Proteome-Wide Protein Expression Profiling Across Five Pancreatic Cell Lines

Hallmarks of the metabolic secretome

The identification of novel secreted factors is advancing at an unprecedented pace. However, there is a critical need to consolidate and integrate this knowledge to provide a framework of their diverse mechanisms, functional significance, and inter-relationships. Complicating this effort are challenges related to nonstandardized methods, discrepancies in sample handling, and inconsistencies in the annotation of unknown molecules. This Review aims to synthesize the rapidly expanding field of the metabolic secretome, encompassing the five major types of secreted factors: proteins, peptides, metabolites, lipids, and extracellular vesicles. By systematically defining the functions and detection of the components within the metabolic secretome, this Review provides a primer into the advances of the field, and how integration of the techniques discussed can provide a deeper understanding of the mechanisms underlying metabolic homeostasis and its disorders.

A Peptidisc-Based Survey of the Plasma Membrane Proteome of a Mammalian Cell

Membrane proteins play critical roles at the cell surface and their misfunction is a hallmark of many human diseases. A precise evaluation of the plasma membrane proteome is therefore essential for cell biology and for discovering novel biomarkers and therapeutic targets. However, the low abundance of this proteome relative to soluble proteins makes it difficult to characterize, even with the most advanced proteomics technologies. Here, we apply the peptidisc membrane mimetic to purify the cell membrane proteome. Using the HeLa cell line as a reference, we capture 500 different integral membrane proteins, with half annotated to the plasma membrane. Notably, the peptidisc library is enriched with several ABC, SLC, GPCR, CD, and cell adhesion molecules that generally exist at low to very low copy numbers in the cell. We extend the method to compare two pancreatic cell lines, Panc-1 and hPSC. Here we observe a striking difference in the relative abundance of the cell surface cancer markers L1CAM, ANPEP, ITGB4, and CD70. We also identify two novel SLC transporters, SLC30A1 and SLC12A7, that are highly present in the Panc-1 cell only. The peptidisc library thus emerges as an effective way to survey and compare the membrane proteome of mammalian cells. Furthermore, since the method stabilizes membrane proteins in a water-soluble state, members of the library, here SLC12A7, can be specifically isolated.

NCOA4-Mediated Ferritinophagy Is a Pancreatic Cancer Dependency via Maintenance of Iron Bioavailability for Iron-Sulfur Cluster Proteins

Pancreatic ductal adenocarcinomas (PDAC) depend on autophagy for survival; however, the metabolic substrates that autophagy provides to drive PDAC progression are unclear. Ferritin, the cellular iron storage complex, is targeted for lysosomal degradation (ferritinophagy) by the selective autophagy adaptor NCOA4, resulting in release of iron for cellular utilization. Using patient-derived and murine models of PDAC, we demonstrate that ferritinophagy is upregulated in PDAC to sustain iron availability, thereby promoting tumor progression. Quantitative proteomics reveals that ferritinophagy fuels iron-sulfur cluster protein synthesis to support mitochondrial homeostasis. Targeting NCOA4 leads to tumor growth delay and prolonged survival but with the development of compensatory iron acquisition pathways. Finally, enhanced ferritinophagy accelerates PDAC tumorigenesis, and an elevated ferritinophagy expression signature predicts for poor prognosis in patients with PDAC. Together, our data reveal that the maintenance of iron homeostasis is a critical function of PDAC autophagy, and we define NCOA4-mediated ferritinophagy as a therapeutic target in PDAC.

SIGNIFICANCE

Autophagy and iron metabolism are metabolic dependencies in PDAC. However, targeted therapies for these pathways are lacking. We identify NCOA4-mediated selective autophagy of ferritin ("ferritinophagy") as upregulated in PDAC. Ferritinophagy supports PDAC iron metabolism and thereby tumor progression and represents a new therapeutic target in PDAC. See related commentary by Jain and Amaravadi, p. 2023. See related article by Ravichandran et al., p. 2198. This article is highlighted in the In This Issue feature, p. 2007.

Respiratory Supercomplexes Promote Mitochondrial Efficiency and Growth in Severely Hypoxic Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is characterized by extensive fibrosis and hypovascularization, resulting in significant intratumoral hypoxia (low oxygen) that contributes to its aggressiveness, therapeutic resistance, and high mortality. Despite oxygen being a fundamental requirement for many cellular and metabolic processes, and the severity of hypoxia in PDAC, the impact of oxygen deprivation on PDAC biology is poorly understood. Investigating how PDAC cells survive in the near absence of oxygen, we find that PDAC cell lines grow robustly in oxygen tensions down to 0.1%, maintaining mitochondrial morphology, membrane potential, and the oxidative metabolic activity required for the synthesis of key metabolites for proliferation. Disrupting electron transfer efficiency by targeting mitochondrial respiratory supercomplex assembly specifically affects hypoxic PDAC proliferation, metabolism, and in vivo tumor growth. Collectively, our results identify a mechanism that enables PDAC cells to thrive in severe, oxygen-limited microenvironments.

Chemical Biology Toolkit for DCLK1 Reveals Connection to RNA Processing

Doublecortin-like kinase 1 (DCLK1) is critical for neurogenesis, but overexpression is also observed in multiple cancers and is associated with poor prognosis. Nevertheless, the function of DCLK1 in cancer, especially the context-dependent functions, are poorly understood. We present a "toolkit" that includes the DCLK1 inhibitor DCLK1-IN-1, a complementary DCLK1-IN-1-resistant mutation G532A, and kinase dead mutants D511N and D533N, which can be used to investigate signaling pathways regulated by DCLK1. Using a cancer cell line engineered to be DCLK1 dependent for growth and cell migration, we show that this toolkit can be used to discover associations between DCLK1 kinase activity and biological processes. In particular, we show an association between DCLK1 and RNA processing, including the identification of CDK11 as a potential substrate of DCLK1 using phosphoproteomics.

Proteomic Analysis of Cell Lines and Primary Tumors in Pancreatic Cancer Identifies Proteins Expressed Only In Vitro and Only In Vivo

OBJECTIVES

A limited repertoire of good pancreatic ductal adenocarcinoma (PDAC) models is one of the main barriers in developing effective new PDAC treatments. We aimed to characterize 6 commonly used PDAC cell lines and compare them with PDAC patient tumor samples using proteomics.

METHODS

Proteomic methods were used to generate an extensive catalog of proteins from 10 PDAC surgical specimens, 9 biopsies of adjacent normal tissue, and 6 PDAC cell lines. Protein lists were interrogated to determine what extent the proteome of the cell lines reflects the proteome of primary pancreatic tumors.

RESULTS

We identified 7973 proteins from the cell lines, 5680 proteins from the tumor tissues, and 4943 proteins from the adjacent normal tissues. We identified 324 proteins unique to the cell lines, some of which may play a role in survival of cells in culture. Conversely, a list of 63 proteins expressed only in the patient samples, whose expression is lost in culture, may place limitations on the degree to which these model systems reflect tumor biology in vivo.

CONCLUSIONS

Our work offers a catalog of proteins detected in each of the PDAC cell lines, providing a useful guide for researchers seeking model systems for PDAC functional studies.

Proteomic analysis of pancreatic ductal adenocarcinoma

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC), which represents approximately 80% of all pancreatic cancers, is a highly aggressive malignant disease and one of the most lethal among all cancers. Overall, the 5-year survival rate among all pancreatic cancer patients is less than 9%; these rates have shown little change over the past 30 years. A more comprehensive understanding of the molecular mechanisms underlying this complex disease is crucial to the development of new diagnostic tools for early detection and disease monitoring, as well as to identify new and more effective therapeutics to improve patient outcomes.

AREA COVERED

We summarize recent advances in proteomic strategies and mass spectrometry to identify new biomarkers for early detection and monitoring of disease progression, predict response to therapy, and to identify novel proteins that have the potential to be 'druggable' therapeutic targets. An overview of proteomic studies that have been conducted to further our mechanistic understanding of metastasis and chemotherapy resistance in PDAC disease progression will also be discussed.

EXPERT COMMENTARY

The results from these PDAC proteomic studies on a variety of PDAC sample types (e.g., blood, tissue, cell lines, exosomes, etc.) provide great promise of having a significant clinical impact and improving patient outcomes.

Selective Alanine Transporter Utilization Creates a Targetable Metabolic Niche in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) evolves a complex microenvironment comprised of multiple cell types, including pancreatic stellate cells (PSC). Previous studies have demonstrated that stromal supply of alanine, lipids, and nucleotides supports the metabolism, growth, and therapeutic resistance of PDAC. Here we demonstrate that alanine cross-talk between PSCs and PDAC is orchestrated by the utilization of specific transporters. PSCs utilize SLC1A4 and other transporters to rapidly exchange and maintain environmental alanine concentrations. Moreover, PDAC cells upregulate SLC38A2 to supply their increased alanine demand. Cells lacking SLC38A2 fail to concentrate intracellular alanine and undergo a profound metabolic crisis resulting in markedly impaired tumor growth. Our results demonstrate that stromal-cancer metabolic niches can form through differential transporter expression, creating unique therapeutic opportunities to target metabolic demands of cancer. SIGNIFICANCE: This work identifies critical neutral amino acid transporters involved in channeling alanine between pancreatic stellate and PDAC cells. Targeting PDAC-specific alanine uptake results in a metabolic crisis impairing metabolism, proliferation, and tumor growth. PDAC cells specifically activate and require SLC38A2 to fuel their alanine demands that may be exploited therapeutically.This article is highlighted in the In This Issue feature, p. 890.

Monitoring protein communities and their responses to therapeutics

Most therapeutics are designed to alter the activities of proteins. From metabolic enzymes to cell surface receptors, connecting the function of a protein to a cellular phenotype, to the activity of a drug and to a clinical outcome represents key mechanistic milestones during drug development. Yet, even for therapeutics with exquisite specificity, the sequence of events following target engagement can be complex. Interconnected communities of structural, metabolic and signalling proteins modulate diverse downstream effects that manifest as interindividual differences in efficacy, adverse effects and resistance to therapy. Recent advances in mass spectrometry proteomics have made it possible to decipher these complex relationships and to understand how factors such as genotype, cell type, local environment and external perturbations influence them. In this Review, we explore how proteomic technologies are expanding our understanding of protein communities and their responses to large- and small-molecule therapeutics.

NCOA4 maintains murine erythropoiesis via cell autonomous and non-autonomous mechanisms

Ncoa4 mediates autophagic degradation of ferritin, the cytosolic iron storage complex, to maintain intracellular iron homeostasis. Recent evidence also supports a role for Ncoa4 in systemic iron homeostasis and erythropoiesis. However, the specific contribution and temporal importance of Ncoa4-mediated ferritinophagy in regulating systemic iron homeostasis and erythropoiesis is unclear. Here, we show that Ncoa4 has a critical role in basal systemic iron homeostasis and both cell autonomous and non-autonomous roles in murine erythropoiesis. Using an inducible murine model of Ncoa4 knockout, acute systemic disruption of Ncoa4 impaired systemic iron homeostasis leading to tissue ferritin and iron accumulation, a decrease in serum iron, and anemia. Mice acutely depleted of Ncoa4 engaged the Hif2a-erythropoietin system to compensate for anemia. Mice with targeted deletion of Ncoa4 specifically in the erythroid compartment developed a pronounced anemia in the immediate postnatal stage, a mild hypochromic microcytic anemia at adult stages, and were more sensitive to hemolysis with higher requirements for the Hif2a-erythropoietin axis and extramedullary erythropoiesis during recovery. These studies demonstrate the importance of Ncoa4-mediated ferritinophagy as a regulator of systemic iron homeostasis and define the relative cell autonomous and non-autonomous contributions of Ncoa4 in supporting erythropoiesis in vivo.

Streamlined Tandem Mass Tag (SL-TMT) Protocol: An Efficient Strategy for Quantitative (Phospho)proteome Profiling Using Tandem Mass Tag-Synchronous Precursor Selection-MS3

Mass spectrometry (MS) coupled toisobaric labeling has developed rapidly into a powerful strategy for high-throughput protein quantification. Sample multiplexing and exceptional sensitivity allow for the quantification of tens of thousands of peptides and, by inference, thousands of proteins from multiple samples in a single MS experiment. Accurate quantification demands a consistent and robust sample-preparation strategy. Here, we present a detailed workflow for SPS-MS3-based quantitative abundance profiling of tandem mass tag (TMT)-labeled proteins and phosphopeptides that we have named the streamlined (SL)-TMT protocol. We describe a universally applicable strategy that requires minimal individual sample processing and permits the seamless addition of a phosphopeptide enrichment step ("mini-phos") with little deviation from the deep proteome analysis. To showcase our workflow, we profile the proteome of wild-type Saccharomyces cerevisiae yeast grown with either glucose or pyruvate as the carbon source. Here, we have established a streamlined TMT protocol that enables deep proteome and medium-scale phosphoproteome analysis.

Global quantitative analysis of the human brain proteome in Alzheimer's and Parkinson's Disease

Patients with Alzheimer's disease (AD) and Parkinson's disease (PD) often have overlap in clinical presentation and brain neuropathology suggesting that these two diseases share common underlying mechanisms. Currently, the molecular pathways linking AD and PD are incompletely understood. Utilizing Tandem Mass Tag (TMT) isobaric labeling and synchronous precursor selection-based MS3 (SPS-MS3) mass spectrometry, we performed an unbiased quantitative proteomic analysis of post-mortem human brain tissues (n=80) from four different groups defined as controls, AD, PD, and co-morbid AD/PD cases across two brain regions (frontal cortex and anterior cingulate gyrus). In total, we identified 11 840 protein groups representing 10 230 gene symbols, which map to ~65% of the protein coding genes in brain. The utility of including two reference standards in each TMT 10-plex assay to assess intra- and inter-batch variance is also described. Ultimately, this comprehensive human brain proteomic dataset serves as a valuable resource for various research endeavors including, but not limited to, the identification of disease-specific protein signatures and molecular pathways that are common in AD and PD.

Filter-Based Protein Digestion (FPD): A Detergent-Free and Scaffold-Based Strategy for TMT Workflows

High-throughput proteome profiling requires thorough optimization to achieve comprehensive analysis. We developed a filter aided sample preparation (FASP)-like, detergent-free method, termed Filter-Based Protein Digestion (FPD). We compared FPD to protein extraction methods commonly used in isobaric tag-based proteome profiling, namely trichloroacetic acid (TCA) and chloroform-methanol (C-M) precipitation. We divided a mammalian whole cell lysate from the SH-SY5Y neuroblastoma cell line for parallel protein processing with TCA (n = 3), C-M (n = 2), and FPD using either 10 kDa (n = 3) or 30 kDa (n = 3) molecular weight cutoff membranes. We labeled each sample with tandem mass tag (TMT) reagents to construct a TMT11-plex experiment. In total, 8654 proteins were quantified across all samples. Pairwise comparisons showed very little deviation for individual protein abundance measurements between the two FPD methods, whereas TCA and FPD showed the most difference. Specifically, membrane proteins were more readily quantified when samples were processed using TCA precipitation than other methods tested. However, globally, only 4% of proteins differed greater than 4-fold in the most divergent pair of protein extraction methods (i.e., FPD10 and TCA). We conclude that the detergent-free FPD strategy, particularly using the faster-flowing 30 kDa filter, is a seamless alteration to high-throughput TMT workflows.

Improved Method for Determining Absolute Phosphorylation Stoichiometry Using Bayesian Statistics and Isobaric Labeling

Phosphorylation stoichiometry, or occupancy, is one element of phosphoproteomics that can add useful biological context (Gerber et al. Proc. Natl. Acad. Sci. U. S. A. 2003, 100, 6940-5). We previously developed a method to assess phosphorylation stoichiometry on a proteome-wide scale (Wu et al. Nat. Methods 2011, 8, 677-83). The stoichiometry calculation relies on identifying and measuring the levels of each nonphosphorylated counterpart peptide with and without phosphatase treatment. The method, however, is problematic in that low stoichiometry phosphopeptides can return negative stoichiometry values if measurement error is larger than the percent stoichiometry. Here, we have improved the stoichiometry method through the use of isobaric labeling with 10-plex TMT reagents. In this way, five phosphatase treated and five untreated samples are compared simultaneously so that each stoichiometry is represented by five ratio measurements with no missing values. We applied the method to determine basal stoichiometries of HCT116 cells growing in culture. With this method, we analyzed five biological replicates simultaneously with no need for phosphopeptide enrichment. Additionally, we developed a Bayesian model to estimate phosphorylation stoichiometry as a parameter confined to an interval between 0 and 1 implemented as an R/Stan script. Consequently, both point and interval estimates are consistent with the plausible range of values for stoichiometry. Finally, we report absolute stoichiometry measurements with credible intervals for 6772 phosphopeptides containing at least a single phosphorylation site.