Nanoparticles Associate with Intrinsically Disordered RNA-Binding

Jan 25, 2017 - In turn, this indicates that within a cell, nanoparticles might interfere with protein synthesis triggering a range of cellular respons...
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Nanoparticles Associate with Intrinsically Disordered RNA-Binding Proteins Alexander V. Romashchenko,†,‡ Tsung-Wai Kan,§ Dmitry V. Petrovski,†,⊥ Ludmila A. Gerlinskaya,† Mikhail P. Moshkin,*,† and Yuri M. Moshkin*,†,∥ †

The Federal Research Center, Institute Cytology and Genetics, ‡Institute of Computational Techniques, and ∥Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia § Department of Biochemistry, Erasmus Medical Center, Rotterdam 3015CE, The Netherlands ⊥ Institute of Systematics and Ecology of Animals, SB RAS, Novosibirsk 630091, Russia S Supporting Information *

ABSTRACT: Nanoparticles are capable of penetrating cells, but little is known about the way they interact with intracellular proteome. Here we show that inorganic nanoparticles associate with low-complexity, intrinsically disordered proteins from HeLa cytosolic protein extracts in nondenaturing in vitro nanoparticle pull-down assays. Intrinsic protein disorder associates with structural mobility, suggesting that side-chain flexibility plays an important role in the driving of a protein to nanoparticle absorption. Disordered protein domains are often found in a diverse group of RNA-binding proteins. Consequently, the nanoparticle-associated proteomes were enriched in subunits of RNA-processing protein complexes. In turn, this indicates that within a cell, nanoparticles might interfere with protein synthesis triggering a range of cellular responses. KEYWORDS: nanoparticles, proteomics, RNA-binding proteins, stress granules, intrinsically disordered proteins

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affinity, but low-abundant proteins. As a rule of thumb, albumin, immunoglobulins, and fibrinogen absorb first on a surface of nanoparticles followed by coagulation factors, apolipoproteins, and other low-abundant proteins.14,19−21 Collectively, such an exchange in protein absorption is referred to as Vroman effect.22 Proteins associate with nanoparticles in layers consisting of so-called “hard” and “soft” protein “coronas”. Proteins of “hard corona” are absorbed first, while “soft corona” proteins associate with nanoparticles through interactions with proteins of “hard corona”. “Hard corona” is relatively stable with dissociation and exchange times within hours. In contrast, “softcorona” is dynamic with lifetimes within minutes.14,19,23,24 Binding of proteins to nanoparticles is driven by hydrophobic and electrostatic interactions and, therefore, the resulting composition of “corona” depends on physicochemichal properties of nanoparticles’ surface. In brief, higher charge density and hydrophobicity of nanoparticles increases the “corona” thickness and protein conformational change. Higher surface curvature thickens the “corona” but decreases conformational change.19,20,25 Changes in composition and dynamics of nanoparticles’ “corona” influence their biodistribution and

anoparticles of various natures enter cells via distinct routes. Depending on nanoparticle structure and cell type, these include phagocytosis, pinocytosis, receptor-mediated endocytosis, clathrin- or caveolin-dependent endocytosis, and so on.1−5 Likewise, the rates of nanoparticles internalization depend on their composition and cell type and span over a broad range of 102−107 particles per cell.6,7 At the upper limit, these values are on a par with the copy number of one of the most abundant proteins in human cells, Vimentin, estimated at ∼107 copies per HeLa cell.8 Although upon internalization nanoparticles are often trapped inside vesicles, such as endolysosomes, there exists several means by which nanoparticles can escape into cytosol or other cellular compartments. These include, but are not limited to, osmotically driven endosome burst, membrane rupture, or fusion with other organelles. To that, there is a growing number of designer nanoparticles with the enhanced ability to escape endosomes or cross plasma membranes directly.9−13 Thus, it is not uncommon for nanoparticles to access cytosol, raising the possibility of their interactions with intracellular proteome. So far, biological associations of nanoparticles with proteins were studied in great detail for blood serum proteins, which form a so-called protein “corona” around blood-circulating nanoparticles.14−18 Composition of the protein “corona” evolves over time resulting in the displacement of lower affinity, but high-abundant blood serum proteins with higher © 2017 American Chemical Society

Received: September 5, 2016 Accepted: January 25, 2017 Published: January 25, 2017 1328

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Figure 1. Mass spectrometry analysis of nanoparticle-associated proteomes. (a) Identification of HeLa cytosolic proteins associated with nanoparticles. Proteins precipitated by nanoparticles were resolved by SDS-PAGE, visualized by Coomassie staining, and identified by nanoflow LC-MS/MS mass-spectrometry (Tables S3 and S4). As a control, 2% of the input cytosolic protein extract was loaded on a gel and analyzed by mass spectrometry. Molecular weights (kDa) are indicated on the left, and percentages of the input cytosolic proteins precipitated by nanoparticles (NP pull-down) estimated by gel densitometry are show at the bottom. (b) Pearson correlation analysis of proteins enriched in nanoparticle pull-downs. Protein enrichment was scored as a log2 ratio of mol % of nanoparticle-associated protein contents to the input cytosolic protein contents. Proteins below the detection limit (emPAI = 0) were set to the minimum emPAI value to avoid divisions by zero. (c) Heatmap depicting log2 cytosolic protein enrichments in nanoparticle pull-downs. Proteins (rows) and pull-down experiments (columns) were ordered by hierarchical cluster analysis. Color key of the log2 protein enrichment is shown next to the heatmap. (d) Six-way Venn diagram depicting overlaps between nanoparticle-associated proteomes. Proteins enriched in nanoparticle pull-downs with the log2 enrichment score ≥1.5 were selected. A core nanoparticle-associated proteome comprises 50 proteins strongly enriched (log2 enrichment score ≥1.5) in each nanoparticle pull-down.

cellular uptake.20,21,26−29 For example, increased binding of immunoglobulins, complement factors, and fibrinogen to nanoparticles promotes their phagocytosis and elimination from systemic circulation.28,30 Preferential binding of certain apolipoproteins, such as ApoE, ApoA-I, ApoB-100, etc., to the surface of nanoparticles facilitates their redistribution across the blood−brain barrier.31−34 Thus, despite a seeming structural simplicity and a lack of the specific binding sites, the surface of nanoparticles creates a hub for extensive protein−nanoparticle interactions defining their biodistribution and biological properties. Upon entering cells, nanosized materials affect cellular proteome, triggering a plethora of cellular responses.35−37 However, despite a wealth of data on extracellular nanoparticleassociated proteomes, knowledge on their potential intracellular targets is limited. Here, we performed an extensive proteomics survey of associations of cytosolic proteins, isolated from cultured human HeLa cells, with inorganic nanoparticles

differing in size, morphology, and structure. We show a substantial degree of similarities in intracellular proteomes absorbed by distinct nanoparticles with a significant enrichment in RNA-processing proteins. Structural analysis of nanoparticleassociated proteomes revealed prevalence of low-complexity protein motifs, which are intrinsically disordered in solutions.38 This suggests that intrinsic protein disorder facilitates the binding of proteins to nanoparticles.

RESULTS AND DISCUSSION In Vitro Proteomics Analysis of Cytosolic HeLa Proteins Binding to Inorganic Nanoparticles. To address a spectrum of intracellular nanoparticle−protein interactions, we assessed absorption of cytosolic proteins to distinct inorganic nanoparticles. To this end, we incubated cytosolic protein extracts prepared from the human HeLa cancer cell line with dispersions of Au, Mn2O3, MnFe2O4, Gd2O3, and SiO2 spherical (SiO2-S) and porous (SiO2-P) nanoparticles (Table 1329

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Figure 2. Structure−functional annotation of nanoparticle-associated proteomes. (a) Annotation of protein domains revealed significant overrepresentation of RNA-binding domains in nanoparticle-associated proteomes identified in the high-stringency pull-downs. Proteins were annotated with DAVID tools, and fold enrichments of InterPro protein domains are shown.45,46 A fold enrichment above 1 indicates overrepresented domains and below 1 indicates underrepresented. Filled bars correspond to significantly overrepresented domains (EASE p value 0.05). Detailed annotation results are listed in Table S5a for individual nanoparticle associated proteomes and in Table S5b for the core nanoparticle-associated proteome. (b) Functional annotation of biological pathways associated with nanoparticles interacting proteins revealed significant overrepresentation of RNA-processing pathways: ribosome, spliceosome, etc. Proteins were annotated with DAVID tools for the KEGG-annotated biological pathways.47 (c) Additional KEGGannotated biological pathways associated with nanoparticles target proteins. Detailed results are listed in Table S6a for individual nanoparticle-associated proteomes and in Table S6b for the core nanoparticle-associated proteomes.

S1) and, second, to reduce nonspecific protein−surface and protein−protein interactions.40,41 Following the incubations, protein−nanoparticle complexes were pulled down by centrifugation, washed, and resolved by SDS-PAGE along with the 2% input of cytosolic protein extract (Figure 1a). Quantification of precipitated cytosolic proteins from the pull-downs by gel densitometry revealed that protein to nanoparticle stoichiometry varied significantly from ∼200:1 to ∼10000:1 (Table S2). Likewise, the surface area normalized

S1). Before incubation, protein extracts were precleared by centrifugation and adjusted to a final protein concentration of ∼20 mg/mL (∼1/10th of intracellular protein concentration8), in order to reduce a nonspecific aggregation caused by macromolecular crowding.39 The resulting protein to nanoparticle ratios were ∼104−106 (Table S2), corresponding to saturated binding conditions. The binding reactions were performed under stringent conditions at ionic strength (I) of ∼0.4 M: first, to reduce nanoparticles’ surface charge (Figure 1330

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ACS Nano stoichiometry ranged between ∼0.2 and ∼2.7 proteins per nm2 (Table S2). Approximate thickness of absorbed protein “corona”, estimated from idealized geometry (spherical or cubical for Gd2O3) and averaged protein parameters (density ∼1.37 g/cm3, molecular weight ∼50 kDa),42 ranged from ∼6 to ∼34 nm (Table S2). The thickness of “corona” was the least for hydrophilic SiO2-S and SiO2-P nanoparticles (∼6 nm) and varied from ∼21 to ∼34 nm for other nanoparticles. Combined, these estimations suggest extensive coating of nanoparticles by cytosolic proteins in multiple layers, as, on average, the radius of human protein is ∼2.4 nm.42 This, in turn, is consistent with the binding of native multisubunit protein complexes to nanoparticles expected under nondenaturing pull-down conditions. Alternatively, high values of protein to nanoparticle stoichiometry may result from nanoparticles-induced protein aggregation.43 Precipitated proteins were then detected and quantified by nanoflow LC-MS/MS. In total, we identified 3247 of cytosolic HeLa proteins in the input cytosolic protein extract and highstringency pull-down experiments (Table S3). Protein contents were expressed as molar fraction percentages (mol %) based on the emPAI scores (Table S4). Enrichment scores for nanoparticle-associated proteins were calculated as a log2 ratio of the pull-down mol % to the input mol %. Positive scores indicate association of a protein with nanoparticle, while negative scores indicate exclusion of a protein from nanoparticle precipitates. Comparing the log2 protein enrichment scores revealed a high degree of correlations (r > 0.5) for all studied nanoparticleassociated proteomes (Figure 1b). Likewise, pairwise intersections of highly enriched proteins (log2 enrichment score ≥1.5) in distinct nanoparticle-associated proteomes showed a significant degree of overlap, as judged by hypergeometric probability (Figure S2). However, correlation and hierarchical cluster analysis suggests some differences for cytosolic proteomes associated with the cubic Gd2O3 and porous SiO2-P nanoparticles (Figure 1c). Of note, their proteomes are more diverse (632 for SiO2-P and 853 for Gd2O3) than that for other nanoparticles (443 for Au, 433 for Mn2O3, 496 for MnFe2O4, and 560 for SiO2-S), as judged by the number of proteins with the log2 enrichment score ≥1.5. Thus, nanoparticles geometry (as for example the porosity) or other physicochemichal properties might affect the number of distinct proteins tethered to a surface of nanoparticles. It has to be noted, however, that the diversity of nanoparticle-associated proteomes is unrelated to their stoichiometries (Table S2). We also surveyed nanoparticle-associated proteomes under more physiological ionic strength (∼0.1 M) for Au, Gd2O3, and SiO2-P ( Figure S3a, Tables S3 and S4). Pairwise comparisons of the log2 enrichment scores revealed strong correlations ( r > 0.65, Figure S3b) and a high degree of overlaps (Figure S3c) between nanoparticle-associated proteomes precipitated under high- and low-stringency (I ∼ 0.4 M and 0.1 M, respectively) conditions. However, despite precipitating comparable amounts of cytosolic proteins in high- and low-stringency pull-downs (Figure 3a), the low-stringency pull-downs resulted in somewhat larger proteomes (Figure S3c). We also identified 491 of additional proteins associated with nanoparticles, which otherwise were missing from the previous analysis (Tables S3 and S4). Weak, transient, or nonspecific protein−protein interactions are increased by decreasing of ionic strength,44 thus precipitation of additional proteins by nanoparticles under lower ionic strength was rather expected. At the same time, a

bulk of nanoparticle−protein interactions and protein to nanoparticle stoichiometries were broadly comparable between high- and low-stringency pull-downs. Finally, taking an advantage of deducing cytosolic proteomes associated with distinct nanoparticles, we established a core nanoparticle-associated proteome. To this end, we intersected proteins enriched in all high-stringency nanoparticle pull-downs with the log2 enrichment score ≥1.5 (Figure 1d). This core proteome comprises 50 proteins, which are likely to represent a set of prime protein targets for nanoparticles in cells. Functional Annotation of Nanoparticle-Associated Cytosolic Proteomes. Having established the proteomes associated with distinct nanoparticles, we wondered about their biological functions. We piped the nanoparticle-associated proteomes (log2 enrichment score ≥1.5) to DAVID annotation tools45 and analyzed their domain composition and biological pathways according to InterPro46 and KEGG47 databases, respectively (Materials and Methods). All input cytosolic proteins detected by LC-MS/MS along with the proteins detected in high-stringency nanoparticle pull-downs were used as a background set. First, we noted a significant overrepresentation of InterProannotated RNA-binding domains in proteins precipitated by nanoparticles under high- and low-stringency conditions (Figure 2a, Table S5a). These included the nucleotide-binding α-β plait domain, which also encompasses RNA recognition motif (RRM); the K homology domain (KH) found in heterogeneous nuclear ribonucleoproteins (hnRNP); the double-stranded RNA binding domain; the LSm domain found in small nuclear RNPs; and the SAP protein domain. We also noted a significant overrepresentation of the helicaseassociated domain along with the DEAD/DEAH box helicase domain found in a diverse set of RNA helicases (Figure 2a, Table S5a). Second, in agreement with these results, KEGG annotation of biological pathways of nanoparticle-associated proteomes revealed a significant degree of overrepresentation of RNA-processing pathways: ribosome, spliceosome, RNA transport, mRNA surveillance, and RNA degradation (Figure 2b, Table S6a). Finally, the core nanoparticle-associated proteome consisting of the proteins enriched in all highstringency pull-downs (Figure 1d) was also overrepresented in RNA-binding protein domains, such as Fragile-X related, ataxin-2, etc., and RNA-processing pathways: ribosome, spliceosome and RNA transport (Figure S4a,b, Tables S5b and S6b). It has to be noted, however, that Gd2O3 and SiO2-S(P) proteomes are more diverse as compared to others and enriched in additional functional groups (Figure 2c, Table S6a). To that, both porous (P) and nonporous (S) SiO2 nanoparticles share a number of exclusive interactions, for example, proteins involved in N- and O-glycan biosynthesis (Figure 2c, Table S6a). Thus, despite common associations with annotated RNA-processing pathways, nanoparticle-associated proteomes display a certain degree of specializations, which might have an impact on their biological functions. To verify whether the proteins associated with nanoparticles are indeed involved in RNA processing, we compared nanoparticle-associated proteomes with mRNA-bound proteomes. mRNA-bound proteomes represent proteins detected by purification of RNA-cross-linked proteins via oligo(dT) from HeLa and human embryonic kidney cell line, HEK293.48,49 Although HeLa and HEK293 cells are of distinct origins, a bulk of the RNA-processing machinery is conserved 1331

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Figure 3. Nanoparticles associate with the subunits of mRNA-processing machinery and stress granules. Cytosolic proteins enriched in the high-stringency nanoparticle pull-downs (log2 enrichment score ≥1.5) intersected with the HeLa mRNA-bound proteome48 and the U-2 OS stress granule proteome.60 Venn diagrams depicting overlaps are scaled to the size of proteomes. Hypergeometric probabilities (phyper) indicate the significance of pairwise overlaps between nanoparticles and mRNA-bound proteomes and nanoparticles and stress granule proteomes. Note that there is a significant overlap between mRNA-bound and stress granule proteomes (phyper = 2.48 × 10−49). Nanoparticleassociated proteomes also overlap significantly with the HEK293 mRNA-bound proteomes49 (Figure S5).

Although, there is no single algorithm to predict intrinsic protein disorder, a combination of several bioinformatics approaches allows IDR prediction in protein sequences (Materials and Methods). First, IDRs often coincide with low-complexity regions (LCRs).51,52 On average, proteins associated with nanoparticles under high ionic strength are characterized by the increased percentages of LCRs as compared to all detected cytosolic proteins (Figure 4a). Second, it is expected that IDRs exist outside of any of the three folded states (α- or 310-helices or βsheet) or, in other words, are confined within protein coils. DisEMBL artificial neural network model predicts coil domains in protein sequences,52 and with the exception of SiO2-S(P) nanoparticles, nanoparticle-associated proteins exhibit an increase in the percentage of coils as compared to the input cytosolic proteins (Figure 4b). To further assess the relations between nanoparticleassociated proteomes and IDRs, we used DisEMBL models to predict hot loops and missing coordinates from remark 465 (REM465) entries. Hot loops are defined as coils with increased values of B-factor (a mean square displacement in the backbone Cα atoms), while REM465 represents nonassigned electron densities in Protein Data Bank (PDB) X-ray structures.52 On average, proteins associated with nanoparticles are characterized by higher percentages of the predicted hot loops and missing coordinates as compared to a set of all identified cytosolic proteins (Figure 4c,d). Hot loops and REM465 entries are likely to reflect increased chain flexibility and, therefore, represent candidate IDRs. To this, a SLIDER prediction model trained on a wellcurated protein disorder data set (MxD)53,54 also revealed an

between cell types, resulting in a significant overlap between them: 406 shared proteins out of 618 for HeLa and 570 for HEK293 cells. Pairwise intersections confirmed significant overlaps between nanoparticle-associated proteomes and mRNA-bound proteomes of HeLa and HEK293 cells (Figures 3 and S5). For example, about 22% of the HeLa mRNA-bound proteins also associate with SiO2-P nanoparticles, and ∼40% bind to Gd2O3 nanoparticles. Finally, ∼60% of the core nanoparticle-associated proteome is represented by mRNAbound proteins (Figure S4c). Combined with these results, we conclude that RNA-processing machinery is one of the prime intracellular targets for nanoparticles. Intrinsic Protein Disorder in Nanoparticle-Associated Proteomes. High correlations between nanoparticle-associated proteomes suggest a common mechanism for protein absorption to a surface of nanoparticles. We noted that RNAprocessing proteins represent a major group of cytosolic proteins precipitated by nanoparticles. It is worth noting that many RNA-binding proteins and RNA recognition domains possess intrinsic protein disorder.50 As opposed to order, whereby a protein domain is folded and confined, intrinsically disordered proteins (IDPs) and regions (IDRs) are present in unfolded and highly flexible conformation. Due to the high levels of intrinsic dynamics and almost unlimited flexibility, IDPs and IDRs are capable of very diverse modes of unusual interactions with ordered, globular substrates, such as wrapping, winding, hugging, stacking, penetrating, and so on.38 This, in turn, provides an attractive mechanism for IDPs and IDRs to interact with curved, but structurally uniform, surfaces of nanoparticles. 1332

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Figure 4. Nanoparticles associate with low-complexity IDPs. (a) Tukey’s boxplots of the percentages of LCRs in proteins associated with nanoparticles. Low complexity sequences were identified by the SEG algorithm.51 Boxes represent the first and third quartile ranges, whiskers extend to 1.5 interquartile range, and midlines indicate medians. The core nanoparticle-associated proteome (core proteome) is shown on the left (red box). Enrichments of nanoparticle-associated proteins in LCRs were compared to all mass spectrometry identified cytosolic proteins (all detected, gray box) by one-sided Student’s t test (*p < 0.05, **p < 0.01, ***p < 0.001). (b−d) Boxplots of the percentages of putative disordered regions predicted by one of the DisEMBL prediction algorithms:52 coils (b), hot loops (c), and REM465 missing coordinates (d). (e) Boxplots of LDR scores in nanoparticle-associated proteomes predicted by the SLIDER model trained on MxD data set of disordered proteins.53,54 (f) Boxplots of the percentages of flexible regions predicted by DynaMine S2 order parameter.56 Flexible regions were selected at predicted S2 ≤ 0.7. (g) Boxplots of the percentages of short protein disordered motifs predicted by IUPred.57 Disordered regions were selected at IUPred score >0.5. (h) Boxplots of the percentages of the disordered regions favoring interactions with globular protein partners according to the ANCHOR prediction model.58 ANCHOR scores exceeding 0.5 indicate favorable energy gains from IDR association with a globular protein.

Complementing these predictions we also estimated the flexibility of nanoparticle-associated proteins with the DynaMine model, which predicts S2 order parameters of N−H bonds in a protein sequence.56 The S2 order parameter is related to the rotational angle of a N−H bond vector in a protein backbone, providing a physically meaningful measure of polypeptide flexibility. As compared to the input cytosolic proteins, nanoparticle-associated proteins display a marked

increased probability of long disordered regions (LDRs) in nanoparticle-associated proteins, except for the SiO2-P proteome (Figure 4e). LDRs represent disordered protein domains of more than 30 residues, which, given a peptide unit’s length of 0.38 nm,55 scale to ≥11.4 nm in length. Thus, such an unstructured LDR chain is capable of wrapping over ∼35% of the spherical circumference of a nanoparticle measuring 10 nm in diameter. 1333

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Figure 5. Spontaneous aggregation of low-complexity IDPs. (a) Mass spectrometry analysis of spontaneously aggregating proteins. HeLa cytosolic protein extracts were incubated at 4 °C, and aggregated proteins were precipitated by centrifugation and identified by nanoflow LCMS/MS. The emPAI scores and mol % protein contents are listed in Tables S3 and S4, respectively, as Mock. A percentage of aggregating cytosolic proteins is shown at the bottom. Note that the amount of proteins precipitated in the mock pull-down is significantly lower than that precipitated in high-stringency nanoparticle pull-downs (compare with Figure 1a). (b) Pearson correlation analysis of the log2 protein enrichments in the mock and high-stringency nanoparticle pull-down experiments. (c) Seven-way Venn diagram depicting intersections between spontaneously aggregating (mock) and nanoparticle-associated proteomes. The mock proteome is outlined in black, and the core nanoparticle-associated proteome is outlined in red. (d) Tukey’s boxplots of the percentages of LCRs, IDRs, LDRs, and flexible regions in the spontaneously aggregated proteome as compared to all detected proteins in the cytosolic protein extract (same as in Figure 4).

increase in the percentages of flexible regions (Figure 4f), substantiating the importance of protein disorder and flexibility in nanoparticle−protein interactions. IDRs can also be defined as protein domains with low intrachain interaction energy. Such energies are predicted by the IUPred model trained on pairwise interaction energy matrices for the known proteins (Materials and Methods).57,58 In agreement with other predictions, the IUPred model confirmed association of nanoparticles (except for SiO2-P) with proteins that are enriched in IDRs (Figure 4g). Finally, extending the IUPred’s approach, it becomes feasible to predict the propensity of IDRs to bind to globular proteins with an ANCHOR prediction model.58 To this end, in addition to intrachain interaction energies, interchain interaction energies for each residue with a globular protein partner are estimated for a protein sequence (Materials and Methods).58 Comparison of inter- and intrachain interaction energies indicates whether energy gains are favorable enough for IDR to bind to a globular protein partner. Although extrapolation of interaction energetics from IDR-protein to IDR-nanoparticle is far from being precise, this analysis at least suggests that IDRs

in nanoparticle-associated proteomes favor interactions with an averaged protein globule (Figure 4h). Together, we conclude that proteins absorbed by nanoparticles are characterized by an increased disorder, suggesting that intrinsic flexibility of IDRs facilitates protein interactions with “smooth” surfaces of nanoparticles. Of note, however, the porosity, as in SiO2-P nanoparticles, and other surface features may also drive associations of ordered, globular proteins with nanoparticles. On average, IDRs are located at the N- and/or C-terminus of a protein sequence (Figure S6), suggesting that protein termini contribute more to protein−nanoparticle interactions. Remarkably, the core nanoparticle-associated proteome is significantly enriched in LCRs, IDRs, and LDRs as assessed by all of the protein disorder criteria (Figures 4 and S6). Spontaneous Aggregation and Nanoparticle-Associated Proteomes. Under certain cellular conditions, proteins containing LCRs and IDRs tend to aggregate.38,50 For example, upon nutrient deprivation or other metabolic stress conditions, RNA binding proteins enriched in LCR and IDR domain transition into a hydrogel-like phase, resulting in formation of stress granules.59 Interestingly, nanoparticle-associated pro1334

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Estimations of protein to nanoparticle stoichiometry suggest that nanoparticles attract a large protein mass to a surface from cytosolic protein extracts. The surface normalized stoichiometry is the most for Au nanoparticles (∼2.7 proteins/nm2) and the least for SiO2-S(P) nanoparticles (∼0.2 proteins/nm2) (Table S2). Except for the SiO2-S(P) nanoparticles, the estimated stoichiometries significantly exceed those, which are expected from monolayer protein packaging and are observed in the in vitro binding experiments with isolated proteins.14,63 At the same time, however, this is consistent with nondenaturing conditions of the pull-downs, under which protein complexes remain largely intact. Thus, the resulting “corona” is thick consisting of macromolecular protein assemblages tethered to nanoparticles via available IDRs (Figure S6f). Alternatively, high protein to nanoparticle ratios might be a result of nanoparticle-induced aggregation of disordered proteins, which otherwise tend to aggregate themselves under low temperature (Figure 5) or form hydrogels under cellular stress.43,59−61 From cytosolic extracts, nanoparticles predominately associate with RNA-processing machinery. Sequestering RNAbinding proteins to a nanoparticle’s surface is expected to affect protein synthesis and intracellular proteomes, inducing a range of cellular responses.35 For example, stress granules comprising low-complexity RNA-binding proteins, which, as we showed, bind to nanoparticles (Figure 3), communicate “a state of emergency” message to cells, triggering a plethora of stress responses. Formation of stress granules inhibits protein translation, thus interfering with cell growth and survival, production of reactive oxygen species, and many other cellular pathways.66 Thus, uncovering of potential intracellular targets for nanoparticles provides a foundation for future studies of the mechanisms of cellular responses to man-made nanomaterials.

teomes overlap significantly with the proteins constituting stress granules in mammalian U-2 OS cells (Figure 3).60 Stress granules assembly dynamics and key protein players are largely conserved between HeLa, U-2 OS, and other cell types, making such proteomic comparisons adequate.61 Thus, we wondered whether LCR- and IDR-containing proteins of nanoparticleassociated proteomes are capable of a spontaneous aggregation. To induce spontaneous aggregation, we incubated precleared cytosolic protein extracts at a low temperature (4 °C) and precipitated high molecular weight protein aggregates by centrifugation. As compared to nanoparticle pull-downs, the amount of precipitated proteins was significantly smaller (compare Figure 5a with Figure 1a). However, mass spectrometry analysis of the spontaneously aggregated proteome revealed significant correlations and overlaps with individual and the core nanoparticle-associated proteomes (Figure 5b,c). Finally, by all criteria, the spontaneously aggregating proteome consists of proteins enriched in LCRs, IDRs and LDRs as compared to a reference set of cytosolic proteins (Figure 5d). Thus, we conclude that nanoparticleassociated proteomes and the spontaneously aggregating proteome are closely related. Both are comprised of disordered RNA-binding proteins, which are sequestered to various RNA granules and stress granules under metabolic stress.50,59,60

CONCLUSIONS In the present study we provide insights into intracellular nanoparticle-associated proteomes. Overall, we show that proteins harboring LCRs/IDRs/LDRs tend to absorb on the surfaces of distinct inorganic nanoparticles under competitive conditions characteristic to a complex composition of cellular proteome. Intrinsically disordered regions are capable of the promiscuous interactions with a diverse set of globular proteins by wrapping, hugging, penetrating, or interwinding.38,62 Entropic penalties associated with the disorder-to-order transition are compensated by their ability to adjust to a structure of the binding partner resulting in the extensive interaction surface and interaction energy gains.38,58 Thus, we propose a potential mechanism for protein−nanoparticle interactions, whereby highly flexible, disordered regions might wrap or hug the surface of nanoparticles. Indeed, in most of the cases, the surface of nanoparticles is relatively smooth, limiting pocket docking of globular proteins. However, in blood, nanoparticles associate with the globular serum albumin proteins triggering their unfolding.43,63 Protein unfolding exposes hydrophobic residues buried inside, leading to stable binding of some globular proteins to nanoparticles.64,65 Thus, there are two considerations that might affect a nanoparticle’s choice for a binding partner. First, for disordered proteins, the interaction energy gains must suffice to compensate for entropic penalties associated with disorder-toorder transitions. Second, for globular proteins, the interaction energy must be sufficient to overcome the unfolding energy barrier. Our results favor the former scenario, while globular proteins might be tethered to a surface of nanoparticle via protein−protein interactions or fall into pores. Although further mechanistic insights for interactions of nanoparticles with disordered proteins are still needed, our proteomics survey of nanoparticle interactions expands a repertoire of their potential protein targets, identifies a set of proteins consistently associated with all studied nanoparticles, and reveals a prevalence of intrinsic protein disorder in nanoparticleassociated proteomes.

MATERIALS AND METHODS Nanoparticles. All nanoparticles used in this study were purchased from the U.S. Research Nanomaterials, Inc. (http://www.us-nano. com/) and are listed in Table S1. Stock solutions were prepared by dissolving nanoparticles in water at concentrations of 1 mg/mL. Nanoparticle solutions were treated by sonication using QSonica Q700 ultrasound homogenizer (QSonica) to achieve colloidal dispersions with a hydrodynamic diameter of ∼100−200 nm, as measured by a dynamic light scattering on Zetasizer NanoZS (Malvern). Extraction of HeLa Cytosolic Proteins. HeLa cells were cultured under standard conditions in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% fetal bovine serum (Thermo Fisher Scientific). Cells were detached by trypsinization and harvested by centrifugation for 5 min at 200 RCF (relative centrifugal force). Cells were washed once in ice-cold phosphate buffered saline (10 mM Na2HPO4; 1.8 mM KH2PO4; 137 mM NaCl; 2.7 mM KCl; pH 7.4) and resuspended in Schaffner’s hypotonic buffer (10 mM 4-(2hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES)-NaOH, pH 7.9; 10 mM KCl; 0.1 mM ethylenediaminetetraacetic acid (EDTA); 0.1 mM ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′- tetraacetic acid (EGTA); 1 mM DTT supplied with cOmplete protease inhibitors cocktail (Roche) and with 0.5 mM of serine protease inhibitor phenylmethane sulfonyl fluoride (PMSF) at 400 μL per 106 cells (chemical abbreviations are listed in the Supporting Information). Cells were swelled on ice for 15 min. Then, nonionic, nondenaturing detergent Nonidet P-40 (octylphenoxypolyethoxyethanol) (NP-40) was added to a final concentration of 0.55%, and cells were vigorously vortexed for 10 s. Nuclei were collected by centrifugation at 4 °C, 18000 RCF for 30 s, while the supernatant was used further as a cytosolic protein fraction. For pull-down experiments, cytosolic extracts were pretreated with a recombinant Benzonase nuclease 1335

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ACS Nano (Sigma-Aldrich) according to manufacturer’s protocol to degrade all forms of RNA and DNA. KCl was added to a final concentration of 100 mM or 400 mM depending on the desired ionic strength. Protein concentration was adjusted to a final concentration of 20 mg/mL. Protein concentration was estimated by Bradford protein assay (BioRad). Nondenaturing Nanoparticles Pull-down with Cytosolic Protein Extracts and Mass Spectrometry Analysis. Cytosolic protein extracts were thawed on ice and precleared by centrifugation at 4 °C, 18000 RCF for 30 min to remove high molecular weight protein aggregates. Nanoparticles were added to 0.5 mL of precleared cytosolic protein extracts at the final concentration of 50 μg/mL and incubated for 1 h at room temperature on a rotating wheel. Protein−nanoparticle complexes were precipitated by centrifugation at 18000 RCF for 10 min. Following extensive washes in Schaffner’s buffer containing 100 mM or 400 mM KCl (depending on the initial ionic strength of cytosolic protein extracts), precipitated proteins were resolved by 10% sodium dodecyl sulphate (SDS) polyacrylamide gel electrophoreses (SDS-PAGE) and visualized with Coomassie stain. Given the fact that cytosolic protein extracts were prepared under nondenaturing conditions, we expect precipitation of largely intact protein complexes by nanoparticles. For the mock pull-downs, precleared cytosolic protein extracts adjusted to 400 mM KCl were incubated on a rotating wheel for 1 h at 4 °C and centrifuged at 4 °C, 18000 RCF for 10 min. Spontaneously aggregated proteins were resolved by 10% SDS-PAGE and visualized with Coomassie stain. Amounts of precipitated proteins in mock and nanoparticles pull-downs were quantified by gel densitometry with ImageJ (https://imagej.nih.gov/ij/) and compared to the 2% of input cytosolic proteins. Nanoflow liquid chromatography-tandem mass spectrometry (nanoflow LC-MS/MS) analysis was performed on a LTQ-Orbitrap XL hybrid ion trap-orbitrap mass spectrometer (Thermo Fisher Scientific) as described67 and according to Erasmus MC Proteomics Center protocols. Detected peptides were matched against the UniProt/Swiss-Prot database (www.uniprot.org) using a Mascot search algorithm (Matrix Science). For each protein, exponentially modified protein abundance indices (emPAI) were determined by Mascot as emPAI = 10Nobsd/Nobsbl − 1; where Nobsd is the number of observed unique peptides, and Nobsbl is the number of all possible unique peptides calculated for each protein (Table S3).68 Protein contents in molar fraction percentages (mol %) were computed from emPAI as mol % = 100 emPAIi/ΣiemPAI; where emPAIi is emPAI value for a given protein (i) in a protein list, and ΣiemPAI is the sum of all emPAI values (Table S4).68 Protein enrichment scores were calculated for each protein as a log2 ratio of molar fraction percentages of protein (i) in a pull-down proteome to /mol %input ). that in the input cytosolic proteome: log2(mol %pull‑down i i All identified cytosolic proteins are listed in Tables S3 and S4, including proteins detected in the input cytosolic protein extract. Structure−Functional Annotation of Nanoparticle-Associated Proteomes. Proteins with the log2 protein enrichment score ≥1.5 were annotated with DAVID (database for annotation, visualization, and integrated discovery) annotation tools.45 DAVID extracts functional and/or structural (protein domain) annotations for every protein in a given input list (nanoparticle-associated proteins) by accessing various databases, such as KEGG (Kyoto Encyclopedia of Genes and Genomes),47 InterPro,46 etc. The number of proteins (m) in the input list belonging to a KEGG-annotated pathway or harboring an InterPro-annotated domain is compared to the number of proteins (M) in a background list belonging to the same annotation. As a background, we used all (3247) proteins identified by the mass spectrometry of the HeLa cytosolic extract and of the high-stringency nanoparticle pull-downs. Fold enrichment (overrepresentation) is given by (m / n) ratio, where n is the size of the input list, and N is the

sented by few proteins and favoring those represented by more proteins. It has to be noted, however, that overrepresentation significances are only indicative, when describing the complex biological functions, as interfering with even a single protein that may cause a substantial impact on all cellular functions. Therefore, we list KEGG-annotated pathways and InterPro-annotated domains extracted with DAVID for nanoparticle-associated proteomes (Tables S5 and S6), regardless of their fold enrichment significances. To simplify presentation, we used a cutoff of m ≥ 5 for individual proteomes (Tables S5a and S6a), and, for the core nanoparticleassociated proteome, we listed KEGG, InterPro entries represented by at least two proteins, m ≥ 2 (Tables S5b and S6b). The complete analysis can be readily reproduced with mol % protein enrichments listed in Table S4 and with the DAVID tools (https://david.ncifcrf. gov/). Prediction of Low Complexity and Intrinsic Protein Disorder in Nanoparticle-Associated Proteomes. LCRs and IDRs, respectively, in protein sequences were predicted with SEG,51 DisEMBL,52 SLIDER,54 and IUPred57 algorithms. We also estimated protein backbone flexibility with DynaMine56 and a propensity of IDRs to interact with globular protein partners with ANCHOR.58 The algorithms are well detailed in the corresponding references, and here we provide their brief description. SEG estimates LCRs based on a compositional complexity (CC) 1 score: CC = L logN Ω , where L is the window size (12 residues), N is the alphabet size (20 amino acids), and Ω is the multinomial i coefficient, Ω = L! /∏ N (ni ! ), where ni is the number of each amino acid of type i appearing in the window L. CC ranges from 0 to 1 with small values corresponding to low complexity sequences. SEG marks LCRs in a protein sequence, and for each protein, we computed the percentage of LCRs from the total protein length. Importantly, as it has been pointed by J. C. Wootton, LCRs often coincide with IDRs.51 SEG is available from (https://ftp.ncbi.nlm.nih.gov/blast/executables/ blast+/) as part of blast+ package. DisEMBL scores IDRs based on artificial neural networks trained on coils, hot loops, or missing coordinates from X-ray structures.52 Coils are defined as any polypeptide conformation not being in any of the three ordered states (α- or 310-helices or β-sheet), according to the dictionary of protein secondary structure (DSSP).70 Thus, although coils do not necessarily represent IDRs, all IDRs should be contained within coils. Hot loops are defined as flexible coils with large temperature factors of Cα backbone carbons (B-factor): B = 8π2u2, where u2 is the mean square displacement of the Cα. B-factors are available from X-ray structures in Protein Data Bank (PDB). Missing coordinates represent nonassigned electron densities in PDB, which in most cases are due to increased flexibility of associated polypeptides, and are listed in remark 465 (REM465) entries. Thus, DisEMBL predicts flexible regions (hot loops, REM465) lacking regular secondary structure (coils), which are indicative of IDRs in a protein sequence. For each protein sequence, we computed the percentage of coils, hot loops, and missing coordinates predicted by DisEMBL from the total protein length. DisEMBL is available for download from (http://dis.embl.de/).52 SLIDER (superfast predictor of proteins with long intrinsically disordered regions) employs a similar approach as DisEMBL and predicts LDRs from the logistic regression model trained on a mixed disorder (MxD) data set of the disordered proteins with low pairwise sequence identities.53,54 LDRs are defined as protein domains composed of ≥30 consecutive disordered residues. For each protein sequence, SLIDER computes the probability of LDR occurrence. SLIDER is available for download from (http://biomine-ws.ece. ualberta.ca/SLIDER/).54 DynaMine predicts S2 order parameter for the N−H bond in protein backbone.56 To this end, the linear prediction model is trained on S2 values extracted from NMR chemical shifts. S2 order parameter is related to the rotational angle of N−H bond vector as 2 cosθ·(1 + cosθ) ⎤ 2 S2 = ⎡⎣ ⎦ . Thus, small S values (large θ) point to flexible 2 protein domains, while large S2 values (small θ) indicate ordered

(M / N )

size of the background list. A significance of the fold enrichment is assessed by an EASE score.69 The EASE score is computed as onetailed Fisher exact probability for (m − 1) entries in the input list, thus penalizing the significance of functional/structural categories repre1336

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ACS Nano protein domains. Increased polypeptide chain flexibility is viewed as one of the distinctive features of IDRs. DynaMine predicted S2 values of ≤0.7 represent IDRs,56 and for each protein sequence, we computed the percentage of IDRs from the total protein length. DynaMine is available for download from (http://dynamine.ibsquare. be/).56 IUPred (intrinsically unstructured proteins prediction) identifies IDRs based on the estimation of intrachain pairwise interaction energies.57 For each amino acid of type i at kth position, the intrachain j interaction energy can be locally approximated as Eik = ∑20 Pijf j (w),

*E-mail: [email protected]. ORCID

Yuri M. Moshkin: 0000-0003-2964-8823 Author Contributions

A.V.R., D.V.P., L.A.G., Y.M.M., and M.P.M. designed the experiments; A.V.R., T.W.K., and D.V.P. performed the experiments; L.A.G., Y.M.M., and M.P.M. analyzed the data; and Y.M.M. and M.P.M. wrote the manuscript.

where f i(w) is the frequency of each of the 20 amino acids of type j within window of size w spanning the kth position, and P is the energy predictor matrix. The P matrix is estimated from the measured pairwise interaction energies Mij between amino acids of types i and j for a set of globular proteins. The resulting position-specific predictions of intrachain interaction energies are averaged over a sliding window of 21 residues and normalized to fall between [0,1]. IUPred score of >0.5 is indicative of IDRs,57 and for each protein sequence, we computed the percentage of IDRs predicted by IUPred from the total protein length. IUPred is available for download from (http://iupred.enzim.hu/).57 ANCHOR extends the IUPred algorithm by estimating the energy gains from intermolecular interactions of IDRs with a globular protein, as compared to intrachain folding.58 To this end, the position-specific j intrachain interaction energy Eiint , k = ∑20 Pijf j (w) is calculated as

Notes

above and compared to interchain interactions with a globular protein j Eiglob = ∑20 Pijfglob, ̅ j using averaged frequencies of amino acids for

REFERENCES

The authors declare no competing financial interest.

ACKNOWLEDGMENTS Laboratories of Peter Verrijzer and Jeroen Demmers from the Erasmus Medical Center are thankfully acknowledged for fruitful collaboration on proteomics. Computational resources were provided by the Federal Research Center, Institute Cytology and Genetics. We are also thankful to Olga Derkatch for help in preparing the manuscript. This work has been supported by grant from the Russian Science Foundation RNF (grant no. 14-14-00221).

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globular proteins ( fglob,j ̅ ). The resulting energy gain (Egain,k = Eint,k − i i 58 ) indicates a propensity of IDR to bind to a globular protein. For Eglob i each protein sequence, we computed the percentage of IDR interacting domains predicted by ANCHOR from the total protein length. ANCHOR is available for download from (http://anchor. enzim.hu/).58 Combined these methods represent a set of independent predictions of protein disorder from a given amino acid sequence. All statistical analysis was done in R (https://www.R-project.org/).

ASSOCIATED CONTENT S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsnano.6b05992. Characteristics of nanoparticles (Table S1); stoichiometry of proteins-nanoparticle interactions (Table S2); zeta-potential of nanoparticles as a function of ionic strength (Figure S1); Venn diagrams of nanoparticleassociated proteomes (Figure S2); mass spectrometry analysis of nanoparticle-associated proteomes under low ionic strength (Figure S3); structure−functional annotation of the core nanoparticle-associated proteome (Figure S4); nanoparticle-associated proteomes also overlap significantly with HEK293 mRNA-bound proteome (Figure S5); distribution of IDRs along nanoparticle-associated proteins (Figure S6) (PDF) Lists of emPAI scores (XLSX) Protein contents in molar fraction percentages for all cytosolic proteins determined in this study and proteins associated with nanoparticles (XLSX) InterPro-annotated protein domains (XLSX) KEGG-annotated biological pathways overrepresented in nanoparticle-associated proteomes (XLSX)

AUTHOR INFORMATION Corresponding Authors

*E-mail: [email protected]. 1337

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