Use Of Quantitative Proteomics Research Articles

Histodenz Separation of Lysosomal Subpopulations for Analysis of Chaperone-mediated Autophagy.

Chaperone-mediated autophagy (CMA) is the most selective form of lysosomal proteolysis, in which proteins are individually selected for lysosomal degradation. CMA degradation targets bear a pentapeptide consensus motif that is recognized by the cytosolic chaperone HSPA8 (Hsc70), which participates in the trafficking of the target to the lysosomal surface. From there, it is translocated into the lysosomal lumen, independent of vesicle fusion, in a process dependent upon the lysosomal transmembrane protein LAMP2A. There are limited tools for studying CMA in whole cells and tissues, and many of the best techniques for studying CMA rely on the preparation of lysosome enriched fractions. Such experiments include (1) the in vitro evaluation of CMA substrate uptake activity, (2) the characterization of changes to lysosomal resident and CMA regulatory proteins, and (3) lysosomal targetomics, i.e., the use of quantitative proteomics to characterize lysosomal degradation targets. Previous studies using discontinuous metrizamide gradients have shown that a subpopulation of liver lysosomes is responsible for the majority of CMA activity ("CMA+ "). These CMA+ lysosomes are low density and have higher levels of MTORC2 relative to the "CMA- " lysosomes, which are high density and have higher levels of MTORC1. Because of safety concerns surrounding metrizamide, however, this compound is difficult to obtain, and it is impractically expensive. Here, we have provided protocols for isolation of lysosomal subpopulations for CMA-related analyses from mouse liver using Histodenz, a safe and affordable alternative to metrizamide. Supplementary protocols show how to perform CMA activity assays with appropriate statistical analysis, and how to analyze for lysosomal breakage/membrane integrity. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Isolation of lysosomal subpopulations from mouse liver using discontinuous Histodenz gradients Alternate Protocol: Isolation of lysosomes from cultured cells using discontinuous Histodenz gradients Support Protocol 1: Verifying enrichment of lysosomal markers in lysosome-enriched fractions Support Protocol 2: Measuring in vitro uptake of CMA substrates Support Protocol 3: Measuring lysosomal membrane integrity by hexosaminidase assay.

Proteome of follicular fluid from Murrah buffaloes (Bubalus bubalis): Normal cycling versus repeat-breeder females

Clinical evaluations have shown that repeat breeding is a major cause of infertility in buffaloes. The follicular fluid (FF) composition reflects directly metabolic status and fertility in females. Given this scenario, this study aimed to perform quantitative proteomic analysis of the FF collected from normal cycling (NC) and compared to repeat-breeder females. According to farm records, buffaloes were divided into two groups: normal cycling (NC, n = 7) and repeat-breeder (RB, n = 8) females. After estrus synchronization and using ultrasound-guided ovum pick-up, FF was aspirated from large follicles (˃ 8 mm). Posteriorly, proteins were identified by the shotgun method. A total of 119 proteins were identified and, among theses, three were uncharacterized and a protein (LOC123334375) was identified only in the NC group. The protein HP-25 homolog 2 was expressed only in RB females. The LFQ (label-free quantitation)-intensity of the proteins afamin (AFM), transthyretin (TTR), clotting factor IX (F9) and Xaa-Pro dipeptidase (PEPD) was significantly (P < 0.05) higher in RB than in NC females. In conclusion, the use of quantitative proteomics proved to be an important tool for the study of RB in buffaloes. The identification of HP-25 homolog 2 protein only in the RB females suggests that it can be used as a biomarker for this reproductive disorder. Additionally, an uncharacterized (LOC123334375) protein was identified only in normal cycling females and compensatory oxidative stress proteins were more expressed in repeat-breeders.

Using SILAC to Develop Quantitative Data-Independent Acquisition (DIA) Proteomic Methods.

Proteins are integral to biological systems and functions. Identifying and quantifying proteins can therefore offer systems-wide insights into protein-protein interactions, cellular signaling, and biological pathway activity. The use of quantitative proteomics has become a method of choice for identifying and quantifying proteins in complex matrices. Proteomics allows researchers to survey hundreds to thousands of proteins in a less biased manner than classical antibody-based protein capture strategies. Typically,discovery approacheshave used data-dependent acquisition (DDA) methods, but this approach suffers from stochasticity that compromises quantitation. Recent developments in data-independent acquisition (DIA) proteomics workflows enable proteomic profiling of thousands of samples with increased peak picking consistency making it an excellent candidate for discovering and assessing biomarkers in clinical samples. However, quantitation of peptides from DIA datasets is computationally intensive, and guidelines on how to establish DIA methods are lacking. Method development and optimization require novel tools to visualize and filter DIA datasets appropriately. Here, a protocol and novel script workflow for the optimization of quantitative DIA methods using stable isotope labeling of amino acids in culture (SILAC) are presented. This protocol includes steps for cell growth and labeling, peptide digestion and preparation, and optimization of quantitative DIA methods. In addition, important steps for (1) computational analysis to identify and quantify peptides, (2) data visualizations to identify the linear abundance ranges for all peptides in the sample, and (3) descriptions of how to find high confidence quantitation abundance thresholds are described herein.

Cell-Free Hemoglobin Does Not Attenuate the Effects of SARS-CoV-2 Spike Protein S1 Subunit in Pulmonary Endothelial Cells.

SARS-CoV-2 primarily infects epithelial airway cells that express the host entry receptor angiotensin-converting enzyme 2 (ACE2), which binds to the S1 spike protein on the surface of the virus. To delineate the impact of S1 spike protein interaction with the ACE2 receptor, we incubated the S1 spike protein with human pulmonary arterial endothelial cells (HPAEC). HPAEC treatment with the S1 spike protein caused disruption of endothelial barrier function, increased levels of numerous inflammatory molecules (VCAM-1, ICAM-1, IL-1β, CCL5, CXCL10), elevated mitochondrial reactive oxygen species (ROS), and a mild rise in glycolytic reserve capacity. Because low oxygen tension (hypoxia) is associated with severe cases of COVID-19, we also evaluated treatment with hemoglobin (HbA) as a potential countermeasure in hypoxic and normal oxygen environments in analyses with the S1 spike protein. We found hypoxia downregulated the expression of the ACE2 receptor and increased the critical oxygen homeostatic signaling protein, hypoxia-inducible factor (HIF-1α); however, treatment of the cells with HbA yielded no apparent change in the levels of ACE2 or HIF-1α. Use of quantitative proteomics revealed that S1 spike protein-treated cells have few differentially regulated proteins in hypoxic conditions, consistent with the finding that ACE2 serves as the host viral receptor and is reduced in hypoxia. However, in normoxic conditions, we found perturbed abundance of proteins in signaling pathways related to lysosomes, extracellular matrix receptor interaction, focal adhesion, and pyrimidine metabolism. We conclude that the spike protein alone without the rest of the viral components is sufficient to elicit cell signaling in HPAEC, and that treatment with HbA failed to reverse the vast majority of these spike protein-induced changes.

Closing the protein gap in plant chronobiology.

Our modern understanding of diel cell regulation in plants stems from foundational work in the late 1990s that analysed the dynamics of selected genes and mutants in Arabidopsis thaliana. The subsequent rise of transcriptomics technologies such as microarrays and RNA sequencing has substantially increased our understanding of anticipatory (circadian) and reactive (light- or dark-triggered) diel events in plants. However, it is also becoming clear that gene expression data fail to capture critical events in diel regulation that can only be explained by studying protein-level dynamics. Over the past decade, mass spectrometry technologies and quantitative proteomic workflows have significantly advanced, finally allowing scientists to characterise diel protein regulation at high throughput. Initial proteomic investigations suggest that the diel transcriptome and proteome generally lack synchrony and that the timing of daily regulatory events in plants is impacted by multiple levels of protein regulation (e.g., post-translational modifications [PTMs] and protein-protein interactions [PPIs]). Here, we highlight and summarise how the use of quantitative proteomics to elucidate diel plant cell regulation has advanced our understanding of these processes. We argue that this new understanding, coupled with the extraordinary developments in mass spectrometry technologies, demands greater focus on protein-level regulation of, and by, the circadian clock. This includes hitherto unexplored diel dynamics of protein turnover, PTMs, protein subcellular localisation and PPIs that can be masked by simple transcript- and protein-level changes. Finally, we propose new directions for how the latest advancements in quantitative proteomics can be utilised to answer outstanding questions in plant chronobiology.

ITRAQ-based analysis for the identification of MARCH8 targets in human esophageal squamous cell carcinoma

MARCH8 is an E3 ligase, primarily involved in immune-modulation. Recently, we reported its aberrant expression in human esophageal squamous cell carcinoma. However, exact mechanisms by which it regulates cancer have been poorly understood. We applied high-throughput quantitative proteomics approach to identify downstream protein targets of MARCH8. Silencing of endogenous MARCH8 in ESCC cells followed by LC-MS/MS analysis led to identification of 1,029 unique proteins showing altered expression post MARCH8 knockdown. Several previously reported MARCH8 target proteins viz. TFR1, syntaxin-4, e-cadherin and CD44 were found to be upregulated. Furthermore, new putative targets of MARCH8, including β2M, were identified in the present study. We demonstrated that MARCH8 interacts with and ubiquitinates CDH1 and β2M. Inhibiting proteasome activity with MG132 prevented CDH1 and β2M degradation, indicating that MARCH8 might be targeting CDH1 and β2M for proteasomal degradation. Further, loss of β2M and CDH1 expression significantly and inversely correlated with MARCH8 expression in ESCC tissues (r = −0.737 and − 0.651, respectively; p < 0.01). In conclusion, our present study has led to identification of new targets of MARCH8 and suggests the role of MARCH8 in regulating CDH1 and β2M turnover in esophageal cancer cells. SignificanceThe use of quantitative proteomics carried out has led to the recognition of new targets of MARCH8. The present study gives a broad understanding of the molecular remodeling arising in the ESCC after MARCH8 knockdown. The study also solidifies the idea that role of MARCH8 is not just limited to immunomodulation as silencing of MARCH8 affects various other processes such as protein processing and localization. This study might help in understanding the regulation of MARCH8 in ESCCs and the mechanism by which MARCH8 might be facilitating cancer cells to evade immune surveillance.

Dynamic proteome profiling of human pluripotent stem cell-derived pancreatic progenitors.

A comprehensive characterization of the molecular processes controlling cell fate decisions is essential to derive stable progenitors and terminally differentiated cells that are functional from human pluripotent stem cells (hPSCs). Here, we report the use of quantitative proteomics to describe early proteome adaptations during hPSC differentiation toward pancreatic progenitors. We report that the use of unbiased quantitative proteomics allows the simultaneous profiling of numerous proteins at multiple time points, and is a valuable tool to guide the discovery of signaling events and molecular signatures underlying cellular differentiation. We also monitored the activity level of pathways whose roles are pivotal in the early pancreas differentiation, including the Hippo signaling pathway. The quantitative proteomics data set provides insights into the dynamics of the global proteome during the transition of hPSCs from a pluripotent state toward pancreatic differentiation.

Proteomic comparison of three clinical diarrhoeagenic drug-resistant Escherichia coli isolates grown on CHROMagar™STEC media

Shiga-toxin-producing Escherichia coli (STEC) and enteropathogenic Escherichia coli (EPEC) are key diarrhoea-causing foodborne pathogens. We used proteomics to characterize the virulence and antimicrobial resistance protein profiles of three clinical pathogenic E. coli isolates (two EPEC [one resistant to ciprofloxacin] and one STEC) cultured on CHROMagar™STEC solid media after minimal laboratory passage. We identified 4767 unique peptides from 1630 protein group across all three clinical E. coli strains. Label-free proteomic analysis allowed the identification of virulence and drug resistance proteins that were unique to each of the clinical isolates compared in this study. The B subunit of Shiga toxin, ToxB, was uniquely detected in the STEC strain while several other virulence factors including SheA, OmpF, OmpC and OmpX were significantly more abundant in the STEC strain. The ciprofloxacin resistant EPEC isolate possessed reduced levels of key virulence proteins compared to the ciprofloxacin susceptible EPEC and STEC strains. Parallel reaction monitoring assays validated the presence of biologically relevant proteins across biologically-replicated cultures. Propagation of clinical isolates on a relevant solid medium followed by mass spectrometry analysis represents a convenient means to quantify virulence factors and drug resistance determinants that might otherwise be lost through extensive in vitro passage in enteropathogenic bacteria. SignificanceThrough the use of quantitative proteomics, we have characterized the virulence and antimicrobial resistance attributes of three clinically isolated, pathogenic E. coli strains cultured on solid media. Our results provide new, quantitative data on the expressed proteomes of these tellurite-resistant, diarrhoeagenic E. coli strains and reveal a subset of antimicrobial resistance and virulence proteins that are differentially abundant between these clinical strains. Our quantitative proteomics-based approach should thus have applicability in microbiological diagnostic labs for the identification of pathogenic/drug resistant E. coli in the future.

Quantitative Temporal in Vivo Proteomics Deciphers the Transition of Virus-Driven Myeloid Cells into M2 Macrophages.

Myeloid cells play a central role in the context of viral eradication, yet precisely how these cells differentiate throughout the course of acute infections is poorly understood. In this study, we have developed a novel quantitative temporal in vivo proteomics (QTiPs) platform to capture proteomic signatures of temporally transitioning virus-driven myeloid cells directly in situ, thus taking into consideration host–virus interactions throughout the course of an infection. QTiPs, in combination with phenotypic, functional, and metabolic analyses, elucidated a pivotal role for inflammatory CD11b+, Ly6G–, Ly6Chigh-low cells in antiviral immune response and viral clearance. Most importantly, the time-resolved QTiPs data set showed the transition of CD11b+, Ly6G–, Ly6Chigh-low cells into M2-like macrophages, which displayed increased antigen-presentation capacities and bioenergetic demands late in infection. We elucidated the pivotal role of myeloid cells in virus clearance and show how these cells phenotypically, functionally, and metabolically undergo a timely transition from inflammatory to M2-like macrophages in vivo. With respect to the growing appreciation for in vivo examination of viral–host interactions and for the role of myeloid cells, this study elucidates the use of quantitative proteomics to reveal the role and response of distinct immune cell populations throughout the course of virus infection.

Proteomic and Microscopic Strategies towards the Analysis of the Cytoskeletal Networks in Major Neuropsychiatric Disorders.

Mental health disorders have become worldwide health priorities. It is estimated that in the next 20 years they will account for a 16 trillion United State dollars (US$) loss. Up to now, the underlying pathophysiology of psychiatric disorders remains elusive. Altered cytoskeleton proteins expression that may influence the assembly, organization and maintenance of cytoskeletal integrity has been reported in major depressive disorders, schizophrenia and to some extent bipolar disorders. The use of quantitative proteomics, dynamic microscopy and super-resolution microscopy to investigate disease-specific protein signatures holds great promise to improve our understanding of these disorders. In this review, we present the currently available quantitative proteomic approaches use in neurology, gel-based, stable isotope-labelling and label-free methodologies and evaluate their strengths and limitations. We also reported on enrichment/subfractionation methods that target the cytoskeleton associated proteins and discuss the need of alternative methods for further characterization of the neurocytoskeletal proteome. Finally, we present live cell imaging approaches and emerging dynamic microscopy technology that will provide the tools necessary to investigate protein interactions and their dynamics in the whole cells. While these areas of research are still in their infancy, they offer huge potential towards the understanding of the neuronal network stability and its modification across neuropsychiatric disorders.

Abstract B46: The use of quantitative proteomics to increase the knowledge about metabolic symbiosis in a tumor microenvironment

Abstract For more than a century, it is known that in a solid tumor, malignant cancer cells co-reside with stromal cells including fibroblasts and immune cells. In addition, changes in metabolic requirements of malignant cells are necessary to cover their high proliferative rate. Although it has been demonstrated that complementary metabolic functions of tumor cells and their supporting local stroma (e.g. cancer-associated fibroblasts, adipocytes) contribute to the progression of cancer, less is known about the metabolic needs of immune cells in this local tumor microenvironment. However, in the context of cancer, immune cells, including tumor-associated macrophages (TAMs) are of high interest since it is known that these cells are able to influence tumor progression by allowing the establishment of an attractive environment for tumor growth and metastasis. Studying the expression profiles of proteins involved in metabolic processes of these cells might be a good strategy to elucidate this question. In this study, we applied quantitative proteomics to study the differences in protein expression of TAMs vs. control macrophages (both bone marrow-derived and peritoneal macrophages) isolated from an in vivo murine model for non-small cell lung adenocarcinoma. In our peptide-centric two dimensional liquid chromatography (LC)- tandem mass spectrometry (MS/MS) setup where Tandem Mass Tag (TMT) labeling is applied, we were able to identify 1250 unique proteins, of which 1178 proteins were quantified with all six TMT reporter ions. Interestingly, analysis of the differentially expressed proteins indicate that several Gene Ontology terms regarding metabolic pathways including glutathione metabolic processes (p= 3.47 e-14) and pentose phosphate shunt (p= 2.29 e-8) were enriched. These data suggest that TAMs can reprogram their metabolism in order to survive to the oxidative stress existing in this kind of microenvironment. In conclusion, our results illustrate that proteome analysis is a good strategy to obtain insights in the biology of tumor-associated macrophages. Citation Format: Evelyne Maes, Mathias Wenes, Dirk Valkenborg, Inge Mertens, Hans Prenen, Massimiliano Mazzone, Geert Baggerman. The use of quantitative proteomics to increase the knowledge about metabolic symbiosis in a tumor microenvironment. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr B46.

Quantitative Proteomics Identifies Serum Response Factor Binding Protein 1 as a Host Factor for Hepatitis C Virus Entry.

SummaryHepatitis C virus (HCV) enters human hepatocytes through a multistep mechanism involving, among other host proteins, the virus receptor CD81. How CD81 governs HCV entry is poorly characterized, and CD81 protein interactions after virus binding remain elusive. We have developed a quantitative proteomics protocol to identify HCV-triggered CD81 interactions and found 26 dynamic binding partners. At least six of these proteins promote HCV infection, as indicated by RNAi. We further characterized serum response factor binding protein 1 (SRFBP1), which is recruited to CD81 during HCV uptake and supports HCV infection in hepatoma cells and primary human hepatocytes. SRFBP1 facilitates host cell penetration by all seven HCV genotypes, but not of vesicular stomatitis virus and human coronavirus. Thus, SRFBP1 is an HCV-specific, pan-genotypic host entry factor. These results demonstrate the use of quantitative proteomics to elucidate pathogen entry and underscore the importance of host protein-protein interactions during HCV invasion.

Chronic intermittent ethanol exposure and withdrawal leads to adaptations in nucleus accumbens core postsynaptic density proteome and dendritic spines

Alcohol use disorder is a chronic relapsing brain disease characterized by the loss of ability to control alcohol (ethanol) intake despite knowledge of detrimental health or personal consequences. Clinical and pre-clinical models provide strong evidence for chronic ethanol-associated alterations in glutamatergic signaling and impaired synaptic plasticity in the nucleus accumbens (NAc). However, the neural mechanisms that contribute to aberrant glutamatergic signaling in ethanol-dependent individuals in this critical brain structure remain unknown. Using an unbiased proteomic approach, we investigated the effects of chronic intermittent ethanol (CIE) exposure on neuroadaptations in postsynaptic density (PSD)-enriched proteins in the NAc of ethanol-dependent mice. Compared with controls, CIE exposure significantly changed expression levels of 50 proteins in the PSD-enriched fraction. Systems biology and functional annotation analyses demonstrated that the dysregulated proteins are expressed at tetrapartite synapses and critically regulate cellular morphology. To confirm this latter finding, the density and morphology of dendritic spines were examined in the NAc core of ethanol-dependent mice. We found that CIE exposure and withdrawal differentially altered dendrite diameter and dendritic spine density and morphology. Through the use of quantitative proteomics and functional annotation, these series of experiments demonstrate that ethanol dependence produces neuroadaptations in proteins that modify dendritic spine morphology. In addition, these studies identified novel PSD-related proteins that contribute to the neurobiological mechanisms of ethanol dependence that drive maladaptive structural plasticity of NAc neurons.

Proteomics and mitochondrial disease

Disorders of mitochondrial oxidative phosphorylation (OXPHOS) present with almost any symptom, at any age with any mode of inheritance. OXPHOS disorders are the most common group of inherited metabolic diseases and occur in ~1/5000 births. There is no ‘gold standard’ diagnostic test and most paediatric diagnoses rely on finding an enzyme defect while molecular investigations are typically restricted to testing for common mutations or sequencing of specific candidate genes. While next generation sequencing (NGS) approaches have yielded great insights into the identification of pathogenic mutations, these generally focus on sequencing of known mitochondrial and disease related genes, yielding a molecular diagnosis in ~60% of cases. Recent advances in proteomics have improved such that much of the human cellular proteome can now be determined in a matter of hours. In addition, with the use of quantitaive approaches including stable isotope labeling of cells in culture (SILAC), differences between the proteomes of diseased and controls cells can be measured. We are now investigating the use of quantitative proteomics as a complementery tool to NGS in the molecular diagnosis of OXPHOS disease, and to ultimately identify novel candidate genes that cause disease.

SILAC-Based Quantitative Proteomics Approach to Identify Transcription Factors Interacting with a Novel Cis-Regulatory Element

The motif TMTCGCGANR (M being C or A, R being A or G, and N any nucleotide) called M8 was discovered as a putative cis-regulatory element present in 368 human gene promoters. Of these, 236 (64%) are conserved within promoter sequences of four related organisms: human, mouse, rat and dog. However, transcription factors (TFs) interacting with the M8 motif has not yet been described. We previously reported the use of quantitative proteomics coupled to one-step DNA affinity purification as a means of screening for TFs associated with given functional DNA elements. The procedure is performed in-vitro employing SILAC-labeled nuclear extracts and making use of a well-characterized cis-regulatory motifs. Building on that, in this study we have combined our method with statistical analysis to filter out false positive hits from the one-step DNA affinity pull-down experiments. This resulted in the identification of zinc finger BED domain-containing protein 1 (ZBED1), alpha globin transcription factor CP2 (TFCP2), upstream binding protein 1 (UBP) and transcription factor CP2 like 1(TFCP2L1), as specific M8 interacting factors. We validated our screen demonstrating the in vivo binding of alpha globin transcription factor TFCP2 to selected genes harboring M8-containing promoters using ChIP (chromatin immuno-precipitation) assays. This not only implicates a functional role of the above proteins in regulating M8 motif containing genes, but also suggests the potential use of our approach to decipher protein-DNA interactions occurring in living cells.

The use of quantitative proteomics towards biomarker discovery in lung squamous cell carcinoma.

The high mortality rate in advanced lung cancer, due to a preponderance of tumors discovered at advanced stage, demands the discovery and clinical validation of biomarkers for diagnosing early stage disease. Quantitative proteomics technologies are capable of identifying protein biomarkers with diagnostic, prognostic, and predictive value. Recent works have demonstrated the utility in using quantitative proteomics across normal, pre-cancerous, and cancerous lesions towards the discovery of biomarkers for early stage lung cancer, as well as discovering novel mechanisms of lung carcinogenesis.

Quantitative proteomics identifies the membrane-associated peroxidase GPx8 as a cellular substrate of the hepatitis C virus NS3-4A protease

The hepatitis C virus (HCV) NS3-4A protease is not only an essential component of the viral replication complex and a prime target for antiviral intervention but also a key player in the persistence and pathogenesis of HCV. It cleaves and thereby inactivates two crucial adaptor proteins in viral RNA sensing and innate immunity, mitochondrial antiviral signaling protein (MAVS) and TRIF, a phosphatase involved in growth factor signaling, T-cell protein tyrosine phosphatase (TC-PTP), and the E3 ubiquitin ligase component UV-damaged DNA-binding protein 1 (DDB1). Here we explored quantitative proteomics to identify novel cellular substrates of the NS3-4A protease. Cell lines inducibly expressing the NS3-4A protease were analyzed by stable isotopic labeling using amino acids in cell culture (SILAC) coupled with protein separation and mass spectrometry. This approach identified the membrane-associated peroxidase GPx8 as a bona fide cellular substrate of the HCV NS3-4A protease. Cleavage by NS3-4A occurs at Cys 11, removing the cytosolic tip of GPx8, and was observed in different experimental systems as well as in liver biopsies from patients with chronic HCV. Overexpression and RNA silencing studies revealed that GPx8 is involved in viral particle production but not in HCV entry or RNA replication. We provide proof-of-concept for the use of quantitative proteomics to identify cellular substrates of a viral protease and describe GPx8 as a novel proviral host factor targeted by the HCV NS3-4A protease.

Quantitative phosphoproteomic profiling of human non-small cell lung cancer tumors

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths worldwide. Within the molecular scope of NCSLC, a complex landscape of dysregulated cellular signaling has emerged, defined largely by mutations in select mediators of signal transduction, including the epidermal growth factor receptor (EGFR) and anaplastic lymphoma (ALK) kinases. Consequently, these mutant kinases become constitutively activated and targets for chemotherapeutic intervention. Encouragingly, small molecule inhibitors of these pathways have shown promise in clinical trials or are approved for clinical use. However, many protein kinases are dysregulated in NSCLC without genetic mutations. To quantify differences in tumor cell signaling that are transparent to genomic methods, we established a super-SILAC internal standard derived from NSCLC cell lines grown in vitro and labeled with heavy lysine and arginine, and deployed them in a phosphoproteomic workflow. We identified 9019 and 8753 phosphorylation sites in two separate tumors. Relative quantification of phosphopeptide abundance between tumor samples allowed for the determination of specific hubs and pathways differing between each tumor. Sites downstream of Ras showed decreased inhibitory phosphorylation (Raf/Mek) and increased activating phosphorylation (Erk1/2) in one tumor versus another. In this way, we were able to quantitatively access oncogenic kinase signaling in primary human tumors. Through the use of quantitative proteomics, we demonstrated the feasibility and coverage that large scale mass spectrometry can leverage for understanding kinase networks in cancer. By incorporating Super-SILAC based quantitation into a typical pathology workflow, we were able to access and compare tumors from multiple patients in this analysis with high accuracy and dynamic range. We analyzed tumors from patients diagnosed with non-small cell lung cancer and were able to detect comprehensive phosphorylation networks relaying through known hubs of oncogenesis in lung cancer. We hereby show that it is possible to track changes to phosphorylation networks across multiple tumors, opening up the possibility that drug susceptibility and patient-specific stratification can be implemented downstream of classical pathology.

Quantitative proteomics by amino acid labeling identifies novel NHR-49 regulated proteins inC. elegans

Stable isotope labeling by amino acids combined with mass spectrometry is a widely used methodology to quantitatively examine metabolic and signaling pathways in yeast, fruit flies, plants, cell cultures and mice. However, only metabolic labeling using 15N has been applied to examine such events in the nematode Caenorhabditis elegans. We have recently shown that C. elegans can be completely labeled with heavy-labeled lysine by feeding worms on prelabeled lysine auxotroph Escherichia coli for just one generation. We applied this methodology to examine the organismal response to functional loss or RNAi mediated knock down of the transcription factor NHR-49, and found numerous proteins involved in lipid metabolism to be downregulated, which is consistent with its previously proposed function as a transcriptional regulator of fatty acid metabolism. The combined use of quantitative proteomics and selective gene knockdown by RNAi provides a powerful tool with broad implications for C. elegans biology.

Quantitative proteomics by amino acid labeling in C. elegans

We demonstrate labeling of Caenorhabditis elegans with heavy isotope-labeled lysine by feeding them with heavy isotope-labeled Escherichia coli. Using heavy isotope-labeled worms and quantitative proteomics methods, we identified several proteins that are regulated in response to loss or RNAi-mediated knockdown of the nuclear hormone receptor 49 in C. elegans. The combined use of quantitative proteomics and selective gene knockdown is a powerful tool for C. elegans biology.