The folding of monomeric antigens and their subsequent assembly into higher ordered structures are crucial for robust and effective production of nanoparticle (NP) vaccines in a timely and reproducible manner. Despite significant advances in in silico design and structure-based assembly, most engineered NPs are refractory to soluble expression and fail to assemble as designed, presenting major challenges in the manufacturing process. The failure is due to a lack of understanding of the kinetic pathways and enabling technical platforms to ensure successful folding of the monomer antigens into regular assemblages. Capitalizing on a novel function of RNA as a molecular chaperone (chaperna: chaperone + RNA), we provide a robust protein-folding vehicle that may be implemented to NP assembly in bacterial hosts. The receptor-binding domain (RBD) of Middle East respiratory syndrome-coronavirus (MERS-CoV) was fused with the RNA-interaction domain (RID) and bacterioferritin, and expressed in Escherichia coli in a soluble form. Site-specific proteolytic removal of the RID prompted the assemblage of monomers into NPs, which was confirmed by electron microscopy and dynamic light scattering. The mutations that affected the RNA binding to RBD significantly increased the soluble aggregation into amorphous structures, reducing the overall yield of NPs of a defined size. This underscored the RNA-antigen interactions during NP assembly. The sera after mouse immunization effectively interfered with the binding of MERS-CoV RBD to the cellular receptor hDPP4. The results suggest that RNA-binding controls the overall kinetic network of the antigen folding pathway in favor of enhanced assemblage of NPs into highly regular and immunologically relevant conformations. The concentration of the ion Fe2+, salt, and fusion linker also contributed to the assembly in vitro, and the stability of the NPs. The kinetic “pace-keeping” role of chaperna in the super molecular assembly of antigen monomers holds promise for the development and delivery of NPs and virus-like particles as recombinant vaccines and for serological detection of viral infections.

A novel protein-folding function of RNA has been recognized, which can outperform previously known molecular chaperone proteins. The RNA as a molecular chaperone (chaperna) activity is intrinsic to some ribozymes and is operational during viral infections. Our purpose was to test whether influenza hemagglutinin (HA) can be assembled in a soluble, trimeric, and immunologically activating conformation by means of an RNA molecular chaperone (chaperna) activity. An RNA-interacting domain (RID) from the host being immunized was selected as a docking tag for RNA binding, which served as a transducer for the chaperna function for de novo folding and trimeric assembly of RID-HA1. Mutations that affect tRNA binding greatly increased the soluble aggregation defective in trimer assembly, suggesting that RNA interaction critically controls the kinetic network in the folding/assembly pathway. Immunization of mice resulted in strong hemagglutination inhibition and high titers of a neutralizing antibody, providing sterile protection against a lethal challenge and confirming the immunologically relevant HA conformation. The results may be translated into a rapid response to a new influenza pandemic. The harnessing of the novel chaperna described herein with immunologically tailored antigen-folding functions should serve as a robust prophylactic and diagnostic tool for viral infections.—Yang, S. W., Jang, Y. H., Kwon, S. B., Lee, Y. J., Chae, W., Byun, Y. H., Kim, P., Park, C., Lee, Y. J., Kim, C. K., Kim, Y. S., Choi, S. I., Seong, B. L. Harnessing an RNA-mediated chaperone for the assembly of influenza hemagglutinin in an immunologically relevant conformation.

Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student’s t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student’s t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of < 0.0001 and < 0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.

Eukaryotic lysyl-tRNA synthetases (LysRS) have an N-terminal appended tRNA-interaction domain (RID) that is absent in their prokaryotic counterparts. This domain is intrinsically disordered and lacks stable structures. The disorder-to-order transition is induced by tRNA binding and has implications on folding and subsequent assembly into multi-tRNA synthetase complexes. Here, we expressed and purified RID from human LysRS (hRID) in Escherichia coli and performed a detailed mutagenesis of the appended domain. hRID was co-purified with nucleic acids during Ni-affinity purification, and cumulative mutations on critical amino acid residues abolished RNA binding. Furthermore, we identified a structural ensemble between disordered and helical structures in non-RNA-binding mutants and an equilibrium shift for wild-type into the helical conformation upon RNA binding. Since mutations that disrupted RNA binding led to an increase in non-functional soluble aggregates, a stabilized RNA-mediated structural transition of the N-terminal appended domain may have implications on the functional organization of human LysRS and multi-tRNA synthetase complexes in vivo.

Knee osteoarthritis (KOA) is a leading cause of disability among elderly adults, and it causes pain and discomfort and limits the functional independence of such adults. The aim of this study was the development of an automated classification model for KOA, based on the Kellgren-Lawrence (KL) grading system, using radiographic imaging and gait analysis data. Gait features highly associated with the radiological severity of KOA identified from our previous study, in addition to radiographic image features extracted from a deep learning network, namely, Inception-ResNet-v2, were exploited using a support vector machine for KOA multi-classification. The area under the curve (AUC) of the receiver operating characteristic curve from KL Grades 0-4 were 0.93, 0.82, 0.83, 0.88, and 0.97, respectively. The sensitivity, precision, and F1-score of the model were 0.70, 0.76, and 0.71, respectively. The proposed model outperformed a common deep learning approach that is based on using only radiographic images as the input data. This result indicates that gait data and radiographic images are complementary with respect to KOA classification, and the use of both data can improve the accuracy of the automated diagnosis of multiclass KOA.