Tools for Pathogen Proteomics: Fishing with Biomimetic Nanosponges

Tools for Pathogen Proteomics: Fishing with Biomimetic Nanosponges Ute Distler and Stefan Tenzer* Institute for Immunology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131 Mainz, Germany

ABSTRACT: The identification of the major virulence factors that drive pathogenicity is critical for gaining insight into the underlying molecular mechanisms of diseases. Although genetic approaches combined with functional analyses have markedly increased the rate of virulence factor discovery, the divergence between genome and proteome can impair the identification of important markers, in particular, of those that act in concert or depend on specific environmental factors. Recently, membrane-coated nanomaterials mimicking source cells of interest have emerged as powerful tools that can be used for improved tumor targeting and as “nanotraps” to capture chemokines and bacterial toxins. In this issue of ACS Nano, Lapek et al. demonstrate that membrane-coated nanosponges in combination with quantitative proteomics can also be used as efficient “fishing devices” for the identification of cell-type-specific virulence factors. PEGylation.5,6 Complementary to the fully synthetic approach, biomimetic nanomaterials combine synthetic and natural elements by coating a preformed NP with the cell membrane of a source cell.7 This approach combines synthetic flexibility to incorporate compounds of interest into the NP core with high biocompatibility provided by the cell membrane “coat”.8 Cell-membrane-coated NPs were introduced by the Zhang laboratory in 2011.9 In the original study, red blood cells (RBCs) were used as source cells in combination with a core consisting of poly(lactic-co-glycolic acid) (PLGA), a biodegradable polymer, but the technology has since been expanded to a wide range of host cell types as well as different NP cores.7 Cloaking nanomaterials with membranes derived from different types of cells or biovesicles redefines their respective surface properties. Thus, biomimetic materials can be tailored to achieve prolonged circulation times and/or tumor-targeting depending on the nature of their shell membrane.10 Since their introduction, biomimetic NPs or nanosponges (NSs) have proved to be powerful tools for a variety of applications, including the targeted delivery of drugs to tumors or sites of inflammation, the modulation of the immune system, and the

N

anomaterials have found widespread use in biomedical research. Upon contact with biological fluids, they rapidly bind both proteins and lipids, leading to the formation of a so-called corona.1 Quantitative proteomics has evolved as the method of choice for the detailed characterization of corona components.2,3 Using this technology, several groups have shown that the formation and composition of the corona is dependent on the biophysical characteristics of the respective nanomaterials, including size, charge, shape, and, most importantly, surface chemistry.4 Therefore, many researchers have focused primarily on synthetic strategies to tailor nanoparticle (NP) properties specifically toward their intended applications. However, this targeting often requires challenging chemical synthesis and optimization steps if multiple functional modalities are to be incorporated into a single nanocarrier. Active targeting of NPs toward selected cell populations, such as antigen-presenting cells, B-cells, or tumors, can be achieved by coupling antibodies, peptides, or aptamers to the surface of nanomaterials. These modifications can enhance cell-mediated immunity and improve vaccination or treatment efficacy. As the biocompatibility and in vivo stability of nanomaterials also depend on the protein corona formed in vivo, significant efforts have recently been made to prevent or to modify the formation of a protein corona, including © 2017 American Chemical Society

Published: November 20, 2017 11768

DOI: 10.1021/acsnano.7b07363 ACS Nano 2017, 11, 11768−11772

Perspective

www.acsnano.org

ACS Nano

Perspective

clearance of toxins (detoxification).7 As cell-membrane-coated NPs preserve cell-specific markers, they display high natural affinity toward different kinds of toxins, including, for example, bacterial virulence factors. It has been shown that NSs can act as bait for toxins that target membrane components such as (glyco)proteins, (glyco)lipids, and glucosaminoglycans.11 By retaining those toxins, NSs efficiently prevent (bacterial) virulence factors from attacking their intended targets.12 Moreover, as NSs are capable of delivering multiple factors in parallel, they have been used for antivirulence vaccination strategies eliciting efficient immune responses.13

centrifugation steps are required not only for initial membrane isolation17 but also for the interaction and pulldown experiments. In this issue of ACS Nano, Lapek et al. used membranecoated NSs as biochemical tools to overcome the challenges associated with the identification of virulence factors.18 The approach, termed biomimetic virulomics by the authors, interfaces nanotechnology-based affinity enrichment with multiplexed quantitative proteomics to identify novel virulence factors. It is therefore comparable to classical pulldown experiments for interactome studies but does not require a priori knowledge regarding the target structures on the host cell membrane. To benchmark their novel workflow, the authors focused on the human pathogenic group A Streptococcus (GAS) (see Figure 1). Secretion of hemolytic proteins followed by the lysis of RBCs (hemolysis) is a typical virulence mechanism of GAS. To enrich selectively and to characterize hemolytic GAS proteins in a proof-of-concept experiment, Lapek et al. first coated PLGA polymeric cores with membranes derived from RBCs and macrophages, which served as a control. Afterward, they incubated cell-free GAS supernatants with RBCs and macrophage biomimetic NPs. Membrane-bound fractions as well supernatants after incubation were characterized by quantitative MS. The quantitative MS approach enabled the authors to distinguish three groups of proteins: host-cellspecific interactors, supernatant-specific proteins, and nonspecific binders. Moreover, by comparing RBCs and macrophage data, they could identify previously known RBC-specific virulence factors of GAS, such as the pore-forming toxins CAMP factor and streptolysin O. To understand the (inter)actions and mechanisms of pathogen-derived effector proteins on membrane components and to plan future mechanistic studies of virulence factor action, Lapek et al. not only examined the (bacterial) virulence factors using quantitative proteomics but also characterized the proteome of the host-cell-specific membranes cloaking the NS. By comparing RBCs and macrophage-derived biomimetic NPs, they identified cell-type-specific differences in the respective membrane proteomes. Besides proteins, there are

In this issue of ACS Nano, Lapek et al. used membrane-coated nanosponges as a biochemical tool to overcome the challenges associated with the identification of virulence factors. Although biomimetic NSs efficiently target and neutralize virulence factors due to their intrinsic properties, previous studies did not focus on the de novo identification of virulence factors targeted by the NS. The identification of pathogenderived effector molecules is challenging for multiple reasons: (i) genomics-based approaches may miss important virulence factors due to differences between genome and proteome, particularly when virulence factors are only expressed under certain (environmental) conditions or act in concert;14 (ii) classical interactome studies are often hampered by the fact that the respective target proteins on the host cells are unknown;15 (iii) high backgrounds of host cell proteins often obscure the direct identification of virulence-associated proteins by mass spectrometry (MS) when working with intact cells. 16 Furthermore, endocytosis and phagocytosis of virulence factors may hamper their identification on intact cells. Therefore, isolated host cell membrane extracts, e.g., erythrocyte ghosts or macrophage cell membranes, have been used to reduce the complexity of the host cell proteome. However, handling of the respective membrane fractions is often difficult, as ultra-

Figure 1. Scheme of the biomimetic virulomics workflow introduced by Lapek et al. Biomimetic nanosponges (NSs) are generated by fusing PLGA nanoparticles with cell membranes that have been obtained from source cells of interest (such as red blood cells, RBCs, or macrophages) using, for example, differential centrifugation. Coated NSs are mixed and incubated with protein containing supernatants derived from pathogen culture (e.g., group A Streptococcus, GAS). Membrane-bound proteins as well as proteins that remain in the supernatant after incubation are subjected to tryptic digestion and analyzed by quantitative LC-MS. The comparison of membrane-bound proteins with proteins from the supernatant enables the host-cell-specific identification of virulence factors. 11769

DOI: 10.1021/acsnano.7b07363 ACS Nano 2017, 11, 11768−11772

ACS Nano

Perspective

present study. An additional point to take into account is the high number of proteins identified by the authors that are enriched on the biomimetic NSs but are unlikely to be virulence factors, including, for example, ribosomal proteins or metabolic enzymes (see Figure 2). This point is especially

other players at the (host) cell surface that are involved in infectious processes contributing to different types of virulence mechanisms. It is well-known that plasma membrane lipids as well as carbohydrates play critical roles in cell−cell communication and serve as cell-surface receptors for various pathogens or, in other cases, contribute to host defense mechanisms, biofilm formation, or drug resistance.19,20 Using quantitative MS, the proteomics approach presented by Lapek et al. could be further expanded and complemented analyzing other families of biomolecules. Such multiomic strategies could enlarge the spectrum of virulence factors that could be identified and, thus, provide a more complete picture of the underlying mechanisms of host−pathogen interactions.

The data presented by Lapek et al. show that biomimetic virulomics is a powerful tool to characterize known virulence factors and, notably, to identify novel effector proteins released by pathogens, which display high affinity toward specific host cell types.

Figure 2. Meta-analysis of the 25 proteins displaying the highest enrichment on biomimetic nanosponges (NSs) cloaked with red blood cell (RBC) membranes after incubation with supernatants of group A Streptococcus (GAS) cultures (data obtained from Lapek et al., Supplementary Table 3). Identified proteins were ranked according to their ratio between RBC-coated NSs and supernatant. Proteins were annotated into four categories: ribosomal proteins (P66567, P66648, P66496, P66376, P0C0D6, P0C0D4, Q9A1 V0, P66084, Q9A1W5, Q99XW8, P68901), metabolic enzymes (Q9A1X7, P65458, P65925, P63415, Q99YE2, Q99YD4), uncharacterized proteins (Q99ZW4, Q99XH6, Q9A0F1), and known and potential novel virulence factors (P0C0I3, Q99ZD8, Q99XP1, Q99XR9, Q99YH7).

FUTURE OUTLOOK AND CHALLENGES Over the past years, quantitative proteomics has evolved as a key tool to elucidate protein−protein interactions. In their study, Lapek et al. used a multiplexed quantitative proteomics approach based on tandem mass tags (TMT) and datadependent acquisition (DDA) for their analysis. In future studies, the biomimetics virulomics workflow could also be combined with other MS strategies. While the TMT approach is well suited for the analysis of up to 10 different samples,21 its applicability might be limited regarding the analysis of larger sample cohorts, e.g., the analysis of virulence factors from multiple clinical isolates or the screening for cell-type-specific virulence factors in a high-throughput manner. Here, label-free quantification workflows combined with data-independent acquisition (DIA) could be adapted, which might increase sensitivity and specificity and reduce missing values.22 For example, supernatants from bacterial cultures can be fractionated and analyzed by DDA and the resulting data used to build spectral libraries. Samples can then be acquired by DIA approaches such as SWATH,23 which would enable researchers to increase the number of samples that can be directly compared while concomitantly reducing wet lab costs as no reagents for labeling are required. Additional improvements of the presented method may include the use of NSs with magnetic cores, thereby improving sample handling during isolation and washing steps. As the use of magnetic core NSs would eliminate the requirement for centrifugation steps, this could facilitate the implementation of the workflow on automated liquid handling platforms.24 The data presented by Lapek et al. show that biomimetic virulomics is a powerful tool to characterize known virulence factors and, notably, to identify novel effector proteins released by pathogens, which display high affinity toward specific host cell types. However, novel proteins identified by the presented approach will need to be characterized in detail to identify their respective interactors on the host cell membranes and to verify their biological activity, a point that was not addressed in the

critical for the analysis of pathogens with less well annotated genomes, where many hypothetical or uncharacterized proteins may be identified. Here, systematic studies using multiple biomimetic NSs with different membrane coatings may help to identify unspecific interactors and to enable researchers to establish a repository with common false positives, similar to the CRAPome database.25 Due to the inherent flexibility of the method presented by Lapek et al., we believe that it can be expanded to a multitude of different model systems studying not only different pathogen−host interactions but also any other type of cell− cell communication. As suggested by the authors, the workflow could be readily adapted to study the interplay of cells within the same species, analyzing, for example, the “communication” between different immune cells or identifying cancer-specific virulence factors. Stable isotope labeling with amino acids in cell culture (SILAC) is a powerful tool to assess differential changes in complex protein samples from in vitro cultivated cells, and it enables researchers to trace back cell types of origin for each protein identified by MS.26 Hence, NSs could be coated with endogenous membranes derived from immune cells, endothelial cells, etc. that have been cultivated under normal conditions or even isolated from human patient samples. Incubation of those NPs with supernatants from cancer cells that were grown in “heavy” SILAC medium and have metabolically incorporated amino acids containing heavy isotopes (e.g., 13C-labeled L-lysine) will enable the identification of cell-type-specific effector proteins from cancer cells (see Figure 3). This strategy could provide novel insights into the intercellular communication in the tumor microenvironment.27 In conclusion, Lapek et al. introduced a promising method for the cell-type-specific capture and subsequent identification 11770

DOI: 10.1021/acsnano.7b07363 ACS Nano 2017, 11, 11768−11772

ACS Nano

Perspective

Funding

S.T. was supported by the Deutsche Forschungsgemeinschaft (Grants TE599/1-1, TE599/2-1, and TP B11 of SFB1066) and the Forschungszentrum Immunotherapie (FZI) of the Johannes Gutenberg University Mainz. U.D. was supported by the Focus Program Translational Neurosciences (FTN) of the Johannes Gutenberg University Mainz and the University Medical Center of the Johannes Gutenberg University Mainz (Internal University Research Funding (Stufe I)). Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENTS We thank Doreen Nothmann for critically reading the manuscript. REFERENCES (1) Monopoli, M. P.; Åberg, C.; Salvati, A.; Dawson, K. A. Biomolecular Coronas Provide the Biological Identity of Nanosized Materials. Nat. Nanotechnol. 2012, 7, 779−786. (2) Docter, D.; Distler, U.; Storck, W.; Kuharev, J.; Wünsch, D.; Hahlbrock, A.; Knauer, S. K.; Tenzer, S.; Stauber, R. H. Quantitative Profiling of the Protein Coronas That Form Around Nanoparticles. Nat. Protoc. 2014, 9, 2030−2044. (3) Lai, Z. W.; Yan, Y.; Caruso, F.; Nice, E. C. Emerging Techniques in Proteomics for Probing Nano−Bio Interactions. ACS Nano 2012, 6, 10438−10448. (4) Tenzer, S.; Docter, D.; Kuharev, J.; Musyanovych, A.; Fetz, V.; Hecht, R.; Schlenk, F.; Fischer, D.; Kiouptsi, K.; Reinhardt, C.; Landfester, K.; Schild, H.; Maskos, M.; Knauer, S. K.; Stauber, R. H. Rapid Formation of Plasma Protein Corona Critically Affects Nanoparticle Pathophysiology. Nat. Nanotechnol. 2013, 8, 772−781. (5) Pearson, R. M.; Sen, S.; Hsu, H.; Pasko, M.; Gaske, M.; Král, P.; Hong, S. Tuning the Selectivity of Dendron Micelles through Variations of the Poly(ethylene glycol) Corona. ACS Nano 2016, 10, 6905−6914. (6) Settanni, G.; Zhou, J.; Suo, T.; Schöttler, S.; Landfester, K.; Schmid, F.; Mailänder, V. Protein Corona Composition of Poly(ethylene glycol)- and Poly(phosphoester)-Coated Nanoparticles Correlates Strongly with the Amino Acid Composition of the Protein Surface. Nanoscale 2017, 9, 2138−2144. (7) Fang, R. H.; Jiang, Y.; Fang, J. C.; Zhang, L. Cell MembraneDerived Nanomaterials for Biomedical Applications. Biomaterials 2017, 128, 69−83. (8) Xu, F.; Reiser, M.; Yu, X.; Gummuluru, S.; Wetzler, L.; Reinhard, B. M. Lipid-Mediated Targeting with Membrane-Wrapped Nanoparticles in the Presence of Corona Formation. ACS Nano 2016, 10, 1189−1200. (9) Hu, C.-M. J.; Zhang, L.; Aryal, S.; Cheung, C.; Fang, R. H.; Zhang, L. Erythrocyte Membrane-Camouflaged Polymeric Nanoparticles as a Biomimetic Delivery Platform. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 10980−10985. (10) Zhai, Y.; Su, J.; Ran, W.; Zhang, P.; Yin, Q.; Zhang, Z.; Yu, H.; Li, Y. Preparation and Application of Cell Membrane-Camouflaged Nanoparticles for Cancer Therapy. Theranostics 2017, 7, 2575−2592. (11) Hsiao, F. S.-H.; Sutandy, F. R.; Syu, G.-D.; Chen, Y.-W.; Lin, J.M.; Chen, C.-S. Systematic Protein Interactome Analysis of Glycosaminoglycans Revealed YcbS as a Novel Bacterial Virulence Factor. Sci. Rep. 2016, 6, 28425. (12) Kroll, A. V.; Fang, R. H.; Zhang, L. Biointerfacing and Applications of Cell Membrane-Coated Nanoparticles. Bioconjugate Chem. 2017, 28, 23−32. (13) Wei, X.; Gao, J.; Wang, F.; Ying, M.; Angsantikul, P.; Kroll, A. V.; Zhou, J.; Gao, W.; Lu, W.; Fang, R. H.; Zhang, L. In Situ Capture of Bacterial Toxins for Antivirulence Vaccination. Adv. Mater. 2017, 29, 1701644.

Figure 3. Combining biomimetic virulomics with stable isotope labeling with amino acids in cell culture (SILAC) for same species studies exemplified by the affinity enrichment of cancer-specific virulence factors. Nanosponges (NSs) are coated with endogenous membranes derived, for example, from immune cells or endothelial cells. Cancer cells are cultivated in SILAC medium containing isotopically labeled “heavy” amino acids, which are metabolically incorporated in newly synthesized proteins. Coated NSs are incubated with supernatants from the cancer cell culture. Afterward, proteins bound to the NS as well as those remaining in the supernatant are analyzed by liquid chromatography−mass spectrometry. The utilization of media conditioned with heavy-labeled amino acids enables researchers to quantify cancer-cell-derived virulence factors and to distinguish them from proteins that originate from other source cell types.

of virulence factors. The present work presents an exciting approach to broaden the applicability of quantitative proteomics for the characterization of pathogen-derived effector proteins, especially when the respective targets on the host cells are unknown. This research opens up new avenues toward the identification of novel factors involved in host−pathogen interactions as well as cell−cell communication.

AUTHOR INFORMATION Corresponding Author

*Phone: +49 (0) 6131 17-6199. E-mail: [email protected]. ORCID

Ute Distler: 0000-0002-8031-6384 Stefan Tenzer: 0000-0003-3034-0017 11771

DOI: 10.1021/acsnano.7b07363 ACS Nano 2017, 11, 11768−11772

ACS Nano

Perspective

(14) Muhammad, S. A.; Ahmed, S.; Ali, A.; Huang, H.; Wu, X.; Yang, X. F.; Naz, A.; Chen, J. Prioritizing Drug Targets in Clostridium Botulinum with a Computational Systems Biology Approach. Genomics 2014, 104, 24−35. (15) Lum, K. K.; Cristea, I. M. Proteomic Approaches to Uncovering Virus−Host Protein Interactions During the Progression of Viral Infection. Expert Rev. Proteomics 2016, 13, 325−340. (16) Scott, N. E.; Hartland, E. L. The Role of Mass Spectrometry Analysis in Bacterial Effector Characterization. Biochem. J. 2017, 474, 2779−2784. (17) Fontaine, A.; Pophillat, M.; Bourdon, S.; Villard, C.; Belghazi, M.; Fourquet, P.; Durand, C.; Lefranc, D.; Rogier, C.; Fusai, T.; Almeras, L. Specific Antibody Responses against Membrane Proteins of Erythrocytes Infected by Plasmodium Falciparum of Individuals Briefly Exposed to Malaria. Malar. J. 2010, 9, 276. (18) Lapek, J. D.; Fang, R. H.; Wei, X.; Li, P.; Wang, B.; Zhang, L.; Gonzalez, D. J. Biomimetic Virulomics for Capture and Identification of Cell-Type Specific Effector Proteins. ACS Nano 2017, DOI: 10.1021/acsnano.7b02650. (19) Kato, K.; Ishiwa, A. The Role of Carbohydrates in Infection Strategies of Enteric Pathogens. Trop. Med. Health 2015, 43, 41−52. (20) Kadioglu, A.; Weiser, J. N.; Paton, J. C.; Andrew, P. W. The Role of Streptococcus pneumoniae Virulence Factors in Host Respiratory Colonization and Disease. Nat. Rev. Microbiol. 2008, 6, 288−301. (21) Rauniyar, N.; Yates, J. R. Isobaric Labeling-Based Relative Quantification in Shotgun Proteomics. J. Proteome Res. 2014, 13, 5293−5309. (22) Muntel, J.; Xuan, Y.; Berger, S. T.; Reiter, L.; Bachur, R.; Kentsis, A.; Steen, H. Advancing Urinary Protein Biomarker Discovery by Data-Independent Acquisition on a Quadrupole-Orbitrap Mass Spectrometer. J. Proteome Res. 2015, 14, 4752−4762. (23) Navarro, P.; Kuharev, J.; Gillet, L. C.; Bernhardt, O. M.; MacLean, B.; Röst, H. L.; Tate, S. A.; Tsou, C.-C.; Reiter, L.; Distler, U.; Rosenberger, G.; Perez-Riverol, Y.; Nesvizhskii, A. I.; Aebersold, R.; Tenzer, S. A Multicenter Study Benchmarks Software Tools for Label-Free Proteome Quantification. Nat. Biotechnol. 2016, 34, 1130− 1136. (24) Passos, M. L. C.; Pinto, P. C. A. G.; Santos, J. L. M.; Saraiva, M. L. M. F. S.; Araujo, A. R. T. S. Nanoparticle-Based Assays in Automated Flow Systems: A Review. Anal. Chim. Acta 2015, 889, 22− 34. (25) Mellacheruvu, D.; Wright, Z.; Couzens, A. L.; Lambert, J.-P.; StDenis, N. A.; Li, T.; Miteva, Y. V.; Hauri, S.; Sardiu, M. E.; Low, T. Y.; Halim, V. A.; Bagshaw, R. D.; Hubner, N. C.; Al-Hakim, A.; Bouchard, A.; Faubert, D.; Fermin, D.; Dunham, W. H.; Goudreault, M.; Lin, Z. Y.; et al. The CRAPome: A Contaminant Repository for Affinity Purification−Mass Spectrometry Data. Nat. Methods 2013, 10, 730− 736. (26) Munday, D. C.; Surtees, R.; Emmott, E.; Dove, B. K.; Digard, P.; Barr, J. N.; Whitehouse, A.; Matthews, D.; Hiscox, J. A. Using SILAC and Quantitative Proteomics To Investigate the Interactions between Viral and Host Proteomes. Proteomics 2012, 12, 666−672. (27) Tian, R. Exploring Intercellular Signaling by Proteomic Approaches. Proteomics 2014, 14, 498−512.

11772

DOI: 10.1021/acsnano.7b07363 ACS Nano 2017, 11, 11768−11772