Paradigm Shift: Major Role of Ion-Pairing-Dependent Size Exclusion Effects in Bottom-Up Proteomics Reversed-Phase Peptide Separations.

Can reversed-phase peptide retention be the same for C8 and C18 columns? or increase for otherwise identical columns with a smaller surface area? Can replacing trifluoroacetic acid (TFA) with formic acid (FA) improve the peak shape? According to our common understanding of peptide chromatography, absolutely not. Surprisingly, a thorough comparison of the peptide separation selectivity of 100 and 120 Šfully porous C18 sorbents to maximize the performance of our in-house proteomics LC-MS/MS setup revealed an unexpectedly higher peptide retentivity for a wider pore packing material, despite it having a smaller surface area. Concurrently, the observed increase in peptide retention─which drives variation in separation selectivity between 100 and 120 Špore size materials─was more pronounced for smaller peptides. These findings contradict the central dogmas that underlie the development of all peptide RP-HPLC applications: (i) a larger surface area leads to higher retention and (ii) increasing the pore size should benefit the retention of larger analytes. Based on our intriguing findings, we compared reversed-phase high-performance liquid chromatography peptide retention for a total of 20 columns with pore sizes between 60 and 300 Šusing FA- and TFA-based eluents. Our results unequivocally attest that the larger size of ion pairs in FA- vs TFA-based eluents leads to the observed impact on selectivity and peptide retention. For FA, peptide retention peaks at 200 Špore size, compared to between 120 and 200 Šfor TFA. However, the decrease in retention for narrow-pore particles is more profound in FA. Our findings suggest that common assumptions about analyte size and accessible surface area should be revisited for ion-pair RP separation of small peptides, typical for proteomic applications that are predominantly applying FA eluents. Hybrid silica-based materials with pore sizes of 130-200 Šshould be specifically targeted for bottom-up proteomic applications to obtain both superior peak shape and peptide retentivity. This challenging task of attaining the best RPLC column for proteomics calls for closer collaboration between LC column manufacturers and proteomic LC specialists.

Human Host Defense Peptide LL-37 Suppresses TNFα-Mediated Matrix Metalloproteinases MMP9 and MMP13 in Human Bronchial Epithelial Cells.

INTRODUCTION: TNFα-inducible matrix metalloproteinases play a critical role in the process of airway remodeling in respiratory inflammatory disease including asthma. The cationic host defense peptide LL-37 is elevated in the lungs during airway inflammation. However, the impact of LL-37 on TNFα-driven processes is not well understood. Here, we examined the effect of LL-37 on TNFα-mediated responses in human bronchial epithelial cells (HBECs). METHODS: We used a slow off-rate modified aptamer-based proteomics approach to define the HBEC proteome altered in response to TNFα. Abundance of selected protein candidates and signaling intermediates was examined using immunoassays, ELISA and Western blots, and mRNA abundance was examined by qRT-PCR. RESULTS: Proteomics analysis revealed that 124 proteins were significantly altered, 12 proteins were enhanced by ≥2-fold compared to unstimulated cells, in response to TNFα. MMP9 was the topmost increased protein in response to TNFα, enhanced by ∼10-fold, and MMP13 was increased by ∼3-fold, compared to unstimulated cells. Furthermore, we demonstrated that LL-37 significantly suppressed TNFα-mediated MMP9 and MMP13 in HBEC. Mechanistic data revealed that TNFα-mediated MMP9 and MMP13 production is controlled by SRC kinase and that LL-37 enhances related upstream negative regulators, namely, phospho-AKT (T308) and TNFα-mediated TNFAIP3 or A20. CONCLUSIONS: The findings of this study suggest that LL-37 may play a role in intervening in the process of airway remodeling in chronic inflammatory respiratory disease such as asthma.

Toward an Ultimate Solution for Peptide Retention Time Prediction: The Effect of Column Temperature on Separation Selectivity.

We studied the effect of the column temperature on the selectivity of reversed-phase peptide separation in bottom-up proteomics. The number of peptide identifications from 2 h liquid chromatography with tandem mass spectrometry (LC-MS/MS) acquisitions reaches a plateau at 45-55 °C, driven simultaneously by improved separation efficiency, a gradual decrease in peptide retention, and possible on-column degradation of peptides at elevated temperatures. Performing 2D LC-MS/MS acquisitions at 25, 35, 45, and 55 °C resulted in the identification of ∼100,000 and ∼120,000 unique peptides for nonmodified and tandem mass tags (TMT)-labeled samples, respectively. These peptide collections were used to investigate the temperature-driven retention features. The latter is governed by the specific temperature response of individual residues, peptide hydrophobicity and length, and amphipathic helicity. On average, peptide retention decreased by 0.56 and 0.5% acetonitrile for each 10 °C increase for label-free and TMT-labeled peptides, respectively. This generally linear response of retention shifts allowed the extrapolation of predictive models beyond the studied temperature range. Thus, (trap) column cooling from room temperature to 0 °C will allow the retention of an additional 3% of detectable tryptic peptides. Meanwhile, the application of 90 °C would result in the loss of ∼20% of tryptic peptides that were amenable to MS/MS-based identification.

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.

Concentration-dependent alterations in the human plasma proteome following controlled exposure to diesel exhaust.

Traffic-related air pollution (TRAP) exposure is associated with systemic health effects, which can be studied using blood-based markers. Although we have previously shown that high TRAP concentrations alter the plasma proteome, the concentration-response relationship between blood proteins and TRAP is unexplored in controlled human exposure studies. We aimed to identify concentration-dependent plasma markers of diesel exhaust (DE), a model of TRAP. Fifteen healthy non-smokers were enrolled into a double-blinded, crossover study where they were exposed to filtered air (FA) and DE at 20, 50 and 150 µg/m3 PM2.5 for 4h, separated by ≥ 4-week washouts. We collected blood at 24h post-exposure and used label-free mass spectrometry to quantify proteins in plasma. Proteins exhibiting a concentration-response, as determined by linear mixed effects models (LMEMs), were assessed for pathway enrichment using WebGestalt. Top candidates, identified by sparse partial least squares discriminant analysis and LMEMs, were confirmed using enzyme-linked immunoassays. Thereafter, we assessed correlations between proteins that showed a DE concentration-response and acute inflammatory endpoints, forced expiratory volume in 1 s (FEV1) and methacholine provocation concentration causing a 20% drop in FEV1 (PC20). DE exposure was associated with concentration-dependent alterations in 45 proteins, which were enriched in complement pathways. Of the 9 proteins selected for confirmatory immunoassays, based on complementary bioinformatic approaches to narrow targets and availability of high-quality assays, complement factor I (CFI) exhibited a significant concentration-dependent decrease (-0.02 µg/mL per µg/m3 of PM2.5, p = 0.04). Comparing to FA at discrete concentrations, CFI trended downward at 50 (-2.14 ± 1.18, p = 0.08) and significantly decreased at 150 µg/m3 PM2.5 (-2.93 ± 1.18, p = 0.02). CFI levels were correlated with FEV1, PC20 and nasal interleukin (IL)-6 and IL-1ß. This study details concentration-dependent alterations in the plasma proteome following DE exposure at concentrations relevant to occupational and community settings. CFI shows a robust concentration-response and association with established measures of airway function and inflammation.

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.

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.

Acetic Acid Ion Pairing Additive for Reversed-Phase HPLC Improves Detection Sensitivity in Bottom-up Proteomics Compared to Formic Acid.

Despite the general acceptance of formic acid as the additive of choice for peptide reversed-phase LC-MS/MS applications, some still argue that the selection of acetic acid represents a better option. To settle this debate, we investigated both the difference in MS sensitivity and chromatographic behavior of peptides between these two systems. This interlaboratory study was performed using different MS setups and C18 separation media employing both 0.1% formic and 0.5% acetic acid as ion pairing modifiers. Relative to formic acid, we find an overall ∼2.2-2.5× increase in MS signal and a slight decrease in RP LC retention (-0.7% acetonitrile on average) for acetic acid conditions. While these two features have opposing effects on peptide detectability, we find that acetic acid produces up to 60% higher peptide ID output depending on the type of sample. The drop in RPLC retention increases with peptide net charge at acidic pH. MS signal is dependent on the difference between the charge of the precursor ion and the charge of the peptide in solution, favoring species with a low pI. Lower peptide retention under acetic acid conditions demonstrates its higher hydrophilicity and, as expected, leads to composition and sequence-dependent character of the observed retention shift.

Combination of IL-17A/F and TNF-α uniquely alters the bronchial epithelial cell proteome to enhance proteins that augment neutrophil migration.

BACKGROUND: The heterodimer interleukin (IL)-17A/F is elevated in the lungs in chronic respiratory disease such as severe asthma, along with the pro-inflammatory cytokine tumor necrosis factor-α (TNF-α). Although IL-17A/F and TNF-α are known to functionally cooperate to exacerbate airway inflammation, proteins altered by their interaction in the lungs are not fully elucidated. RESULTS: We used Slow Off-rate Modified Aptamer-based proteomic array to identify proteins that are uniquely and/or synergistically enhanced by concurrent stimulation with IL-17A/F and TNF-α in human bronchial epithelial cells (HBEC). The abundance of 38 proteins was significantly enhanced by the combination of IL-17A/F and TNF-α, compared to either cytokine alone. Four out of seven proteins that were increased > 2-fold were those that promote neutrophil migration; host defence peptides (HDP; Lipocalin-2 (LCN-2) and Elafin) and chemokines (IL-8, GROα). We independently confirmed the synergistic increase of these four proteins by western blots and ELISA. We also functionally confirmed that factors secreted by HBEC stimulated with the combination of IL-17A/F and TNF-α uniquely enhances neutrophil migration. We further showed that PI3K and PKC pathways selectively control IL-17A/F + TNF-α-mediated synergistic production of HDPs LCN-2 and Elafin, but not chemokines IL-8 and GROα. Using a murine model of airway inflammation, we demonstrated enhancement of IL-17A/F, TNF-α, LCN-2 and neutrophil chemokine KC in the lungs, thus corroborating our findings in-vivo. CONCLUSION: This study identifies proteins and signaling mediated by concurrent IL-17A/F and TNF-α exposure in the lungs, relevant to respiratory diseases characterized by chronic inflammation, especially neutrophilic airway inflammation such as severe asthma.

Temporal proteomic analyses of human lung cells distinguish high pathogenicity influenza viruses and coronaviruses from low pathogenicity viruses.

Newly re-emerging viruses are of significant global concern. In late 2019, a new coronavirus, SARS-CoV-2, emerged in China and soon spread worldwide, causing the COVID-19 pandemic, which to date has caused >6 M deaths. There has been a wealth of studies on this new virus since its emergence. The coronaviruses consist of many animal and human pathogens, with some of the human coronavirus, such as strain OC43, normally causing only mild cold-like symptoms. Viruses usurp host cellular processes to successfully replicate. We used tandem mass tag mass spectrometry-based proteomic analyses of human lung MRC-5 cells infected with OC43 for various periods of time to delineate virus-induced host cell alterations. Numerous proteins involved in lipid metabolism, molecular transport, small molecule biochemistry, cell death and survival, humoral immune response, and inflammatory response were dysregulated. Comparison of our findings to previous studies that examined a range of differentially pathogenic influenza A viruses (IAV), and to SARS-CoV-2 data, revealed that proteins involved in the cell cycle, cytokine signaling, DNA replication, and anti-inflammatory responses were generally similarly affected by virtually all tested IAV and CoV. However, proteins involved in necrosis, protein metabolism, ECM regulation, and signal transduction were generally different. In addition, the more pathogenic CoV and IAV activated Rb-dependent repression of E2F-mediated transcription, whereas less pathogenic influenza and coronaviruses either inhibited or had no effect on this pathway.

Myelin-associated glycoprotein alters the neuronal secretome and stimulates the release of TGFß and proteins that affect neural plasticity.

Myelin-associated glycoprotein (MAG) and Nogo inhibit neurite outgrowth by binding to receptors such as NgR1, PirB and LRP1, and they have also been shown to induce phosphorylation of Smad2, a key intermediate in the transforming growth factor ß (TGFß) signalling pathway. In this study, we determined that MAG and Nogo do not transactivate the TGFß receptor through their canonical receptors or discoidin domain receptor 1, which we identified as a novel receptor for MAG and Nogo. Instead, MAG and Nogo promoted Smad2 phosphorylation by stimulating secretion of TGFß. Proteomic analysis of the neuronal secretome revealed that MAG also regulated the secretion of proteins that affect central nervous system plasticity-inducing the secretion of S100A6, septin-7 and neurofascin 186, while inhibiting the secretion of frataxin, MAP6, syntenin-1 and GAP-43. This represents a novel function for MAG that has broad implications for the treatment for spinal cord injury.

Sex Dimorphism of Allergen-Induced Secreted Proteins in Murine and Human Lungs.

Biological sex influences disease severity, prevalence and response to therapy in allergic asthma. However, allergen-mediated sex-specific changes in lung protein biomarkers remain undefined. Here, we report sex-related differences in specific proteins secreted in the lungs of both mice and humans, in response to inhaled allergens. Female and male BALB/c mice (7-8 weeks) were intranasally challenged with the allergen house dust mite (HDM) for 2 weeks. Bronchoalveolar lavage fluid (BALF) was collected 24 hour after the last HDM challenge from allergen-naïve and HDM-challenged mice (N=10 per group, each sex). In a human study, adult participants were exposed to nebulized (2 min) allergens (based on individual sensitivity), BALF was obtained after 24 hour (N=5 each female and male). The BALF samples were examined in immunoblots for the abundance of 10 proteins shown to increase in response to allergen in both murine and human BALF, selected from proteomics studies. We showed significant sex-bias in allergen-driven increase in five out of the 10 selected proteins. Of these, increase in eosinophil peroxidase (EPX) was significantly higher in females compared to males, in both mice and human BALF. We also showed specific sex-related differences between murine and human samples. For example, allergen-driven increase in S100A8 and S100A9 was significantly higher in BALF of females compared to males in mice, but significantly higher in males compared to females in humans. Overall, this study provides sex-specific protein biomarkers that are enhanced in response to allergen in murine and human lungs, informing and motivating translational research in allergic asthma.

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.

Mitochondrial Sirtuin-3 (SIRT3) Prevents Doxorubicin-Induced Dilated Cardiomyopathy by Modulating Protein Acetylation and Oxidative Stress.

BACKGROUND: High doses of doxorubicin put cancer patients at risk for developing dilated cardiomyopathy. Previously, we showed that doxorubicin treatment decreases SIRT3 (sirtuin 3), the main mitochondrial deacetylase and increases protein acetylation in rat cardiomyocytes. Here, we hypothesize that SIRT3 expression can attenuate doxorubicin induced dilated cardiomyopathy in vivo by preventing the acetylation of mitochondrial proteins. METHODS: Nontransgenic, M3-SIRT3 (truncated SIRT3; short isoform), and M1-SIRT3 (full-length SIRT3; mitochondrial localized) transgenic mice were treated with doxorubicin for 4 weeks (8 mg/kg body weight per week). Echocardiography was performed to assess cardiac structure and function and validated by immunohistochemistry and immunofluorescence (n=4-10). Mass spectrometry was performed on cardiac mitochondrial peptides in saline (n=6) and doxorubicin (n=5) treated hearts. Validation was performed in doxorubicin treated primary rat and human induced stem cell derived cardiomyocytes transduced with adenoviruses for M3-SIRT3 and M1-SIRT3 and deacetylase deficient mutants (n=4-10). RESULTS: Echocardiography revealed that M3-SIRT3 transgenic mice were partially resistant to doxorubicin induced changes to cardiac structure and function whereas M1-SIRT3 expression prevented cardiac remodeling and dysfunction. In doxorubicin hearts, 37 unique acetylation sites on mitochondrial proteins were altered. Pathway analysis revealed these proteins are involved in energy production, fatty acid metabolism, and oxidative stress resistance. Increased M1-SIRT3 expression in primary rat and human cardiomyocytes attenuated doxorubicin-induced superoxide formation, whereas deacetylase deficient mutants were unable to prevent oxidative stress. CONCLUSIONS: Doxorubicin reduced SIRT3 expression and markedly affected the cardiac mitochondrial acetylome. Increased M1-SIRT3 expression in vivo prevented doxorubicin-induced cardiac dysfunction, suggesting that SIRT3 could be a potential therapeutic target for mitigating doxorubicin-induced dilated cardiomyopathy.

Proteomic Shifts Reflecting Oxidative Stress and Reduced Capacity for Protein Synthesis, and Alterations to Mitochondrial Membranes in Neurospora crassa Lacking VDAC.

Voltage-dependent anion-selective channels (VDAC) maintain the bidirectional flow of small metabolites across the mitochondrial outer membrane and participate in the regulation of multiple cellular processes. To understand the roles of VDAC in cellular homeostasis, preliminary proteomic analyses of S100 cytosolic and mitochondria-enriched fractions from a VDAC-less Neurospora crassa strain (ΔPor-1) were performed. In the variant cells, less abundant proteins include subunits of translation initiation factor eIF-2, enzymes in the shikimate pathway leading to precursors of aromatic amino acids, and enzymes involved in sulfate assimilation and in the synthesis of methionine, cysteine, alanine, serine, and threonine. In contrast, some of the more abundant proteins are involved in electron flow, such as the α subunit of the electron transfer flavoprotein and lactate dehydrogenase, which is involved in one pathway leading to pyruvate synthesis. Increased levels of catalase and catalase activity support predicted increased levels of oxidative stress in ΔPor-1 cells, and higher levels of protein disulfide isomerase suggest activation of the unfolded protein response in the endoplasmic reticulum. ΔPor-1 cells are cold-sensitive, which led us to investigate the impact of the absence of VDAC on several mitochondrial membrane characteristics. Mitochondrial membranes in ΔPor-1 are more fluid than those of wild-type cells, the ratio of C18:1 to C18:3n3 acyl chains is reduced, and ergosterol levels are lower. In summary, these initial results indicate that VDAC-less N. crassa cells are characterized by a lower abundance of proteins involved in amino acid and protein synthesis and by increases in some associated with pyruvate metabolism and stress responses. Membrane lipids and hyphal morphology are also impacted by the absence of VDAC.

Defining the effects of traffic-related air pollution on the human plasma proteome using an aptamer proteomic array: A dose-dependent increase in atherosclerosis-related proteins.

BACKGROUND: Traffic-related air pollution (TRAP) is a critical risk factor and major contributor to respiratory and cardiovascular disease (CVD). The effects of TRAP beyond the lungs can be related to changes in circulatory proteins. However, such TRAP-mediated changes have not been defined in an unbiased manner using a controlled human model. OBJECTIVE: To detail global protein changes (the proteome) in plasma following exposure to inhaled diesel exhaust (DE), a paradigm of TRAP, using controlled human exposures. METHODS: In one protocol, ex-smokers and never-smokers were exposed to filtered air (FA) and DE (300 µg PM2.5/m3), on order-randomized days, for 2 h. In a second protocol, independent never-smoking participants were exposed to lower concentrations of DE (20, 50 or 150 µg PM2.5/m3) and FA, for 4 h, on order-randomized days. Each exposure was separated by 4 weeks of washout. Plasma samples obtained 24 h post-exposure from ex-smokers (n = 6) were first probed using Slow off-rate modified aptamer proteomic array. Plasma from never-smokers (n = 11) was used for independent assessment of proteins selected from the proteomics study by immunoblotting. RESULTS: Proteomics analyses revealed that DE significantly altered 342 proteins in plasma of ex-smokers (n = 6). The top 20 proteins therein were primarily associated with inflammation and CVD. Plasma from never-smokers (n = 11) was used for independent assessment of 6 proteins, amongst the top 10 proteins increased by DE in the proteomics study, for immunoblotting. The abundance of all six proteins (fractalkine, apolipoproteins (APOB and APOM), IL18R1, MIP-3 and MMP-12) was significantly increased by DE in plasma of these never-smokers. DE-mediated increase was shown to be concentration-dependent for fractalkine, APOB and MMP-12, all biomarkers of atherosclerosis, which correlated with plasma levels of IL-6, a subclinical marker of CVD, in independent participants. CONCLUSION: This investigation details changes in the human plasma proteome due to TRAP. We identify specific atherosclerosis-related proteins that increase concentration-dependently across a range of TRAP levels applicable worldwide.

Zika Virus Infection of Sertoli Cells Alters Protein Expression Involved in Activated Immune and Antiviral Response Pathways, Carbohydrate Metabolism and Cardiovascular Disease.

Zika virus (ZIKV), a re-emerging virus, causes congenital brain abnormalities and Guillain-Barré syndrome. It is mainly transmitted by Aedes mosquitoes, but infections are also linked to sexual transmissions. Infectious ZIKV has been isolated, and viral RNA has been detected in semen over a year after the onset of initial symptoms, but the mode of long-term persistence is not yet understood. ZIKV can proliferate in human Sertoli cells (HSerC) for several weeks in vitro, suggesting that it might be a reservoir for persistent ZIKV infection. This study determined proteomic changes in HSerC during ZIKV infections by TMT-mass spectrometry analysis. Levels of 4416 unique Sertoli cell proteins were significantly altered at 3, 5, and 7 days after ZIKV infection. The significantly altered proteins include enzymes, transcription regulators, transporters, kinases, peptidases, transmembrane receptors, cytokines, ion channels, and growth factors. Many of these proteins are involved in pathways associated with antiviral response, antigen presentation, and immune cell activation. Several immune response pathway proteins were significantly activated during infection, e.g., interferon signaling, T cell receptor signaling, IL-8 signaling, and Th1 signaling. The altered protein levels were linked to predicted activation of immune response in HSerC, which was predicted to suppress ZIKV infection. ZIKV infection also affected the levels of critical regulators of gluconeogenesis and glycolysis pathways such as phosphoglycerate mutase, phosphoglycerate kinase, and enolase. Interestingly, many significantly altered proteins were associated with cardiac hypertrophy, which may induce heart failure in infected patients. In summary, our research contributes to a better understanding of ZIKV replication dynamics and infection in Sertoli cells.

Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis.

Objectives: Patients with Rheumatoid Arthritis (RA) are increasingly achieving stable disease remission, yet the mechanisms that govern ongoing clinical disease and subsequent risk of future flare are not well understood. We sought to identify serum proteomic alterations that dictate clinically important features of stable RA, and couple broad-based proteomics with machine learning to predict future flare. Methods: We studied baseline serum samples from a cohort of stable RA patients (RETRO, n = 130) in clinical remission (DAS28<2.6) and quantified 1307 serum proteins using the SOMAscan platform. Unsupervised hierarchical clustering and supervised classification were applied to identify proteomic-driven clusters and model biomarkers that were associated with future disease flare after 12 months of follow-up and RA medication withdrawal. Network analysis was used to define pathways that were enriched in proteomic datasets. Results: We defined 4 proteomic clusters, with one cluster (Cluster 4) displaying a lower mean DAS28 score (p = 0.03), with DAS28 associating with humoral immune responses and complement activation. Clustering did not clearly predict future risk of flare, however an XGboost machine learning algorithm classified patients who relapsed with an AUC (area under the receiver operating characteristic curve) of 0.80 using only baseline serum proteomics. Conclusions: The serum proteome provides a rich dataset to understand stable RA and its clinical heterogeneity. Combining proteomics and machine learning may enable prediction of future RA disease flare in patients with RA who aim to withdrawal therapy.

Slow Off-Rate Modified Aptamer (SOMAmer) Proteomic Analysis of Patient-Derived Malignant Glioma Identifies Distinct Cellular Proteomes.

Malignant gliomas derive from brain glial cells and represent >75% of primary brain tumors. This includes anaplastic astrocytoma (grade III; AS), the most common and fatal glioblastoma multiforme (grade IV; GBM), and oligodendroglioma (ODG). We have generated patient-derived AS, GBM, and ODG cell models to study disease mechanisms and test patient-centered therapeutic strategies. We have used an aptamer-based high-throughput SOMAscan® 1.3K assay to determine the proteomic profiles of 1307 different analytes. SOMAscan® proteomes of AS and GBM self-organized into closely adjacent proteomes which were clearly distinct from ODG proteomes. GBM self-organized into four proteomic clusters of which SOMAscan® cluster 4 proteome predicted a highly inter-connected proteomic network. Several up- and down-regulated proteins relevant to glioma were successfully validated in GBM cell isolates across different SOMAscan® clusters and in corresponding GBM tissues. Slow off-rate modified aptamer proteomics is an attractive analytical tool for rapid proteomic stratification of different malignant gliomas and identified cluster-specific SOMAscan® signatures and functionalities in patient GBM cells.