extfieldsofstudy:
Materials Science
Medicine

s2fieldsofstudy: Biology Chemistry

text:
Lipiodol nanoemulsions stabilized with polyglycerol-polycaprolactone block copolymers for theranostic applications

Background Polyglycerol is an attractive hydrophilic building block of amphiphilic copolymers for biomedical and pharmaceutical applications due to its biocompatibility, facile chemical modification, and anti-fouling activity. Herein we introduce theranostic nanoemulsions incorporating anti-cancer therapeutic and contrast agents using linear polyglycerol-poly(ε-caprolactone) diblock copolymers (PG-b-PCL). Lipiodol is used as a core oil that dissolves paclitaxel and serves as a contrast agent for computer tomography (CT). Methods PG-b-PCL is synthesized by three-step processes: polymerization of ethoxyethyl glycerol ether; ring-opening polymerization of ε-caprolactone; and deprotection of the PEEGE block. In vitro cytotoxicity of the polyglycerolated lipiodol nanoemulsions is demonstrated using HeLa ovarian cancer cells. The applicability of the prepared nanoemulsions as a contrast agent for CT imaging is also evaluated using micro-CT. Results Three compositions of PG-b-PCL with different block lengths are synthesized to prepare nanoemulsions. The polyglycerolated lipiodol nanoemulsions exhibit excellent anti-cancer activities, while placebo nanoemulsions have no significant cytotoxicity under the same condition. Micro-CT imaging of the nanoemulsions confirms the ability of nanoemulsions as a contrast agent. Conclusions This study suggests that PG-b-PCL is a promising polymeric emulsifier for effective stabilization and surface functionalization of drug delivery nanocarriers for therapeutic and imaging agents. Electronic supplementary material The online version of this article (10.1186/s40824-017-0108-4) contains supplementary material, which is available to authorized users.

Background
Recently, various amphiphilic copolymers have been studied for drug delivery with aiming to enhancing the therapeutic effects of drugs through long-term blood circulation and facile surface modification [1][2][3]. Polyethylene glycol (PEG) has been extensively employed as the hydrophilic, non-sticky corona of drug delivery nanocarriers because PEG efficiently suppresses non-specific protein adsorption and colloidal aggregation in the blood stream [4]. However, recent studies showed that a long PEG chain can effectively hinder the cellular uptake of nanoparticles and the following endosomal escape partially owing to its neutrality or lack of responsiveness to environmental changes [5][6][7]. In addition, although cell adhesion molecules are generally introduced at the end of PEG exposed to the surface, the number of molecules that can be conjugated is very limited, and relatively large, hydrophobic targeting molecules can negatively affect the self-assembly and surface properties of nanocarriers [8][9][10]. Therefore, alternative hydrophilic polymers have been explored to overcome the limitations of PEG.
Polyglycerol (PG) has received increasing attention as a new hydrophilic polymer due to its unique chemical properties [11][12][13][14]. These include a large number of hydroxyl groups, dendritic structures [15], and biocompatibility [16]. PG was also co-polymerized with poly(εcaprolactone) (PCL) [17], PCL-polycitric [18], and PEG [19] to enhance the intracellular delivery of anti-cancer drugs [20]. In comparison to PEG, the hydroxyl groups on each monomeric unit of PG can help increase the hydrophilic properties of the nanoparticle surface, thereby increasing the dispersion stability of nanocarriers in the presence of serum proteins, and provide multiple sites for the conjugation of targeting molecules.
In this work, we employed a linear PG-PCL diblock copolymer (PG-b-PCL) as an emulsifier to stabilize lipiodol nanoemulsions and functionalize the PG block with cancer cell-targeting molecules. Lipiodol, an iodinated derivative of poppy seed oil with a high iodine content (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) wt-%) [21], has been used as a clinically approved computer tomography (CT) contrast-enhancing agent and has been used for trans-arterial chemoembolization for hepatocellular carcinoma [22]. Recently, theranostic nanocarriers have been vigorously investigated for the co-delivery of imaging and therapeutic agents to tumor tissues, enabling to identify the precise location of tumors in a non-invasive manner and provide evidences to determine a suitable treatment regimen [23]. Our previous studies demonstrated that PEG-PCL diblock copolymers can form a semi-solid interphase between oil and water, producing robust oil-in-water emulsions [24][25][26][27]. The present work extended the same concept of emulsion stabilization to PG-b-PCL with lipiodol as a core oil of the nanoemulsions dissolving paclitaxel [28][29][30]. The cytotoxicity and cellular uptake of the prepared nanoemulsions were investigated to demonstrate the feasibility of lipiodol nanoemulsions stabilized with PG-b-PCL as a delivery carrier for anti-cancer drugs.

Synthesis of linear PG-b-PCL
Ethoxyethyl glycerol ether (EEGE) was synthesized from glycidol and ethyl vinyl ether using TsOH as a catalyst at room temperature for 3 h. The product was washed with saturated NaHCO 3 and then isolated using ethyl acetate and dried using MgSO 4 , followed by distillation at 64°C in vacuum. Poly(ethoxyethyl glycerol ether) (PEEGE) was synthesized through polymerization of EEGE in THF at 65°C for 24 h using potassium tert-butoxide as an initiator. The produce was precipitated in cold methanol, and then the solvent was evaporated at 30°C. The synthesized PEEGE was isolated using benzene and dried in vacuum for 24 h. Linear PEEGE-b-PCL was synthesized by the ring opening polymerization of ɛ-caprolactone at 125°C for 20 h using PEEGE and stannous octanoate as an initiator and a catalyst, respectively. The synthesized polymer was dissolved in methylene chloride and precipitated in cold hexane. The de-protection of PEEGE-b-PCL was carried out to obtain PG-b-PCL by mixing the PEEGE-b-PCL solution in acetone with an aqueous solution of oxalic acid. The mixture solution was magnetically stirred for 5 h at room temperature. The mixture was then dialyzed against deionized water through a Spectra/Por membrane for 24 h. The final polymer product was dried by rotary evaporation.

Characterization of PG-b-PCL
Gel permeation chromatography (GPC) analysis was performed using Agilent high performance liquid chromatography (HPLC) 110 series (Agilent Technologies, Palo Alto, CA, USA). Three PL gel columns (300 × 7.5 mm, pore sizes = 10 3 , 10 4 , and 10 5 Å) and a refractive index detector were used. Tetrahydrofuran was used as an isocratic mobile phase at a flow rate of 1.0 mL min −1 . The molecular weight of the polymers was obtained through calibration against monodispersed polystyrene standards (Polysciences, Inc., Warrington, PA, USA). 1 H Nuclear magnetic resonance (NMR) analysis was performed at 25°C using a Bruker NMR (300 MHz) using CDCl 3 as a solvent. Chemical shifts were measured in parts per million (ppm) using tetramethylsilane as an internal reference. Differential scanning calorimetry (DSC) analysis was performed using DSC 204 F1 Phoenix (Netzsch-Gerätebau GmbH, Selb, Germany). Heating and cooling scans were run between −50 and 200°C at a scan rate of 10°C min −1 .

Preparation of Lipiodol Nanoemulsions
Various preparation conditions for placebo nanoemulsions (denoted as 'bNEs') and paclitaxel-incorporating nanoemulsions (denoted as 'pNEs') were investigated. Briefly, the pre-determined amounts of PG-b-PCL, lipiodol, and paclitaxel were dissolved completely in acetone by sonication for 10 min. The solutions were then added dropwisely into 10 mL deionized water with vigorous magnetic stirring or homogenization. Various stirring/ homogenizing speeds (500-10,000 rpm) and temperatures (25-50°C) were examined. The mixtures were then stirred overnight for solvent evaporation. The produced nanoemulsions were stored at 4°C until use.

Characterization of Lipiodol Nanoemulsions
The hydrodynamic size distribution of the nanoemulsions was determined by dynamic laser light scattering (DLS) using a particle size analyzer ELSZ-1000 (Otsuka Electronics Co., Ltd., Osaka, Japan). The morphology of the nanonemulsions was observed via transmission electron microscopy (TEM) (JEM-3010, JEOL, Japan). TEM specimen was prepared by mixing 10 μL of the nanoemulsions with 10 μL of 2% uranyl acetate for negative staining. The mixture was dropped on a carbon-coated copper grid (Ted Pella, Inc., Redding, CA, USA), followed by incubation at room temperature for 5 min. The drop was removed using a filter paper, and the grids were dried in air.

Determination of loading yield and efficiency
The nanoemulsions (500 μL) were freeze-dried and dissolved in 1.5 mL ethanol with sonication for 1 h. The solution was filtered through a 0.45 μm syringe filter, and the amount of lipiodol was determined at 260 nm using UV-visible absorption spectrophotometry (UV-1601, Shimadzu, Kyoto, Japan). As for the loading yield and efficiency of paclitaxel, the nanoemulsions (1 mL) were freeze-dried and dissolved in 1 mL acetonitrile with sonication for 1 h. The solution was filtered through a 0.45 μm syringe filter, paclitaxel in the filtrate was determined using reversed phase HPLC (Agilent 1100 series, Agilent Technologies, Palo Alto, USA) equipped with a Waters Spherisorb ODS2 column (C18, 5 μm, 4.6 mm × 250 mm). Acetonitrile was used as an isocratic mobile phase at a rate of 1 mL min −1 . The absorption of the eluted solution was determined at 227 nm using a standard curve of paclitaxel in acetonitrile.

Conjugation of folic acids to Nanoemulsions
Folic acid (FA, 1 mg) was dissolved in N,N-dimethylformamide (DMF, 5 mL) at room temperature with magnetic stirring for 20 min. EDC (440 mg) and NHS (260 mg) was added to the solution, and then the solution was magnetically stirred on ice for 1 h. Then, 3-APBA (411 mg) in DMF (2 mL) was then mixed with the FA solution followed by stirring in an ice bath for 1 h and at room temperature for 24 h. The solution was then added dropwisely into deionized water (50 mL). Yellow precipitates were gathered by filtration and then washed several times with acetone. The folate boronic acid (FBA) powder was dried under vacuum overnight, dissolved in DMSO, and then diluted by deionized water to the final concentration of 20 μg mL −1 . The FBA solution (5 mL) was mixed with pNEs (1 mL) by magnetic stirring at room temperature for 24 h for esterification, and then non-conjugated FBA were removed by dialysis against deionized water through a Spectra/Por membrane (MWCO = 12 kDa) for 12 h. To determine the degree of FA substitution, the prepared FA-conjugated pNEs (denoted as 'fpNEs') were freeze-dried and then re-dissolved in DMSO by sonication for 15 min. The UV absorbance of folates was measured at 363 nm, and the number of FA conjugated to the nanoemulsions was calculated using a calibration curve of FA in DMSO.

Serum stability and in vitro release
The serum stability of pNEs was determined by incubating the nanoemulsions in PBS containing 10% FBS at 37°C. The particle size distribution of the nanoemulsions were determined using DLS. The In vitro release profile of paclitaxel was obtained by incubating 20 mL of the nanoemulsions (0.3 mg mL −1 of paclitaxel) in a Spectra/Por dialysis bag (MWCO = 12 kDa) placed in 100 mL of PBS containing various percentages of methanol. At pre-determined time intervals, a 1 mL aliquot of the medium was taken, freeze-dried, and dissolved in acetonitrile to determine the concentration of paclitaxel using reversed phase high performance liquid chromatography (RP-HPLC).

Micro-CT analysis
HeLa cells were seeded onto a 100 mm dish at a density of 10 7 cells per dish, followed by incubation in DMEM at 37°C for 24 h. The medium was then replaced by 10 mL of fresh DMEM containing the nanoemulsions, followed by incubation for 4 h. The cells were trypsinized and collected by centrifugation, and then fixed using 4% formaldehyde at 4°C overnight. The cells were then mixed with 1% agarose gel in a 1.5 mL tube and visualized on a micro-CT (Simens Inveon Micro-CT, Siemens Medical Solutions, Knoxville, TN, USA).

Cellular uptake of fluorescently labeled Nanoemulsions
To probe the cellular uptake of lipiodol nanoemulsions, 1.64 wt-% of Nile red was incorporated in the core of the nanoemulsions. HeLa cells were seeded in a chamber slide (4-well Culture Slides, BD Falcon, MA) at a density of 10 5 cells per well, and pre-incubated in DMEM at 37°C for 24 h. The medium was then replaced by Nile red-loaded nanoemulsions at a concentration of 1.2 μg mL −1 in a fresh medium, and the cells were treated for 4 h at 37°C. Afterwards, the nanoemulsions were removed, and the cells were washed several times with PBS. Then the cells were fixed using 4 wt-% formaldehyde for 30 min at room temperature. The nuclei of cells were stained using DAPI (1.5 mg mL −1 ) for 2 min, and the specimens were examined using a confocal laser scanning microscope (LSM510, Carl Zeiss, Oberkochen, Germany). Confocal images were quantitatively analyzed using ImageJ (U. S. National Institutes of Health, Bethesda, Maryland, USA).

Cytotoxicity
HeLa cells were cultured in DMEM (10% FBS and 1% penicillin/streptomycin) at 37°C. The cells were seeded onto a 96-well-plate at a density of 10 4 cells per well, followed by incubation at 37°C for 24 h. The medium was replaced by a fresh DMEM containing pNEs at various concentrations. The cells were then incubated for 4 h, and the pNEs were removed from the culture medium. The cells were further incubated for 2 days in a fresh medium, and then treated with a CCK-8 assay solution. The absorbance at 450 nm was recorded using a microplate reader (CLARIO Star, BMG Labtech, Germany).

Results
The synthetic scheme of PG-b-PCL with a linear block of PG is summarized in Fig. 1a. The hydroxyl group of glycidol was temporarily protected through the reaction of glycidol with ethyl vinyl ether. The resulting EEGE was then polymerized by ring-opening polymerization to PEEGE. The synthesized PEEGE was used as an initiator for the ring-opening polymerization of ε-caprolactone to PEEGE-b-PCL. The hydroxyl groups of PEEGE-b-PCL were finally de-protected to obtain linear PG-b-PCL. The chemical structure of the synthesized block copolymer was analyzed using 1 H NMR (Fig. 1b). Peaks number '1' to '5' in the spectrum were assigned to the hydrogen atoms in PCL, and peaks number '6' to '8' to PG. The integrations of the corresponding peaks in the NMR spectrum were analyzed to calculate the molar ratio of the two blocks, as shown in Table 1. Three compositions of PG-b-PCL with different molar ratios of the two blocks were selected for further investigation: the molar ratios of PCL to PG were 2, 2.5, and 4.5, and the corresponding polymers are denoted as PG 40 -b-PCL 80, PG 47 -b-PCL 118 , and PG 32 -b-PCL 144 (Additional file 1: Figure S1). The subscripts represent the number of repeating units calculated from the molecular weight and the molar ratios. The number average molecular weights  Fig. 1c. The melting temperatures (49, 52, and 55°C) and crystallinity (enthalpy = 58, 71, and 67 J g -1 ) of the copolymers increased as the length of the PCL increased, which might have been caused by the crystallization of the PCL chain.
Polymeric micelles (denoted as 'bMCs') were prepared using PG-b-PCL with different lengths of the PCL block, and their critical aggregate concentrations (c.a.c.) were determined using pyrene. As expected, the c.a.c. value of PG 40 -b-PCL 80 (7.4 × 10 −3 g L -1 ) was much higher than that of PG 47 -b-PCL 118 and (4.0 × 10 −3 g L −1 ), and PG 32b-PCL 144 (3.5 × 10 −3 g L -1 ) (Fig. 2a). This is because the relatively larger segment of PCL decreased c.a.c. due to its increased hydrophobicity. However, the size distribution of bMCs of PG 32 -b-PCL 144 and PG 40 -b-PCL 80 was significantly broader than that of PG 47 -b-PCL 118 (the hydrodynamic diameter = 113.9 nm, PDI = 0.11) (Fig. 2b). The bMCs exhibit a relatively uniform spherical morphology with a diameter of 83 ± 29 nm (Fig. 2c). The smaller particle size from TEM might be due to the shrinkage during the drying process. Figure 2d and e show the size distributions and TEM image of blank lipiodol nanoemulsions (bNEs). The result indicates that bNE-1, which was prepared using 10 mg PG 47 -b-PCL 118 and 50 mg of lipiodol by a stirring method, had the smallest diameter (181 nm). Considering the physiochemical characteristics of self-assembled aggregates, we chose PG 47 -b-PCL 118 for further investigation on the preparation of nanoemulsions incorporating lipiodol as a core using various emulsification parameters listed in Table 2. The loading yield and efficiency of lipiodol were 75.5 and 90.6%, respectively, as determined using the   Figure S2). Nanoemulsions with various concentrations of paclitaxel were prepared using PG 47 -b-PCL 118 , as described in Table 3, and the loading yield and efficiency were determined. The nanoemulsions fed with 3 mg (2.6%) of paclitaxel (pNE-3) showed the highest encapsulation efficiency (55%) (Fig. 3a). The lower efficiency at a higher loading yield might be caused by the limited solubility of paclitaxel in lipiodol, which is about 10 mg mL −1 . Despite the different loading yield of paclitaxel, no significant difference in the size distribution of pNEs was observed (Fig. 3b). To evaluate the PG-b-PCL nanoemulsions as drug delivery vehicles, we examined the dispersion stability of pNE-3 in 10 and 50% FBS. No significant change of their hydrodynamic diameter was observed for a week at 37°C (Fig. 3c). This is attributed to excellent biocompatibility and the high resistance of PG to non-specific protein adsorption.
Next, we determined the in vitro release profiles of paclitaxel from pNEs (pNE-3). Due to the negligible solubility of paclitaxel in an aqueous solution, surfactants or polar organic solvents are commonly added to the release medium to maintain the sinking condition.
However, there is no standardized experimental condition presumably because of the differences in the properties of materials used for paclitaxel formulations. In this work, a mixture of PBS and methanol was used to examine how solvent polarity affects the release of paclitaxel from pNEs (Fig. 3d). The sink condition was maintained if the volume percentage of methanol was higher than 20%. In 50% methanol, 37.4% of paclitaxel was released from pNEs for 24 h at the initial stage. After 24 h, the release rate became lower, and for 15 days only 54.3% of the paclitaxel was released. The drug release is controlled by slow diffusion of paclitaxel as a result of partitioning from the hydrophobic core of the nanoemulsions into 40 and 50% methanol during the first day, followed by slow release. However, in 20% methanol, the drug release was very limited to less than 20% for 2 weeks, and no significant release of pactlitaxel was observed in 10% methanol. The results indicate that the hindered diffusion of paclitaxel through the PCL-based interphase is a limiting factor for the diffusion of paclitaxel from the lipiodol core to the release medium.
To achieve active targeting via FA receptor (FAR)-mediated endocytosis, pNEs were conjugated with FA by boronic acid ester linkage to produce fpNEs as described  Polydispersity index (PDI) = μ 2 /Γ 2 , estimated by the cumulant method Emulsification condition of every batch is the same with that of bNE-1 (magnetic stirring at 10,000 rpm at 40-50°C) in the experimental section. Boronic acid binds to alcohol with high affinity through the formation of boronate ester, which is a strong single-pair reversible functional group, interaction in an aqueous milieu [10]. FBA was synthesized through simple amide coupling between FA and 3-aminophenylboronic acid. Boronic acid can simply react with the vicinal diol groups at the end of the PG block to form double ester linkages with each boronate unit with a degree of folate substitution (19.2%).
To visualize the cellular uptake of pNEs, Nile red was incorporated as a hydrophobic fluorescent dye into the nanoemulsions. HeLa cells, which overexpress FAR type α, were incubated with the nanoemulsions loaded with 1.64 wt-% Nile red, and examined using a confocal fluorescence microscope (Fig. 4a). The Nile red-loaded nanoemulsions are shown in red, while DAPI staining showed the area and distribution of the nuclei. The fluorescence intensity per individual cell is represented in Fig. 4b. The average intensity per single cell was almost twice higher in the cells treated with fpNEs than those treated with pNEs. These results indicated that the FA conjugation efficiently increased the cellular uptake of the nanoemulsions presumably through binding to FARs in the cellular membrane. Moreover, assuming that the density of PG-b-PCL is 1.1 g mL −1 , FA ligands were located on the surface of nanoemulsions at a density of 6.8 FA molecules per nm 2 [31]. It can be inferred that the high density of FA on the nanoemulsions enhanced the cellular uptake efficiency by multivalent binding to HeLa cells. It was also confirmed by competition assay of folate receptor mediated intracellular uptake of fpNEs. In case that folate receptors are blocked by cultivating cells in folic acid-rich medium, the intracellular uptake efficiency of fpNEs decreased to 30.7% of folate receptor open system (Additional file 1: Figure S3).
Lipiodol within the nanoemulsions can serve as a CT contrast agent, so the diagnostic capability of the lipiodol nanoemulsions was evaluated using micro-CT analysis. HeLa cells were treated with PBS, pNEs, and fpNEs, followed by the fixation in an agarose gel phantom (Fig. 4c). The emulsions concentration was varied to achieve from 10 to 100 μg mL −1 of lipiodol. The CT values of the cells (1 × 10 7 ) treated with fpNEs and pNEs, where the concentration of lipoidol was 10 μg mL −1 , were 73.4 ± 3.6 and 40.3 ± 1.4 HU, respectively, where HU is a hounsfield unit (Fig. 4d) 233.4 ± 6.4 HU at 50 and 100 μg mL −1 of lipiodol, respectively. These values were much higher than those from treatment with pNEs. The CT values of most soft tissues are in the range of 30-100 HU, and the difference of 50-100 HU can be used to distinguish the tissues of interest. Therefore, it can be said that fpNEs are applicable as contrast agents for CT imaging of tumors. Interestingly, regardless of the lipiodol concentration, the CT value of fpNEs was about 1.8 times higher than that of pNEs, which corresponds to the difference in the fluorescence intensity per cell. The difference seems to result from the cellular uptake efficiency of nanoemulsions. Compared with our previous study on the feasibility of CT imaging using lipiodol nanoemulsions [30] (therein, denoted 'sr-NEs') composed of LPEI-g-cholesterol, cholesterol, DSPE-PEG, siRNAs, and paclitaxel, the contrast enhancement by fpNEs was 1.8-fold better than that from the use of sr-NEs. This was true even with the lesser amount of lipiodol (ca. 72% of fpNEs), probably due to difference in the cellular uptake. Thus, it is expected that fpNEs could be further developed as an in vivo tumor-targeting CT contrast agent.
The effects of FA conjugation on the cytotoxicity of paclitaxel-loaded lipiodol nanoemulsions were examined using HeLa cells (Fig. 5). bMCs and bNEs were also used as negative controls to determine the cytotoxicity of while fpNEs showed 41% cell viability at the same concentration. However, at 3 ng mL −1 , fpNEs exhibited 52% cell viability while pNEs had 85%. The result indicates that the FA conjugation to the surface of lipiodol nanoemulsions effectively promotes the intracellular delivery of the nanocarriers to boost the efficacy of paclitaxel.

Discussion
Although most widely used as a hydrophilic corona of polymeric nanoparticles for biomedical applications, PEG has several limitations such as lowering cellular uptake of nanoparticles, a lack of responsiveness to environmental changes, and insufficient number of sites for chemical modification. Hence, a development of a novel hydrophilic moiety is highly demanded. PG is one of the most prominent alternatives to PEG due to tis excellent dispersion stability and biocompatibility. We synthesized PG-b-PCL amphiphilic diblock copolymers having critical aggregation concentrations in the range of 10 −6 -10 −7 M which is comparable to that of PEG based polymeric emulsifiers. Theranostic nanoemulsions co-encapsulating lipiodol and paclitaxel were prepared using PG 47 -b-PCL 118 . The potency of our theranostic nanoemulsions is attributed to the high loading yield of paclitaxel in lipiodol core and the high degree of cell-targeting ligand on the PG corona. As reported previously, to achieve efficient drug encapsulation, it is critical to select a suitable surrounding medium which has a high miscibility and solubility to the payloads. Besides serving as a CT contrast agent, lipiodol which has paclitaxel solubility of 10 mg mL −1 was a good medium for encapsulated paclitaxel. Moreover, lipiodol played a role for a reservoir of paclitaxel by hindering diffusion of paclitaxel from the core through the PCL-based interphase. Considering that small molecular payloads of nanoparticles are easily diffused out during blood circulation and found in plasma or accumulated in non-target-tissues like kidney, this encapsulation stability resulted from lipiodol core can be one of the outstanding advantage for the delivery efficiency in vivo.
In addition, multiple hydroxyl groups on the PG's backbone enable to introduce a high degree of hydrophobic cancer-targeting ligand on the surface of nanoemulsions without a loss of dispersion stability in aqueous. The conjugation strategy between the vicinal diol of PG and the folate boronic acid (FBA) addressed the laborious work for introducing targeting moiety to nanoemulsions and simplified the process by enabling post-modification of nanoemulsion surface in aqueous media. The folate decorated nanoemulsions increased intracellular uptake by HeLa cells via receptor-mediated endocytosis, which consequently led to enhancement of CT imaging and anticancer therapeutic effect.
Although the theranostic efficacy of PG-PCL lipiodol nanoemulsion was only evaluated by in vitro assay in this study, it is expected that this nanoemulsion system could be further developed as an in vivo tumor-targeting theranostic contrast agent.

Conclusion
This study demonstrated that PG-b-PCL diblock copolymers can serve as a semi-solid polymeric emulsifier for theranostic nanoemulsions for cancer. The PCL block can generate a robust polymeric layer at the oil/water interface because it is not soluble in lipiodol. The PG block has a hydroxyl group in each monomeric unit, exhibiting excellent biocompatibility and anti-fouling property. In vitro cell studies with HeLa cells demonstrated that ligand conjugation efficiently increased the cellular uptake of PG-b-PCL nanoemulsions. The FA-conjugated lipiodol nanoemulsions loaded with 2.6% paclitaxel showed excellent anti-cancer activity (LD 50 < 3 ng mL −1 ), while placebo nanoemulsions did not show any significant cytotoxicity at the same concentration. In addition, the lipiodol nanoemulsions greatly enhanced the CT contrast of tissue-mimicking phantoms of HeLa cells due to efficient intracellular translocation. This study suggests that PG-b-PCL can serve as a robust polymeric emulsifier to stabilize nanoemulsions while enabling facile surface functionalization with multiple targeting molecules for targeted delivery of therapeutic and imaging agents.

Additional file
Additional file 1: Supporting information. 1


extfieldsofstudy: Biology Medicine

text:
Pathways to zoonotic spillover

Zoonotic diseases present a substantial global health burden. In this Opinion article, Plowrightet al. present an integrative conceptual and quantitative model that reveals that all zoonotic pathogens must overcome a hierarchical series of barriers to cause spillover infections in humans. Supplementary information The online version of this article (doi:10.1038/nrmicro.2017.45) contains supplementary material, which is available to authorized users.

The phenomenon of cross-species spillover is the defining characteristic of pathogens that transmit from vertebrate animals to humans (zoonoses). The public health burden that is presented by zoonoses includes outbreaks of pathogens such as Ebola virus, influenza A virus (H1N1) pdm09 and Middle East respiratory syndrome coronavirus (MERS-CoV), as well as the ongoing transmission of endemic pathogens, such as Salmonella spp., Leptospira spp., Trypanosoma spp., Mycobacterium spp. and West Nile virus [1][2][3][4][5][6] . Spillover transmission is promoted by successive processes that enable an animal pathogen to establish infection in a human. The probability of zoonotic spillover is determined by interactions among several factors, including disease dynamics in the reservoir host, pathogen exposure and the within-human factors that affect susceptibility to infections. These factors can be partitioned into three functional phases that describe all major routes of transmission (FIG. 1). In the first phase, the amount of pathogen available to the human host at a given point in space and time, known as the pathogen pressure, is determined by interactions among reservoir Although many recent articles have examined the fields of zoonoses or emerging pathogens 2,3,10-15 , a synthetic mechanistic understanding of animal-to-human transmission is lacking 14,16 . Much attention has been dedicated to the characterization of emerging infections 3,11,12,15 ; for example, the high frequency of zoonoses among emerging infections 3,12 , their socio-economic, environmental and ecological drivers 2,13,17,18 , and their phylogenetic and geographical distribution 3 . Similarly, the phases of zoonotic emergence in the human population 11,14,18 , adaptation and compatibility of zoonoses in humans 10,11,19 , and approaches to modelling the transmission of zoonoses 14,16 , have also been addressed in the literature. However, a comprehensive understanding of the processes that enable a pathogen from a vertebrate animal to establish infection in a human, and how these processes are hierarchically, functionally and quantitatively linked, remains a fundamental deficit in research on zoonoses 14,16 . In this Opinion article, we present a mechanistic structure that integrates the determinants of spillover and the interactions among them (FIG. 1). However, we do not address broader determinants of pathogen emergence or factors that affect disease severity or onward transmission in humans.
Although many of the individual determinants of spillover are subjects of intensive study, each is usually addressed in isolation in a specialized discipline (FIG. 2). Accordingly, the better-characterized factors become the focus of public health interventions. For example, reservoir hosts or vectors are often targeted for control before the concatenation and relative influence of processes that lead to spillover are understood, which sometimes leads to inefficient or even counterproductive interventions 20 . In other cases, multiple mechanisms are aggregated in analyses that obscure the interactions or heterogeneities that drive risk. Although the aggregation of mechanisms may be appropriate at times, identifying discrete mechanisms and how they interact to drive spillover is essential to recognize the assumptions that are implicit in simpler models, and to clarify which processes must be modelled explicitly and host distribution, pathogen prevalence and pathogen release from the reservoir host, followed by pathogen survival, development and dissemination outside of the reservoir hosts. Second, human and vector behaviour determine pathogen exposure; specifically, the likelihood, route and dose of exposure. Third, genetic, physiological and immunological attributes of the recipient human host, together with the dose and route of exposure, affect the probability and severity of infection.
Each phase presents multiple barriers to the flow of a pathogen from a reservoir host to a recipient host. Spillover requires the pathogen to pass every barrier and thus can only occur when gaps align in each successive barrier within an appropriate window in space and time (FIG. 2). Consequently, zoonotic spillover is a relatively rare event, and although humans are continually exposed to many potentially infectious pathogens that are derived from other species, most of these microorganisms cannot infect or cause disease in humans [7][8][9][10] .
This Opinion article focuses on spillover transmission, strictly defined as the processes that enable a pathogen from a vertebrate animal to establish infection in a human. which can be combined. For example, does assessment of the risk of acquiring a zoonotic infection require the measurement of the pathogen burden carried by individual reservoir hosts, or is it sufficient to estimate the cumulative abundance of a pathogen in the environment over time? This is a key question for pathogens such as Leptospira interrogans, Giardia spp., and Escherichia coli O157, and the answer may depend on modes of contact and dose-response relationships in humans (see below). Models that integrate data from experiments, the field and epidemiological studies, even if only partially parameterized, may be necessary to make such determinations.
We describe how pathogens overcome a series of barriers to pass from reservoir hosts to humans. Crucially, nonlinear interactions among the barriers create bottlenecks in the flow of a pathogen between species. Such bottlenecks provide opportunities for public health interventions that could lead to substantial reductions in the risk of spillover. Alternatively, changing environmental or social conditions can alleviate these bottlenecks, which can cause surges in spillover infections. Our framework provides the foundation for operational models that are required for quantitative evidence-based risk analysis, preparedness, surveillance and control.

Barriers to spillover
The probability of spillover is determined by the interactions among the barriers and the associated bottlenecks that might prevent cross-species transmission. Many of these interactions are nonlinear and dynamic in space and time.
Pathogen pressure. The series of processes that culminate in pathogen pressure (the amount of a pathogen that is available to humans at a given point in time and space) includes pathogen dynamics in reservoir hosts, pathogen release from reservoir hosts, and pathogen survival or dispersal outside of reservoir hosts.
Pathogen dynamics in reservoir hosts can be represented as three variables that determine the distribution and intensity of infection in time and space: the density of reservoir hosts, the prevalence of infection among reservoir hosts, and the average intensity of infection in an infected reservoir host in time and space (Supplementary information S1 (box)). Many ecological and physiological factors influence these variables in communities of reservoir animals; however, two sets of factors are dominant.
The mode of pathogen release from reservoir hosts determines the major routes of transmission. Pathogens may be released in host excretions, through slaughter or through an arthropod vector (FIG. 1). The probability of a pathogen being released from a reservoir host is affected by its presence and viability in relevant tissues, such as the blood for many vector-borne pathogens, tissues contacted or consumed during butchering and eating for some food-borne pathogens, and tissues through which external shedding occurs for direct or environmental routes. For example, the viral load and excretion rates in the salivary glands are key determinants for the transmission of rabies virus from carnivores, whereas viral loads in the intestinal and respiratory tracts affect the transmission of avian influenza virus from poultry [24][25][26] . Likewise, the release of pathogenic Leptospira The first set is the natural history of infection in hosts, which includes the duration, intensity and severity of infection and the level of shedding. Second, the movement and behaviour of hosts affect contact and the likelihood of exposure within and between species. These factors interact with the abundance, density, demographic turnover, spatial distribution and physiological state of hosts to determine the efficiency of spread 21 . Collectively, these processes determine how the pathogen is distributed across reservoir host populations. Such pathogen distribution can be highly variable (for example, pulses of Sin Nombre virus infections in deer mice (Peromyscus maniculatus) populations in response to climate-driven increases in population density) 22 , or stable (as illustrated by Mycobacterium bovis infections in populations of livestock and wildlife) 23 .  Figure 1 | Pathways to spillover. The risk of spillover is determined by a series of processes that link the ecological dynamics of infection in reservoir hosts, the microbiological and vector determinants of survival and dissemination outside of reservoir hosts, the epidemiological and behavioural determinants of exposure, and the within-host biological factors that shape the susceptibility of recipient hosts. The distribution and intensity of infection in reservoir hosts, followed by pathogen release, movement, survival and possible development to infectious stage, determine the pathogen pressure, which is defined as the amount of pathogen available to the recipient host at a given point in space and time. Pathogen pressure then interacts with the behaviour of the recipient host (and vector for vector-borne pathogens) to determine the likelihood, dose and route of exposure. A series of withinhost barriers then determine host susceptibility, and, therefore, the probability and severity of infection for a given pathogen dose.
spp. from animal hosts requires colonization of the renal tubules 27 . The excreted pathogen load depends on the quantity of leptospires that effectively colonize the tubules 28 , the rate of release and the urinary output of the host 29 . Moreover, the pathogen undergoes several changes in its lipopolysaccharide content and proteome during colonization and shedding in the urine 30,31 , which suggests that priming in the renal milieu is required to adapt for survival and infectivity in the external environment. The rate of pathogen release is a crucial determinant of spillover risk, and care must be taken to appropriately formulate models that represent the rate of release for each route of transmission (BOX 1; Supplementary information S1 (box)).
information S1 (box)). Spillover of pathogens that have short survival times (for example, influenza A virus when transmitted through the respiratory route) 33,34 may require close interactions between reservoir and recipient hosts. Consequently, spillover patterns in recipient hosts correspond to the prevalence patterns in reservoir hosts. By contrast, if pathogens survive for sufficient periods of time outside of their reservoir hosts, they may be dispersed beyond the home range of the host through fomites or environmental transport. In this case, the release of a pathogen from its reservoir host and human exposure to the pathogen may become disconnected in space and time. An example is the spread of aerosolized Coxiella burnetii Following the release of a pathogen from its reservoir host, the opportunity for spillover transmission is influenced by the duration of pathogen survival outside of its host, the extent of spatial dispersal through passive transport (for example, through water, on fomites or in the air), and possible pathogen reproduction or obligate developmental stages outside of the primary host (for example, Yersinia pestis, the causative agent of plague, must multiply within flea vectors before it can be transmitted to humans 32 ). These processes can be represented as the probability that the pathogen (shed, harvested or colonized in a vector) survives and is infectious at a given point in time, and is dispersed or transported to a particular location (  a | Determinants of spillover are being studied by researchers in many disciplines. b | A pathogen must overcome a series of barriers to transmit from one species to another. If any of these barriers is impenetrable, spill over cannot occur. Spillover of some pathogens requires that gaps (depicted as holes) in all of the barriers align within a narrow window in space and time (indicated by the blue arrow, see Supplementary information S2 (movie)). For other patho gens, protracted survival in the environment (for example, Bacillus anthracis spores 109 ), or wide dissemination (for example, the spread of aerosolized Coxiella burnetii by wind 35 ), may stagger the alignment of barriers to spillover. c | Top panel: hypothetical dose available over time for a given pathogen. In scenario 1 (dashed light blue line), the pathogen is excreted consistently from infected reservoir hosts. In scenario 2 (solid light blue line), the pathogen is excreted in regular but short high-intensity pulses over time. In both scenarios, the mean dose over the time interval is the same. Bottom panel: the likelihood that this dose will translate into infection depends on the functional form of the dose-response relationship. If the dose-response relationship is linear (green line), these two excretion scenarios generate the same total probability of spillover over the time interval shown. However, for nonlinear dose-response relationships, the total probability of spillover differs between scenarios. If the relationship is sigmoidal (red line), there is some probability of spillover whenever the dose exceeds zero (indicated by the intensity of the red shading in the top panel), but the total spillover probability in scen ario 2 is markedly higher. In the extreme case in which the recipient host can be infected only by a dose that exceeds a sharp threshold, as suspected for Bacillus anthracis 67,68,79 , the pathogen in scenario 2 will spill over when the dose peaks above the threshold (blue solid line near peak), but the pathogen in scenario 1 will never spill over.
by wind, which can lead to outbreaks of Q fever in humans that live several kilometres from the livestock reservoir hosts 35 .
As illustrated by rabies virus, pathogenic Leptospira spp. and E. coli O157 (FIG. 3), the bottlenecks that hinder the transfer of pathogens between species depend on the ecology of the reservoir host and the pathogen, and the interactions among the determinants of spillover. For example, the primary driver of pathogen pressure for rabies virus is the prevalence of infection in key hosts (such as domestic dogs 36 ). Nonlinearities in rabies transmission generate a threshold effect in susceptible host density below which the pathogen cannot persist. These thresholds can be used to set vaccination targets for disease elimination 37 . By contrast, pathogen pressure of L. interrogans is also affected by fluctuations in reservoir host density (such as rodents 29 ), and prevalence and shedding from infected animals 29 . However, if human exposure occurs through mechanisms that aggregate and disperse pathogens shed by many individuals (through accumulation in the environment, sustained survival after exiting the host 38 , and dispersal through rain, rivers and flood waters 39 ), the detailed dynamics in reservoir hosts do not matter because they get integrated out by the environmental reservoir. In this scenario, spillover risk is determined by the aggregate pathogen pressure, human behaviours that determine exposure and the integrity of within-human barriers to infection. For example, when flooding mobilizes Leptospira spp. during the wet season in Brazil, human exposures can become widespread and epidemics of spillover infection can occur 40 . During these extreme environmental events, control efforts must focus on preventing exposure to contaminated sources (for example, by wearing protective clothing and boots 41 ) and reducing the infectious inoculum rather than reducing the source of pathogen shedding, as the release of Leptospira spp. into the environment by animal reservoirs occurs before the extreme precipitation. Similarly, pathogen pressure of E. coli O157 is affected by the density of its cattle host population 42 , by variation in shedding among individuals and by prevalence in herds 43 . Each of these factors can be highly skewed and seasonal 44,45 . If spillover events are driven by contact between humans and cattle, then variation in pathogen load among animals would interact with nonlinear doseresponse functions to determine spillover risk (see below). However, this individual variation matters less if human exposure to exposure through different routes of transmission 52 . Human behaviours, such as occupational interactions with reservoir host animals, the consumption of certain animal products or the use of particular environments, may increase the risk of infection 53 .
Exposure is often conceptualized as a simple point of contact. However, nonlinear interactions between pathogen pressure, human risk behaviour and environmental factors can lead to unexpected complexity, especially for vector-borne diseases. For example, in rats, both a high prevalence of Y. pestis and high mortality may be necessary to drive outbreaks of bubonic plague in humans. Widespread exposure of humans through flea bites occurs only after a decrease in the abundance of rats, which are the primary hosts of Y. pestis in peridomestic settings 54 . Indeed, historically, high rat mortality ('rat-fall') was an indication of an imminent human plague epidemic 32 . Thus, killing rodents in response to cases of bubonic plague in humans could inadvertently increase the severity of the epidemic 54 . Conversely, and controversially, zooprophylaxis, which involves diverting vector bites from humans by increasing the local population density of another animal host, may decrease the risk of human exposure 55 . For example, the presence of chickens and dogs in rural areas of Argentina occurs after human-mediated dispersal of the pathogen through irrigation, meat processing and food transportation [46][47][48] . In this instance, outbreaks of E. coli O157 are determined by the pathogen pressure on vegetables or in hamburger meat, potentially derived from many sources. As the dose that is required for E. coli O157 spillover is thought to be very low 49,50 , public health policies aim to completely eliminate pathogen pressure in food that is processed for human consumption 50 . To achieve this goal, interventions are focused on creating successive bottlenecks in several barriers to spillover, including decreasing cattle density, preventing faecal contamination during meat processing and increasing cooking temperatures to reduce exposure dose in ground beef 43,47,51 . Cumulatively, these efforts are usually successful, but high levels of shedding from cattle during summer can sometimes overwhelm interventions 47 .
Exposure. The next phase of spilloverexposure -bridges the upstream processes that generate pathogen pressure and the within-host processes in the recipient that determine whether a given dose generates a spillover infection (see below). The interaction between recipient hosts and pathogen pressure determines both the dose and the route of exposure. Different behaviours of the recipient host are relevant

Box 1 | The mathematics of spillover
The opportunities for cross-species transmission are influenced by processes that occur at scales from molecules to landscapes (FIG. 1). These processes are subjects of intense study, and their characterization is complicated by their variability in space and time, nonlinear responses and interactions with outside factors. Consequently, it is impossible to integrate all of the determinants of spillover transmission -or to assess the effects of gaps in our knowledge about these determinants -without appropriate tools, such as mathematical and computational models 107 .
In Supplementary information S1 (box), we present a general mathematical model of the spillover process, which provides a template for integrating our knowledge of processes for specific disease systems. This model framework essentially translates FIG. 1 into mathematical expressions. It allows for variation in space and time, and uses different formulations for transmission through pathogen excretion, slaughter or arthropod vectors.
The mathematical model reflects the modular nature of the spillover process, as emphasized in the main text, while highlighting dependencies among factors in ways such as the following: • Factors that are linked to disease ecology of the reservoir host and the mode of pathogen release determine the amount of pathogen released to the environment or vector. • Pathogen survival and transport outside of the animal host, which give rise to pathogen pressure at a particular place and time, are modelled with simple probability kernels. • Human risk behaviours determine how this pathogen pressure translates to exposure dose. • The probability of infection for a given dose and route of exposure is encapsulated in the dose-response relationship (FIG. 2c).
Mathematically, the focal point of this process is the dose to which the recipient host is exposed. All upstream factors come together, with appropriate functional dependencies, to shape this dose. To a reasonable approximation, which is consistent with current practice in quantitative microbial risk assessment 108 , the consequent risk of infection can be modelled independently through the dose-response relationship.
decreased the rate at which Triatoma infestans transmitted Trypanosoma cruzi, the causative agent of Chagas disease, to humans 56 . However, increasing the population density of reservoir hosts may also affect vector survival, vector abundance and pathogen prevalence in reservoir hosts, which, in turn, increases pathogen pressure and offsets reductions in human-vector contact rates 56,57 . These complexities highlight the need to understand the mechanisms that contribute to particular routes of spillover.
All of the factors that precede human exposure, mediated by human behaviour and environmental factors (FIG. 1), cumulate individual receptivity 58 . Physical barriers include the skin, mucous membranes, mucus, stomach acid or the absence of functional receptors that enable the pathogen to enter its target cells or tissues 10 . Interferoninduced and other innate immune responses may be triggered after the initial infection of a cell, resulting in protective mechanisms such as apoptosis or the induction of interferon-induced resistance in surrounding cells 59 . In addition, interfering defensive proteins in the host cell cytoplasm may block the replication of intracellular pathogens. In other cases, cells lack functional host factors that are required for the replication of the in the dose to which a host is exposed at a given location and time (the integral of the pathogen pressure in space and time to which the host has been exposed (Supplementary information S1 (box)).

Probability of infection.
Following cross-species exposure of a recipient host, the within-host barriers and their interactions with the strain of pathogen determine the functional relationship between the pathogen dose and the likelihood that an infection will establish. Within-host barriers to infection vary widely and depend on the specific combinations of pathogen, host species and  Figure 3 | Bottlenecks to spillover. Different barriers permit or constrain the flow of pathogens from one species to another. The figure is illustrative, owing to the lack of sufficient data for more than one or two barriers for any given system. The width of the gaps in barriers represents the ease with which a pathogen can flow through the barriers and will vary depending on context. The question marks represent points at which the barriers are especi ally poorly understood and highlight gaps in our knowledge of some patho gens that are of global concern (for example, the lack of information on disease dynamics in reservoir hosts of Ebola virus). Many rabies virus reservoirs, such as domestic dogs, are widely distributed. The prevalence of rabies virus is generally low and the incidence of spillover closely tracks the prevalence of infection in the reservoir host. Rabies virus is almost always fatal to spillover hosts 25 . Interventions are usually aimed at reducing the preva lence in reservoir hosts through vaccination 37 . Leptospira interrogans survives in water and soil after being shed in the urine of a wide range of rodents and other reservoir hosts 29 . Key bottlenecks to the zoonotic spillover of this patho gen are exposure and within-host barriers. For example, during floods in Brazil, many humans that are exposed do not become infected, probably because the initial within-host barrier, the skin, is not penetrated 41 . However, once L. interrogans penetrates the skin (for example, through skin wounds), 1-10 leptospires may be sufficient to cause systemic infection 110 . Therefore, wearing protective clothing and boots is an effective control measure 41 . Important bottlenecks to Escherichia coli O157 spillover include heterogeneous shedding from cattle 43,44 (although it is still unknown whether super-shedding is a characteristic of particular individuals or is a transient phase that occurs in most cattle 42 ). In some contexts, exposure is an important bottleneck; for example, when the pathogen is eliminated from food through cooking. Widespread dispersal leads to uncertainties about the source of many outbreaks 46,47 , and weak within-human barriers enable low doses of E. coli to cause infection 49,50 . Humans are frequently exposed to Toxoplasma gondii carried by domestic cats and intermediate hosts, but the parasite rarely causes disease because most humans have strong within-host immunological barriers. Cats are widely and densely distributed, but the prevalence of T. gondii is low and cats shed oocysts only once in their lifetime 111 . However, sporulated oocysts survive in the environment for long periods of time 112 . Limiting exposure to oocysts may prevent spillover; however, this is challenging when it is unclear whether cats or the environment are the major sources of infection in humans 111,113 . Ebola virus has not been isolated from bats and the definitive reservoir bat species is unknown 114 ; therefore, characteristics of infection in bats are unknown 114,115 . The pathogen is released through excretion or slaughter, then survives for up to a week, depending on the environmental conditions 116 . The most tractable bottlenecks for intervention may be the zoonotic exposure of humans through interaction with bats, bushmeat or the carcasses of other species 97,117,118 , because once exposed, the within-host barriers to Ebola virus may be extremely low 119 . pathogen 60,61 . Even when pathogens can replicate within cells, several barriers can prevent their transmission to other cells 62,63 and thus the establishment of an infection. For example, avian influenza virus must pass through a series of within-host barriers to infect a human, including mucins in respiratory tract excretions, specific receptor molecules that constrain virus entry into cells and have different distributions in the respiratory tracts of different host species, suboptimal viral polymerase that restricts the ability of the virus to replicate in cells of the human respiratory tract, viral neuraminidase that is inefficient in its role in the release of influenza viruses from infected cells, and innate immune responses that are initiated early and that block infection in both infected and neighbouring cells 63,64 .
From an epidemiological perspective, these within-host interactions between zoonotic pathogens and hosts can be encapsulated by the functional relationship between pathogen dose and the probability of an infection. Although there is much to learn about dose-response relationships, they are expected to be nonlinear as, at minimum, they must saturate at high doses because the probability of infection cannot exceed one 65 . This nonlinearity imposes a filter on the dynamics of pathogen pressure and exposure (FIG. 2c). If the dose-response relationship is highly nonlinear, such that small changes in dose lead to large changes in the probability of an infection, then variation in any of the upstream factors that culminate in an exposure dose (including released dose, pathogen survival and human behaviour) may have disproportionate effects on the probability of spillover. Such effects could generate opportunities for targeted control measures. Moreover, nonlinear dose-response relationships may imply that infrequent high-intensity exposures are more likely to cause spillover infections than continuous low-intensity excretion. This phenomenon has been reported for occupational exposure to Bacillus anthracis aerosols; tannery workers who were exposed to infrequent high doses of B. anthracis spores in imported goat hair were more likely to die of anthrax than those who were exposed to frequent low doses of B. anthracis spores [66][67][68] . Conversely, if doses are far below the inflection point on the dose-response curve (FIG. 2c), then the system may be insensitive to changes in dose. If the doseresponse function is close to linear, the total exposure dose over time is equal and host responses do not change as a consequence of early exposures, then longer-term exposure Once a pathogen has penetrated the within-host barriers to replicate and disseminate in the new host, the outcome of the infection may range from subclinical elimination of the microorganism to the death of the new host, and from dead-end spillover infection to sustained human-to-human transmission. For many important zoonotic pathogens, such as HIV or Zika virus, the transmission that drives the current public health crisis is human-to-human 81,82 and the events that led to spillover are long past. Although understanding disease severity and onward transmission is essential for understanding the consequences of emerging infectious diseases, these processes are beyond the scope of this article. Our current knowledge of the biological features of pathogens and characteristics of host-pathogen interactions that determine these outcomes are described elsewhere (for example, see REFS 83,84).

Assessing zoonotic risk
When gaps in barriers to spillover are highly dynamic in time and space, they may vary asynchronously, so that the alignment of gaps in all barriers may be fleeting and spillover may seem random (Supplementary information S2 (movie)). Research methods that group multiple barriers or integrate data over space and time may not capture these dynamics. For example, ecological niche models are often used to study zoonotic risk by assessing the distribution of reservoir hosts or vectors 85 , but this approach overlooks variation in downstream barriers that might drive risk. Alternatively, niche models that are based on the documented occurrence of spillover may capture the accumulated distribution of all conditions that enabled barriers to be breached over time (FIG. 1), but they cannot isolate the precise barriers that affect spillover risk (for example, see REF. 86). Therefore, niche models tend to overestimate the spatial range of spillover risk and do not readily enable extrapolation to novel conditions 87 . Examples of this include Hendra virus and Marburg virus, which can be excreted in discrete temporal and spatial pulses from their bat reservoir hosts 20,88,89 . However, for spillover, shedding must align with environmental and bat population conditions that generate levels of pathogen pressure that are sufficient to produce an infectious dose (FIG. 2), and with exposure behaviours and susceptibility of the recipient hosts. As some of these conditions vary among seasons and years, the pattern of outbreaks in livestock or humans has high spatial and temporal variability 20,89 . However, to a low but constant dose may generate the same probability of infection as intermittent high-intensity exposures (FIG. 2c).
The genetic, immunological and physiological state of the host also can modulate the dose-response relationship. Immunosuppression (for example, due to AIDS, immunosuppressive drugs, co-infections or malnutrition) increases gaps in within-host barriers, which shifts dose-response curves and increases susceptibility 69,70 . For example, in immunosuppressed hosts, the decreased number or activity of lymphocytes can reduce the dose that is required to establish an infection with the widespread pathogen Toxoplasma gondii, or cause the loss of control of T. gondii infections that are usually kept in check by sustained immune pressure 71 (FIG. 3). Seasonality in human immune function (for example, enhanced baseline inflammation and altered cellular composition of the immune system in winter compared with summer) may also alter the permeability of within-host barriers by altering the magnitude and speed of immune responses 72 . Finally, the probability and severity of infection at a given dose are shaped by host genetics 73 ; triathletes with a particular gene polymorphism were at increased risk of leptospirosis after swallowing lake water compared with athletes who lacked this polymorphism 74 .
Many of the interactions at the crossroads of exposure, inoculum dose and host response are poorly understood. Therefore, very little is known about the interactions between dose, timing of exposure and probability of infection. The current dose-response paradigm is based on discrete transient exposures, but the effects of protracted or cumulative exposure to environmental pathogens (for example, to low concentrations of Leptospira spp. in floodwater) are unclear 75 . Repeated low-dose exposure can increase host immunity to infection (for example, as postulated for poultry handlers who are exposed to avian influenza 76 , dairy farmers who are exposed to E. coli O157 (REF. 77) and mice that are exposed to continuous infections of parasites 78 ). However, increases in immunity are not always observed; for example, such effects on immunity were not observed in tannery workers who were exposed to B. anthracis 67,68,79 . Moreover, it may be difficult to differentiate between a cumulative dose effect and the increasing opportunity to initiate an infection with each additional low-dose exposure (if each infectious unit has a probability of causing an infection that is above zero) 20,80 . as niche models often summarize risk across large areas and long durations, they overlook important heterogeneities and they lack the specificity that is required for public health intervention. Although niche models can help to identify regional-to-continental concentrations of risk 90,91 , risk assessments that are more quantitative and more precise with regard to space, time and which barriers they address are needed to guide concrete action.
Epidemiological investigations of spillover also need to account for conditions that are highly dynamic in space and time. If the alignment of gaps in all barriers is fleeting, delayed diagnoses or inconsistent case detection may delay outbreak investigations until the conditions that enabled spillover have changed. Similarly, investigations are sometimes triggered once the case count becomes high. These challenges differ among pathogens with different values of R 0 (the basic reproductive number or expected number of secondary infections caused by a typical infected individual in a susceptible population). For supercritical pathogens with R 0 >1, which can cause major epidemics through sustained transmission in human populations (for example, Ebola virus, Zika virus and the pandemic strain of severe acute respiratory syndrome coronavirus (SARS-CoV) 4,81,92 ), spillover becomes challenging to study because a given human case is likely to be far removed in time or space from the spillover event that triggered an outbreak. Subcritical pathogens with 0 <R 0 <1, which cause self-limiting outbreaks or 'stuttering chains' in human populations (for example, monkeypox or avian influenza viruses 93,94 ), raise distinct challenges because any given individual could have been infected by either an animal or a human source 16 . It is easiest to study the spillover of pathogens with R 0 = 0 that are not transmitted between humans (for example, rabies virus or West Nile virus 25,95 ), in which every case is an instance of spillover. The 2014-2015 Ebola virus epidemic in West Africa is a prime example whereby delayed response and investigation prevented researchers from reconstructing the conditions that initiated the human epidemic of a supercritical pathogen 96,97 . Ebola virus infection is an extreme example of spillover infection that only occurs during the rare alignment of gaps in barriers, and, accordingly, the precise determinants of risk are poorly understood (FIG. 3). By contrast, for other zoonoses, such as trypanosomiasis in some parts of Africa, incidence is high because the pathogen flows through consistently wide gaps in barriers to infection

Outlook
The framework presented in this Opinion article highlights that an important frontier in research on zoonotic spillover is to understand the functional and quantitative links among the determinants of spillover. To our knowledge, all of the processes that are necessary to achieve spillover have not been connected, compared and quantified for any single zoonotic pathogen. We address this gap, in part, by introducing a conceptual and quantitative model that can be used to integrate existing data, identify high-priority data gaps, investigate conditions that widen or align gaps in barriers to spillover, and identify the best gaps on which to focus intervention efforts. We suggest that future research focuses on developing case studies that contribute to fully quantifying the determinants of spillover and their linkages, with the goal of making operational contributions to risk assessment. We provide a mathematical framework that formalizes the ideas presented here to guide the formulation of mechanistic spillover models for particular zoonotic pathogens (BOX 1; Supplementary information S1 (box)). We anticipate that this synthetic framework will provide a foundation for cross-scale data integration, transdisciplinary investigation, and a new body of theory on spillover that is necessary for risk assessment and public health planning.
(for example, common exposure to infected animal hosts and tsetse fly vectors, and low resistance in humans due to the ability of trypanosomes to neutralize or avoid human innate immune activity 98,99 ). In all scenarios, irrespective of the frequency with which gaps align, the concept of hierarchical barriers can be used to organize and quantify the conditions that enable spillover.
The influence of particular barriers may vary in space and time, and this variation -coupled with data on realized spillover events -can help elucidate factors that shape infection risk, even in the absence of information on other barriers. In the westernmost province of the Democratic Republic of Congo, the observed lack of monkeypox spillover, despite high seroprevalence in the suspected reservoir hosts (Heliosciurus spp. and Funisciuris spp.), was attributed to cultural norms that forbade the consumption of small rodents 100 . The inconsistency between ecological data that suggested high pathogen pressure and epidemiological data that indicated a lack of spillover, focused attention on human behaviours that affect the probability of exposure. Research approaches that integrate data on multiple barriers are more likely to discern such behavioural effects.
Broad-scale discovery of novel microorganisms has the potential to characterize the pool of possible zoonotic pathogens and provide valuable baseline information 101,102 . However, each of the ~63,000 species of mammals, birds, reptiles, amphibians and fish 103 contains a multitude of infectious viruses, bacteria and parasites 101,102,[104][105][106] . Although each of these microorganisms and parasites can be viewed as a potential pathogen, the vast majority may not cause disease in their natural hosts, and the extent to which they infect or cause pathology in other species, including humans, is unknown 7,9,10 . Therefore, discovery alone cannot address the potential risk of spillover. The translation of new discoveries of microorganisms into guidance for public health practitioners requires the identification of the barriers to microbial infection of humans, the conditions that facilitate the breaching of these barriers, and, therefore, the microbiological and environmental contexts that pose the greatest risk to human populations. For the foreseeable future, the greatest practical contribution of pathogen discovery and sequence characterization to the epidemiology of emerging pathogens is likely to be in the rapid post hoc identification of novel pathogens after spillover.


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The nucleocapsid proteins of mouse hepatitis virus and severe acute respiratory syndrome coronavirus share the same IFN-β antagonizing mechanism: attenuation of PACT-mediated RIG-I/MDA5 activation

Coronaviruses (CoVs) are a huge threat to both humans and animals and have evolved elaborate mechanisms to antagonize interferons (IFNs). Nucleocapsid (N) protein is the most abundant viral protein in CoV-infected cells, and has been identified as an innate immunity antagonist in several CoVs, including mouse hepatitis virus (MHV) and severe acute respiratory syndrome (SARS)-CoV. However, the underlying molecular mechanism(s) remain unclear. In this study, we found that MHV N protein inhibited Sendai virus and poly(I:C)-induced IFN-β production by targeting a molecule upstream of retinoic acid-induced gene I (RIG-I) and melanoma differentiation gene 5 (MDA5). Further studies showed that both MHV and SARS-CoV N proteins directly interacted with protein activator of protein kinase R (PACT), a cellular dsRNA-binding protein that can bind to RIG-I and MDA5 to activate IFN production. The N–PACT interaction sequestered the association of PACT and RIG-I/MDA5, which in turn inhibited IFN-β production. However, the N proteins from porcine epidemic diarrhea virus (PEDV) and porcine reproductive and respiratory syndrome virus (PRRSV), which are also classified in the order Nidovirales, did not interact and counteract with PACT. Taken together, our present study confirms that both MHV and SARS-CoV N proteins can perturb the function of cellular PACT to circumvent the innate antiviral response. However, this strategy does not appear to be used by all CoVs N proteins.

INTRODUCTION
Coronaviruses (CoVs) are a type species of enveloped viruses with a single-stranded positive RNA genome of approximately 26-32 kilobases. CoVs belong to the Coronaviridae family, classified into the order Nidovirales along with Arteriviridae [1,2]. All CoVs share similarities in the organization and expression of their genome, which is arranged into open reading frame (ORF) 1a, ORF1b, S, ORF3, E, M and N in order, with both termini flanked by 5′-and 3′-untranslated regions. CoVs are separated into four genera based on phylogeny: alpha-CoV, beta-CoV, gamma-CoV and delta-CoV. The beta-CoV genus contains important veterinary and human etiological agents, such as mouse hepatitis virus (MHV) and severe acute respiratory syndrome coronavirus (SARS-CoV). MHV infects mice liver or neuron, but it is a well-studied coronavirus model for similar patterns of pathogenesis [5]. SARS-CoV is the etiological agent behind an outbreak of severe respiratory disease through China during 2002-2003 [6][7][8].

Research Paper
Oncotarget 49656 www.impactjournals.com/oncotarget Virus infection can be sensed in host cells through the interaction of pathogen-associated molecular patterns with host pattern recognition receptors [13]. During CoVs infection, viral components or replication intermediates can be detected by retinoic acid-induced gene I (RIG-I) and melanoma differentiation gene 5 (MDA5) [14][15][16][17]. Activated RIG-I and/or MDA5 recruit the mitochondrial adaptor protein MAVS/IPS-1/Cardif through caspase activation and recruitment domain (CARD)-CARD interactions to activate the downstream κB kinase (IKK)related kinases, such as TANK-binding kinase 1 (TBK1) and IKKε. The TBK1-IKKε complex then mediates downstream activation of the critical transcription factors, interferon regulation factor 3 (IRF3) and nuclear factor κB (NF-κB), triggering their nuclear translocation, where they form active transcriptional complexes that bind to IFN promoter elements and activate type I IFN gene expression [18][19][20]. Recent studies have identified a new molecule that acts as a protein activator of protein kinase R (PKR), namely protein activator of the interferoninduced protein kinase (PACT) [21,22], and also promotes RIG-I signaling in response to viral infection and dsRNA through interacting with the carboxy-terminal domain (CTD) of RIG-I, which in turn stimulates RIG-I ATPase activity [23,24].
Many CoVs, including MHV, SARS-CoV, Middle East respiratory symdrome (MERS)-CoV, porcine epidemic diarrhea virus (PEDV), and porcine deltacoronavirus (PDCoV), have been reported to cause dysregulated type I IFN expression to counteract the host innate immune defense, and this is mostly due to virus produced-proteins involved in IFN evasion [25][26][27][28][29][30][31][32][33][34][35]. Nucleocapsid (N) proteins of CoVs are a substantial part of the viral proteins, seemingly dedicated to suppressing the antiviral response [36]. SARS-CoV and PEDV N proteins can inhibit IFN-β synthesis, and this occurs by an unknown mechanism in SARS-CoV [37]. We have previously found that PEDV N protein blocks IFN-β signaling by targeting TBK1 to prevent its interaction with IRF3. We also investigated whether MHV and SARS-CoV N proteins use the same strategy to antagonize IFN-β production; however, the N proteins of MHV and SARS-CoV do not interact with TBK1 [38].
In the present study, we showed that MHV N protein is responsible for IFN-β inhibition, and exerts its IFN-antagonizing function by targeting PACT, thereby impairing PACT-RIG-I/MDA5 interactions and RIG-I/ MDA5 activation for downstream IFN production. This is the same evasion strategy developed by SARS-CoV N protein. However, this mechanism was not shared by PEDV or porcine reproductive and respiratory syndrome virus (PRRSV) N proteins. Our findings thus shed light on the IFN-regulatory mechanism employed by beta-CoV N proteins.

MHV N protein blocks Sendai virus-induced IFN-β production
The MHV N protein has been reported to block type I IFN-induced antiviral responses, but the effect on IFN-β production is still unclear [39]. To test this, HEK-293T cells were co-transfected with a DNA expression construct pCMV-tag2b-MHV-N encoding MHV N protein and luciferase reporter plasmid IFN-β-Luc, together with the internal control plasmid pRL-TK, and then infected with Sendai virus (SEV) or treated with poly(I:C). As shown in Figure 1A and 1B, the overexpression of MHV N protein blocked the SEV/poly(I:C)-induced IFN-β promoter activities. To further confirm that IFN-β expression was inhibited by MHV N protein, HEK-293T cells were transiently transfected with pCMV-tag2b-MHV-N plasmid and then infected with SEV for 12 h. Cells were collected for detection of IFN-β relative mRNA expression and SEV NH gene copies by real-time RT-PCR. The expression of MHV N protein was detected by immunoblotting with an anti-Flag antibody. As shown in Figure 1C, SEVinduced mRNA expression of IFN-β was significantly reduced in MHV N protein-expressing cells, meanwhile SEV NH RNA in mock cells equaled to cells expressing MHV N protein. This indicated MHV N perturbed IFN-β production not result from inhibiting SEV replication. The coordinated and cooperative actions of transcription factors IRF3 and NF-κB are essential to mediate type I IFN expression [40,41], therefore IRF3 and NF-κB were additionally examined to investigate the role of MHV N protein in type I IFN suppression. Luciferase reporter gene assays were performed with luciferase reporter plasmids IRF-3-Luc ( Figure 1D) or NF-κB-Luc ( Figure 1E). Our results indicated that SEV-induced promoter activities of IRF-3 and NF-κB are also blocked by MHV N protein.
SEV-induced IFNs initiate a series of signaling cascades through the Janus kinase/signal transducer and activator of transcription pathway to direct antiviral and immune gene expression, resulting in the suppression of virus replication [42]. Vesicular stomatitis virus encoding green fluorescent protein (VSV-GFP) is sensitive to this antiviral response [43][44][45], which makes it a good model system to screen IFN-β antagonists. To further confirm the inhibitory effect of MHV N protein on the SEV-induced antiviral response, HEK-293T cells were transfected or mock transfected with pCMV-tag2b-MHV-N plasmid prior to infection with SEV and VSV-GFP. Dramatic reduction of VSV-GFP proliferation was observed after SEV infection, but a robust restorative effect was observed in N protein-expressing cells ( Figure 1F). These results further confirmed that MHV N protein antagonizes the production of IFN-β.
Oncotarget 49657 www.impactjournals.com/oncotarget MHV N protein blocks IFN-β production by targeting molecule(s) upstream of RIG-I and MDA5 RIG-I and MDA5 are RNA sensors and can be activated by their respective RNA ligands to induce the downstream cascade pathway [46,47]. To identify the molecular target of MHV N protein in the IFN-induction signaling pathway, HEK-293T cells were co-transfected with pCMV-tag2b-MHV-N vector and luciferase reporter plasmids containing the IFN-β promoter and Renilla luciferase-expressing plasmid, pRL-TK, and a series of IFN-β promoter stimulators in the RIG-I-like receptor (RLR) signaling pathway, including RIG/RIG-IN, MDA5, IPS-1, TBK1, IKKε, IRF3 and IRF3-5D. Luciferase assays were performed at 28 h after co-transfection. The overexpression of any molecule in the RLR signaling pathway dramatically stimulated IFN-β promoter activation, but interestingly, MHV N had no inhibitory effect on this activation (Figure 2A-2E). This is coincident with SARS-CoV N protein inhibition of IFN-β production, which also fails to affect the activation of these signaling molecules. We re-confirmed that overexpression of SARS-CoV N protein did not inhibit IFN-β promoter activation induced by RIG-I ( Figure 2F) and MDA5 ( Figure 2G) under our experimental conditions. If MHV N protein involved the identical IFN-β-antagonizing mechanisms to SARS-CoV N protein, which has been demonstrated not interact with RIG-I or MDA5 [37], MHV N protein should not be supposed to target RIG-I or MDA5. To test this hypothesis, expression plasmids of hemagglutinin (HA)-tagged MHV N and Flag-tagged RIG-I ( Figure 2H) or MDA5 ( Figure 2I) were co-transfected into HEK-293T cells. At 28 h posttransfection, co-immunoprecipitations were performed with anti-Flag antibodies. As speculated, The results represent the means and standard deviations of three independent experiments. The relative firefly luciferase activity was normalized to the Renilla reniformis luciferase activity, and the uninfected or untreated empty vector control value was set to 1. (C) HEK-293T cells were transfected with MHV-N expression plasmid. After infection with SEV for 12 h, cells were lysed to extract total RNA, which was used for detecting the expression of the IFN-β, SEV HN and GAPDH genes by quantitative real-time RT-PCR. The results are expressed as increases in mRNA levels or SEV HN gene copies relative to those in cells transfected without SEV infection and were normalized to the expression of the GAPDH housekeeping gene. (F) HEK-293T cells were transfected with MHV-N expression plasmid and then infected with SEV for 12 h. The cells were then re-infected with VSV-GFP for 24 h, followed by analysis for fluorescence by microscopy. *P < 0.05; **P < 0.01; ***P < 0.001.
Oncotarget 49658 www.impactjournals.com/oncotarget MHV N protein was not detected in neither Flag-tagged RIG-I nor MDA5 precipitates, suggesting that MHV N protein did not associate with RIG-I or MDA5. All together, these data indicated that MHV N and SARS-CoV N proteins suppress the activation of an upstream molecule in the IFN signaling cascade.

MHV and SARS-CoV N proteins attenuate PACT-mediated RIG-I/MDA5 activation of the IFN-β promoter
Previous studies have revealed that optimal activity of RIG-I and MDA5 requires PACT, a cellular dsRNAbinding protein which binds to RIG-I and MDA5 to induce IFN production [24,48,49]. MHV and SARS-CoV N proteins with RIG-I-signaling antagonistic and RNA binding properties were speculated to target PACT. To test this possibility, HEK-293T cells were co-transfected with MHV N or SARS-CoV N expression plasmids and PACT, and either RIG-I or MDA5, along with luciferase reporter plasmids. Luciferase assays were performed to assess the suppressive activity in the presence of PACT and either RIG-I or MDA5 at 28 h after co-transfection. As shown in Figure 3, PACT promoted the activation of RIG-I and MDA5 for induction of IFN-β promoter activity, but this effect was reversed by the N proteins of MHV and SARS-CoV. These findings indicated that MHV ( Figure 3A and 3B) and SARS-CoV ( Figure 3C and 3D) N proteins counteract the activity of PACT.

MHV and SARS-CoV N proteins bind to PACT and impair interactions between PACT and RIG-I/MDA5
If MHV or SARS-CoV N proteins target PACT, then colocalization between these proteins should occur within cells. To test this association, Flag-tagged MHV or SARS-CoV N proteins and HA-tagged PACT were co-transfected into HEK-293T cells. Indirect immunofluorescence assay (IFA), reciprocal co-immunoprecipitation and immunoblotting experiments were then performed. IFA showed that MHV and SARS-CoV N proteins were both distributed in the cytoplasm, as was PACT ( Figure 4A). found that PACT was present in the MHV and SARS-CoV N protein precipitates and vice versa, MHV and SARS-CoV N proteins were present in the PACT precipitate ( Figure 4B-4E). These results indicated that MHV and SARS-CoV N proteins can interact with PACT.

Co-immunoprecipitation and immunoblotting experiments
PACT physically binds to the C-terminal repression domain of RIG-I and potently stimulates RIG-I-induced type I interferon production, resulting in stimulation of its ATPase activity [24]. N protein-PACT interactions may impair the association between PACT and RIG-I. To assess this hypothesis, HEK-293T cells were transfected with increasing amounts of Myc-tagged MHV or SARS-CoV N proteins, together with constant amounts of pCAGGS-HA-PACT and Flag-tagged RIG-I. Immunoprecipitation was performed with anti-HA antibody, and the coprecipitated N proteins and RIG-I were analyzed by immunoblotting with anti-Myc and anti-Flag antibodies. As shown in Figure 5A and 5B, PACT efficiently pulled down RIG-I. However, the amount of RIG-I present in PACT precipitates gradually decreased as the amount of MHV and SARS-CoV N proteins increased. These results suggested that MHV and SARS-CoV N proteins inhibit the PACT-RIG-I interaction in a concentration-dependent manner.
Considering that co-expression of MDA5 and PACT resulted in enhancement of MDA5induced activation of the IFN-β promoter [48,49], we hypothesized that the activation of MDA5 might plausibly be mediated by a direct interaction with PACT. We therefore performed co-immunoprecipitation experiments with lysates of co-transfected or single vector HA-tagged PACT and Flag-tagged MDA5 transfected HEK-293T cells. As shown in Figure  5C and 5D, both HA-tagged PACT and Flag-tagged MDA5 proteins were found to co-precipitate upon detection with anti-HA and anti-Flag antibodies. This indicated that PACT also interact with MDA5. To investigate this interaction further, we repeated the Oncotarget 49660 www.impactjournals.com/oncotarget co-immunoprecipitation experiment to detect whether N-PACT interactions could also disrupt the PACT-MDA5 association. Likewise, gradually decreasing levels of MDA5 were detected in the anti-HA precipitate as MHV and SARS-CoV N protein concentrations were increased ( Figure 5E and 5F), indicating that formation of the PACT-MDA5 complex is impaired by the viral N proteins. Since MHV and SARS-CoV N proteins and PACT are dsRNA-binding proteins [50], we questioned whether the interactions between N proteins and PACT were mediated by RNA. The co-immunoprecipitation experiments were repeated in the presence of increasing amount of RNase A. The amounts of MHV and SARS-CoV N proteins detected in the PACT precipitates were gradually reduced but not vanished with increasing dose of RNase A ( Figure 6A and 6B), indicating that MHV or SARS-CoV N protein can directly RNA-independently interact with PACT. However, N-PACT interaction was weakened when endogenous RNA was digested. PACT contains three domains (domain 1 to 3). Domain 1 and domain 2 possess dsRNA binding activity, while domain 3 is not capable of binding RNA [51]. Theoretically, PACT mutant with RNA binding domain deletion could also interact with MHV and SARS-CoV N proteins. To test this, PACT mutant (ΔPACT) deleting domain 1 and domain 2 was constructed ( Figure 6C) and verified no RNA binding activity ( Figure 6D). Then ΔPACT-N association was assessed with co-immunoprecipitation experiment using anti-HA antibodies. As shown in Figure 6E and 6F, both MHV and SARS-CoV N proteins were detected in ΔPACT precipitates, with a weaker band compared with wild type PACT precipitates. This confirmed that PACT mutant with no RNA binding activity is sufficient for N interaction.

PEDV and PRRSV N proteins do not counteract PACT
We wished to determine whether this N-PACT targeting mechanism was universal for CoV-encoded N proteins, and potentially even commonly used by the N proteins from arteriviruses, which are of the same phylogenetic order as CoVs. To test this possibility, the N proteins of PEDV, a member of the alpha-CoV genus and PRRSV, a member of the arteriviridae family, were chosen for co-immunoprecipitation experiments; both N proteins are also known to be IFN-antagonizing proteins Oncotarget 49662 www.impactjournals.com/oncotarget but function downstream of RIG-I and MDA5 [38,52]. As shown in Figure 7A and 7B, neither PEDV nor PRRSV N protein was present in the PACT precipitates. Thus, PEDV and PRRSV N proteins were not supposed to impair PACT-RIG-I/MDA5 complex conformation. To test this, Flag-tagged RIG-I or MDA5 expression plasmids, HA-tagged PACT, together with increasing amounts of pCAGGS-Myc-PEDV-N or pCAGGS-Myc-PRRSV-N vectors, were co-transfected into HEK-293T cells for 28 h. Cell lysates were harvested for co-immunoprecipitation experiments using anti-HA antibodies. As shown in Figure  7C-7F, neither PEDV nor PRRSV N proteins disrupted RIG-I or MDA5 solid interaction with PACT. Hence, these supported the notion that MHV and SARS-CoV N proteins are unique in their counteraction with PACT.

DISCUSSION
Previously, MHV N protein was reported to antagonize the IFN response, with limited information about MHV N protein effects on IFN synthesis. In this study, we found that MHV N protein was responsible for this IFN-antagonizing activity and the molecular mechanism was identical to that of SARS-CoV N protein.
We further investigated the shared IFN-antagonistic mechanism of MHV and SARS-CoV N proteins, and found that they targeted PACT to impede the interaction of this molecule with RIG-I/MDA5, leading to blockade of PACT-activated RIG-I/MDA5, and ultimately inhibition of IFN-β synthesis.
CoV N proteins share a similar overall topology structure and highly conserved function of packaging the viral genome with structural proteins to form ribonucleoprotein complexes for viral assembly [55]. However, their behaviors in host innate immune mediation were quite distinct from this structural function. Among CoV N proteins, MHV, SARS-CoV, and PEDV N proteins were identified as IFN antagonists [38]. MHV and SARS-CoV N proteins were proved by us to target PACT to inhibit IFN-β synthesis, but the N protein of MHV also inhibited RNaseL-mediated host translation shut-off [39], which has not yet been shown for SARS-CoV N. PEDV N protein was previously reported to target TBK1, thus disrupting the interaction between IRF3 and TBK1 [38]. Surprisingly, MERS-CoV N protein was demonstrated to be unable to suppress IFN production [56]. But recently, SARS-CoV and MERS-CoV N proteins were discrepantly reported to prohibit RIG-IN induced IFN-β production with same methods [57]. In addition, given the mutual RNA binding properties of CoV N proteins [58], it will be of interest to determine whether coating of dsRNA is another common mechanism by which N proteins antagonize IFN-β expression, whereby dsRNA is masked from immunosurveillance sensing molecules.
PACT harbors multiple functions. Among these, PACT heterodimerizes with PKR and activates it via protein-protein interactions between the dsRNAbinding domain present in each protein [59][60][61]. All three domains of PACT can interact with but only domain 3 activates PKR [51]. In this study, we demonstrated that domain 3 of PACT is sufficient for N proteins association, but much weakly compared with wild type PACT, which is share similarity to PACT-PKR interaction. PACT can directly interact with the CTD of RIG-I, thus stimulating its ATPase activity and mediating RIG-I activation. Here, we also identified that PACT induced MDA5 activation is via directly interaction. However, MDA5 seems to be lower activated than RIG-I upon PACT stimuli. Even though RIG-I and MDA5 share a highly homologous structure, they are activated by distinct agonists. Considering the domain of PACT which associates with and activates RIG-I/MDA5 has not been mapped, the mechanisms of PACT-mediated RIG-I/ MDA5 activation and N proteins involved in PACT-RIG-I/MDA5 perturbation remain obscure. Besides, PACT associates with TAR RNA binding protein (TRBP) and/or Dicer, thus participating the RNA interference process [62][63][64][65]. In the context of different binding partners, N-PACT counteraction may involve in distinct physiological process. The Ebola virus protein VP35-PACT interaction has been verified to interfere with RIG-I activation. Furthermore, this was also proposed to contribute to functions of RNA silencing and PKR prohibition in the absence of virus infection or IFN [66][67][68][69]. Previous study suggested that SARS-CoV N protein suppresses RNA interference [70]. Whether the CoV N-PACT association can also contribute to the biogenesis of regulatory RNAs requires further study. PACT-mediated IFN production was described by two mechanisms: PACT potentiation of the dsRNAinduced activation of RIG-I, or direct binding of PACT to the CTD of RIG-I to augment ATPase activation [24,71]. Thereafter, an expanding list of dsRNA-binding, IFN-antagonizing, and PACT-targeting viral proteins were identified. According to the feature of PACT combination, proteins can be classified into two types: RNA dependent and RNA independent. Us11 protein of Herpes simplex virus 1, VP35 of Ebola virus, and NS1 protein of influenza A virus interactions with PACT require an RNA binding motif, and Us11 or VP35-PACT interactions are verified totally RNA independent [49,72,73]. This contributes to the hypothesis that Us11 and VP35 have higher affinities for PACT than RNA, to impair direct binding of PACT to RIG-I. However, targeting of PACT by ORF4a of MERS-CoV and the N proteins of MHV and SARS-CoV are mediated by RNA [48]. Mechanistically, these three proteins may compete with RIG-I/MDA5 for dsRNA and inhibit dsRNA recognition. As mentioned above, PACT can augment RNA agonists in RIG-I/MDA5 activation. However, the interaction of MHV or SARS-CoV N proteins with dsRNA and PACT is proposed to disrupt this process. Alternatively, the antagonist-RNA-PACT complex may render RNA-PACT nonfunctional in terms of RIG-I/MDA5 activation, or prevent its subsequent combination with RIG-I/MDA5.
Despite the high homology in genetic sequence [3], IFN antagonists produced by CoVs exploit divergent strategies with complicated characteristics. Although the majority of proteins from the various CoVs display the same phenotypic trends in IFN production, there are exceptions. While nsp1, -3, -7, -14, -15, and -16 from both SARS-CoV and PEDV were found to inhibit IFN production, as was PEDV nsp5, but not SARS-CoV nsp5 [33,34,[77][78][79][80][81][82]. Thus, IFN-antagonizing activity regulated by the same antagonist with similar function from differing CoVs may do so by the same mechanism, or may not. The strategy exploited by both MERS-CoV and SARS-CoV M protein is attenuation of the formation of functional TBK1containing complexes [83,84]. Alternative examples include the papain-like proteases (PLPs). PLP from SARS-CoV, PEDV, and MHV can deubiquitinate innate immune modulators, but the specific targets are STING/ RIG-I and STING/TBK1 [33,85,86]. It would appear that a strategy of tailoring several inhibitors to one target has been adopted. As demonstrated in the present study, both MHV and SARS-CoV N proteins can target PACT. There may be compensatory mechanisms involving other viral proteins to counter PACT-mediated IFN signaling in place of the N protein, such as the inhibition achieved by ORF4a protein of MERS-CoV [48]. Whether the anti-PACT mechanism could be used by other RNA-binding and IFN-antagonistic proteins requires further research, especially with regards to the proteins from the recently emerged severe pathogens MERS-CoV and PDCoV, the IFN-inhibitory effects of which have not yet been thoroughly investigated.

Luciferase reporter gene assay
HEK-293T cells grown in 24-well plates were cotransfected with 0.1 μg/well reporter plasmid, 0.02 μg/ well pRL-TK plasmid (Promega; as an internal control for the normalization of transfection efficiency), and the indicated expression plasmid or an empty control plasmid. Where indicated, the cells were also mock-infected/ treated or infected/treated with SEV (10 hemagglutinating activity units/well)/poly(I:C) 24 h after co-transfection. The cells were lysed 12 h later and the firefly luciferase and Renilla luciferase activities were determined with the Dual-Luciferase Reporter Assay System (Promega), according to the manufacturer's protocol. The data are the relative firefly luciferase activities normalized to the Renilla luciferase activities and are representative of three independently conducted experiments. Data are presented as means and standard deviations (SD). Values of P < 0.05 were considered statistically significant, and P values of < 0.01 were considered highly statistically significant.

RNA extraction and quantitative real-time RT-PCR
To determine the effects of MHV N protein on the expression of IFN-β, HEK-293T cells in 24-well plates were transfected with 1 µg of empty vector or plasmid encoding MHV N protein. After 24 h, the cells were mockinfected or infected with SEV for 12 h. Total RNA was extracted from the cells with RNA-Solv Reagent (Omega, GA, USA) and an aliquot (1 µg) was reverse transcribed to cDNA using AMV reverse transcriptase (Roche, Basel, Switzerland). The cDNA (1 µl of the 20 µl RT reaction) was then used as the template in a SYBR Green PCR assay (Applied Biosystems). The abundance of the individual mRNA or viral RNA transcript in each sample was assayed three times and normalized to that of the internal control, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA. The primers were designed using Primer Express software v.3.0 (Applied Biosystems) as shown in Table 2.

Co-immunoprecipitation and immunoblotting analyses
For the transient transfection experiments, HEK-293T cells were transfected with the appropriate plasmids for 28 h. The transfected cells were lysed in 200 μL of lysis buffer (4% SDS, 3% DTT, 0.065 mM Tris-HCl, [pH 6.8], 30% glycerine) supplemented with protease inhibitor (PMSF, Sigma-Aldrich). The lysates were boiled at 100°C for 10 min before separation by SDS-PAGE and electroblotting onto a polyvinylidene difluoride membrane (Bio-Rad), and analyzed by immunoblotting with the indicated antibodies. For co-immunoprecipitation analysis, cells were washed with PBS and lysed for 20 min at 4°C in lysis buffer containing 50 mM Tris-HCl (pH 7.4), 150 mM NaCl, 1% NP-40, 10% glycerin, 0.1% SDS, and 2 mM Na 2 EDTA. The lysates were then cleared by centrifugation, and the proteins were immunoprecipitated overnight at 4°C with affinity antibodies and protein A+G agarose beads (Beyotime, Shanghai, China). The immunoprecipitates were washed three times with 1 mL of lysis buffer and then analyzed by standard immunoblotting procedures.