Miro A. Astore

Viral RNA genomes contain structured elements that recruit the machinery necessary for their replication. However, these mechanisms remain poorly understood because of RNA structure’s dynamic nature. Using cryo-EM and single-molecule Förster resonance energy transfer, we show that the flaviviral stem-loop A (SLA)—which recruits the viral polymerase NS5 to initiate negative-strand synthesis—populates a conserved, multi-state conformational ensemble governed by a dynamic three-dimensional (3D) motif. Structures of Zika and dengue SLA–NS5 complexes reveal that NS5 engages a preorganized SLA conformation. Notably, destabilizing this conformation impairs negative-strand synthesis without affecting NS5 binding. Thus, the conserved SLA 3D ensemble guides a post-binding conformational search that converts an initial encounter complex into a productive replication-initiation complex—revealing how RNA conformational ensembles, rather than static structures, dictate function. SUMMARY: Viral genomes encode dynamic RNA structures that are specifically recognized by proteins to regulate critical steps of the viral life cycle. Although these RNAs exist as multi-state conformational ensembles, structural studies have largely captured single conformations, limiting our mechanistic understanding of RNA-protein recognition and function. Here, using single-particle cryo-electron microscopy (cryo-EM), single-molecule Förster resonance energy transfer, and biochemical assays, we show how flaviviral genomes use structural dynamics encoded in their stem-loop A (SLA) to guide a conserved, multi-step mechanism of negative-strand synthesis initiation by non-structural protein 5 (NS5). Cryo-EM ensembles of SLAs from dengue, Zika, West Nile, and yellow fever viruses reveal a conserved conformational landscape dictated by alternative stacking configurations of a central three-way junction (3WJ). Cryo-EM structures of SLA-NS5 complexes from dengue and Zika show that NS5 engages a specific state within SLA 3D ensemble, one in which the 3WJ adopts a GRR/A tetraloop-like motif that we identify across diverse structural RNAs. Strikingly, mutations that substantially destabilize this state impair negative-strand synthesis without affecting NS5 binding—decoupling binding from function and ruling out a simple conformational-capture mechanism. Instead, the SLA’s intrinsic structural dynamics direct a post-binding conformational search that converts an initial, conformationally heterogeneous encounter complex into a productive replication-initiation complex. These results demonstrate a mechanism of RNA-protein recognition in which the 3D conformational ensemble of an RNA guides formation of the functional state downstream of binding, and identify a ubiquitous, intrinsically dynamic 3D motif within the SLA’s 3WJ as a critical determinant of flavivirus negative-strand synthesis.

While cryo-electron microscopy (cryo-EM) has come to prominence in the last decade due to its ability to resolve biomolecular complexes at atomic resolution, advancements in experimental and computational methods have made cryo-EM promising for investigating intracellular organization and heterogeneous molecular states. A primary challenge for these alternative applications is the development of techniques for cryo-EM data analysis, which are very computationally demanding. To this end, it is advantageous to leverage advanced scientific computing frameworks for statistical analysis. One such framework is JAX, an emerging array-oriented Python numerical computing package for automatic differentiation and vectorization with a growing ecosystem for statistical inference and machine learning. We have developed cryoJAX, a cryo-EM image-simulation library for building computational data-analysis applications in JAX. CryoJAX is a flexible modeling language for cryo-EM image formation and therefore can support a wide range of data analysis downstream. By integrating with the JAX ecosystem, cryoJAX enables the development and deployment of algorithms for the growing breadth of scientific applications for cryo-EM.

While cryo-electron microscopy (cryo-EM) has come to prominence in the last decade due to its ability to resolve biomolecular complexes at atomic resolution, advancements in experimental and computational methods have made cryo-EM promising for investigating intracellular organization and heterogeneous molecular states. A primary challenge for these alternative applications is the development of techniques for cryo-EM data analysis, which are very computationally demanding. To this end, it is advantageous to leverage advanced scientific computing frameworks for statistical analysis. One such framework is JAX, an emerging array-oriented Python numerical computing package for automatic differentiation and vectorization with a growing ecosystem for statistical inference and machine learning. We have developed cryoJAX, a cryo-EM image simulation library for building computational data analysis applications in JAX. CryoJAX is a flexible modeling language for cryo-EM image formation and therefore can support a wide range of data analysis downstream. By integrating with the JAX ecosystem, cryoJAX enables the development and deployment of algorithms for the growing breadth of scientific applications for cryo-EM. Synopsis: The authors have developed cryoJAX, a cryo-EM image simulation library for developing data analysis techniques across cryo-EM modalities. CryoJAX is built on JAX, an emerging scientific computing framework in Python well suited for cryo-EM data analysis.

Despite the rise of single particle cryo-electron microscopy (cryo-EM) as a premier method for resolving macromolecular structures at atomic resolution, methods to address molecular heterogeneity in vitrified samples have yet to reach maturity. With an increasing number of new methods to analyze the multitude of heterogeneous states captured in single particle images, a systematic approach to validation in this field is needed. With this motivation, we issued a challenge to the community to analyze two cryo-EM particle image sets of thyroglobulin that exhibit continuous conformational heterogeneity. The first dataset was experimental and the second was generated with a simulator, allowing control over the distribution of molecular structures and enabled direct comparison between participants’ submissions and the ground truth molecular structures and distributions. Participants were asked to submit 80 volumes representing the heterogeneous ensemble and estimate their respective populations in the image sets provided. Participation of the research community in the challenge was strong, with submissions from nearly all developers of heterogeneity methods, resulting in 41 submissions across both datasets. Submissions qualitatively exceeded expectations, with the molecular motions identified by methods resembling both each other and the ground truth motion. However, quantitatively assessing these similarities was a challenge in and of itself. In the process of assessing the submissions, we developed several validation metrics, most of which require reference to the underlying ground truth volumes. However, we have also explored the use of metrics that do not necessarily reference ground truth. This is particularly apt for experimental datasets where ground truth is inaccessible. These approaches allowed us to assess the similarity and accuracy in volume quality, molecular motions, and conformational distribution of di!erent submissions. These metrics and the e!orts of all participants help chart a path forward for the improvements of heterogeneity methods for cryo-EM and for future challenges to validate these new methods as they continue to be developed by the community.

Resolving continuous conformational heterogeneity in single-particle cryo-electron microscopy (cryo-EM) is a field in which new methods are now emerging regularly. Methods range from traditional statistical techniques to state-of-the-art neural network approaches. Such ongoing efforts continue to enhance the ability to explore and understand the continuous conformational variations in cryo-EM data. One of the first methods was the manifold embedding approach or ManifoldEM. However, comparing it with more recent methods has been challenging due to software availability and usability issues. In this work, we introduce a modern Python implementation that is user-friendly, orders of magnitude faster than its previous versions and designed with a developer-ready environment. This implementation allows a more thorough evaluation of the strengths and limitations of methods addressing continuous conformational heterogeneity in cryo-EM, paving the way for further community-driven improvements.

Cryogenic electron microscopy (cryo-EM) has emerged as a powerful method for resolving the atomistic details of cellular components. In recent years, several computational methods have been developed to study the heterogeneity of molecules in single-particle cryo-EM. In this study, we analyzed a publicly available single-particle dataset of TRPV1 using five of these methods: 3D Flexible Refinement, 3D Variability Analysis, cryoDRGN, ManifoldEM, and Bayesian ensemble reweighting. Beyond what we initially expected, we have found that this dataset contains significant heterogeneity— indicating that single particle datasets likely contain a rich spectrum of biologically relevant states. Further, we have found that different methods are best suited to studying different kinds of heterogeneity, with some methods being more sensitive to either compositional or conformational heterogeneity. We also apply a combination of Bayesian ensemble reweighting and molecular dynamics as supporting evidence for the presence of these rarer states within the sample. Finally, we developed a quantitative metric based on the analysis of the singular value decomposition and power spectra to compare the resulting volumes from each method. This work represents a detailed view of the variable outcomes of different heterogeneity methods used to analyze a single real dataset and presents a pathway to a deeper understanding of the biology of complex macromolecules like the TRPV1 ion channel.

Summary Insulin resistance (IR) is a complex metabolic disorder that underlies several human diseases, including type 2 diabetes and cardiovascular disease. Despite extensive research, the precise mechanisms underlying IR development remain poorly understood. Here, we provide new insights into the mechanistic connections between cellular alterations associated with IR, including increased ceramides, deficiency of coenzyme Q (CoQ), mitochondrial dysfunction, and oxidative stress. We demonstrate that elevated levels of ceramide in the mitochondria of skeletal muscle cells results in CoQ depletion and loss of mitochondrial respiratory chain components, leading to mitochondrial dysfunction and IR. Further, decreasing mitochondrial ceramide levels in vitro and in animal models (under chow and high fat diet) increased CoQ levels and was protective against IR. CoQ supplementation also rescued ceramide-associated IR. Examination of the mitochondrial proteome from human muscle biopsies revealed a strong correlation between the respirasome system and mitochondrial ceramide as key determinants of insulin sensitivity. Our findings highlight the mitochondrial Ceramide-CoQ-respiratory chain nexus as a potential foundation of an IR pathway that may also play a critical role in other conditions associated with ceramide accumulation and mitochondrial dysfunction, such as heart failure, cancer, and aging. These insights may have important clinical implications for the development of novel therapeutic strategies for the treatment of IR and related metabolic disorders.

Summary: Insulin resistance (IR) is a complex metabolic disorder that underlies several human diseases, including type 2 diabetes and cardiovascular disease. Despite extensive research, the precise mechanisms underlying IR development remain poorly understood. Here, we provide new insights into the mechanistic connections between cellular alterations associated with IR, including increased ceramides, deficiency of coenzyme Q (CoQ), mitochondrial dysfunction, and oxidative stress. We demonstrate that elevated levels of ceramide in the mitochondria of skeletal muscle cells results in CoQ depletion and loss of mitochondrial respiratory chain components, leading to mitochondrial dysfunction and IR. Further, decreasing mitochondrial ceramide levels in vitro and in animal models (under chow and high fat diet) increased CoQ levels and was protective against IR. CoQ supplementation also rescued ceramide-associated IR. Examination of the mitochondrial proteome from human muscle biopsies revealed a strong correlation between the respirasome system and mitochondrial ceramide as key determinants of insulin sensitivity. Our findings highlight the mitochondrial Ceramide-CoQ-respiratory chain nexus as a potential foundation of an IR pathway that may also play a critical role in other conditions associated with ceramide accumulation and mitochondrial dysfunction, such as heart failure, cancer, and aging. These insights may have important clinical implications for the development of novel therapeutic strategies for the treatment of IR and related metabolic disorders.

A significant challenge to making targeted cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapies accessible to all individuals with cystic fibrosis (CF) are many mutations in the CFTR gene that can cause CF, most of which remain uncharacterized. Here, we characterized the structural and functional defects of the rare CFTR mutation R352Q, with a potential role contributing to intrapore chloride ion permeation, in patient-derived cell models of the airway and gut. CFTR function in differentiated nasal epithelial cultures and matched intestinal organoids was assessed using an ion transport assay and forskolin-induced swelling assay, respectively. CFTR potentiators (VX-770, GLPG1837, and VX-445) and correctors (VX-809, VX-445, with or without VX-661) were tested. Data from R352Q-CFTR were compared with data of 20 participants with mutations with known impact on CFTR function. R352Q-CFTR has residual CFTR function that was restored to functional CFTR activity by CFTR potentiators but not the corrector. Molecular dynamics simulations of R352Q-CFTR were carried out, which indicated the presence of a chloride conductance defect, with little evidence supporting a gating defect. The combination approach of in vitro patient-derived cell models and in silico molecular dynamics simulations to characterize rare CFTR mutations can improve the specificity and sensitivity of modulator response predictions and aid in their translational use for CF precision medicine.