Peptide Copolymers - American Chemical Society

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Biomacromolecules 2008, 9, 724–732

Monomolecular Assembly of siRNA and Poly(ethylene glycol)-Peptide Copolymers Jason DeRouchey,*,†,⊥ Claudia Schmidt,† Greg F. Walker,‡ Christian Koch,4 Christian Plank,4 Ernst Wagner,‡,§ and Joachim O. Rädler†,§ Department of Physics, Department of Chemistry and Pharmacy, and Center for NanoScience, Ludwig-Maximilians-Universität, 80539 Munich, Germany, Institute of Experimental Oncology, Technische Universität, 81675 Munich, Germany, and National Institutes of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 20692 Received October 16, 2007; Revised Manuscript Received November 30, 2007

In this work, we design and investigate the complex formation of highly uniform monomolecular siRNA complexes utilizing block copolymers consisting of a cationic peptide moiety covalently bound to a poly(ethylene glycol) (PEG) moiety. The aim of the study was to design a shielded siRNA construct containing a single siRNA molecule to achieve a sterically stabilized complex with enhanced diffusive properties in macromolecular networks. Using a 14 lysine-PEG (K14-PEG) linear diblock copolymer, formation of monomolecular siRNA complexes with a stoichiometric 1:3 grafting density of siRNA to PEG is realized. Alternatively, similar PEGylated monomolecular siRNA particles are achieved through complexation with a graft copolymer consisting of six cationic peptide side chains bound to a PEG backbone. The hydrodynamic radii of the resulting complexes as measured by fluorescence correlation spectroscopy (FCS) were found to be in good agreement with theoretical predictions using polymer brush scaling theory of a PEG decorated rodlike molecule. It is furthermore demonstrated that the PEG coating of the siRNA-PEG complexes can be rendered biodegradable through the use of a pH-sensitive hydrazone or a reducible disulfide bond linker between the K14 and the PEG blocks. To model transport under in vivo conditions, diffusion of these PEGylated siRNA complexes is studied in various charged and uncharged matrix materials. In PEG solutions, the diffusion coefficient of the siRNA complex is observed to decrease with increasing polymer concentration, in agreement with theory of probe diffusion in semidilute solutions. In charged networks, the behavior is considerably more complex. FCS measurements in fibrin gels indicate complete dissociation of the diblock copolymer from the complex, while transport in collagen solutions results in particle aggregation.

Introduction Silencing of gene expression by the RNA interference (RNAi) mechanism in a sequence-specific manner offers possibilities for applications in therapy of diseases that arise from the expression of a pathogenic gene or the overexpression of a gene.1–7 In RNAi, small double-stranded RNAs silence gene expression via several mechanisms: by targeting mRNA for degradation, preventing mRNA translation, or by establishing regions of silenced chromatin. Although targeting specificity must be improved, the prospect of specifically suppressing the expression of disease-causing genes has generated a lot of enthusiasm for developing RNAi-based therapies. Therapies integrating RNA interference rely on the efficient delivery of short interfering RNA (siRNA) to target cells in vivo. So far, the delivery of siRNA to eukaryotic cells in vitro has been achieved following transfection methods developed for nonviral delivery of plasmid DNA. Most efficient among commercially available transfection reagents are cationic lipid formulations.8–10 To date polymer-based delivery systems seem to be less efficient for siRNA delivery. Although plasmid complexes and siRNA complexes share many of the known physical barriers to efficient * Corresponding author. E-mail: [email protected]. † Department of Physics, Ludwig-Maximilians-Universität. ‡ Department of Chemistry and Pharmacy, Ludwig-Maximilians-Universität. § Center for NanoScience, Ludwig-Maximilians-Universität. 4 Institute of Experimental Oncology, Technische Universität Munich. ⊥ National Institutes of Child Health and Human Development.

delivery, there are subtle differences due to their differing chemistry and size that will ultimately need to be addressed for an optimized design. To enhance delivery efficiency and to overcome systemic barriers, artificial gene delivery complexes are proposed that provide for the efficient packing, shielding, and targeting of nucleic acids.11,12 For in vivo applications, gene delivery systems need to be optimized for efficient transport to specific target tissue, cell binding, and entry through specific cell receptors and successful release into the cytoplasm and nucleus. Furthermore the unpacking and activation of the genetic material needs to be ensured. A molecular architecture that achieves all the requirements will most likely consist of a viruslike layered structure of several components. Polycationic materials can protect nucleic acids from degradation and facilitate entry into the target cells. For systemic applications, unspecific interactions of the cationic complex can occur with negatively charged serum proteins and other blood components. By covalently binding poly(ethylene glycol) (PEG) to the polycation, undesired effects such as immune response, unspecific interactions, and degradation can be greatly reduced. PEG can shield and stabilize the complex and is biocompatible. In a rational design approach for artificial viruses, PEGylation can be implemented by using PEGylated components in the initial complex formation. Alternatively, PEG shielding can be applied to preformed complexes in a secondary processing step by using either self-assembly or chemical grafting.13–15 While PEGylation is a necessity to improve extracellular stability and

10.1021/bm7011482 CCC: $40.75  2008 American Chemical Society Published on Web 01/26/2008

Monomolecular siRNA Complexes

circulation half-life, it often decreases the transfection efficiency due to reduced specificity and inhibiting cell association and uptake. Furthermore, PEGylation can hinder endosomal release and hence lessen the efficiency at a later phase of the entry pathway.16 A pH-cleavable PEG shielding is specifically designed to strip the protective shielding during endosomal uptake, which is associated with a pronounced acidification. Uptake of plasmid delivery particles is most efficient via receptor-mediated endocytosis. Lipofectamine-mediated siRNA delivery also follows the endocytotic pathway. However, siRNA complexes by the size of the siRNA itself could be of considerably smaller size than plasmid DNA particles. Recently, Nakamura et al. reported uptake of siRNA nanoparticles via macropinocytosis.17 A smaller size of gene delivery vehicles presumably also enhances extracellular transport in vivo. Gene complexes diffuse to their target sites within cells and the extracellular matrix (ECM) in a crowded, interacting environment of biomacromolecules. For in vivo applicability, both the transport properties and efficient transfer of the gene delivery complexes to the target tissue are crucial. Monomolecular gene delivery vehicles, i.e., a carrier with a single nucleic acid molecule encapsulated, represent the ultimate solution as far as the smallest possible size is concerned. On the other hand, stability and functionality inside and outside of the cells are important. Dedicated experiments of gene carriers in gels and complex biological fluids such as blood plasma, extracellular matrix components, glycoproteins, and cellular cytoplasm are vital to gaining insight into transport and interactions in biological systems.18,19 In this paper, we demonstrate the feasibility of forming PEGshielded monomolecular siRNA complexes. Two copolymer constructs, a linear and a branched version consisting of a short cationic polypeptide and a PEG moiety, are compared. The hydrodynamic size and number density are measured with FCS and are shown to be in good agreement with a micellar-type decorated complex. The measured hydrodynamic radius is compared to the predicted value based on a theoretical polymer brush model. Last, the stability of the novel complexes is tested in PEG, collagen, and fibrin networks. While siRNA particles diffuse freely in uncharged networks of PEG without aggregation, the diffusion in the collagen and fibrin networks exhibit adverse and destabilizing interactions and warrants further investigations.

Materials and Methods Materials. A 14 lysine-cysteine peptide (K14-Cys) was custom synthesized by GenScript Corporation (Piscataway, NJ). siRNA and siGLO (Cy3-siRNA) were purchased from Dharmacon. Monofunctional polyethylene glycol (PEG) derivatives of maleimide (PEG-MAL), ortho-pyridyldisulfide (PEG-OPSS), and butyraldehyde (PEG-ALD) of 5000 kDa were purchased from Nektar therapeutics (Huntsville, AL). Maleimido-6-hydraziniumpyridine (MHPH) was purchased from Solulink Inc. (San Diego, CA). Gel filtration media Superdex 75 Prep grade and Sephadex G25 Superfine were obtained from Amersham Biosciences (Piscataway, NJ). Dithiothreitol (DTT) was purchased from Sigma-Aldrich (Steinheim, Germany) and used as received. Collagen and PEG (Mw ) 35 kDa, PEG35) were purchased from Fluka (collagen from calf skin, soluble, BioChemika). Synthesis of K14 Copolymers. K14Cys (12.5 µmol) was dissolved in 1 mL of 20 mM Hepes buffer pH 7.4 and stored at -80 °C. The amount of mercapto groups was determined by using the Ellman’s assay.20 The amount of K14Cys was quantified by the 2,4,6-trinitrobenzenesulfonic acid (TNBS) assay using the K14Cys stock as a standard.21 Stocks of PEG-ALD (2 µmol/mL) and MHPH (50 µmol/mL) were

Biomacromolecules, Vol. 9, No. 2, 2008 725 prepared in anhydrous DMSO and were combined at 1 and 4 µmol, respectively. The solution stood at room temperature in the dark for 3 h. The reaction mixture was separated on a Sephadex G-25 gel filtration column equilibrated in 20 mM Hepes (pH 7.4) containing 0.25 M NaCl at a flow rate of 1 mL/min, and the product (PEG-HZNMAL) was collected in the void fractions. For copolymer synthesis, K14Cys (0.6 µmol) was mixed with 0.2 µmol of PEG-HZN-MAL, PEG-MAL, or PEG-OPSS prepared in 1 mL of 20 mM Hepes buffer pH 7.4 (all PEGs used in K14 copolymer synthesis were 5 kDa). All reactions were mixed in the dark; the maleimide PEG derivatives were mixed for 10 min, while the OPSS derivative was mixed for 1 h. The PEG conjugates were subsequently purified from unreacted K14Cys by applying the reaction to a gel filtration column of Superdex G-75 HR 10/30 column with a running buffer of 20 mM Hepes pH 7.4 containing 0.25 M NaCl at a flow rate of 1 mL/min. Void fractions containing the PEGylated copolymers were collected. K14-HZN-PEG was immediately stored at -80 °C, the K14S-PEG (referred throughout the text as simply K14-PEG), and K14-SSPEG were dialyzed overnight against deionized water then lyophilized and stored at -80 °C. Synthesis of the P6-SV202 Copolymer. The peptide SV202 (PKKKRKVG)2 C was synthesized22 by solid-phase peptide chemistry using a chlorotrityl chloride resin and the FastMoc protocol with an Applied Biosystems 431A automated peptide synthesizer. The peptides were purified by reverse phase HPLC. The copolymer intermediate P6TP and the final product P6-SV202 were obtained in several steps by copolymerizing O,O′-bis(2-aminopropyl)polyethylene glycol 6000 (Fluka) with 3-(2′-thiopyridyl)-mercaptopropionyl-glutamic acid and reacting the purified intermediate P6-TP with the peptide SV202, respectively. The basic synthetic protocol has been described in detail elsewhere.23 Briefly, the intermediate product P6-TP was purified from the copolymerization mixture using a Superdex HiLoad 26/60 Superdex 75pg column (GE Healthcare, Freiburg, Germany) with PBS as elution buffer at 4.4 mL/min flow rate. Pooled product fractions were reduced in volume by ultrafiltration. P6-TP was mixed with a 1.2-fold excess of SV202 (excess referring to thiol groups in the peptides over thiopyridine groups in P6-TP). The final product P6-SV202 was obtained upon size exclusion chromatography (Superdex HiLoad as described above), volume reduction by rotary evaporation, and extensive dialysis against water in Slide-A-Lyzer cassettes (MWCO 10 kD; Pierce, Perbio Science, Bonn, Germany). The concentrations of the copolymercoupled peptides were determined by a ninhydrin assay using the free uncoupled peptides as standards. Preparation of Copolymer/siRNA Polyplexes. cy5-siRNA was made in house from unlabeled siRNA labeled directly with the Mirus Bio-Labeling kit. For both labeled siRNAs (siGLO and cy5-siRNA), the labeling efficiency is generally one label for every 20–60 base pairs or approximately one dye per siRNA on average. For FCS, 0.1–0.2 µg of labeled siRNA was mixed with the corresponding copolymer concentration to achieve the desired molar ratio of the total number of amino groups in the polymer to the total number of siRNA phosphates (N/P) charge ratio. These samples were subsequently diluted in Hepes (pH 7.4) or TE (10 mM Tris, 1 mM EDTA, pH 7.6) buffer to a final volume of 300 µL. Complexes were vortexed briefly, spun down, and incubated at room temperature for at least 15 min before measurements. Preparation of PEG, Collagen, and Fibrin Solutions. PEG35 stock solutions of 50 mg/mL were prepared in 20 mM Hepes buffer (pH 7.4), mixed thoroughly, and incubated at room temperature for at least 4 h before use. Subsequent dilutions with 20 mM Hepes were made from the stock solution, resulting in final concentrations with nanoparticles of 8–42 mg/mL. The corresponding macroscopic viscosities ηP of the PEG solutions were measured at 21 °C using a capillary automatic microviscometer (AVM, Anton Paar). Collagen stock solutions of 5 mg/mL were prepared in 20 mM Hepes using type I collagen derived from calf skin (Fluka). The solution was vortexed and incubated at 37 °C for 5–10 min and then incubated at room temperature for 2 h. Solutions were diluted from the 5 mg/mL

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stock solution to give concentrations ranging from 0.25 to 5 mg/mL (final concentrations with nanoparticles: 0.2–4 mg/mL). Salmon fibrinogen and thrombin were extracted and purified as described previously.24–26 Fibrinogen was dissolved in 50 mM Tris, 150 mM NaCl, pH 7.4, and thrombin was dissolved in 20 mM Tris, 1 M NaCl, pH 7.4. For FCS experiments, 50 µL of siRNA nanoparticles were prepared and mixed with 200 µL of fibrinogen (concn 5 mg/ mL). Fibrinogen was polymerized in an Ibidi chamber (ibidi, Munich, Germany) at 21 °C by adding 50 µL of 5 U/mL thrombin. FCS Setup. FCS experiments were performed using a commercially available FCS setup (Carl Zeiss, Germany), consisting of an Axiovert 200 inverted microscope equipped with the Confocor 2 module. A Zeiss C-Apochromat 40×, NA 1.2 water immersion objective was used. The illumination source was a HeNe laser (543 nm, 1 mW) for cy3-siRNA (siGLO) with emission passed through a 560–615 nm band pass before detection. For cy5-siRNA, samples were run with a HeNe laser (633 nm, 5 mW) and emission was passed through a 650 nm long pass before detection. Samples were pipetted directly into 8 well Laboratory-Tek chambers (Nalgene Nunc International, Rochester, NY) or 8 well plasma treated ibidi µslides (Ibidi, Munich Germany). Determination of the focal volume was established via calibration against an aqueous solution of 10 nM Rhodamine 6G or Cy5 before each data acquisition. All FCS results are an average of 10–50 measurements, with sampling times ranging from 20 to 60 s. For FCS experiments in network solutions, 50 µL of siRNA complexes were prepared and added to 250 µL PEG, fibrin, or collagen solutions, pipetted up and down, or vortexed and allowed to equilibrate for 2 h before measurements. FCS Measurements. As FCS is an established optical technique,27 here we will give only a brief overview. The raw signal in an FCS experiment is the time-dependent fluorescence intensity emitted by labeled objects diffusing through a highly focused illuminated volume (∼1 fL). The size of the effective illumination volume is fixed by the confocal detection optics and the excitation profile of the focused laser beam and characterized by measurements against a standard of a known diffusion constant. As labeled particles diffuse through the detection volume, a signal I(t) is generated with fluctuations around a mean value 〈I(t)〉 ) I(t) - δI(t). For identical fluorescent particles undergoing ideal Brownian diffusion, dynamic information can be determined from the intensity fluctuations by means of a time autocorrelation given by

G(τ) ) 1 +

1 × gdiff(τ) N

(1)

with

(

gdiff(τ) ) 1 +

τ τD

)( -1

1+

τ B τD 2

)

-1⁄2

(2)

Here, N denotes the number of particles per effective volume, and the structure parameter B ) zo/ωo is the ratio of the axial to radial dimensions of the focused excitation beam as determined by calibration measurements (B was approximately 5–7 in our experiments). The diffusion time τD is related to the translational diffusion coefficient D by the simple relationship τD ) ω02/4D. In general, one also sees correlations arising from intramolecular dynamic processes involving dark states, such as triplet states, or from experimental afterpulsing.27,28 Assuming that these processes are well separated in the time domain, the full autocorrelation function can then be written as

G(τ) ) 1 +

1 × gdiff(τ) × gtr(τ) N

(3)

where gtr(τ) ) [1 + exp(-τ/τT)T/1-T)] is the autocorrelation function related with dark fractions, T, and characteristic flickering times, τT, typically observed to be of the order of 2 µs, thus well separated from the time scales of interest in these experiments. In dilute solution, the autocorrelation can then be normalized by

Gnorm(τ) )

G(τ) - 1 G(0) - 1

(4)

For spherical particles with hydrodynamic radius RH smaller than the detection volume (RH , ωo), the hydrodynamic radius, RH, can be calculated from the Stokes–Einstein relation

RH )

kBT 6πηDt

(5)

where η is the solvent viscosity, kB is the Boltzmann constant, and T is the temperature.

Results and Discussion Monomolecular siRNA Complexes. Let us consider a simple schematic for controlled self-assembly of monomolecular siRNA nanoparticles. By using a diblock copolymer where one block is positively charged and the other block is an uncharged shielding block, one can envision building a complex as depicted in Figure 1. Here, we expect self-assembly is driven by electrostatics. If the charged block is kept sufficiently short, to ensure a high grafting density of PEG chains along the surface area of the siRNA, a “decorated rod” particle is created using the stiff double-stranded siRNA as a molecular scaffold. This approach to a controlled self-assembly of monomolecular complexes was earlier shown to be successful, with linearized DNA fragments of various lengths and short poly(ethylene imine)s linked to 20 kDa PEGs.29 Here, we have directly controlled the complexation through the use of a specific peptide block length (14 lysines) covalently bound to a 5 kDa PEG. As depicted, such a system would be expected to bind 3 copolymer chains to every siRNA to achieve electric neutrality. Block copolymers are useful constructs, as a variety of chemistries allows for the controlled release of the PEG through cleavable linking groups (Figure 1B), such as disulfide and hydrazone linkers, as well as the potential of adding external targeting on the free end of the shielding block. Complexation with the repeating copolymer P6SV202, consisting of 6 kDa PEG blocks integrated with a 16 amino acid peptide unit, is predicted to form a “loop” complex (figure 1C). Here each peptide repeat consists of 10 charged amines at neutral pH, thus one copolymer chain can bind a single siRNA, resulting in a slight overcharging of the complex and 6 bound PEGs. FCS Measurements of siRNA Complexes. FCS measurements were performed on complexes of siRNA with K14-PEG and P6SV202 copolymers at various N/P charge ratios in Hepes buffer at room temperature (Figure 2 and 3, respectively). Normalized autocorrelation curves for cy5-siRNA complexed with K14-PEG for 8 different N/P ratios from 0 to 8 in increments of 0.5 are shown in Figure 2. Identical results were obtained for cy3 labeled siRNA in TE or Hepes buffer. All autocorrelation curves can be described by a single mean passage time with a shift to longer diffusion times with increasing copolymer concentration. This increase continues up to N/P ∼ 2, which we associate with a fully PEGylated siRNA, where no additional copolymer chains can bind and the corresponding translational diffusion coefficient reaches a minimum plateau. Comparing 50 single measurements for each N/P ratio shows diffusion times are highly constant between batches and over time (at least 3 h), suggesting very stable monodisperse samples. Fluctuations in τdiff < 10% were observed. The diffusion coefficient as a function of N/P ratio is plotted in Figure 2B. The hydrodynamic radius (RH) can be determined from eq 5 as RH ) 5.6 nm for K14-PEG/siRNA at N/P 2. Although PEG shielding is often used to suppress


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Figure 1. Schematic drawing of the self-assembly of siRNA diblock-copolymer nanoparticles (referred to as “decorated rods”). (A) siRNA nanoparticle consisting of one siRNA and three K14-PEG diblock copolymers (N/P charge ratio 1). Each + or - in the drawing stands for seven charges. (B) siRNA nanoparticle consisting of one siRNA and three K14-HZN-PEG of K14-SS-PEG diblock copolymers, which both contain cleavable linkers between the peptide and PEG blocks (represented as circles). (C) siRNA nanoparticle consisting of one siRNA and one P6SV202 diblock copolymer. The P6SV202 molecule consists of six PEG units (P6) and six charged peptide side units (SV202: (PKKKRKVG)2 C). Here, one - in the drawing stands for seven negative charges, one + stands for 10 positive charges.

aggregation in polyplexes resulting from unspecific interactions with salt or proteins, typical polyplex formulations can still show appreciable aggregation at physiological salt for typical polyplex formulations. Because our monomolecular siRNA complexes have significantly higher PEG density, we should therefore expect increased particle stability. To investigate this, K14-PEG/ siRNA complexes (N/P 2) were investigated as a function of additional NaCl concentration up to 150 mM by FCS. Complexes remained highly stable with no discernible aggregation over the whole range of salt concentrations investigated. Figure 3 shows the normalized autocorrelation curves and translational diffusion coefficients for siRNA complexed with P6SV202 with N/P charge ratios ranging from 0 to 10. Similarly, P6SV202 complexes plateau to a minimum diffusion coefficient at N/P ∼ 2, which we associate with a complete saturation of the siRNA charge. Analogous results are obtained whether mixing directly to the desired N/P or adding stepwise titration of copolymer for both systems. An advantage of FCS is it not only provides diffusion data for the complexes but also gives information related to the average number of particles in the system. Upon complexation, both types of siRNA nanoparticles show an ∼20% decrease in N, however, the complexes show highly uniform diffusion times while the counts per molecule remain constant, suggesting only one siRNA has been incorporated into the complex. While some minor aggregation is possible, the decrease in N may also be due to an influence of the copolymer on the fluorescence properties of the label on the siRNA. As discussed below, we later show that upon cleaving the PEG from K14-HZN-PEG/ siRNA complexes we regain this ∼20% in N, strongly cor-

roborating that the particles formed here result in predominantly monomolecular siRNA complexation. For comparison, we also studied complexation of siRNA with a longer polycation (22 kDa linear poly(ethylene imine) (PEI22)) and LF2000, a lipofectamine typically used for knockdown formulations (data not shown). PEI22/siRNA results in fairly uniform aggregates with RH ∼60 nm, while LF2000/siRNA results in very large aggregates of the order of a micrometer, consistent with previously published results.30,31 Because of the incorporation of multiple siRNA per aggregate, both systems show large excitation bursts in the count rate. As a rough estimate of siRNAs per particle, the corresponding decrease in N in our experiments suggests lPEI22/siRNA incorporates ∼20 siRNA particles per aggregate. This is in reasonable agreement with ∼40 siRNA per aggregate estimated from the previously measured close packing distance of 3 nm from small-angle X-ray scattering experiments on PEI/DNA complexes.32 LF2000/ siRNA incorporates hundreds of siRNA per micrometer-sized aggregate. Predicting Particle Size with Polymer Brush Theory. To model our PEGylated siRNA particles, we consider the schematic shown in Figure 1A in terms of polymer brush scaling theory, an approach referred to as a “decorated rod” model described previously.29 Simple scaling arguments to determine static and dynamic properties of polymers adsorbed or endgrafted to a planar surface were first derived by Alexander and de Gennes.33,34 Two distinct regimes are observed depending on the grafting density of chains on the surface Γ. If we consider a polymer chain in solution, its dimensions can be described by the Flory radius RF ∼ an3/5, where n is the degree of


DeRouchey et al.

Figure 2. (A) Normalized FCS autocorrelation curves of siRNA/K14PEG nanoparticles at eight different N/P charge ratios (represented by differently dotted lines). (B) Variation of the diffusion coefficient as a function of increasing N/P charge ratio. The dashed line is drawn to guide the eye.

polymerization and a is the effective monomer length. With a uniform grafting density, the average distance between polymer chains on the surface dp ∼ Γ -1/2. If dp is large compared to RF, polymers adsorb on the surface as “mushrooms” with dimensions comparable to RF. As the grafting density increases, the chains come into close contact and are forced to interact. Because of excluded volume interactions, the chains stretch away from the surface, resulting in the formation of a “brush” layer. The equilibrium polymer brush height, H0, is therefore simply understood as a balancing between these enthalpic and entropic terms. Assuming a relatively dense, strongly stretched brush regime on a planar surface, it can be shown that H0 scales as ndp-2/3. Extending this scaling for the case of polymers attached to a convex surface of radius R, it can be shown that H0 scales as35

() ()

H0 ) an3⁄d+3

dp a

-2⁄d+3

R a

d⁄d+3

(6)

where the dimensionality constant d ) 0, 1, or 2 for planar, cylindrical, or spherical surfaces, respectively. Clearly the siRNA molecule is best represented as a rodlike object. However, it can be shown that, within the context of polymer brush scaling theory, in our system, the discrepancy between the brush height H0 for a spherical surface and for a cylindrical surface is negligible (for the siRNA/K14-PEG complex H0 ) 4.8 nm for a cylindrical surface vs 4.6 nm for a spherical surface, siRNA/P6SV202 H0 ) 3.88 and 3.89 nm,

Figure 3. (A) Normalized FCS autocorrelation curves of siRNA + P6SV202 nanoparticles for seven different N/P charge ratios (represented by differently dotted lines). (B) Variation of the diffusion coefficient as a function of increasing N/P charge ratio.

respectively). As siRNA is only 7 nm in length and 2 nm in diameter, upon PEGylation, the particle dimensions are deviating from cylindrical and approaching spherical dimensions, thus we adapt the spherical model, using a sphere of identical surface area to siRNA, for the theoretical calculations as well as for the calculation of the hydrodynamic radius from the experimental data. Using an effective monomer length a of 0.35 nm for PEG,36 a 5 kDa PEG chain has a RF ≈ 6 nm. Assuming charge neutralization, we expect 3 PEGs per siRNA corresponding to a PEG grafting density of ∼1 chain/15 nm2 or a dp ) 3.9 nm between PEG chains. As dp < RF, this system is expected to be commensurate with a brush regime. The equilibrated brush height can therefore be calculated for the siRNA/K14- PEG complex as ∼4.6 nm, giving a full radius of the nanoparticle of rparticle ) 6.6 nm. This is in good agreement with the measured RH of 5.6 nm for PEGylated siRNA complexes at N/P 2 obtained from FCS measurements. Similarly, we can calculate the theoretical size of the nanoparticles obtained from complexing siRNA with P6SV202. With 6 PEGs (each 6 kDa) per siRNA molecule, dp is decreased to 2.8 nm. The PEG chains of the P6SV202 copolymer are attached to the surface with both ends and form “loops” (see figure 1C). For the calculation of the theoretical size of the particles, therefore, a degree of polymerization of N/2 is used. This gives an expected H0 ) 3.9 nm and an expected particle radius of rparticle ) 5.9 nm comparable to the RH obtained from FCS that gives RH ) 5.3 nm upon full decoration.


Figure 4. (A) Diffusion coefficient of siRNA/K14-SS-PEG nanoparticles as a function of N/P charge ratio. (B) Comparison of diffusion coefficient of various stoichiometric mixtures of siRNA with cleavable K14-SS-PEG and noncleavable K14-PEG conjugates (noncleavable) before and after cleaving with DTT. (C) FCS autocorrelation curves for siRNA and siRNA/K14-HZN-PEG complexes before and after cleaving. Here cleaving was achieved at neutral pH after several hours.

Stoichiometric Complexes with Cleavable Diblocks. Diffusion properties of cleavable diblock systems were also studied and are shown in Figure 4. Here, two types of cleavable K14PEG systems were used. First, a disulfide linkage was created between the lysine and PEG blocks (K14-SS-PEG), which can be cleaved through the use of a reducing agents such as DTT. Second, a hydrazone based linkage was used (K14-HZN-PEG) that can be cleaved via a lowering of pH. The hydrazone linker


has previously been shown useful in poly(ethylene imine)-HZNPEG complexes by increasing transfection efficiencies in a variety of cell lines including K562, Neuro2A, and Huh7.37 Figure 4a shows the decrease in the translational diffusion coefficient with increasing N/P ratio for K14-SS-PEG, which again plateaus at N/P ∼ 2 and gives an identical hydrodynamic radius as the K14-PEG particles. Similar results were observed with the K14-HZN-PEG system. Figure 4B shows stoichiometric mixtures made by combining the K14-PEG and K14-SS-PEG systems to give various ratios of cleaving to noncleaving copolymer chains per particle. All complexes were mixed at N/P 2 and allowed to equilibrate for 15 min before each set of measurements. As shown, upon full PEGylation, all the stoichiometric complexes show good agreement in D. After 30 min, a 50-fold excess of DTT was added to cleave the PEG block from the disulfide linked copolymer. After cleaving, no aggregation was observed and the particles showed wellbehaving diffusion described by a single τD. The average translational diffusion coefficient increased correspondingly to the stoichiometric mixture with a linear increase in D with increasing average number of bound cleavable PEGs per siRNA. siRNA bound purely with K14-SS-PEG showed nearly identical diffusive properties as naked siRNA after cleaving. Similar results were obtained with the K14-HZN-PEG system. The autocorrelation curves of siRNA before and after complexation with K14-HZN-PEG at N/P 2 are shown in Figure 4C. Upon reducing the pH, or equivalently long incubation times at room temperature in neutral pH, the PEG is observed to cleave and the autocorrelation curve is nearly identical to the initial naked siRNA autocorrelation curve. Note that the number of particles, as measured by the inverse of the autocorrelation curve plateau regime, is recovered, strongly suggesting that the resulting PEGylated complexes consist of only one siRNA per particle before and after cleaving. Modeling Probe Diffusion in Polymer Solutions. Concepts of diffusion theory and polymer physics can be applied to describe the diffusion in a ternary system consisting of a probe particle suspended in a solution of macromolecules and a solvent.38 The theoretical models are based on different physical concepts such as obstruction effects, hydrodynamic interactions, and free volume effects with no agreement yet on which aspects most affect probe diffusion. The diffusing particles have been referred to as probes because their diffusion behavior may provide information about microstructure and dynamics of the polymeric solution in which they are dispersed. In pure solvents, diffusion of probe molecules of radius Rp is understood through the Stokes–Einstein (SE) relation given in eq 5. Implicit in the SE relation is the assumption that the dispersing fluid is a homogeneous, continuum medium described by a single viscosity η. Such an assumption is not necessarily valid for a semidilute polymeric solution. For an ideal polymer solution, the semidilute regime is defined when the polymer concentrations are above the overlap concentration, c*, and chains start to overlap with each other. The overlap concentration can be determined experimentally with knowledge of either the polymer chain radius of gyration Rg or the intrinsic viscosity [η] through the relation c*) MW/(4π/3)NARg3 ) 1/[η]. The thermodynamic behavior of semidilute solutions is governed by the polymer–polymer correlation length ξ, which reflects the average distance between polymer entanglements of a fluctuating polymer network. Here, ξ can be calculated from prior knowledge of both Rg and c* as ξ ) Rg(c*/c)3/4.39–41 For probe diffusion, three regimes can be identified depending on the ratio of probe size, Rp, to the correlation length, ξ. When Rp/ξ . 1,


DeRouchey et al.

the probe cannot penetrate the network and diffusion is often simply described by SE with a single viscosity characteristic of the polymer/solvent solution. In contrast, when Rp/ξ , 1, the probe is so small it detects only the pure solvent and is unimpeded by the presence of the polymer network. When Rp/ξ ∼ 1, the probe sees an inhomogeneous local environment and the corresponding diffusive behavior deviates from Stokes–Einstein diffusion because the particle diffusion is dominated by a local microscopic viscosity ηµ, which may differ greatly from the macroscopic viscosity. To interpret probe diffusion in networks, various physical models based on hydrodynamic interactions have been used.39,40,42–46 These theories presume that different forces dominate the probe diffusion behavior. For instance, Langevin and Rondelez47 argue that topological constraints dominate the hydrodynamic forces and use arguments derived from reptation theory, where Phillies42,43 argues probe diffusion is largely governed by hydrodynamic interactions. Several of these models, despite developing from different physical mechanisms, all derive equations that can be expressed in the form of a stretched exponential

D ) D0exp(-Rcν)

(7)

where D0 is the diffusion coefficient of the probe in pure solvent, c is the polymer concentration, and ν and R are constants dependent on the system. Typically, ν reflects the solvent quality and R depends on critical sizes in the system (probe radius, molecular weight, etc.) depending on the specific model. Diffusion of siRNA Complexes in PEG Solutions. To begin to model how the siRNA nanoparticles move in an environment similar to the cytoplasm or extracellular matrix (ECM), we first investigated their diffusion in uncharged PEG solutions. Figure 5A shows the normalized FCS autocorrelation curves and corresponding residuals for Cy5-labeled siRNA complexed with P6SV202 at six different PEG35 concentrations ranging from 8 to 42 mg/mL. The autocorrelation curves shift to larger diffusion times with increasing PEG concentration with no signs of aggregation. Solid lines represent the best fit to the data following eq 4. The residuals suggest that a one component model for data evaluation is sufficient and there is no anomalous diffusion. Similar results were observed for the diffusion of K14PEG/siRNA particles in PEG35 solutions. Figure 5B shows the scaled diffusion coefficient (D/D0) as a function of PEG concentration (% wt/vol). The diffusion coefficient for siRNA complexes in PEG halves from 3.74 × 10-11 m2/s to 1.72 × 10-11 m2/s over this PEG concentration range. The solid line depicts a best fit to the data using the stretched exponential of eq 7, resulting in R ) 0.31 and ν ) 0.77. ν ≈ 0.75 is in keeping with probe diffusion in a good solvent. An R ) 0.31 is in reasonable agreement with recent experiments of probe diffusion of similar radii in aqueous poly(vinyl alcohol) solutions.46 If the Stokes–Einstein equation is obeyed, the product ηPD(cP) of the macroscopic viscosity with the diffusion coefficient of the complexes should be independent of PEG concentration. Figure 5C shows the experimental data compared to SE diffusion. ηPD(cP)/η0D0 is greater than unity at all PEG concentrations, with complexes diffusing faster than predicted by SE diffusion. The positive deviation continually increases and has doubled at the maximum PEG concentration measured. To explain this positive deviation from the Stokes–Einstein equation, the radius of the probe particles can be compared with the mesh size of the fluctuating polymer network in the semidilute regime. Using the measured macroscopic viscosities, the overlap concentration of the PEG35 solutions was deter-

Figure 5. (A) FCS autocorrelation curves and fit residuals of siRNA + P6SV202 nanoparticles in PEG solutions at six different PEG concentrations (represented by differently dotted lines). (B) Variation of the scaled diffusion coefficient as a function of the PEG concentration. The solid line is a fit of the data with a stretched exponential (eq 7). (C) Deviation from the Stokes–Einstein relation as a function of PEG concentration. The dotted line at unity indicates Stokes–Einstein consistent behavior.

mined to be c* ∼ 22 mg/mL by determining the intrinsic viscosity through a least-squares polynomial fit.48 This number is also in good agreement with previously published theory from Tanaka et al., who measured Rg ) (0.02 MW0.58), resulting in a c* ) 21 mg/mL for PEG35.49 Using this c*, we estimate for



Figure 7. (A) FCS autocorrelation curves for siRNA and siRNA/ P6SV202 (N/P 3) in Hepes and fibrin (represented by markers). The solid lines represent the fits to the experimental data. (B) Count rate of a single measurement in fibrin.

Figure 6. (A) FCS autocorrelation curves for siRNA/K14-PEG nanoparticles (N/P 2) at four different collagen concentrations (represented by markers). (B) Count rate of a single measurement in a collagen suspension of c ) 0.2 mg/mL. (C) LSM picture in a collagen suspension of c ) 0.2 mg/mL.

PEG35 concentrations ranging from 25 to 42 mg/mL, and ξ varies from 8.1 to 5.5 nm comparable to the measured P6SV202/ siRNA complex RH. Positive deviations from the Stokes–Einstein relation have been reported previously.40,45,46,50 If RH ∼ ξ and there is no interaction of the polymer with the probe molecule, the frictional force that acts on the particle and the diffusion of the nanoparticle is controlled by the local microscopic viscosity ηµ, which can be smaller than the macroscopic viscosity ηP. This corresponds to the complexes diffusing through the solutions without waiting for the chains to relax their conformations. Additional explanations used for positive deviations involve coupling of the dynamics of the probe particle and the polymer, resulting in cooperative movement,51–53 shear thinning,54 and polymer depletion surrounding the probe particles.39,45,50,55 Because our siRNA complexes are PEGylated and diffusing in a PEG network, a depletion layer based on entropic excluded volume effects is not probable. Diffusion of siRNA Complexes in Charged Networks. Transport properties of the monomolecular siRNA complexes in charged matrixes are observed to be more complicated. Figure 6A shows the measured FCS autocorrelation curves for K14PEG complexes at N/P 2 for different collagen concentrations.

These correlations show clear signs of significant aggregation despite the protective PEG brush layer with large variability in the average diffusion times for the collagen/nanoparticle suspensions. The average number of particles shows an ∼90% decrease already at a collagen concentration of 0.5 mg/mL. In addition, the count rate shown in Figure 6B exhibits large bursts in the intensity fluctuations where aggregates containing multiple siRNA are diffusing through the focal beam. LSM pictures, shown in Figure 6C, also indicate the presence of micrometersized regions of labeled siRNA aggregates within the collagen matrix. Similar aggregation was observed with LSM for various collagen concentrations and particle N/P charge ratios. Aggregation was also observed for naked siRNA in these same collagen gels. Collagen molecules have both positively and negatively charged side chains at physiological pH, so the negatively charged siRNA as well as positively charged peptides can presumably bind to collagen. In contrast, our siRNA complexes display complete disassembly in fibrin networks. Figure 7A shows the FCS autocorrelation curves for naked siRNA and P6SV202/siRNA complexes in both Hepes buffer and in fibrin gel. Fibrin is slightly negatively charged at physiological pH. We observe that both the complex and naked siRNA diffuse identically in fibrin, suggesting a complete disassociation of the copolymer from the siRNA within the fibrin network. τD of naked siRNA decreases from 140 µs in Hepes to 300 µs in fibrin. For the P6SV202/ siRNA complex, τD decreases from 430 to 300 µs. Looking at the count rate, shown in Figure 7B, we see stable, uniform count rates, consistent with no aggregation and free diffusing siRNA. Disassociation is probable as binding of polylysine to fibrin has been previously reported.56 Varying the chemical nature or peptide length of the cationic moiety could provide a means to regulate and suppress these unspecific interactions.

Conclusions Utilizing two different peptide-PEG copolymers, we have shown a successful route to the formation of PEG-stabilized,


monomolecular siRNA complexes with predictable hydrodynamic size. These complexes serve as a model system for better understanding biomacromolecular self-assembly as well as probe diffusion in networks. As diblock copolymers give great flexibility to incorporate additional components such as targeting or cleavable linkers, these monomolecular siRNA complexes may also serve as an ideal system for controlling and improving delivery strategies for gene silencing. In this work, we show that peptide length can be utilized to control the PEG grafting density along a siRNA scaffold. Using simple polymer brush arguments, the resulting complex size can then be predicted. For both K14-PEG and P6SV202/siRNA complexes, the measured hydrodynamic radii are in excellent agreement with the calculated theoretical values. The diffusion of the resulting PEGylated siRNA complexes were also investigated in various polymer matrixes as a first step to modeling diffusion behavior in the cytoplasm or extracellular matrix. In uncharged matrices, such as PEG solutions, the PEGylated siRNA particles diffuse freely and show positive deviations from Stokes–Einstein behavior with increasing PEG concentration. In charged networks, the particle diffusive behavior is more complex. In the negatively charged fibrin network, particles are observed to disassemble completely. In the more complex collagen networks, with areas of positive and negative charge, the particles aggregate. The experiments serve to mimic the transport properties of siRNA carriers in vivo and will help to improve the molecular design of these artificial vectors such that in the future the molecular integrity is preserved in a tissue environment. Acknowledgment. This project was partly supported by the Deutsche Forschungsgemeinschaft by grant SFB-486-B2 and the BMBF through grant 0312019B. Financial support of the German Excellence Initiative via the “Nanosystems Initiative Munich (NIM)” is gratefully acknowledged. Additional financial support by the Alexander von Humboldt Foundation is gratefully acknowledged by J.D. We thank Per Lyngs Hansen for discussions, Paul Janmey for kindly providing the salmon fibrinogen and thrombin and for the helpful explanations on preparation and fibrin properties, and Axel Stemberger for valuable discussions on collagen.

References and Notes (1) Agami, R. Curr. Opin. Chem. Biol. 2002, 6, 829–834. (2) Dykxhoorn, D. M.; Novina, C. D.; Sharp, P. A. Nat. ReV. Mol. Cell Biol. 2003, 4, 457–467. (3) Elbashir, S. M.; Harborth, J.; Lendeckel, W.; Yalcin, A.; Weber, K.; Tuschl, T. Nature 2001, 411, 494–498. (4) Elbashir, S. M.; Lendeckel, W.; Tuschl, T. Genes DeV. 2001, 15, 188– 200. (5) Hassani, Z.; Lemkine, G. F.; Erbacher, P.; Palmier, K.; Alfama, G.; Giovannangeli, C.; Behr, J. P.; Demeneix, B. A. J. Gene Med. 2005, 7, 198–207. (6) Sioud, M. Trends Pharmacol. Sci. 2004, 25, 22–28. (7) Rana, T. M. Nat. ReV. Mol. Cell Biol. 2007, 8, 23–36. (8) Li, C. X.; Parker, A.; Menocal, E.; Xiang, S. L.; Borodyansky, L.; Fruehauf, J. H. Cell Cycle 2006, 5, 2103–2109. (9) Aagaard, L.; Rossi, J. J. AdV. Drug DeliVery ReV. 2007, 59, 75–86. (10) Meyer, M.; Wagner, E. Hum. Gene Ther. 2006, 17, 1062–1076. (11) Schatzlein, A. G. J. Biomed. Biotechnol. 2003, 2003, 149–158. (12) Wagner, E.; Kloeckner, J. Gene delivery using polymer therapeutics. In Polymer Therapeutics I: Polymers as Drugs, Conjugates and Gene DeliVery Systems; Advances in Polymer Science, Vol. 192; Springer: New York, 2006; pp 135–173. (13) Verbaan, F. J.; Oussoren, C.; Snel, C. J.; Crommelin, D. J. A.; Hennink, W. E.; Storm, G. J. Gene Med. 2004, 6, 64–75.

DeRouchey et al. (14) Fisher, K. D.; Ulbrich, K.; Subr, V.; Ward, C. M.; Mautner, V.; Blakey, D.; Seymour, L. W. Gene Ther. 2000, 7, 1337–1343. (15) Ogris, M.; Brunner, S.; Schuller, S.; Kircheis, R.; Wagner, E. Gene Ther. 1999, 6, 595–605. (16) Remaut, K.; Lucas, B.; Braeckmans, K.; Sanders, N. N.; Demeester, J.; De Smedt, S. C. J. Controlled Release 2005, 110, 212–226. (17) Nakamura, Y.; Kogure, K.; Futaki, S.; Harashima, H. J. Controlled Release 2007, 119, 360–367. (18) Bertschinger, M.; Backliwal, G.; Schertenleib, A.; Jordan, M.; Hacker, D. L.; Wurm, F. M. J. Controlled Release 2006, 116, 96–104. (19) Elouahabi, A.; Ruysschaert, J. M. Mol. Ther. 2005, 11, 336–347. (20) Riddles, P. W.; Blakeley, R. L.; Zerner, B. Anal. Biochem. 1979, 94, 75–81. (21) Snyder, S. L.; Sobocinski, P. Z. Anal. Biochem. 1975, 64, 284–288. (22) Ritter, W.; Plank, C.; Lausier, J.; Rudolph, C.; Zink, D.; Reinhardt, D.; Rosenecker, J. J. Mol. Med. 2003, 81, 708–717. (23) Finsinger, D.; Remy, J. S.; Erbacher, P.; Koch, C.; Plank, C. Gene Ther. 2000, 7, 1183–1192. (24) Janmey, P. A.; Lamb, J. A.; Ezzell, R. M.; Hvidt, S.; Lind, S. E. Blood 1992, 80, 928–936. (25) Laidmae, I.; McCormick, M. E.; Herod, J. L.; Pastore, J. J.; Salum, T.; Sawyer, E. S.; Janmey, P. A.; Uibo, R. Biomaterials 2006, 27, 5771–5779. (26) Wang, L. Z.; Gorlin, J.; Michaud, S. E.; Janmey, P. A.; Goddeau, R. P.; Kuuse, R.; Uibo, R.; Adams, D.; Sawyer, E. S. Thromb. Res. 2000, 100, 537–548. (27) Rigler, R.; Elson, E. S. Fluorescence Correlation Spectroscopy: Theory and Applications; Springer: Berlin, 2001. (28) Widengren, J.; Mets, U.; Rigler, R. J. Phys. Chem. 1995, 99, 13368– 13379. (29) DeRouchey, J.; Walker, G. F.; Wagner, E.; Radler, J. O. J. Phys. Chem. B 2006, 110, 4548–4554. (30) Cardoso, A. L. C.; Simoes, S.; de Almeida, L. P.; Pelisek, J.; Culmsee, C.; Wagner, E.; de Lima, M. C. P. J. Gene Med. 2007, 9, 170–183. (31) Mao, S. R.; Neu, M.; Germershaus, O.; Merkel, O.; Sitterberg, J.; Bakowsky, U.; Kissel, T. Bioconjugate Chem. 2006, 17, 1209–1218. (32) DeRouchey, J.; Netz, R. R.; Radler, J. O. Eur. Phys. J. E 2005, 16, 17–28. (33) Alexander, S. J. Phys. 1977, 38, 983–987. (34) Degennes, P. G. Macromolecules 1980, 13, 1069–1075. (35) Sevick, E. M. Macromolecules 1996, 29, 6952–698. (36) Hansen, P. L.; Cohen, J. A.; Podgornik, R.; Parsegian, V. A. Biophys. J. 2003, 84, 350–355. (37) Walker, G. F.; Fella, C.; Pelisek, J.; Fahrmeir, J.; Boeckle, S.; Ogris, M.; Wagner, E. Mol. Ther. 2005, 11, 418–425. (38) Masaro, L.; Zhu, X. X. Prog. Polym. Sci. 1999, 24, 731–775. (39) Gold, D.; Onyenemezu, C.; Miller, W. G. Macromolecules 1996, 29, 5700–5709. (40) Ye, X.; Tong, P.; Fetters, L. J. Macromolecules 1998, 31, 5785–5793. (41) Degennes, P. G. Macromolecules 1976, 9, 594–598. (42) Phillies, G. D. J.; Brown, W.; Zhou, P. Macromolecules 1992, 25, 4948–4954. (43) Phillies, G. D. J.; Clomenil, D. Macromolecules 1993, 26, 167–170. (44) Busch, N. A.; Kim, T.; Bloomfield, V. A. Macromolecules 2000, 33, 5932–5937. (45) Gold, D.; Onyenemezu, C.; Miller, W. G. Macromolecules 1996, 29, 5710–5716. (46) Michelman-Ribeiro, A.; Horkay, F.; Nossal, R.; Boukari, H. Biomacromolecules 2007, 8, 1595–1600. (47) Langevin, D.; Rondelez, F. Polymer 1978, 19, 875–882. (48) Davidson, N. S.; Fetters, L. J.; Funk, W. G.; Hadjichristidis, N.; Graessley, W. W. Macromolecules 1987, 20, 2614–2619. (49) Tanaka, S.; Ataka, M.; Onuma, K.; Kubota, T. Biophys. J. 2003, 84, 3299–3306. (50) Won, J.; Onyenemezu, C.; Miller, W. G.; Lodge, T. P. Macromolecules 1994, 27, 7389–7396. (51) Zhou, P.; Brown, W. Macromolecules 1990, 23, 1131–1139. (52) Brown, W.; Zhou, P. Macromolecules 1989, 22, 4031–4039. (53) Brown, W.; Rymden, R. Macromolecules 1988, 21, 840–846. (54) Lin, T. H.; Phillies, G. D. J. Macromolecules 1984, 17, 1686–1691. (55) Krabi, A.; Donath, E. Colloids Surf., A 1994, 92, 175–182. (56) Jockenhoevel, S.; Zund, G.; Hoerstrup, S. P.; Chalabi, K.; Sachweh, J. S.; Demircan, L.; Messmer, B. J.; Turina, M. Eur. J. CardioThoracic Surg. 2001, 19, 424–430.

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