RESC14 and RESC8 cooperate to mediate RESC function and dynamics during trypanosome RNA editing.

Mitochondrial transcripts in Trypanosoma brucei require extensive uridine insertion/deletion RNA editing to generate translatable open reading frames. The RNA editing substrate binding complex (RESC) serves as the scaffold that coordinates the protein-protein and protein-RNA interactions during editing. RESC broadly contains two modules termed the guide RNA binding complex (GRBC) and the RNA editing mediator complex (REMC), as well as organizer proteins. How the protein and RNA components of RESC dynamically interact to facilitate editing is not well understood. Here, we examine the roles of organizer proteins, RESC8 and RESC14, in facilitating RESC dynamics. High-throughput sequencing of editing intermediates reveals an overlapping RESC8 and RESC14 function during editing progression across multiple transcripts. Blue native PAGE analysis demonstrates that RESC14 is essential for incorporation of RESC8 into a large RNA-containing complex, while RESC8 is important in recruiting a smaller ribonucleoprotein complex (RNP) to this large complex. Proximity labeling shows that RESC14 is important for stable RESC protein-protein interactions, as well as RESC-RECC associations. Together, our data support a model in which RESC14 is necessary for assembly of editing competent RESC through recruitment of an RNP containing RESC8, GRBC and gRNA to REMC and mRNA.

Translational control by Trypanosoma brucei DRBD18 contributes to the maintenance of the procyclic state.

Trypanosoma brucei occupies distinct niches throughout its life cycle, within both the mammalian and tsetse fly hosts. The immunological and biochemical complexity and variability of each of these environments require a reshaping of the protein landscape of the parasite both to evade surveillance and face changing metabolic demands. In kinetoplastid protozoa, including T. brucei, posttranscriptional control mechanisms are the primary means of gene regulation, and these are often mediated by RNA-binding proteins. DRBD18 is a T. brucei RNA-binding protein that reportedly interacts with ribosomal proteins and translation factors. Here, we tested a role for DRBD18 in translational control. We validate the DRBD18 interaction with translating ribosomes and the translation initiation factor, eIF3a. We further show that DRBD18 depletion by RNA interference leads to altered polysomal profiles with a specific depletion of heavy polysomes. Ribosome profiling analysis reveals that 101 transcripts change in translational efficiency (TE) upon DRBD18 depletion: 41 exhibit decreased TE and 60 exhibit increased TE. A further 66 transcripts are buffered, that is, changes in transcript abundance are compensated by changes in TE such that the total translational output is expected not to change. In DRBD18-depleted cells, a set of transcripts that codes for procyclic form-specific proteins is translationally repressed while, conversely, transcripts that code for bloodstream form- and metacyclic form-specific proteins are translationally enhanced. RNA immunoprecipitation/qRT-PCR indicates that DRBD18 associates with members of both repressed and enhanced cohorts. These data suggest that DRBD18 contributes to the maintenance of the procyclic state through both positive and negative translational control of specific mRNAs.

Fibroblast growth factor receptor 1 inhibition suppresses pancreatic cancer chemoresistance and chemotherapy-driven aggressiveness.

AIMS: Pancreatic ductal adenocarcinoma (PDAC) is often intrinsically-resistant to standard-of-care chemotherapies such as gemcitabine. Acquired gemcitabine resistance (GemR) can arise from treatment of initially-sensitive tumors, and chemotherapy can increase tumor aggressiveness. We investigated the molecular mechanisms of chemoresistance and chemotherapy-driven tumor aggressiveness, which are understood incompletely. METHODS: Differential proteomic analysis was employed to investigate chemotherapy-driven chemoresistance drivers and responses of PDAC cells and patient-derived tumor xenografts (PDX) having different chemosensitivities. We also investigated the prognostic value of FGFR1 expression in the efficacy of selective pan-FGFR inhibitor (FGFRi)-gemcitabine combinations. RESULTS: Quantitative proteomic analysis of a highly-GemR cell line revealed fibroblast growth factor receptor 1 (FGFR1) as the highest-expressed receptor tyrosine kinase. FGFR1 knockdown or FGFRi co-treatment enhanced gemcitabine efficacy and decreased GemR marker expression, implicating FGFR1 in augmentation of GemR. FGFRi treatment reduced PDX tumor progression and prolonged survival significantly, even in highly-resistant tumors in which neither single-agent showed efficacy. Gemcitabine exacerbated aggressiveness of highly-GemR tumors, based upon proliferation and metastatic markers. Combining FGFRi with gemcitabine or gemcitabine+nab-paclitaxel reversed tumor aggressiveness and progression, and prolonged survival significantly. In multiple PDAC PDXs, FGFR1 expression correlated with intrinsic tumor gemcitabine sensitivity. CONCLUSION: FGFR1 drives chemoresistance and tumor aggressiveness, which FGFRi can reverse.

Comparative Proteomic Analysis Identifies Key Metabolic Regulators of Gemcitabine Resistance in Pancreatic Cancer.

Pancreatic adenocarcinoma (PDAC) is highly refractory to treatment. Standard-of-care gemcitabine (Gem) provides only modest survival benefits, and development of Gem resistance (GemR) compromises its efficacy. Highly GemR clones of Gem-sensitive MIAPaCa-2 cells were developed to investigate the molecular mechanisms of GemR and implemented global quantitative differential proteomics analysis with a comprehensive, reproducible ion-current-based MS1 workflow to quantify ∼6000 proteins in all samples. In GemR clone MIA-GR8, cellular metabolism, proliferation, migration, and 'drug response' mechanisms were the predominant biological processes altered, consistent with cell phenotypic alterations in cell cycle and motility. S100 calcium binding protein A4 was the most downregulated protein, as were proteins associated with glycolytic and oxidative energy production. Both responses would reduce tumor proliferation. Upregulation of mesenchymal markers was prominent, and cellular invasiveness increased. Key enzymes in Gem metabolism pathways were altered such that intracellular utilization of Gem would decrease. Ribonucleoside-diphosphate reductase large subunit was the most elevated Gem metabolizing protein, supporting its critical role in GemR. Lower Ribonucleoside-diphosphate reductase large subunit expression is associated with better clinical outcomes in PDAC, and its downregulation paralleled reduced MIAPaCa-2 proliferation and migration and increased Gem sensitivity. Temporal protein-level Gem responses of MIAPaCa-2 versus GemR cell lines (intrinsically GemR PANC-1 and acquired GemR MIA-GR8) implicate adaptive changes in cellular response systems for cell proliferation and drug transport and metabolism, which reduce cytotoxic Gem metabolites, in DNA repair, and additional responses, as key contributors to the complexity of GemR in PDAC. These findings additionally suggest targetable therapeutic vulnerabilities for GemR PDAC patients.

Proteomics Analysis of the Polyomavirus DNA Replication Initiation Complex Reveals Novel Functional Phosphorylated Residues and Associated Proteins.

Polyomavirus (PyV) Large T-antigen (LT) is the major viral regulatory protein that targets numerous cellular pathways for cellular transformation and viral replication. LT directly recruits the cellular replication factors involved in initiation of viral DNA replication through mutual interactions between LT, DNA polymerase alpha-primase (Polprim), and single-stranded DNA binding complex, (RPA). Activities and interactions of these complexes are known to be modulated by post-translational modifications; however, high-sensitivity proteomic analyses of the PTMs and proteins associated have been lacking. High-resolution liquid chromatography tandem mass spectrometry (LC-MS/MS) of the immunoprecipitated factors (IPMS) identified 479 novel phosphorylated amino acid residues (PAARs) on the three factors; the function of one has been validated. IPMS revealed 374, 453, and 183 novel proteins associated with the three, respectively. A significant transcription-related process network identified by Gene Ontology (GO) enrichment analysis was unique to LT. Although unidentified by IPMS, the ETS protooncogene 1, transcription factor (ETS1) was significantly overconnected to our dataset indicating its involvement in PyV processes. This result was validated by demonstrating that ETS1 coimmunoprecipitates with LT. Identification of a novel PAAR that regulates PyV replication and LT's association with the protooncogenic Ets1 transcription factor demonstrates the value of these results for studies in PyV biology.

Species-Deconvolved Proteomics for In Situ Investigation of Tumor-Stroma Interactions after Treatment of Pancreatic Cancer Patient-Derived Xenografts with Combined Gemcitabine and Paclitaxel.

Tumor-stroma interactions are critical in pancreatic ductal adenocarcinoma (PDAC) progression and therapeutics. Patient-derived xenograft (PDX) models recapitulate tumor-stroma interactions, but the conventional antibody-based immunoassay is inadequate to discriminate tumor and stromal proteins. Here, we describe a species-deconvolved proteomics approach embedded in IonStar that can unambiguously quantify the tumor (human-derived) and stromal (mouse-derived) proteins in PDX samples, enabling unbiased investigation of tumor and stromal proteomes with excellent quantitative reproducibility. With this strategy, we studied tumor-stroma interactions in PDAC PDXs that responded differently to Gemcitabine combined with nab-Paclitaxel (GEM+PTX) treatment. By analyzing 48 PDX animals 24 h/192 h after treatment with/without GEM+PTX, we quantified 7262 species-specific proteins under stringent cutoff criteria, with high reproducibility. For the PDX sensitive to GEM+PTX, the drug-dysregulated proteins in tumor cells were involved in suppressed oxidative phosphorylation and the TCA cycle, and in the stroma, inhibition of glycolytic activity was predominant, suggesting a relieved reverse Warburg effect by the treatment. In GEM+PTX-resistant PDXs, protein changes suggested extracellular matrix deposition and activation of tumor cell proliferation. Key findings were validated by immunohistochemistry (IHC). Overall, this approach provides a species-deconvolved proteomic platform that could advance cancer therapeutic studies by enabling unbiased exploration of tumor-stroma interactions in the large number of PDX samples required for such investigations.

Highly Reproducible Quantitative Proteomics Analysis of Pancreatic Cancer Cells Reveals Proteome-Level Effects of a Novel Combination Drug Therapy That Induces Cancer Cell Death via Metabolic Remodeling and Activation of the Extrinsic Apoptosis Pathway.

Pancreatic cancer patients have poor survival rates and are frequently treated using gemcitabine (Gem). However, initial tumor sensitivity often gives way to rapid development of resistance. Gem-based drug combinations are employed to increase efficacy and mitigate resistance, but our understanding of molecular-level drug interactions, which could assist in the development of more effective therapeutic regimens, is limited. Global quantitative proteomic analysis could provide novel mechanistic insights into drug combination interactions, but it is challenging to achieve high-quality quantitative proteomics analysis of the large sample sets that are typically required for drug combination studies. Here, we investigated molecular-level temporal interactions of Gem with BGJ398 (infigratinib), a recently approved pan-FGFR inhibitor, in multiple treatment groups (N = 42 samples) using IonStar, a robust large-scale proteomics method that employs well-controlled, ultrahigh-resolution MS1 quantification. A total of 5514 proteins in the sample set were quantified without missing data, requiring >2 unique peptides/protein, <1% protein false discovery rate (FDR), <0.1% peptide FDR, and CV < 10%. Functional analysis of the differentially altered proteins revealed drug-dysregulated processes such as metabolism, apoptosis, and antigen presentation pathways. These changes were validated experimentally using Seahorse metabolic assays and immunoassays. Overall, in-depth analysis of large-scale proteomics data provided novel insights into possible mechanisms by which FGFR inhibitors complement and enhance Gem activity in pancreatic cancers.

Comparative Proteomics Analysis of Exosomes Identifies Key Pathways and Protein Markers Related to Breast Cancer Metastasis.

Proteomics analysis of circulating exosomes derived from cancer cells represents a promising approach to the elucidation of cell-cell communication and the discovery of putative biomarker candidates for cancer diagnosis and treatment. Nonetheless, the proteome of exosomes derived from cell lines with different metastatic capabilities still warrants further investigation. Here, we present a comprehensive quantitative proteomics investigation of exosomes isolated from immortalized mammary epithelial cells and matched tumor lines with different metastatic potentials in an attempt to discover exosome markers specific to breast cancer (BC) metastasis. A total of 2135 unique proteins were quantified with a high confidence level from 20 isolated exosome samples, including 94 of the TOP 100 exosome markers archived by ExoCarta. Moreover, 348 altered proteins were observed, among which several metastasis-specific markers, including cathepsin W (CATW), magnesium transporter MRS2 (MRS2), syntenin-2 (SDCB2), reticulon-4 (RTN), and UV excision repair protein RAD23 homolog (RAD23B), were also identified. Notably, the abundance of these metastasis-specific markers corresponds well with the overall survival of BC patients in clinical settings. Together, these data provide a valuable dataset for BC exosome proteomics investigation and prominently facilitate the elucidation of the molecular mechanisms underlying primary tumor development and progression.

Quantitative Proteomics of Human Retinal Pigment Epithelium Reveals Key Regulators for the Pathogenesis of Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) is the leading cause of blindness in elderly people, with limited treatment options available for most patients. AMD involves the death of retinal pigment epithelium (RPE) and photoreceptor cells, with mitochondria dysfunction being a critical early event. In the current study, we utilized our unique resource of human donor RPE graded for AMD presence and severity to investigate proteome-wide dysregulation involved in early AMD. Organelle-enriched fractions of RPE were isolated from donors with early AMD (n = 45) and healthy age-matched controls (n = 32) and were analyzed by UHR-IonStar, an integrated proteomics platform enabling reliable and in-depth proteomic quantification in large cohorts. A total of 5941 proteins were quantified with excellent analytical reproducibility, and with further informatics analysis, many biological functions and pathways were found to be significantly dysregulated in donor RPE samples with early AMD. Several of these directly pinpointed changes in mitochondrial functions, e.g., translation, ATP metabolic process, lipid homeostasis, and oxidative stress. These novel findings highlighted the value of our proteomics investigation by allowing a better understanding of the molecular mechanisms underlying early AMD onset and facilitating both treatment development and biomarker discovery.

Influence of Tyrosine Kinase Inhibition on Organic Anion Transporting Polypeptide 1B3-Mediated Uptake.

The organic anion transporting polypeptide family member (OATP) 1B3 is a hepatic uptake transporter that has a broad substrate recognition and plays a significant role in regulating elimination of endogenous biomolecules or xenobiotics. OATP1B3 works in tandem with OATP1B1, with which it shares approximately 80% sequence homology and a high degree of substrate overlap. Despite some substrates being recognized solely by OATP1B3, its ability to compensate for loss of OATP1B1-mediated elimination and recognition by regulatory agencies, little is known about OATP1B3 regulatory factors and how they are involved with drug-drug interaction. It was recently discovered that OATP1B1 function is mediated by the activity of a particular tyrosine kinase that is sensitive to a variety of tyrosine kinase inhibitors (TKIs). This study reports that OATP1B3 is similarly regulated, as at least 50% of its activity is reduced by 20 US Food and Drug Administration -approved TKIs. Nilotinib was assessed as the most potent OATP1B3 inhibitor among the investigated TKIs, which can occur at clinically relevant concentrations and acted predominantly through noncompetitive inhibition without impacting membrane expression. Finally, OATP1B3 function was determined to be sensitive to the knockdown of the Lck/Yes novel tyrosine kinase that is sensitive to nilotinib and has been previously implicated in mediating OATP1B1 activity. Collectively, our findings identify tyrosine kinase activity as a major regulator of OATP1B3 function which is sensitive to kinase inhibition. Given that OATP1B1 is similarly regulated, simultaneous disruption of these transporters can have drastic effects on systemic drug concentrations, which would promote adverse events. SIGNIFICANCE STATEMENT: The organic anion transporting polypeptide family member (OATP) 1B3 is a facilitator of hepatic drug elimination, although much is unknown of how OATP1B3 activity is mediated, or how such regulators contribute to drug-drug interactions. This study reports that OATP1B3 activity is dependent on the Lck/Yes novel tyrosine kinase, which is sensitive to numerous tyrosine kinase inhibitors. These findings provide insight into the occurrence of many clinical drug-drug interactions, and a rationale for future study of tyrosine kinases regulating drug disposition.

Parallel, High-Quality Proteomic and Targeted Metabolomic Quantification Using Laser Capture Microdissected Tissues.

Quantitative proteomics/metabolomics investigation of laser-capture-microdissection (LCM) cell populations from clinical cohorts affords precise insights into disease/therapeutic mechanisms, nonetheless high-quality quantification remains a prominent challenge. Here, we devised an LC/MS-based approach allowing parallel, robust global-proteomics and targeted-metabolomics quantification from the same LCM samples, using biopsies from prostate cancer (PCa) patients as the model system. The strategy features: (i) an optimized molecular weight cutoff (MWCO) filter-based separation of proteins and small-molecule fractions with high and consistent recoveries; (ii) microscale derivatization and charge-based enrichment for ultrasensitive quantification of key androgens (LOQ = 5 fg/1k cells) with excellent accuracy/precision; (iii) reproducible/precise proteomics quantification with low-missing-data using a detergent-cocktail-based sample preparation and an IonStar pipeline for reproducible and precise protein quantification with excellent data quality. Key parameters enabling robust/reproducible quantification have been meticulously evaluated and optimized, and the results underscored the importance of surveying quantitative performances against key parameters to facilitate fit-for-purpose method development. As a proof-of-concept, high-quality quantification of the proteome and androgens in LCM samples of PCa patient-matched cancerous and benign epithelial/stromal cells was achieved (N = 16), which suggested distinct androgen distribution patterns across cell types and regions, as well as the dysregulated pathways involved in tumor-stroma crosstalk in PCa pathology. This strategy markedly leverages the scope of quantitative-omics investigations using LCM samples, and combining with IonStar, can be readily adapted to larger-cohort clinical analysis. Moreover, the capacity of parallel proteomics/metabolomics quantification permits precise corroboration of regulatory processes on both protein and small-molecule levels, with decreased batch effect and enhanced utilization of samples.

Ultra-High-Resolution IonStar Strategy Enhancing Accuracy and Precision of MS1-Based Proteomics and an Extensive Comparison with State-of-the-Art SWATH-MS in Large-Cohort Quantification.

Quantitative proteomics in large cohorts is highly valuable for clinical/pharmaceutical investigations but often suffers from severely compromised reliability, accuracy, and reproducibility. Here, we describe an ultra-high-resolution IonStar method achieving reproducible protein measurement in large cohorts while minimizing the ratio compression problem, by taking advantage of the exceptional selectivity of ultra-high-resolution (UHR)-MS1 detection (240k_FWHM@m/z = 200). Using mixed-proteome benchmark sets reflecting large-cohort analysis with technical or biological replicates (N = 56), we comprehensively compared the quantitative performances of UHR-IonStar vs a state-of-the-art SWATH-MS method, each with their own optimal analytical platforms. We confirmed a cutting-edge micro-liquid chromatography (LC)/Triple-TOF with Spectronaut outperforms nano-LC/Orbitrap for SWATH-MS, which was then meticulously developed/optimized to maximize sensitivity, reproducibility, and proteome coverage. While the two methods with distinct principles (i.e., MS1- vs MS2-based) showed similar depth-of-analysis (∼6700-7000 missing-data-free proteins quantified, 1% protein-false discovery rate (FDR) for entire set, 2 unique peptides/protein) and good accuracy/precision in quantifying high-abundance proteins, UHR-IonStar achieved substantially superior quantitative accuracy, precision, and reproducibility for lower-abundance proteins (a category that includes most regulatory proteins), as well as much-improved sensitivity/selectivity for discovering significantly altered proteins. Furthermore, compared to SWATH-MS, UHR-IonStar showed markedly higher accuracy for a single analysis of each sample across a large set, which is an inadequately investigated albeit critical parameter for large-cohort analysis. Finally, we compared UHR-IonStar vs SWATH-MS in measuring the time courses of altered proteins in paclitaxel-treated cells (N = 36), where dysregulated biological pathways have been very well established. UHR-IonStar discovered substantially more well-recognized biological processes/pathways induced by paclitaxel. Additionally, UHR-IonStar showed markedly superior ability than SWATH-MS in accurately depicting the time courses of well known to be paclitaxel-induced biomarkers. In summary, UHR-IonStar represents a reliable, robust, and cost-effective solution for large-cohort proteomic quantification with excellent accuracy and precision.

IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts.

Reproducible quantification of large biological cohorts is critical for clinical/pharmaceutical proteomics yet remains challenging because most prevalent methods suffer from drastically declined commonly quantified proteins and substantially deteriorated quantitative quality as cohort size expands. MS2-based data-independent acquisition approaches represent tremendous advancements in reproducible protein measurement, but often with limited depth. We developed IonStar, an MS1-based quantitative approach enabling in-depth, high-quality quantification of large cohorts by combining efficient/reproducible experimental procedures with unique data-processing components, such as efficient 3D chromatographic alignment, sensitive and selective direct ion current extraction, and stringent postfeature generation quality control. Compared with several popular label-free methods, IonStar exhibited far lower missing data (0.1%), superior quantitative accuracy/precision [∼5% intragroup coefficient of variation (CV)], the widest protein abundance range, and the highest sensitivity/specificity for identifying protein changes (<5% false altered-protein discovery) in a benchmark sample set (n = 20). We demonstrated the usage of IonStar by a large-scale investigation of traumatic injuries and pharmacological treatments in rat brains (n = 100), quantifying >7,000 unique protein groups (>99.8% without missing data across the 100 samples) with a low false discovery rate (FDR), two or more unique peptides per protein, and high quantitative precision. IonStar represents a reliable and robust solution for precise and reproducible protein measurement in large cohorts.

Potential Neuroprotective Mechanisms of Methamphetamine Treatment in Traumatic Brain Injury Defined by Large-Scale IonStar-Based Quantitative Proteomics.

Although traumatic brain injury (TBI) causes hospitalizations and mortality worldwide, there are no approved neuroprotective treatments, partly due to a poor understanding of the molecular mechanisms underlying TBI neuropathology and neuroprotection. We previously reported that the administration of low-dose methamphetamine (MA) induced significant functional/cognitive improvements following severe TBI in rats. We further demonstrated that MA mediates neuroprotection in part, via dopamine-dependent activation of the PI3K-AKT pathway. Here, we further investigated the proteomic changes within the rat cortex and hippocampus following mild TBI (TM), severe TBI (TS), or severe TBI plus MA treatment (TSm) compared to sham operated controls. We identified 402 and 801 altered proteins (APs) with high confidence in cortical and hippocampal tissues, respectively. The overall profile of APs observed in TSm rats more closely resembled those seen in TM rather than TS rats. Pathway analysis suggested beneficial roles for acute signaling through IL-6, TGFß, and IL-1ß. Moreover, changes in fibrinogen levels observed in TSm rats suggested a potential role for these proteins in reducing/preventing TBI-induced coagulopathies. These data facilitate further investigations to identify specific pathways and proteins that may serve as key targets for the development of neuroprotective therapies.

Evidence of Allosteric Coupling between Substrate Binding and Adx Recognition in the Vitamin D Carbon-24 Hydroxylase CYP24A1.

Metabolic inactivation of 1,25(OH)2D3 requires molecular recognition between the mitochondrial enzyme cytochrome P450 24A1 (CYP24A1) and its cognate redox partner adrenodoxin (Adx). Recent evidence supports a model of CYP24A1 function in which substrate binding and Adx recognition are structurally linked. However, the details of this allosteric connection are not clear. In this study, we utilize chemical cross-linking coupled to mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy, and CYP24A1 functional assays to inform a working model of a CYP24A1-Adx complex. We report that differential cross-linking internal to CYP24A1 points toward an Adx-induced conformational change that perturbs the F and G helices, which are required for substrate binding. Moreover, the modeled complex suggests that a semiconserved nonpolar interaction at the interface may influence CYP24A1 regioselectivity. Taken together, these findings contribute to our understanding of Adx recognition in a critical vitamin D-inactivating enzyme and provide broader insight regarding the variability inherent in CYP-Adx interactions.

Toward Accurate and Robust Liquid Chromatography-Mass Spectrometry-Based Quantification of Antibody Biotherapeutics in Tissues.

Liquid chromatography-mass spectrometry (LC-MS) affords a highly promising solution for absolute quantification of biotherapeutics/targets in tissues, which is critical for drug development. Nonetheless, accurate/robust tissue quantification remains challenging largely owing to the lack of optimal approaches to address the following fundamental prerequisites: (i) efficient removal of residual blood without losing tissue-associated biotherapeutics; (ii) an optimal method to exhaustively/quantitatively recover target proteins from tissues; and (iii) an appropriate strategy to prepare calibration/quality-control samples to ensure accurate tissue analysis. Here, we devised novel analytical procedures enabling extensive and systematic investigation of the above issues and thereby development of optimal strategies for accurate tissue analysis. Key discoveries include: first, using a novel procedure of sequential administration of nonlabeled and then stable-isotope-labeled monoclonal antibody (mAb); it was determined that perfusion with three blood volumes of heparinized saline is optimal, achieving efficient blood removal (95-99%) and low quantitative bias (0.5-13%); second, a reference sample set established by mass-balanced, exhaustive extraction, permitted accurate measurement of absolute protein recovery from tissues of dosed animals; with this method, we found mAb biotherapeutics present in free-(49.3-75.4%) and bound-forms (24.6-50.7%) in tissues, even without a target; therefore, a denaturing detergent buffer is necessary for exhaustive extraction (recovery>90%); third, overnight-incubation of calibration samples after spiking mAb to tissue was found to improve quantitative accuracy, especially for nondenaturing buffer extraction. These investigations established the critical parameters and optimal protocols that can be universally applied to achieve accurate and robust quantification of biotherapeutics/targets in tissues. As a proof of concept, we conducted the first-ever extensive pharmacokinetics measurement of mAb in major tissues with a LC-MS-based method, where interesting features of mAb tissue disposition were observed.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts.

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Temporal Effects of Combined Birinapant and Paclitaxel on Pancreatic Cancer Cells Investigated via Large-Scale, Ion-Current-Based Quantitative Proteomics (IonStar).

Despite decades of effort, pancreatic adenocarcinoma (PDAC) remains an intractable clinical challenge. An insufficient understanding of mechanisms underlying tumor cell responses to chemotherapy contributes significantly to the lack of effective treatment regimens. Here, paclitaxel, a first-line chemotherapeutic agent, was observed to interact synergistically with birinapant, a second mitochondrial-derived activator of caspases mimetic. Therefore, we investigated molecular-level drug interaction mechanisms using comprehensive, reproducible, and well-controlled ion-current-based MS1 quantification (IonStar). By analyzing 40 biological samples in a single batch, we compared temporal proteomic responses of PDAC cells treated with birinapant and paclitaxel, alone and combined. Using stringent criteria (e.g. strict false-discovery-rate (FDR) control, two peptides/protein), we quantified 4069 unique proteins confidently (99.8% without any missing data), and 541 proteins were significantly altered in the three treatment groups, with an FDR of <1%. Interestingly, most of these proteins were altered only by combined birinapant/paclitaxel, and these predominantly represented three biological processes: mitochondrial function, cell growth and apoptosis, and cell cycle arrest. Proteins responsible for activation of oxidative phosphorylation, fatty acid ß-oxidation, and inactivation of aerobic glycolysis were altered largely by combined birinapant/paclitaxel compared with single drugs, suggesting the Warburg effect, which is critical for survival and proliferation of cancer cells, was alleviated by the combination treatment. Metabolic profiling was performed to confirm substantially greater suppression of the Warburg effect by the combined agents compared with either drug alone. Immunoassays confirmed proteomic data revealing changes in apoptosis/survival signaling pathways, such as inhibition of PI3K/AKT, JAK/STAT, and MAPK/ERK signal transduction, as well as induction of G2/M arrest, and showed the drug combination induced much more apoptosis than did single agents. Overall, this in-depth, large-scale proteomics study provided novel insights into molecular mechanisms underlying synergy of combined birinapant/paclitaxel and describes a proteomics/informatics pipeline that can be applied broadly to the development of cancer drug combination regimens.

High-Throughput, Sensitive LC-MS Quantification of Biotherapeutics and Biomarkers Using Antibody-Free, Peptide-Level, Multiple-Mechanism Enrichment via Strategic Regulation of pH and Ionic and Solvent Strengths.

Sensitive and high-throughput measurement of biotherapeutics and biomarkers in plasma and tissues is critical for protein-drug development. Enrichment of target signature peptide (SP) after sample digestion permits sensitive LC-MS-based protein quantification and carries several prominent advantages over protein-level enrichment; however, developing high-quality antipeptide antibodies is challenging. Here we describe a novel, antibody-free, peptide-level-enrichment technique enabling high-throughput, sensitive, and robust quantification of proteins in biomatrices, by highly selective removal of matrix peptides and components via cation-exchange (CX) reversed-phase (RP) SPE with strategically regulated pH and ionic and organic strengths. Multiple-mechanism washing and elution achieved highly selective separation despite the low plate number of the SPE cartridge. We first investigated the adsorption-desorption behaviors of peptides on CX-RP sorbent and the coexisting, perplexing effects of pH, and ionic and organic strengths on the selectivity for SP enrichment, which has not been previously characterized. We demonstrated that the selectivity for separating target SPs from matrix peptides was closely associated with buffer pH relative to the pI of the SP, and pH values of pI - 2, pI, and pI + 2 respectively provided exceptional specificity for the ionic wash, the hydrophobic wash, and selective elution. Furthermore, desorption of peptides from the mixed-mode sorbent showed exponential and linear dependence, respectively, on organic-solvent percentage and salt percentage. On the basis of these findings, we established a streamlined procedure for rapid and robust method development. Quantification of biotherapeutics, targets, and biomarkers in plasma and tissues was used as the model system. Selective enrichment of target SPs was achieved along with elimination of 87-95% of matrix peptides, which improved the LOQ by 20-fold (e.g., 2 ng per gram of tissue). Application was demonstrated by sensitive quantification of time courses of mAb (T84.66) and target (CEA) in plasma and tumor tissues from a low-dose mouse PK study. For the first time, down-regulation of membrane-associated antigen following mAb treatment was observed. The CX-RP enrichment is robust, high-throughput, and universally applicable and thus is highly valuable for ultrasensitive, large-scale measurement of target protein in plasma and tissues.

Surfactant Cocktail-Aided Extraction/Precipitation/On-Pellet Digestion Strategy Enables Efficient and Reproducible Sample Preparation for Large-Scale Quantitative Proteomics.

For quantitative proteomics, efficient, robust, and reproducible sample preparation with high throughput is critical yet challenging, especially when large cohorts are involved, as is often required by clinical/pharmaceutical studies. We describe a rapid and straightforward surfactant cocktail-aided extraction/precipitation/on-pellet digestion (SEPOD) strategy to address this need. Prior to organic solvent precipitation and on-pellet digestion, SEPOD treats samples with a surfactant cocktail (SC) containing multiple nonionic/anionic surfactants, which achieves (i) exhaustive/reproducible protein extraction, including membrane-bound proteins; (ii) effective removal of detrimental nonprotein matrix components (e.g., >94% of phospholipids); (iii) rapid/efficient proteolytic digestion owing to dual (surfactants + precipitation) denaturation. The optimal SC composition and concentrations were determined by Orthogonal-Array-Design investigation of their collective/individuals effects on protein extraction/denaturation. Key parameters for cleanup and digestion were experimentally identified as well. The optimized SEPOD procedures allowed a rapid 6 h digestion providing a clean digest with high peptide yields and excellent quantitative reproducibility (especially low-abundance proteins). Compared with filter-assisted sample preparation (FASP) and in-solution digestion, SEPOD showed superior performance by recovering substantially more peptide/proteins (including integral membrane proteins), yielding significantly higher peptide intensities and improving quantification for peptides with extreme physicochemical properties. SEPOD was further applied in a large-cohort temporal investigation of 44 IAV-infected mouse lungs, providing efficient and reproducible peptide yields (77.9 ± 4.6%) across all samples. With the IonStar pipeline, >6 400 unique protein groups were quantified (≥2 peptide/protein, peptide-FDR < 0.05%), ∼99% without missing data in any sample with <7% technical median-intragroup CV. Altered proteome patterns revealed interesting novel insights into pathophysiological changes by IAV infection. In summary, SEPOD offers a feasible solution for rapid, efficient, and reproducible preparation of biological samples, facilitating high-quality proteomic quantification of large sample cohorts.