelet activation is known to be an extremely rapid process and involves multiple post-translational mechanisms at sub second time scale, including proteolysis and phosphorylation. Multi-omics technologies and biochemical approaches can be exploited to precisely probe and define these posttranslational pathways. Notably, the absence of a nucleus in platelets significantly reduces the number of present proteins, simplifying mass spectrometry-based proteomics and metabolomics approaches.

Proteomic Assessment of the Murine Mammary Gland Extracellular Matrix.

The extracellular matrix (ECM) is a molecular scaffold mainly comprising fibrous proteins, glycoproteins, proteoglycans, and polysaccharides. Aside from acting as a structural support, the ECM's composition dictates cell-matrix interactions at the biochemical and biophysical level. In the context of cancer, the ECM is a critical component of the tumor microenvironment (TME) and dysregulation of its deposition and remodelling has been shown to promote tumor onset, progression, and metastasis. Here, we describe a robust protocol for the isolation and subsequent proteomic analysis of the ECM of murine mammary glands, for downstream assays studying the role of the ECM in breast cancer. The protocol yields sufficient protein amounts to enable not only the global quantitation of protein expression but furthermore the enrichment and quantitative analysis of post-translationally modified peptides to study aberrant signalling.

FYCO1 Increase and Effect of Arimoclomol-Treatment in Human VCP-Pathology.

Dominant VCP-mutations cause a variety of neurological manifestations including inclusion body myopathy with early-onset Paget disease and frontotemporal dementia 1 (IBMPFD). VCP encodes a ubiquitously expressed multifunctional protein that is a member of the AAA+ protein family, implicated in multiple cellular functions ranging from organelle biogenesis to ubiquitin-dependent protein degradation. The latter function accords with the presence of protein aggregates in muscle biopsy specimens derived from VCP-patients. Studying the proteomic signature of VCP-mutant fibroblasts, we identified a (pathophysiological) increase of FYCO1, a protein involved in autophagosome transport. We confirmed this finding applying immunostaining also in muscle biopsies derived from VCP-patients. Treatment of fibroblasts with arimoclomol, an orphan drug thought to restore physiologic cellular protein repair pathways, ameliorated cellular cytotoxicity in VCP-patient derived cells. This finding was accompanied by increased abundance of proteins involved in immune response with a direct impact on protein clearaqnce as well as by elevation of pro-survival proteins as unravelled by untargeted proteomic profiling. Hence, the combined results of our study reveal a dysregulation of FYCO1 in the context of VCP-etiopathology, highlight arimoclomol as a potential drug and introduce proteins targeted by the pre-clinical testing of this drug in fibroblasts.

Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning.

The recent surge of coronavirus disease 2019 (COVID-19) hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reliable tests predictive of disease severity and mortality. Using multiplexed targeted mass spectrometry assays on a robust triple quadrupole MS setup which is available in many clinical laboratories, we determined the precise concentrations of hundreds of proteins and metabolites in plasma from hospitalized COVID-19 patients. We observed a clear distinction between COVID-19 patients and controls and, strikingly, a significant difference between survivors and nonsurvivors. With increasing length of hospitalization, the survivors' samples showed a trend toward normal concentrations, indicating a potential sensitive readout of treatment success. Building a machine learning multi-omic model that considers the concentrations of 10 proteins and five metabolites, we could predict patient survival with 92% accuracy (area under the receiver operating characteristic curve: 0.97) on the day of hospitalization. Hence, our standardized assays represent a unique opportunity for the early stratification of hospitalized COVID-19 patients.

Phosphorylation of eIF4E in the stroma drives the production and spatial organisation of collagen type I in the mammary gland.

The extracellular matrix (ECM) plays critical roles in breast cancer development. Whether ECM composition is regulated by the phosphorylation of eIF4E on serine 209, an event required for tumorigenesis, has not been explored. Herein, we used proteomics and mouse modeling to investigate the impact of mutating serine 209 to alanine on eIF4E (i.e., S209A) on mammary gland (MG) ECM. The proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD028953. We discovered that S209A knock-in mice, expressing a non-phosphorylatable form of eIF4E, have less collagen-I deposition in native and tumor-bearing MGs, leading to altered tumor cell invasion. Additionally, phospho-eIF4E deficiency impacts collagen topology; fibers at the tumor-stroma boundary in phospho-eIF4E-deficient mice run parallel to the tumor edge but radiate outwards in wild-type mice. Finally, a phospho-eIF4E-deficient tumor microenvironment resists anti-PD-1 therapy-induced collagen deposition, correlating with an increased anti-tumor response to immunotherapy. Clinically, we showed that collagen-I and phospho-eIF4E are positively correlated in human breast cancer samples, and that stromal phospho-eIF4E expression is influenced by tumor proximity. Together, our work defines the importance of phosphorylation of eIF4E on S209 as a regulator of MG collagen architecture in the tumor microenvironment, thereby positioning phospho-eIF4E as a therapeutic target to augment response to therapy.