Secretome proteomics for discovery of cancer biomarkers

Deciphering nasopharyngeal carcinoma pathogenesis via proteomics

Introduction: Nasopharyngeal carcinoma (NPC) is a distinct head and neck squamous cell carcinoma in its etiological association of Epstein-Barr virus (EBV) infection, hidden anatomical location, remarkable racial and geographical distribution, and high incidence of locoregional recurrence or metastasis. Thanks to the advancements in proteomics in recent decades, more understanding of the disease etiology, carcinogenesis, and progression has been gained, potentially deciphering the molecular characteristics of the malignancy. Areas covered: In this review, we provide an overview of the proteomic aberrations that are likely involved or drive NPC development and progression, focusing on the contributions of major EBV-encoded factors, intercommunication with environment, protein features of high metastasis and therapy resistance, and protein-protein interactions that allow NPC cells to evade immune recognition and elimination. Finally, multistep carcinogenesis and subtypes of NPC from a proteomic perspective are inquired. Expert commentary: Proteomic studies have covered various aspects involved in NPC pathogenesis, yet much remains to be uncovered. Coherent study designs, optimal conditions for obtaining high-quality data, and compelling interpretation are critical in ensuring the emergence of good science out of NPC proteomics. NPC proteogenomics and proteoform analysis are two promising fields to promote the application of the proteomic findings from bench to bedside.

Multi-Omics Strategies Uncover Host-Pathogen Interactions

With the success of the Human Genome Project, large-scale systemic projects became a reality that enabled rapid development of the systems biology field. Systems biology approaches to host-pathogen interactions have been instrumental in the discovery of some specifics of Gram-negative bacterial recognition, host signal transduction, and immune tolerance. However, further research, particularly using multi-omics approaches, is essential to untangle the genetic, immunologic, (post)transcriptional, (post)translational, and metabolic mechanisms underlying progression from infection to clearance of microbes. The key to understanding host-pathogen interactions lies in acquiring, analyzing, and modeling multimodal data obtained through integrative multi-omics experiments. In this article, we will discuss how multi-omics analyses are adding to our understanding of the molecular basis of host-pathogen interactions and systemic maladaptive immune response of the host to microbes and microbial products.

A Systematic Investigation of the Malignant Functions and Diagnostic Potential of the Cancer Secretome

The collection of proteins secreted from a cell-the secretome-is of particular interest in cancer pathophysiology due to its diagnostic potential and role in tumorigenesis. However, cancer secretome studies are often limited to one tissue or cancer type or focus on biomarker prediction without exploring the associated functions. We therefore conducted a pan-cancer analysis of secretome gene expression changes to identify candidate diagnostic biomarkers and to investigate the underlying biological function of these changes. Using transcriptomic data spanning 32 cancer types and 30 healthy tissues, we quantified the relative diagnostic potential of secretome proteins for each cancer. Furthermore, we offer a potential mechanism by which cancer cells relieve secretory pathway stress by decreasing the expression of tissue-specific genes, thereby facilitating the secretion of proteins promoting invasion and proliferation. These results provide a more systematic understanding of the cancer secretome, facilitating its use in diagnostics and its targeting for therapeutic development.

Upregulation of STC2 in colorectal cancer and its clinicopathological significance

Background

Stanniocalcin 2 (STC2) is a glycoprotein hormone involved in many biological processes and a secretory protein that regulates malignant tumor progression. The aim of the present study was to further explore the clinicopathological significance and prognostic role of STC2 in colorectal cancer (CRC).

Methods

In this study, STC2 expression was first investigated in Gene Expression Omnibus and The Cancer Genome Atlas, and then validated with the data from our medical center. Univariate and multivariate analyses were performed to assess the association between prognostic factors and survival outcome.

Results

In Gene Expression Omnibus and The Cancer Genome Atlas databases, bioinformatics analysis confirmed that STC2 was significantly increased in CRC compared with that in normal tissues (P<0.01), and CRC patients with high STC2 expression had a shorter overall survival. By analyzing data from our medical center, the results also showed that STC2 expression of CRC tissues was higher than that in normal tissues, whether the transcriptional or protein levels. In the CRC tissues, high STC2 expression was significantly correlated with lymph node metastasis (P=0.047), distant metastasis (P=0.040), and advanced clinical stage (P=0.047). Moreover, Kaplan–Meier analyses indicated that high STC2 expression predicted a worse prognosis, and multivariate Cox regression analysis revealed that STC2 was an independent prognostic factor for overall survival (HR =1.976, 95% CI: 1.092–3.576, P=0.024) in patients with CRC.

Conclusion

Our results suggested that STC2 played an important role in CRC progression and prognosis, and could be a useful biomarker for survival prediction.

Analyzing the Secretome of Gut Microbiota as the Next Strategy For Early Detection of Colorectal Cancer

Dysbiosis of gut microbiome can contribute to inflammation, and subsequently initiation and progression of colorectal cancer (CRC). Throughout these stages, various proteins and metabolites are secreted to the external environment by microorganisms or the hosts themselves. Studying these proteins may help enhance our understanding of the host-microorganism relationship or they may even serve as useful biomarkers for CRC. However, secretomic studies of gut microbiome of CRC patients, until now, are scarcely performed. In this review article, the focus is on the roles of gut microbiome in CRC, the current findings on CRC secretome are highlighted, and the emerging challenges and strategies to drive forward this area of research are addressed.

Interstitial Fluid in Gynecologic Tumors and Its Possible Application in the Clinical Practice

Gynecologic cancers are an important cause of worldwide mortality. The interstitium consists of solid and fluid phases, situated between the blood vessels and cells. The interstitial fluid (IF), or fluid phase, is an extracellular fluid bathing and surrounding the tissue cells. The TIF (tumor interstitial fluid) is a dynamic fluid rich in lipids, proteins and enzyme-derived substances. The molecules found in the IF may be associated with pathological changes in tissues leading to cancer growth and metastatization. Proteomic techniques have allowed an extensive study of the composition of the TIF as a source of biomarkers for gynecologic cancers. In our review, we analyze the composition of the TIF, its formation process, the sampling methods, the consequences of its accumulation and the proteomic analyses performed, that make TIF valuable for monitoring different types of cancers.

Quiescin Sulfhydryl Oxidase 1 (QSOX1) Secreted by Lung Cancer Cells Promotes Cancer Metastasis

As lung cancer shows the highest mortality in cancer-related death, serum biomarkers are demanded for lung cancer diagnosis and its treatment. To discover lung cancer protein biomarkers, secreted proteins from primary cultured lung cancer and adjacent normal tissues from patients were subjected to LC/MS⁻MS proteomic analysis. Quiescin sulfhydryl oxidase (QSOX1) was selected as a biomarker candidate from the enriched proteins in the secretion of lung cancer cells. QSOX1 levels were higher in 82% (51 of 62 tissues) of lung cancer tissues compared to adjacent normal tissues. Importantly, QSOX1 serum levels were significantly higher in cancer patients (p < 0.05, Area Under curve (AUC) = 0.89) when measured by multiple reaction monitoring (MRM). Higher levels of QSOX1 were also uniquely detected in lung cancer tissues, among several other solid cancers, by immunohistochemistry. QSOX1-knock-downed Lewis lung cancer (LLC) cells were less viable from oxidative stress and reduced migration and invasion. In addition, LLC mouse models with QSOX1 knock-down also proved that QSOX1 functions in promoting cancer metastasis. In conclusion, QSOX1 might be a lung cancer tissue-derived biomarker and be involved in the promotion of lung cancers, and thus can be a therapeutic target for lung cancers.

Proteomics and pulse azidohomoalanine labeling of newly synthesized proteins: what are the potential applications?

INTRODUCTION

Measuring the immediate changes in cells that arise from changing environmental conditions is crucial to understanding the underlying mechanisms involved. These changes can be measured with metabolic stable isotope fully labeled proteomes, but requires looking for changes in the midst of a large background. In addition, labeling efficiency can be an issue in primary and fully differentiated cells. Area covered: Azidohomoalanine (AHA), an analog of methionine, can be accepted by cellular translational machinery and incorporated into newly synthesized proteins (NSPs). AHA-NSPs can be coupled to biotin via CuAAC-mediated click-chemistry and enriched using avidin-based affinity purification. Thus, AHA-containing proteins or peptides can be enriched and efficiently separated from the whole proteome. In this review, we describe the development of mass spectrometry (MS) based AHA strategies and discuss their potential to measure proteins involved in immune response, secretome, gut microbiome, and proteostasis as well as their potential for clinical uses. Expert commentary: AHA strategies have been used to identify synthesis activity and to compare two biological conditions in various biological model organisms. In combination with instrument development, improved sample preparation and fractionation strategies, MS-based AHA strategies have the potential for broad application, and the methods should translate into clinical use.

Methods of Isolation, Characterization and Expansion of Human Adipose-Derived Stem Cells (ASCs): An Overview

Considering the increasing interest in adipose-derived stem cells (ASCs) in regenerative medicine, optimization of methods aimed at isolation, characterization, expansion and evaluation of differentiation potential is critical to ensure (a) the quality of stem cells also in terms of genetic stability; (b) the reproducibility of beneficial effects; and (c) the safety of their use. Numerous studies have been conducted to understand the mechanisms that regulate ASC proliferation, growth and differentiation, however standard protocols about harvesting and processing techniques are not yet defined. It is also important to note that some steps in the procedures of harvesting and/or processing have been reported to affect recovery and/or the physiology of ASCs. Even considering the great opportunity that the ASCs provide for the identification of novel molecular targets for new or old drugs, the definition of homogeneous preparation methods that ensure adequate quality assurance and control, in accordance with current GMPs (good manufacturing practices), is required. Here, we summarize the literature reports to provide a detailed overview of the methodological issues underlying human ASCs isolation, processing, characterization, expansion, differentiation techniques, recalling at the same time their basilar principles, advantages and limits, in particular focusing on how these procedures could affect the ASC quality, functionality and plasticity.

Secretome analysis of alkaliphilic bacterium Bacillus lehensis G1 in response to pH changes

Bacillus lehensis G1 is an alkaliphilic bacterium that is capable of surviving in environments up to pH 11. Secretome related to bacterial acclimation in alkaline environment has been less studied compared to cytoplasmic and membrane proteome. The aim of this study was to gain better understanding of bacterial acclimation to alkaline media through analyzing extracellular proteins of B. lehensis. The pH range for B. lehensis growth was conducted, and two-dimensional electrophoresis and MALDI-TOF/TOF MS analysis were conducted to characterize changes in protein profiling in B. lehensis cultured at pH 8 and pH 11 when compared with those cultured at pH 10 (optimal growth pH). B. lehensis could grow well at pH ranging from 8 to 11 in which the bacteria showed to posses thinner flagella at pH 11. Proteomic analyses demonstrated that five proteins were up-regulated and 13 proteins were down-regulated at pH 8, whereas at pH 11, 14 proteins were up-regulated and 8 were down-regulated. Majority of the differentially expressed proteins were involved in the cell wall, main glycolytic pathways, the metabolism of amino acids and related molecules and some proteins of unknown function. A total of 40 differentially expressed protein spots corresponding to 33 proteins were identified; including GlcNAc-binding protein A, chitinase, endopeptidase lytE, flagellar hook-associated proteins and enolase. These proteins may play important roles in acclimation to alkaline media via reallocation of cell wall structure and changes to cell surface glycolytic enzymes, amino acid metabolism, flagellar hook-associated proteins and chaperones to sustain life under pH-stressed conditions.

Stable Isotope Dynamic Labeling of Secretomes (SIDLS) Identifies Authentic Secretory Proteins Released by Cancer and Stromal Cells

Analysis of secretomes critically underpins the capacity to understand the mechanisms determining interactions between cells and between cells and their environment. In the context of cancer cell micro-environments, the relevant interactions are recognized to be an important determinant of tumor progression. Global proteomic analyses of secretomes are often performed at a single time point and frequently identify both classical secreted proteins (possessing an N-terminal signal sequence), as well as many intracellular proteins, the release of which is of uncertain biological significance. Here, we describe a mass spectrometry-based method for stable isotope dynamic labeling of secretomes (SIDLS) that, by dynamic SILAC, discriminates the secretion kinetics of classical secretory proteins and intracellular proteins released from cancer and stromal cells in culture. SIDLS is a robust classifier of the different cellular origins of proteins within the secretome and should be broadly applicable to nonproliferating cells and cells grown in short term culture.

A Strategy for Discovery of Endocrine Interactions with Application to Whole-Body Metabolism

Inter-tissue communication via secreted proteins has been established as a vital mechanism for proper physiologic homeostasis. Here, we report a bioinformatics framework using a mouse reference population, the Hybrid Mouse Diversity Panel (HMDP), which integrates global multi-tissue expression data and publicly available resources to identify and functionally annotate novel circuits of tissue-tissue communication. We validate this method by showing that we can identify known as well as novel endocrine factors responsible for communication between tissues. We further show the utility of this approach by identification and mechanistic characterization of two new endocrine factors. Adipose-derived Lipocalin-5 is shown to enhance skeletal muscle mitochondrial function, and liver-secreted Notum promotes browning of white adipose tissue, also known as "beiging." We demonstrate the general applicability of the method by providing in vivo evidence for three additional novel molecules mediating tissue-tissue interactions.

Correlation of five secretory proteins with the nasopharyngeal carcinoma metastasis and the clinical applications

In our previous study, five different secretory proteins, including GSN, ADAMTSL4, CALR, PPIA and TXN, have been identified to be associated with the nasopharyngeal carcinoma (NPC) metastasis. In this work, the 5 proteins were further investigated. Bioinformatics analysis suggested that they might play an important role in the process of NPC development. Western blotting analysis showed that all of these 5 targets could be secreted into extracellular by both high metastatic NPC 5-8F cells and non-metastatic NPC 6-10B cells. Except for GSN, the expressions of ADAMTSL4, CALR, PPIA and TXN proteins in extracts of the 5-8F and 6-10B cells were significantly different (P < 0.05). Thus, the expressions of these 4 differentially expressed proteins were further tested in a cohort of NPC tissue specimens. The results indicated that the expression levels of ADAMTSL4 and TXN were highly correlated with the lymph node and distant metastasis (P<0.05) in NPC patients. Moreover, Enzyme-linked immunosorbent assay (ELISA) was used to investigate the concentrations of the ADAMTSL4 and TXN in serum specimens of NPC patients. The results revealed that serum ADAMTSL4 expression level was closely correlated with lymph node metastasis and clinical stage (P<0.05) in NPC patients, and it was able to discriminate metastasis NPC from non-metastasis NPC with a sensitivity of 75.6% and a specificity of 64.7%. The present data show for the first time that the ADAMTSL4 and TXN may be novel and potential biomarkers for predicting the NPC metastasis.Furthermore, the serum ADAMTSL4 could be a potential serum tumor biomarker for prognosis of NPC.

Unearthing new genomic markers of drug response by improved measurement of discriminative power

Background

Oncology drugs are only effective in a small proportion of cancer patients. Our current ability to identify these responsive patients before treatment is still poor in most cases. Thus, there is a pressing need to discover response markers for marketed and research oncology drugs. Screening these drugs against a large panel of cancer cell lines has led to the discovery of new genomic markers of in vitro drug response. However, while the identification of such markers among thousands of candidate drug-gene associations in the data is error-prone, an appraisal of the effectiveness of such detection task is currently lacking.

Methods

Here we present a new non-parametric method to measuring the discriminative power of a drug-gene association. Unlike parametric statistical tests, the adopted non-parametric test has the advantage of not making strong assumptions about the data distorting the identification of genomic markers. Furthermore, we introduce a new benchmark to further validate these markers in vitro using more recent data not used to identify the markers.

Results

The application of this new methodology has led to the identification of 128 new genomic markers distributed across 61% of the analysed drugs, including 5 drugs without previously known markers, which were missed by the MANOVA test initially applied to analyse data from the Genomics of Drug Sensitivity in Cancer consortium.

Conclusions

Discovering markers using more than one statistical test and testing them on independent data is unusual. We found this helpful to discard statistically significant drug-gene associations that were actually spurious correlations. This approach also revealed new, independently validated, in vitro markers of drug response such as Temsirolimus-CDKN2A (resistance) and Gemcitabine-EWS_FLI1 (sensitivity).

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0336-z) contains supplementary material, which is available to authorized users.

Development and Comparison of Two Immuno-disaggregation Based Bioassays for Cell Secretome Analysis

Cell secretome analysis has gained increasing attention towards the development of effective strategies for disease treatment. Analysis of cell secretome enables the platform to monitor the status of disease progression, facilitating therapeutic outcomes. However, cell secretome analysis is very challenging due to its versatile and dynamic composition. Here, we report the development of two immuno-disaggregation bioassays using functionalized microparticles for the quantitative analysis of the cell secretome. Methods: We evaluated the feasibility of our developed immuno-disaggregation bioassays using antibody-conjugated MPs and protein-conjugated MPs for the detection of target cell secretome protein. The vascular endothelial growth factor (VEGF)-165 protein was tested as a model cell secretome protein in the serum and serum-free conditions. The status of MP aggregates was examined with a light microscopy and AccuSizer^TM 780 Optical Particle Sizer. The accuracy of our bioassays measurement was compared with standard ELISA method. Results: The developed bioassays successfully detected target VEGF protein present in serum-free buffer and serum-containing complete cell culture medium with high sensitivity and specificity. Additionally, the immuno-disaggregation bioassays using antibody-conjugated MPs and protein-conjugated MPs have a wide detection range from 0.01 ng/mL to 100 ng/mL and 0.5 ng/mL to 100 ng/mL, respectively. The sensitivity of the bioassay using antibody-conjugated MPs was approximately one order of magnitude higher than the bioassay using protein-conjugated MPs. Conclusion: Our promising results indicate the potential of the developed bioassays as powerful platforms for the quantitative analysis of cell secretome.

Cell Line Secretome and Tumor Tissue Proteome Markers for Early Detection of Colorectal Cancer: A Systematic Review

Objective: In order to find low abundant proteins secretome and tumor tissue proteome data have been explored in the last few years for the diagnosis of colorectal cancer (CRC). In this review we aim to summarize the results of studies evaluating markers derived from the secretome and tumor proteome for blood based detection of colorectal cancer. Methods: Observing the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines PubMed and Web of Science databases were searched systematically for relevant studies published up to 18 July 2017. After screening for predefined eligibility criteria a total of 47 studies were identified. Information on diagnostic performance indicators, methodological procedures and validation was extracted. Functions of proteins were identified from the UniProt database and the the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess study quality. Results: Forty seven studies meeting inclusion criteria were identified. Overall, 83 different proteins were identified, with carcinoembryonic Antigen (CEA) being by far the most commonly reported (reported in 24 studies). Evaluation of the markers or marker combinations in blood samples from CRC cases and controls yielded apparently very promising diagnostic performances, with area under the curve >0.9 in several cases, but lack of internal or external validation, overoptimism due to overfitting and spectrum bias due to evaluation in clinical setting rather than screening settings are major concerns. Conclusions: Secretome and tumor proteome-based biomarkers when validated in blood yield promising candidates. However, for discovered protein markers to be clinically applicable as screening tool they have to be specific for early stages and need to be validated externally in larger studies with participants recruited in true screening setting.

Gaining insights into cancer biology through exploration of the cancer secretome using proteomic and bioinformatic tools

INTRODUCTION

Tumor-associated proteins released by cancer cells and by tumor stroma cells, referred as 'cancer secretome', represent a valuable resource for discovery of potential cancer biomarkers. The last decade was marked by a great increase in number of studies focused on various aspects of cancer secretome including, composition and identification of components externalized by malignant cells and by the components of tumor microenvironment. Areas covered: Here, we provide an overview of achievements in the proteomic analysis of the cancer secretome, elicited through the tumor-associated interstitial fluid recovered from malignant tissues ex vivo or the protein component of conditioned media obtained from cultured cancer cells in vitro. We summarize various bioinformatic tools and approaches and critically appraise their outcomes, focusing on problems and challenges that arise when applied for the analysis of cancer secretomic databases. Expert commentary: Recent achievements in the omics- analysis of structural and metabolic aspects of altered cancer secretome contribute greatly to the various hallmarks of cancer including the identification of clinically significant biomarkers and potential targets for therapeutic intervention.

The Epithelial-to-Mesenchymal Transition in Breast Cancer: Focus on Basal-Like Carcinomas

Breast cancer is a heterogeneous disease that is characterized by a high grade of cell plasticity arising from the contribution of a diverse range of factors. When combined, these factors allow a cancer cell to transition from an epithelial to a mesenchymal state through a process of dedifferentiation that confers stem-like features, including chemoresistance, as well as the capacity to migrate and invade. Understanding the complex events that lead to the acquisition of a mesenchymal phenotype will therefore help to design new therapies against metastatic breast cancer. Here, we recapitulate the main endogenous molecular signals involved in this process, and their cross-talk with paracrine factors. These signals and cross-talk include the extracellular matrix; the secretome of cancer-associated fibroblasts, macrophages, cancer stem cells, and cancer cells; and exosomes with their cargo of miRNAs. Finally, we highlight some of the more promising therapeutic perspectives based on counteracting the epithelial-to-mesenchymal transition in breast cancer cells.

HMGA1 regulates the Plasminogen activation system in the secretome of breast cancer cells

Cancer cells secrete proteins that modify the extracellular environment acting as autocrine and paracrine stimulatory factors and have a relevant role in cancer progression. The HMGA1 oncofetal protein has a prominent role in controlling the expression of an articulated set of genes involved in various aspect of cancer cell transformation. However, little is known about its role in influencing the secretome of cancer cells. Performing an iTRAQ LC–MS/MS screening for the identification of secreted proteins, in an inducible model of HMGA1 silencing in breast cancer cells, we found that HMGA1 has a profound impact on cancer cell secretome. We demonstrated that the pool of HMGA1–linked secreted proteins has pro–migratory and pro-invasive stimulatory roles. From an inspection of the HMGA1–dependent secreted factors it turned out that HMGA1 influences the presence in the extra cellular milieu of key components of the Plasminogen activation system (PLAU, SERPINE1, and PLAUR) that has a prominent role in promoting metastasis, and that HMGA1 has a direct role in regulating the transcription of two of them, i.e. PLAU and SERPINE1. The ability of HMGA1 to regulate the plasminogen activator system may constitute an important mechanism by which HMGA1 promotes cancer progression.

Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours

Cancer drug therapies are only effective in a small proportion of patients. To make things worse, our ability to identify these responsive patients before administering a treatment is generally very limited. The recent arrival of large-scale pharmacogenomic data sets, which measure the sensitivity of molecularly profiled cancer cell lines to a panel of drugs, has boosted research on the discovery of drug sensitivity markers. However, no systematic comparison of widely-used single-gene markers with multi-gene machine-learning markers exploiting genomic data has been so far conducted. We therefore assessed the performance offered by these two types of models in discriminating between sensitive and resistant cell lines to a given drug. This was carried out for each of 127 considered drugs using genomic data characterising the cell lines. We found that the proportion of cell lines predicted to be sensitive that are actually sensitive (precision) varies strongly with the drug and type of model used. Furthermore, the proportion of sensitive cell lines that are correctly predicted as sensitive (recall) of the best single-gene marker was lower than that of the multi-gene marker in 118 of the 127 tested drugs. We conclude that single-gene markers are only able to identify those drug-sensitive cell lines with the considered actionable mutation, unlike multi-gene markers that can in principle combine multiple gene mutations to identify additional sensitive cell lines. We also found that cell line sensitivities to some drugs (e.g. Temsirolimus, 17-AAG or Methotrexate) are better predicted by these machine-learning models.

Proteomic profiling of the cancer cell secretome: informing clinical research

INTRODUCTION

Cancer represents one of the major causes of human deaths. Identification of proteins as biomarkers for early detection of cancer and therapeutic targets for cancer treatment are important issues in precision medicine. Secretome of cancer cells represents the collection of proteins secreted or shed from cancer cells. Proteomic profiling of the cancer cell secretome has been proven to be a convenient and efficient way to discover cancer biomarker and/or therapeutic targets. Areas covered: There have been numerous reviews describing the history and application of secretome analysis in cancer biomarker/therapeutic target research. The present review focuses on the technological advancement for profiling low-molecular-mass proteins in secretome, the latest information regarding the new candidate biomarkers and molecular mechanisms discovered on the basis of cancer cell secretome analysis, as well as the previously discovered candidate biomarkers that enter into clinical trials. Expert commentary: Current technologies for protein sample preparation/separation and MS-based protein identification have allowed in-depth analysis of cancer cell secretome. Future efforts should focus on the comprehensiveness of cancer cell secretome, meta-analysis of different secretome datasets and integrated analysis via combining other omics datasets, as well as the incorporation of MS-based biomarker verification pipeline into both preclinical studies and clinical trials.

A multivariate predictive modeling approach reveals a novel CSF peptide signature for both Alzheimer's Disease state classification and for predicting future disease progression

To determine if a multi-analyte cerebrospinal fluid (CSF) peptide signature can be used to differentiate Alzheimer’s Disease (AD) and normal aged controls (NL), and to determine if this signature can also predict progression from mild cognitive impairment (MCI) to AD, analysis of CSF samples was done on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The profiles of 320 peptides from baseline CSF samples of 287 subjects over a 3–6 year period were analyzed. As expected, the peptide most able to differentiate between AD vs. NL was found to be Apolipoprotein E. Other peptides, some of which are not classically associated with AD, such as heart fatty acid binding protein, and the neuronal pentraxin receptor, also differentiated disease states. A sixteen-analyte signature was identified which differentiated AD vs. NL with an area under the receiver operating characteristic curve of 0.89, which was better than any combination of amyloid beta (1–42), tau, and phospho-181 tau. This same signature, when applied to a new and independent data set, also strongly predicted both probability and rate of future progression of MCI subjects to AD, better than traditional markers. These data suggest that multivariate peptide signatures from CSF predict MCI to AD progression, and point to potentially new roles for certain proteins not typically associated with AD.

Plasma and amniotic fluid PPARγ is involved in the lipid metabolism of maternal-fetal interface cells

OBJECTIVES

The aim of this study was at detecting the expression of peroxisome proliferator-activated receptor γ (PPARγ) in patients with gestational diabetes mellitus (GDM) and to investigate whether PPARγ regulates the lipid metabolism of maternal-fetal interface cells.

METHODS

We detected the expression of PPARγ in the maternal plasma, fetal plasma and amniotic fluid of two groups of women with GDM or normal pregnancy using ELISA and western blot. Establish the cell models of the indirect coculture model and the separate culture model using BeWo cells and endometrial stromal cells (ESCs). Oil red O staining was used to detect the lipid uptake, and the mRNA levels of the rate-limiting enzyme stearoyl CoA desaturase (SCD) and glucose transporter 1 (GLUT1) were detected by RT-PCR.

RESULTS

PPARγ is detectable in maternal plasma, fetal plasma and amniotic fluid. Levels of PPARγ in GDM maternal plasma were significant higher than other groups in vitro, with increasing PPARγ, the cytoplasmic lipid uptake levels of BeWo cells and ESCs were both increased in coculture conditions. GLUT1 and SCD mRNA rose significantly with increasing PPARγ in indirect coculture conditions, but both did not respond to PPARγ in ESC-absent culture conditions.

CONCLUSIONS

The extracellular concentration of PPARγ may be involved in the lipid transport of maternal-fetal interface cells and may play a role in the abnormal lipid metabolism of GDM patients.

The Potential of Stimuli-Responsive Nanogels in Drug and Active Molecule Delivery for Targeted Therapy

Nanogels (NGs) are currently under extensive investigation due to their unique properties, such as small particle size, high encapsulation efficiency and protection of active agents from degradation, which make them ideal candidates as drug delivery systems (DDS). Stimuli-responsive NGs are cross-linked nanoparticles (NPs), composed of polymers, natural, synthetic, or a combination thereof that can swell by absorption (uptake) of large amounts of solvent, but not dissolve due to the constituent structure of the polymeric network. NGs can undergo change from a polymeric solution (swell form) to a hard particle (collapsed form) in response to (i) physical stimuli such as temperature, ionic strength, magnetic or electric fields; (ii) chemical stimuli such as pH, ions, specific molecules or (iii) biochemical stimuli such as enzymatic substrates or affinity ligands. The interest in NGs comes from their multi-stimuli nature involving reversible phase transitions in response to changes in the external media in a faster way than macroscopic gels or hydrogels due to their nanometric size. NGs have a porous structure able to encapsulate small molecules such as drugs and genes, then releasing them by changing their volume when external stimuli are applied.

Proteome and Secretome Analysis Reveals Differential Post-transcriptional Regulation of Toll-like Receptor Responses

The innate immune system is the organism's first line of defense against pathogens. Pattern recognition receptors (PRRs) are responsible for sensing the presence of pathogen-associated molecules. The prototypic PRRs, the membrane-bound receptors of the Toll-like receptor (TLR) family, recognize pathogen-associated molecular patterns (PAMPs) and initiate an innate immune response through signaling pathways that depend on the adaptor molecules MyD88 and TRIF. Deciphering the differences in the complex signaling events that lead to pathogen recognition and initiation of the correct response remains challenging. Here we report the discovery of temporal changes in the protein signaling components involved in innate immunity. Using an integrated strategy combining unbiased proteomics, transcriptomics and macrophage stimulations with three different PAMPs, we identified differences in signaling between individual TLRs and revealed specifics of pathway regulation at the protein level.

SheddomeDB: the ectodomain shedding database for membrane-bound shed markers

BACKGROUND

A number of membrane-anchored proteins are known to be released from cell surface via ectodomain shedding. The cleavage and release of membrane proteins has been shown to modulate various cellular processes and disease pathologies. Numerous studies revealed that cell membrane molecules of diverse functional groups are subjected to proteolytic cleavage, and the released soluble form of proteins may modulate various signaling processes. Therefore, in addition to the secreted protein markers that undergo secretion through the secretory pathway, the shed membrane proteins may comprise an additional resource of noninvasive and accessible biomarkers. In this context, identifying the membrane-bound proteins that will be shed has become important in the discovery of clinically noninvasive biomarkers. Nevertheless, a data repository for biological and clinical researchers to review the shedding information, which is experimentally validated, for membrane-bound protein shed markers is still lacking.

RESULTS

In this study, the database SheddomeDB was developed to integrate publicly available data of the shed membrane proteins. A comprehensive literature survey was performed to collect the membrane proteins that were verified to be cleaved or released in the supernatant by immunological-based validation experiments. From 436 studies on shedding, 401 validated shed membrane proteins were included, among which 199 shed membrane proteins have not been annotated or validated yet by existing cleavage databases. SheddomeDB attempted to provide a comprehensive shedding report, including the regulation of shedding machinery and the related function or diseases involved in the shedding events. In addition, our published tool ShedP was embedded into SheddomeDB to support researchers for predicting the shedding event on unknown or unrecorded membrane proteins.

CONCLUSIONS

To the best of our knowledge, SheddomeDB is the first database for the identification of experimentally validated shed membrane proteins and currently may provide the most number of membrane proteins for reviewing the shedding information. The database included membrane-bound shed markers associated with numerous cellular processes and diseases, and some of these markers are potential novel markers because they are not annotated or validated yet in other databases. SheddomeDB may provide a useful resource for discovering membrane-bound shed markers. The interactive web of SheddomeDB is publicly available at http://bal.ym.edu.tw/SheddomeDB/ .

Bottom-up proteomics analysis of the secretome of murine islets of Langerhans in elevated glucose levels

Glucotoxicity is a causative agent of type-2 diabetes, where high glucose levels damage the islets of Langerhans resulting in oxidative damage and endoplasmic reticulum stress. We evaluated the secretomes of healthy CD-1 murine islets. Three experimental conditions were investigated in biological triplicate: a control incubated with 11 mM glucose, 1-day incubation with 25 mM glucose, and 2-day incubation with 25 mM glucose. An SDS-based, filter-aided sample preparation protocol was used to prepare secretomes for analysis. A total of 428 protein groups were identified across the nine samples. Each condition generated between 328-349 protein IDs and intracondition protein overlap was between 66-90% for the biological triplicates. 232 protein groups were identified in all three conditions with 184 quantified at least once in each condition. Significant expression changes were observed for proteins associated with the unfolded protein response, such as proteases, chaperones, and elongation factors, as well as proteins associated with peptide hormone processing and small molecule metabolism.

Collection of Serum- and Feeder-free Mouse Embryonic Stem Cell-conditioned Medium for a Cell-free Approach

The capacity of embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) to generate various cell types has opened new avenues in the field of regenerative medicine. However, despite their benefits, the tumorigenic potential of ESCs and iPSCs has long been a barrier for clinical applications. Interestingly, it has been shown that ESCs produce several soluble factors that can promote tissue regeneration and delay cellular aging, suggesting that ESCs and iPSCs can also be utilized as a cell-free intervention method. Therefore, the method for harvesting mouse embryonic stem cell (mESC)-conditioned medium (mESC-CM) with minimal contamination of serum components (fetal bovine serum, FBS) and feeder cells (mouse embryonic fibroblasts, MEFs) has been highly demanded. Here, the present study demonstrates an optimized method for the collection of mESC-CM under serum- and feeder-free conditions and for the characterization of mESC-CM using senescence-associated multiple readouts. This protocol will provide a method to collect pure mESC-specific secretory factors without serum and feeder contamination.

A Methodology for Comprehensive Analysis of Toll-Like Receptor Signaling in Macrophages

A combination of high-throughput, multiplexed, quantitative methods with computational modeling and statistical approaches is required to obtain system-level understanding of biological function. Mass spectrometry (MS)-based proteomics has emerged as a preferred tool for the analysis of changes in protein abundance and their post-translational modification (PTM) levels at a global scale, comparable with genomic experiments and generating data suitable for use in mathematical modeling of signaling pathways. Here we describe a set of parallel bottom-up proteomic approaches to detect and quantify the global protein changes in total intracellular proteins, their phosphorylation, and the proteins released by active and passive secretion or shedding mechanisms (referred to as the secretome as reviewed in Makridakis and Vlahou, J Proteome 73:2291-2305, 2010) in response to the stimulation of Toll-like receptors (TLRs) with specific ligands in cultured macrophages. The method includes protocols for metabolic labeling of cells (SILAC: stable isotope labeling by amino acids in cell culture; Ong et al., Mol Cell Proteomics 1:376-386, 2002), ligand stimulation, cell lysis and media collection, in-gel and in-solution modification and digestion of proteins, phosphopeptide enrichment for phosphoproteomics, and LC-MS/MS analysis. With these methods, we can not only reliably quantify the relative changes in the TLR signaling components (Sjoelund et al., J Proteome Res 13:5185-5197, 2014) but also use the data as constraints for mathematical modeling.

Evaluation of sulfur isotopic enrichment of urine metabolites for the differentiation of healthy and prostate cancer mice after the administration of 34S labelled yeast

Sulfur isotopic enrichment of urine metabolites in healthy and prostate cancer mice using ³⁴S enriched yeast and High Performance Liquid Chromatography coupled to Multicollector Inductively Coupled Plasma Mass Spectrometry (HPLC-MC-ICP-MS) has been evaluated. A 30 weeks experiment (since the eleventh to the fortieth week of life) was carried out collecting the urine of three healthy mice and three transgenic mice with prostate cancer during 24h after a single oral administration of a ³⁴S enriched yeast slurry. The isotopic enrichment of different sulphur metabolites was monitored by coupling a C18 reverse phase HPLC column with a multicollector ICP-MS using a membrane desolvating system. Quantification of sulfur in the chromatographic peaks was carried out by post-column isotope dilution using a ³³S enriched spike. Differences between the ³⁴S enrichment in the urine metabolites of healthy and prostate cancer mice were found from the beginning of the disease. Both populations could be differentiated using a principal component analysis (PCA). Finally, 7 unknown mice were correctly classified in each population using a linear discriminant analysis.

Global Analysis of Secreted Proteins and Glycoproteins in Saccharomyces cerevisiae

Protein secretion is essential for numerous cellular activities, and secreted proteins in bodily fluids are a promising and noninvasive source of biomarkers for disease detection. Systematic analysis of secreted proteins and glycoproteins will provide insight into protein function and cellular activities. Yeast (Saccharomyces cerevisiae) is an excellent model system for eukaryotic cells, but global analysis of secreted proteins and glycoproteins in yeast is challenging due to the low abundances of secreted proteins and contamination from high-abundance intracellular proteins. Here, by using mild separation of secreted proteins from cells, we comprehensively identified and quantified secreted proteins and glycoproteins through inhibition of glycosylation and mass spectrometry-based proteomics. In biological triplicate experiments, 245 secreted proteins were identified, and comparison with previous experimental and computational results demonstrated that many identified proteins were located in the extracellular space. Most quantified secreted proteins were down-regulated from cells treated with an N-glycosylation inhibitor (tunicamycin). The quantitative results strongly suggest that the secretion of these down-regulated proteins was regulated by glycosylation, while the secretion of proteins with minimal abundance changes was contrarily irrelevant to protein glycosylation, likely being secreted through nonclassical pathways. Glycoproteins in the yeast secretome were globally analyzed for the first time. A total of 27 proteins were quantified in at least two protein and glycosylation triplicate experiments, and all except one were down-regulated under N-glycosylation inhibition, which is solid experimental evidence to further demonstrate that the secretion of these proteins is regulated by their glycosylation. These results provide valuable insight into protein secretion, which will further advance protein secretion and disease studies.

Cancer Secretome May Influence BSP and DSP Expression in Human Salivary Gland Cells

One of the biggest challenges in managing head and neck cancers, especially salivary gland cancers, is the identification of secreted biomarkers of the disease that can be evaluated noninvasively. A relevant source of enriched tumor markers could potentially be found in the tumor secretome. Although numerous studies have evaluated secretomes from various cancers, the influence of the cancer secretome derived from salivary gland cancers on the behavior of normal cells has not yet been elucidated. Our data indicate that secretome derived from salivary gland cancer cells can influence the expression of two potential biomarkers of oral cancer-namely, bone sialoprotein (BSP) and dentin sialoprotein (DSP)-in normal salivary gland cells. Using routine immunohistochemistry, immunofluorescence, and immunoblotting techniques, we demonstrate an enrichment of BSP and DSP in human salivary gland (HSG) cancer tissue, unique localizations of BSP and DSP in HSG cancer cells, and enriched expression of BSP and DSP in normal salivary gland cells exposed to a cancer secretome. The secretome domain of the cancer microenvironment could alter signaling cascades responsible for normal cell proliferation, migration, and invasion, thus enhancing cancer cell survival and the potential for cancer progression. The cancer secretome may be critical in maintaining and stimulating "cancer-ness," thus potentially promoting specific hallmarks of metastasis.

Quantitative secretomic analysis of pancreatic cancer cells in serum-containing conditioned medium

Pancreatic cancer is a highly metastatic and chemo-resistant disease. Secreted proteins involved in cell-cell interactions play an important role in changing the tumor microenvironment. Previous studies generally focus on the secretome of cancer cell line from serum-free media, due to the serious interference of fetal bovine serum (FBS). However, serum-starvation may alter expression patterns of secreted proteins. Hence, efforts to decrease the interference of serum in proteomic analysis of serum-containing media have been hampered to quantitatively measure the tumor secretion levels. Recently, the metabolic labeling, protein equalization, protein fractionation and filter-aided sample preparation (FASP) strategy (MLEFF) has been successfully used to avoid the disturbance of serum on secretome analysis. Here, this efficient method was applied for comparative secretome analysis of two hamster pancreatic cancer cells with differentially metastatic potentials, enabling the observation of 161 differentially expressed proteins, including 106 proteins that had been previously reported and detected in plasma. By integrated analysis of our data and publicly available bioinformatics resources, we found that a combination panel consisting of CDH3, PLAU, and LFNG might improve the prognosis of overall pancreatic cancer survival. These secreted proteins may serve as a potential therapeutic targets for pancreatic cancer metastasis.

Recent advances in mass spectrometry-based proteomics of gastric cancer

The last decade has witnessed remarkable technological advances in mass spectrometry-based proteomics. The development of proteomics techniques has enabled the reliable analysis of complex proteomes, leading to the identification and quantification of thousands of proteins in gastric cancer cells, tissues, and sera. This quantitative information has been used to profile the anomalies in gastric cancer and provide insights into the pathogenic mechanism of the disease. In this review, we mainly focus on the advances in mass spectrometry and quantitative proteomics that were achieved in the last five years and how these up-and-coming technologies are employed to track biochemical changes in gastric cancer cells. We conclude by presenting a perspective on quantitative proteomics and its future applications in the clinic and translational gastric cancer research.

Proteomic Analysis of Exosomes and Exosome-Free Conditioned Media From Human Osteosarcoma Cell Lines Reveals Secretion of Proteins Related to Tumor Progression

Osteosarcomas are the most prevalent bone tumors in pediatric patients, but can also occur later in life. Bone tumors have the potential to metastasize to lung and occasionally other vital organs. To understand how osteosarcoma cells interact with their micro-environment to support bone tumor progression and metastasis, we analyzed secreted proteins and exosomes from three human osteosarcoma cell lines. Exosome isolation was validated by transmission electron microscopy (TEM) and immuno-blotting for characteristic biomarkers (CD63, CD9, and CD81). Exosomal and soluble proteins (less than 100 kDa) were identified by mass spectrometry analysis using nanoLC-MS/MS and classified by functional gene ontology clustering. We identified a secretome set of >3,000 proteins for both fractions, and detected proteins that are either common or unique among the three osteosarcoma cell lines. Protein ontology comparison of proteomes from exosomes and exosome-free fractions revealed differences in the enrichment of functional categories associated with different biological processes, including those related to tumor progression (i.e., angiogenesis, cell adhesion, and cell migration). The secretome characteristics of osteosarcoma cells are consistent with the pathological properties of tumor cells with metastatic potential. J. Cell. Biochem. 118: 351-360, 2017. © 2016 Wiley Periodicals, Inc.

Proteomic studies with a novel nano-magnetic chelating system to capture metalloproteins and its application in the preliminary study of monocyte and macrophage sub-secretome

A new chelating chromatography method was developed based in the use of magnetic iron oxide nanoparticles functionalized with EDTA-TMS ((N-(trimethoxysilylpropyl)ethylenediaminetriacetate trisodium salt). These particles combine a high surface area, biocompatibility and magnetic removal from solution, with the chelating affinity towards metal ions. The particles were used to selectively capture metallo-dependant proteins in secretome obtained from human monocytes and mouse macrophages. Secreted metallo-dependant proteins are highly important sources of information since they are involved in several pathological processes. The identification of secreted proteins involved in these processes is highly important for diagnosis or monitoring the progression of a disease. In this multiple-approach study it was possible to not only selectively capture several secreted metallo-dependant proteins, but also to significantly avoid masking proteins such as the highly abundant albumin form the fetal bovine serum used to supplement the cell culture medium. Overall, the magnetic nanoparticle-based chelating chromatography method developed here has proved to be a sensitive, low cost, and a quick tool for sample treatment in order to selectively enrich metalloproteins while overcoming the contamination of highly abundant proteins.

Integrative analysis of extracellular and intracellular bladder cancer cell line proteome with transcriptome: improving coverage and validity of -omics findings

Characterization of disease-associated proteins improves our understanding of disease pathophysiology. Obtaining a comprehensive coverage of the proteome is challenging, mainly due to limited statistical power and an inability to verify hundreds of putative biomarkers. In an effort to address these issues, we investigated the value of parallel analysis of compartment-specific proteomes with an assessment of findings by cross-strategy and cross-omics (proteomics-transcriptomics) agreement. The validity of the individual datasets and of a “verified” dataset based on cross-strategy/omics agreement was defined following their comparison with published literature. The proteomic analysis of the cell extract, Endoplasmic Reticulum/Golgi apparatus and conditioned medium of T24 vs. its metastatic subclone T24M bladder cancer cells allowed the identification of 253, 217 and 256 significant changes, respectively. Integration of these findings with transcriptomics resulted in 253 “verified” proteins based on the agreement of at least 2 strategies. This approach revealed findings of higher validity, as supported by a higher level of agreement in the literature data than those of individual datasets. As an example, the coverage and shortlisting of targets in the IL-8 signalling pathway are discussed. Collectively, an integrative analysis appears a safer way to evaluate -omics datasets and ultimately generate models from valid observations.

Sample preparation protocol for bottom-up proteomic analysis of the secretome of the islets of Langerhans

We evaluate a set of protocols for preparation of the secretome from murine islets of Langerhans for bottom-up proteomic analysis. Of the protocols evaluated, a filter-aided sample preparation based approach using sodium dodecyl sulfate as a detergent to solubilize proteins generated the most protein identifications. A total of 362 protein groups (average of 3.7 peptides/protein) were identified from the secretome using the SDS-FASP protocol; a combination of data from three protocols generated 413 protein group identifications. As expected, the identified proteins included insulin 1 and 2, somatostatin, and glucagon, the four main secreted components from islets. STRING network analysis classified the other proteins as being associated with extracellular exosomes, membrane-bounded vesicles, vesicles, and the extracellular region.

Secreted IGFBP5 mediates mTORC1-dependent feedback inhibition of IGF-1 signalling

The PI(3)K-Akt-mTORC1 pathway is a highly dynamic network that is balanced and stabilized by a number of feedback inhibition loops. Specifically, activation of mTORC1 has been shown to lead to the inhibition of its upstream growth factor signalling. Activation of the growth factor receptors is triggered by the binding of their cognate ligands in the extracellular space. However, whether secreted proteins contribute to the mTORC1-dependent feedback loops remains unclear. We found that cells with hyperactive mTORC1 secrete a protein that potently inhibits the function of IGF-1. Using a large-scale, unbiased quantitative proteomic platform, we comprehensively characterized the rapamycin-sensitive secretome in TSC2(-/-) mouse embryonic fibroblasts, and identified IGFBP5 as a secreted, mTORC1 downstream effector protein. IGFBP5 is a direct transcriptional target of HIF1, which itself is a known mTORC1 target. IGFBP5 is a potent inhibitor of both the signalling and functional outputs of IGF-1. Once secreted, IGFBP5 cooperates with intracellular branches of the feedback mechanisms to block the activation of IGF-1 signalling. Finally, IGFBP5 is a potential tumour suppressor, and the proliferation of IGFBP5-mutated cancer cells is selectively blocked by IGF-1R inhibitors.