Targeting cancer-associated fibroblast autophagy renders pancreatic cancer eradicable with immunochemotherapy by inhibiting adaptive immune resistance.

Accumulating evidence suggests that cancer-associated fibroblast (CAF) macroautophagy/autophagy is crucial in tumor development and may be a therapeutic target for pancreatic ductal adenocarcinoma (PDAC). However, the role of CAF autophagy during immune surveillance and cancer immunotherapy is unclear. The present study revealed that the inhibition of CAF autophagy suppresses in vivo tumor development in immune-deficient xenografts. This deletion compromises anti-tumor immunity and anti-tumor efficacy both in vitro and in vivo by upregulating CD274/PDL1 levels in an immune-competent mouse model. A block in CAF autophagy reduced the production of IL6 (interleukin 6), disrupting high desmoplastic TME and decreasing USP14 expression at the transcription level in pancreatic cancer cells. We further identify USP14 as the post-translational factor responsible for downregulating CD274 expression by removing K63 linked-ubiquitination at the K280 residue. Finally, chloroquine diphosphate-loaded mesenchymal stem cell (MSC)-liposomes, by accurately targeting CAFs, inhibited CAF autophagy, improving the efficacy of immunochemotherapy to combat pancreatic cancer.Abbreviation: AIR: adaptive immune resistance; ATRA: all-trans-retinoicacid; CAF: cancer-associated fibroblast; CD274/PDL1: CD274 molecule; CM: conditioned medium; CQ: chloroquine diphosphate; CyTOF: Mass cytometry; FGF2/bFGF: fibroblast growth factor 2; ICB: immune checkpoint blockade; IF: immunofluorescence; IHC: immunohistochemistry; IP: immunoprecipitation; MS: mass spectrometer; MSC: mesenchymal stem cell; PDAC: pancreatic ductal adenocarcinoma; TEM: transmission electron microscopy; TILs: tumor infiltrating lymphocytes; TME: tumor microenvironment; USP14: ubiquitin specific peptidase 14.

Impaired AIF-CHCHD4 interaction and mitochondrial calcium overload contribute to auditory neuropathy spectrum disorder in patient-iPSC-derived neurons with AIFM1 variant.

Auditory neuropathy spectrum disorder (ANSD) is a hearing impairment caused by dysfunction of inner hair cells, ribbon synapses, spiral ganglion neurons and/or the auditory nerve itself. Approximately 1/7000 newborns have abnormal auditory nerve function, accounting for 10%-14% of cases of permanent hearing loss in children. Although we previously identified the AIFM1 c.1265 G > A variant to be associated with ANSD, the mechanism by which ANSD is associated with AIFM1 is poorly understood. We generated induced pluripotent stem cells (iPSCs) from peripheral blood mononuclear cells (PBMCs) via nucleofection with episomal plasmids. The patient-specific iPSCs were edited via CRISPR/Cas9 technology to generate gene-corrected isogenic iPSCs. These iPSCs were further differentiated into neurons via neural stem cells (NSCs). The pathogenic mechanism was explored in these neurons. In patient cells (PBMCs, iPSCs, and neurons), the AIFM1 c.1265 G > A variant caused a novel splicing variant (c.1267-1305del), resulting in AIF p.R422Q and p.423-435del proteins, which impaired AIF dimerization. Such impaired AIF dimerization then weakened the interaction between AIF and coiled-coil-helix-coiled-coil-helix domain-containing protein 4 (CHCHD4). On the one hand, the mitochondrial import of ETC complex subunits was inhibited, subsequently leading to an increased ADP/ATP ratio and elevated ROS levels. On the other hand, MICU1-MICU2 heterodimerization was impaired, leading to mCa2+ overload. Calpain was activated by mCa2+ and subsequently cleaved AIF for its translocation into the nucleus, ultimately resulting in caspase-independent apoptosis. Interestingly, correction of the AIFM1 variant significantly restored the structure and function of AIF, further improving the physiological state of patient-specific iPSC-derived neurons. This study demonstrates that the AIFM1 variant is one of the molecular bases of ANSD. Mitochondrial dysfunction, especially mCa2+ overload, plays a prominent role in ANSD associated with AIFM1. Our findings help elucidate the mechanism of ANSD and may lead to the provision of novel therapies.

1-Deoxynojirimycin promotes cardiac function and rescues mitochondrial cristae in mitochondrial hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is the most prominent cause of sudden cardiac death in young people. Due to heterogeneity in clinical manifestations, conventional HCM drugs have limitations for mitochondrial hypertrophic cardiomyopathy. Discovering more effective compounds would be of substantial benefit for further elucidating the pathogenic mechanisms of HCM and treating patients with this condition. We previously reported the MT-RNR2 variant associated with HCM that results in mitochondrial dysfunction. Here, we screened a mitochondria-associated compound library by quantifying the mitochondrial membrane potential of HCM cybrids and the survival rate of HCM-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) in galactose media. 1-Deoxynojirimycin (DNJ) was identified to rescue mitochondrial function by targeting optic atrophy protein 1 (OPA1) to promote its oligomerization, leading to reconstruction of the mitochondrial cristae. DNJ treatment further recovered the physiological properties of HCM iPSC-CMs by improving Ca2+ homeostasis and electrophysiological properties. An angiotensin II-induced cardiac hypertrophy mouse model further verified the efficacy of DNJ in promoting cardiac mitochondrial function and alleviating cardiac hypertrophy in vivo. These results demonstrated that DNJ could be a potential mitochondrial rescue agent for mitochondrial hypertrophic cardiomyopathy. Our findings will help elucidate the mechanism of HCM and provide a potential therapeutic strategy.

RNA-ligand molecular docking: advances and challenges.

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

RLDOCK method for predicting RNA-small molecule binding modes.

RNA molecules play critical roles in cellular functions at the level of gene expression and regulation. The intricate 3D structures and the functional roles of RNAs make RNA molecules ideal targets for therapeutic drugs. The rational design of RNA-targeted drug requires accurate modeling of RNA-ligand interactions. Recently a new computational tool, RLDOCK, was developed to predict ligand binding sites and binding poses. Using an iterative multiscale sampling and search algorithm and a energy-based evaluation of ligand poses, the method enables efficient and accurate predictions for RNA-ligand interactions. Here we present a detailed illustration of the computational procedure for the practical implementation of the RLDOCK method. Using Flavin mononucleotide (FMN) docking to F. nucleatum FMN riboswitch as an example, we illustrate the computational protocol for RLDOCK-based prediction of RNA- ligand interactions. The RLDOCK software is freely accessible at http://https://github.com/Vfold-RNA/RLDOCK.

RLDOCK: A New Method for Predicting RNA-Ligand Interactions.

The ability to accurately predict the binding site, binding pose, and binding affinity for ligand-RNA binding is important for RNA-targeted drug design. Here, we describe a new computational method, RLDOCK, for predicting the binding site and binding pose for ligand-RNA binding. By developing an energy-based scoring function, we sample exhaustively all of the possible binding sites with flexible ligand conformations for a ligand-RNA pair based on the geometric and energetic scores. The model distinguishes from other approaches in three notable features. First, the model enables exhaustive scanning of all of the possible binding sites, including multiple alternative or coexisting binding sites, for a given ligand-RNA pair. Second, the model is based on a new energy-based scoring function developed here. Third, the model employs a novel multistep screening algorithm to improve computational efficiency. Specifically, first, for each binding site, we use a gird-based energy map to rank the binding sites according to the minimum Lennard-Jones potential energy for the different ligand poses. Second, for a given selected binding site, we predict the ligand pose using a two-step algorithm. In the first step, we quickly identify the probable ligand poses using a coarse-grained simplified energy function. In the second step, for each of the probable ligand poses, we predict the ligand poses using a refined energy function. Tests of the RLDOCK for a set of 230 RNA-ligand-bound structures indicate that RLDOCK can successfully predict ligand poses for 27.8, 58.3, and 69.6% of all of the test cases with the root-mean-square deviation within 1.0, 2.0, and 3.0 Å, respectively, for the top three predicted docking poses. The computational method presented here may enable the development of a new, more comprehensive framework for the prediction of ligand-RNA binding with an ensemble of RNA conformations and the metal-ion effects.

Modeling Loop Composition and Ion Concentration Effects in RNA Hairpin Folding Stability.

The ability to accurately predict RNA hairpin structure and stability for different loop sequences and salt conditions is important for understanding, modeling, and designing larger RNA folds. However, traditional RNA secondary structure models cannot treat loop-sequence and ionic effects on RNA hairpin folding. Here, we describe a general, three-dimensional (3D) conformation-based computational method for modeling salt concentration-dependent conformational distributions and the detailed 3D structures for a set of three RNA hairpins that contain a variable, 15-nucleotide loop sequence. For a given RNA sequence, the new, to our knowledge, method integrates a Vfold2D two-dimensional structure folding model with IsRNA coarse-grained molecular dynamics 3D folding simulations and Monte Carlo tightly bound ion estimations of ion-mediated electrostatic interactions. The model predicts free-energy landscapes for the different RNA hairpin-forming sequences with variable salt conditions. The theoretically predicted results agree with the experimental fluorescence measurements, validating the strategy. Furthermore, the theoretical model goes beyond the experimental results by enabling in-depth 3D structural analysis, revealing energetic mechanisms for the sequence- and salt-dependent folding stability. Although the computational framework presented here is developed for RNA hairpin systems, the general method may be applied to investigate other RNA systems, such as multiway junctions or pseudoknots in mixed metal ion solutions.

RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers.

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA 3D structure prediction. With agreement from crystallographers, the RNA structures are predicted by various groups before the publication of the crystal structures. We now report the prediction of 3D structures for six RNA sequences: four nucleolytic ribozymes and two riboswitches. Systematic protocols for comparing models and crystal structures are described and analyzed. In these six puzzles, we discuss (i) the comparison between the automated web servers and human experts; (ii) the prediction of coaxial stacking; (iii) the prediction of structural details and ligand binding; (iv) the development of novel prediction methods; and (v) the potential improvements to be made. We show that correct prediction of coaxial stacking and tertiary contacts is essential for the prediction of RNA architecture, while ligand binding modes can only be predicted with low resolution and simultaneous prediction of RNA structure with accurate ligand binding still remains out of reach. All the predicted models are available for the future development of force field parameters and the improvement of comparison and assessment tools.

Rotational Diffusion of Soft Vesicles Filled by Chiral Active Particles.

We investigate the dynamics of two-dimensional soft vesicles filled with chiral active particles by employing the overdamped Langevin dynamics simulation. The unidirectional rotation is observed for soft vesicles, and the rotational angular velocity of vesicles depends mainly on the area fraction (ρ) and angular velocity (ω) of chiral active particles. There exists an optimal parameter for ω at which the rotational angular velocity of vesicle takes its maximal value. Meanwhile, at low concentration the continuity of curvature is destroyed seriously by chiral active particles, especially for large ω, and at high concentration the chiral active particles cover the vesicle almost uniformly. In addition, the center-of-mass mean square displacement for vesicles is accompanied by oscillations at short timescales, and the oscillation period of diffusion for vesicles is consistent with the rotation period of chiral active particles. The diffusion coefficient of vesicle decreases monotonously with increasing the angular velocity ω of chiral active particles. Our investigation can provide a few designs for nanofabricated devices that can be driven in a unidirectional rotation by chiral active particles or could be used as drug-delivery agent.

Glassy dynamics of nanoparticles in semiflexible ring polymer nanocomposite melts.

By employing molecular dynamics simulations, we explore the dynamics of NPs in semiflexible ring polymer nanocomposite melts. A novel glass transition is observed for NPs in semiflexible ring polymer melts as the bending energy (Kb) of ring polymers increases. For NPs in flexible ring polymer melts (Kb = 0), NPs move in the classic diffusive behavior. However, for NPs in semiflexible ring polymer melts with large bending energy, NPs diffuse very slowly and exhibit the glassy state in which the NPs are all irreversibly caged be the neighbouring semiflexible ring polymers. This glass transition occurs well above the classical glass transition temperature at which microscopic mobility is lost, and the topological interactions of semiflexible ring polymers play an important role in this non-classical glass transition. This investigation can help us understand the nature of the glass transition in polymer systems.

Aggregation-Dispersion Transition for Nanoparticles in Semiflexible Ring Polymer Nanocomposite Melts.

By employing molecular dynamics simulations, we explored the conformation transition of nanoparticles (NPs) in semiflexible ring polymer nanocomposite melts. A novel aggregation-dispersion transition for NPs in ring polymer nanocomposites occurs when the bending energy of ring chains increases. The conformations of flexible ring chains near NPs are radial distribution, and the entropic depletion interactions between a pair of NPs in flexible ring polymer nanocomposite melts are attractive, however, the rod-like ring chains wrap around the NPs and the entropic depletion interactions between NPs in rod-like ring polymer melts are repulsive. The aggregation-dispersion transition for NPs induced by chain topology in polymer nanocomposites can provide a new access to achieve miscibility in producing high-performance polymer-nanoparticle composites by simply varying the topological structure of polymers.

Entropic Interactions in Semiflexible Polymer Nanocomposite Melts.

By employing molecular dynamics simulations, we explored the effective depletion zone for nanoparticles (NP) immersed in semiflexible polymer melts and calculated the entropic depletion interactions between a pair of NPs in semiflexible polymer nanocomposite melts. The average depletion zone volumes rely mainly on polymer chain stiffness and increase with chain stiffness increasing. In the semiflexible polymer nanocomposite melts, the entropic depletion interactions are attractive and anisotropic, and increase with chain stiffness increasing. Meanwhile, the attractive interactions between NPs and polymers can also affect strongly the entropic depletion interactions. For the semiflexible polymer nanocomposite melts in the athermal system, the entropic depletion interactions change from anisotropic to isotropic when the NP/polymer interactions increase. For NPs in the rodlike polymer melts, a mixture structure of contact/"bridging" aggregations for NPs is formed at a strong attractive NP/polymer interaction. Our calculations can provide an effective framework to predict the morphology of NPs immersed in semiflexible polymer melts.

The adsorption-desorption transition of double-stranded DNA interacting with an oppositely charged dendrimer induced by multivalent anions.

The adsorption-desorption transition of DNA in DNA-dendrimer solutions is observed when high-valence anions, such as hexavalent anions, are added to the DNA-dendrimer solutions. In the DNA-dendrimer solutions with low-valence anions, dendrimers bind tightly with the V-shaped double-stranded DNA. When high-valence anions, such as pentavalent or hexavalent anions, are added to the DNA-dendrimer solutions, the double-stranded DNA chains can be stretched straightly and the dendrimers are released from the double-stranded DNA chains. In fact, adding high-valence anions to the solutions can change the charge spatial distribution in the DNA-dendrimer solutions, and weaken the electrostatic interactions between the positively charged dendrimers and the oppositely charged DNA chains. Adsorption-desorption transition of DNA is induced by the overcharging of dendrimers. This investigation is capable of helping us understand how to control effectively the release of DNA in gene/drug delivery because an effective gene delivery for dendrimers includes non-covalent DNA-dendrimer binding and the effective release of DNA in gene therapy.

Wrapping/unwrapping transition of double-stranded DNA in DNA-nanosphere complexes induced by multivalent anions.

Wrapping and unwrapping behaviors of double-stranded DNA around a positively charged nanosphere in solution are studied by using the coarse-grained molecular dynamics (CGMD) simulation method. When monovalent, divalent and trivalent anions are added to the DNA-nanosphere complex solution, double-stranded DNA binds with a nanosphere owing to strong electrostatic attraction. However, when tetravalent anions are added to the DNA-nanosphere complex solution, local charge inversion is observed for a high anion concentration of tetravalent anions and the double-stranded DNA can be unwrapped from the nanosphere because of the local charge inversion near the nanosphere. Moreover, the helical structure of DNA is damaged when double-stranded DNA wraps around the nanosphere and the helical structure can be rebuilt when the double-stranded DNA unwraps from the nanosphere. This study can help us understand how to control the release of DNA in DNA-nanosphere complexes.

Ordered structures of diblock nanorods induced by diblock copolymers.

The self-assembly of diblock copolymer (DBCP)/diblock nanorod (DBNR) mixtures are studied by using a dissipative particle dynamics simulation method. The microstructures of DBCP/DBNR blends depend on the type of DBCPs as well as the number of DBNRs. For the asymmetric DBCPs of A3B7, the morphological transition of DBCP/DBNR blends from cylinder phase to lamellar phase is observed for the DBNRs with suitable length and component. Meanwhile, for the symmetric DBCPs of A5B5, the lamellar morphologies of DBCP/DBNR blends can always maintain during the process of adding DBNRs to the blends except for the case of component length mismatch between the DBCPs and the DBNRs. On the other hand, DBCPs can also affect the orientation and the spatial distributions of DBNRs deeply, and the ordered structures of DBNRs are formed for the DBCP/DBNR blends with a large number of DBNRs. Comparisons with homopolymer/DBNR blends are made, and only the aggregation structures are observed in homopolymer/DBNR blends. This investigation can provide valuable assistance in exploring and designing complicated polymer/nanoparticle composites with the desired properties.

Self-assembly of binary nanoparticles on soft elastic shells.

The self-assembly behaviors and phase transitions of binary nanoparticles (NPs) adsorbed on a soft elastic shell are investigated through molecular dynamics simulation. The conformations of adsorbed binary NPs depend on the bending energy K(b) of elastic shell and the binding energy D0 between the NPs and the elastic shell. The ordered structures of binary NPs are observed at the moderate adhesive strength and bending energy, in which the small NPs are located near the vertices of regular pentagons as well as the large NPs are distributed along the sides of the regular pentagons. The shape of soft elastic shell can be adjusted by adding the adsorbed binary NPs, and this investigation can provide an effective way to regulate and reshape surfaces or membranes with the sizes in the micrometer range or smaller.