APOE regulates the transport of GM1.

Apolipoprotein E (APOE) is responsible for lipid transport, including cholesterol transport and clearance. While the ε4 allele of APOE (APOE4) is associated with a significant genetic risk factor for late-onset Alzheimer's disease (AD), no mechanistic understanding of its contribution to AD etiology has been established yet. In addition to cholesterol, monosialotetrahexosylganglioside (GM1) is a crucial lipid component in cell membranes and has been implicated in promoting the aggregation of amyloid beta protein (Aß), a key protein associated with AD. Here, we ask whether there are direct interactions between APOE and GM1 that further impact AD pathology. We find that both APOE3 and APOE4 exhibit superior binding affinity to GM1 compared to cholesterol and have an enhanced cellular uptake to GM1 lipid structures than cholesterol lipid structures. APOE regulates the transport process of GM1 depending on the cell type, which is influenced by the expression of APOE receptors in different cell lines and alters GM1 contents in cell membranes. We also find that the presence of GM1 alters the secondary structure of APOE3 and APOE4 and enhances the binding affinity between APOE and its receptor low-density lipoprotein receptor (LDLR), consequently promoting the cellular uptake of lipid structures in the presence of APOE. To understand the enhanced cellular uptake observed in lipid structures containing 20% GM1, we determined the distribution of GM1 on the membrane and found that GM1 clustering in lipid rafts, thereby supporting the physiological interaction between APOE and GM1. Overall, we find that APOE plays a regulatory role in GM1 transport, and the presence of GM1 on the lipid structures influences this transport process. Our studies introduce a plausible direct link between APOE and AD etiology, wherein APOE regulates GM1, which, in turn, promotes Aß oligomerization and aggregation.

Systematic review and meta-analysis of dysregulated microRNAs derived from liquid biopsies as biomarkers for amyotrophic lateral sclerosis.

The discovery of disease-specific biomarkers, such as microRNAs (miRNAs), holds the potential to transform the landscape of Amyotrophic Lateral Sclerosis (ALS) by facilitating timely diagnosis, monitoring treatment response, and accelerating drug discovery. Such advancement could ultimately improve the quality of life and survival rates for ALS patients. Despite more than a decade of research, no miRNA biomarker candidate has been translated into clinical practice. We conducted a systematic review and meta-analysis to quantitatively synthesize data from original studies that analyzed miRNA expression from liquid biopsies via PCR and compared them to healthy controls. Our analysis encompasses 807 miRNA observations from 31 studies, stratified according to their source tissue. We identified consistently dysregulated miRNAs in serum (hsa-miR-3665, -4530, -4745-5p, -206); blood (hsa-miR-338-3p, -183-5p); cerebrospinal fluid (hsa-miR-34a-3p); plasma (hsa-miR-206); and neural-enriched extracellular vesicles from plasma (hsa-miR-146a-5p, -151a-5p, -10b-5p, -29b-3p, and -4454). The meta-analyses provided further support for the upregulation of hsa-miR-206, hsa-miR-338-3p, hsa-miR-146a-5p and hsa-miR-151a-5p, and downregulation of hsa-miR-183-5p, hsa-miR-10b-5p, hsa-miR-29b-3p, and hsa-miR-4454 as consistent indicators of ALS across independent studies. Our findings provide valuable insights into the current understanding of miRNAs' dysregulated expression in ALS patients and on the researchers' choices of methodology. This work contributes to the ongoing efforts towards discovering disease-specific biomarkers.

Immunostimulation of Fibrous Nucleic Acid Nanoparticles Can be Modulated through Aptamer-Based Functional Moieties: Unveiling the Structure-Activity Relationship and Mechanistic Insights.

Fibrous nanomaterials containing silica, titanium oxide, and carbon nanotubes are notoriously known for their undesirable inflammatory responses and associated toxicities that have been extensively studied in the environmental and occupational toxicology fields. Biopersistance and inflammation of "hard" nanofibers prevent their broader biomedical applications. To utilize the structural benefits of fibrous nanomaterials for functionalization with moieties of therapeutic significance while preventing undesirable immune responses, researchers employ natural biopolymers─RNA and DNA─to design "soft" and biodegradable nanomaterials with controlled immunorecognition. Nucleic acid nanofibers have been shown to be safe and efficacious in applications that do not require their delivery into the cells such as the regulation of blood coagulation. Previous studies demonstrated that unlike traditional therapeutic nucleic acids (e.g., CpG DNA oligonucleotides) nucleic acid nanoparticles (NANPs), when used without a carrier, are not internalized by the immune cells and, as such, do not induce undesirable cytokine responses. In contrast, intracellular delivery of NANPs results in cytokine responses that are dependent on the physicochemical properties of these nanomaterials. However, the structure-activity relationship of innate immune responses to intracellularly delivered fibrous NANPs is poorly understood. Herein, we employ the intracellular delivery of model RNA/DNA nanofibers functionalized with G-quadruplex-based DNA aptamers to investigate how their structural properties influence cytokine responses. We demonstrate that nanofibers' scaffolds delivered to the immune cells using lipofectamine induce interferon response via the cGAS-STING signaling pathway activation and that DNA aptamers incorporation shields the fibers from recognition by cGAS and results in a lower interferon response. This structure-activity relationship study expands the current knowledge base to inform future practical applications of intracellularly delivered NANPs as vaccine adjuvants and immunotherapies.

Rational peptide design for targeting cancer cell invasion.

Cell invasion is an important process in cancer progression and recurrence. Invasion and implantation of cancer cells from their original place to other tissues, by disabling vital organs, challenges the treatment of cancer patients. Given the importance of the matter, many molecular treatments have been developed to inhibit cancer cell invasion. Because of their low production cost and ease of production, peptides are valuable therapeutic molecules for inhibiting cancer cell invasion. In recent years, advances in the field of computational biology have facilitated the design of anti-cancer peptides. In our investigation, using computational biology approaches such as evolutionary analysis, residue scanning, protein-peptide interaction analysis, molecular dynamics, and free energy analysis, our team designed a peptide library with about 100 000 candidates based on A6 (acetyl-KPSSPPEE-amino) sequence which is an anti-invasion peptide. During computational studies, two of the designed peptides that give the highest scores and showed the greatest sequence similarity to A6 were entered into the experimental analysis workflow for further analysis. In experimental analysis steps, the anti-metastatic potency and other therapeutic effects of designed peptides were evaluated using MTT assay, RT-qPCR, zymography analysis, and invasion assay. Our study disclosed that the IK1 (acetyl-RPSFPPEE-amino) peptide, like A6, has great potency to inhibit the invasion of cancer cells.

Differentiable rotamer sampling with molecular force fields.

Molecular dynamics (MD) is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative to MD, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD enables convergence to thermodynamic equilibrium faster than traditional MD; however, critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks.

Molecular basis and cellular functions of vinculin-actin directional catch bonding.

The ability of cells and tissues to respond differentially to mechanical forces applied in distinct directions is mediated by the ability of load-bearing proteins to preferentially maintain physical linkages in certain directions. However, the molecular basis and biological consequences of directional force-sensitive binding remain unclear. Vinculin (Vcn) is a load-bearing linker protein that exhibits directional catch bonding due to interactions between the Vcn tail domain (Vt) and filamentous (F)-actin. We developed a computational approach to predict Vcn residues involved in directional catch bonding and produced a set of associated Vcn variants with unaltered Vt structure, actin binding, or phospholipid interactions. Incorporation of the variants did not affect Vcn activation but reduced Vcn loading and altered exchange dynamics, consistent with the loss of directional catch bonding. Expression of Vcn variants perturbed the coordination of subcellular structures and cell migration, establishing key cellular functions for Vcn directional catch bonding.

Allosteric regulation of kinase activity in living cells.

The dysregulation of protein kinases is associated with multiple diseases due to the kinases' involvement in a variety of cell signaling pathways. Manipulating protein kinase function, by controlling the active site, is a promising therapeutic and investigative strategy to mitigate and study diseases. Kinase active sites share structural similarities, making it difficult to specifically target one kinase, and allosteric control allows specific regulation and study of kinase function without directly targeting the active site. Allosteric sites are distal to the active site but coupled via a dynamic network of inter-atomic interactions between residues in the protein. Establishing an allosteric control over a kinase requires understanding the allosteric wiring of the protein. Computational techniques offer effective and inexpensive mapping of the allosteric sites on a protein. Here, we discuss the methods to map and regulate allosteric communications in proteins, and strategies to establish control over kinase functions in live cells and organisms. Protein molecules, or 'sensors,' are engineered to function as tools to control allosteric activity of the protein as these sensors have high spatiotemporal resolution and help in understanding cell phenotypes after immediate activation or inactivation of a kinase. Traditional methods used to study protein functions, such as knockout, knockdown, or mutation, cannot offer a sufficiently high spatiotemporal resolution. We discuss the modern repertoire of tools to regulate protein kinases as we enter a new era in deciphering cellular signaling and developing novel approaches to treat diseases associated with signal dysregulation.

REAP-2: An interactive quantitative tool for robust and efficient dose-response curve estimation.

REAP-2 is an interactive dose-response curve estimation tool for Robust and Efficient Assessment of drug Potency. It provides user-friendly dose-response curve estimation for in vitro studies and conducts statistical testing for model comparisons with a redesigned user interface. We also make a major update of the underlying estimation method with penalized beta regression, which demonstrates great reliability and accuracy in dose estimation and uncertainty quantification. In this note, we describe the method and implementation of REAP-2 with a highlight on potency estimation and drug comparison.

Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks.

Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally determined structures of RNA molecules. Unlike DNA structure, RNA structure is far less constrained by basepair hydrogen bonding, resulting in an explosion of potential stable states. Here, we propose a convolutional neural network that predicts all pairwise distances between residues in an RNA, using a recently described smooth parametrization of Euclidean distance matrices. We achieve high-accuracy predictions on RNAs up to 100 nt in length in fractions of a second, a factor of 107 faster than existing molecular dynamics-based methods. We also convert our coarse-grained machine learning output into an all-atom model using discrete molecular dynamics with constraints. Our proposed computational pipeline predicts all-atom RNA models solely from the nucleotide sequence. However, this method suffers from the same limitation as nucleic acid molecular dynamics: the scarcity of available RNA crystal structures for training.

Septins Enable T Cell Contact Guidance via Amoeboid-Mesenchymal Switch.

Lymphocytes exit circulation and enter in-tissue guided migration toward sites of tissue pathologies, damage, infection, or inflammation. By continuously sensing and adapting to the guiding chemo-mechano-structural properties of the tissues, lymphocytes dynamically alternate and combine their amoeboid (non-adhesive) and mesenchymal (adhesive) migration modes. However, which mechanisms guide and balance different migration modes are largely unclear. Here we report that suppression of septins GTPase activity induces an abrupt amoeboid-to-mesenchymal transition of T cell migration mode, characterized by a distinct, highly deformable integrin-dependent immune cell contact guidance. Surprisingly, the T cell actomyosin cortex contractility becomes diminished, dispensable and antagonistic to mesenchymal-like migration mode. Instead, mesenchymal-like T cells rely on microtubule stabilization and their non-canonical dynein motor activity for high fidelity contact guidance. Our results establish septin's GTPase activity as an important on/off switch for integrin-dependent migration of T lymphocytes, enabling their dynein-driven fluid-like mesenchymal propulsion along the complex adhesion cues.

Combining Experimental Restraints and RNA 3D Structure Prediction in RNA Nanotechnology.

Precise RNA tertiary structure prediction can aid in the design of RNA nanoparticles. However, most existing RNA tertiary structure prediction methods are limited to small RNAs with relatively simple secondary structures. Large RNA molecules usually have complex secondary structures, including multibranched loops and pseudoknots, allowing for highly flexible RNA geometries and multiple stable states. Various experiments and bioinformatics analyses can often provide information about the distance between atoms (or residues) in RNA, which can be used to guide the prediction of RNA tertiary structure. In this chapter, we will introduce a platform, iFoldNMR, that can incorporate non-exchangeable imino protons resonance data from NMR as restraints for RNA 3D structure prediction. We also introduce an algorithm, DVASS, which optimizes distance restraints for better RNA 3D structure prediction.

Vitamin D Receptor Antagonist MeTC7 Inhibits PD-L1.

Small-molecule inhibitors of PD-L1 are postulated to control immune evasion in tumors similar to antibodies that target the PD-L1/PD-1 immune checkpoint axis. However, the identity of targetable PD-L1 inducers is required to develop small-molecule PD-L1 inhibitors. In this study, using chromatin immunoprecipitation (ChIP) assay and siRNA, we demonstrate that vitamin D/VDR regulates PD-L1 expression in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) cells. We have examined whether a VDR antagonist, MeTC7, can inhibit PD-L1. To ensure that MeTC7 inhibits VDR/PD-L1 without off-target effects, we examined competitive inhibition of VDR by MeTC7, utilizing ligand-dependent dimerization of VDR-RXR, RXR-RXR, and VDR-coactivators in a mammalian 2-hybrid (M2H) assay. MeTC7 inhibits VDR selectively, suppresses PD-L1 expression sparing PD-L2, and inhibits the cell viability, clonogenicity, and xenograft growth of AML cells. MeTC7 blocks AML/mesenchymal stem cells (MSCs) adhesion and increases the efferocytotic efficiency of THP-1 AML cells. Additionally, utilizing a syngeneic colorectal cancer model in which VDR/PD-L1 co-upregulation occurs in vivo under radiation therapy (RT), MeTC7 inhibits PD-L1 and enhances intra-tumoral CD8+T cells expressing lymphoid activation antigen-CD69. Taken together, MeTC7 is a promising small-molecule inhibitor of PD-L1 with clinical potential.

Allosteric regulation of kinase activity in living cells.

The dysregulation of protein kinases is associated with multiple diseases due to the kinases' involvement in a variety of cell signaling pathways. Manipulating protein kinase function, by controlling the active site, is a promising therapeutic and investigative strategy to mitigate and study diseases. Kinase active sites share structural similarities making it difficult to specifically target one kinase, allosteric control allows specific regulation and study of kinase function without directly targeting the active site. Allosteric sites are distal to the active site but coupled via a dynamic network of inter-atomic interactions between residues in the protein. Establishing an allosteric control over a kinase requires understanding the allosteric wiring of the protein. Computational techniques offer effective and inexpensive mapping of the allosteric sites on a protein. Here, we discuss methods to map and regulate allosteric communications in proteins, and strategies to establish control over kinase functions in live cells and organisms. Protein molecules, or "sensors" are engineered to function as tools to control allosteric activity of the protein as these sensors have high spatiotemporal resolution and help in understanding cell phenotypes after immediate activation or inactivation of a kinase. Traditional methods used to study protein functions, such as knockout, knockdown, or mutation, cannot offer a sufficiently high spatiotemporal resolution. We discuss the modern repertoire of tools to regulate protein kinases as we enter a new era in deciphering cellular signaling and developing novel approaches to treat diseases associated with signal dysregulation.

Microcalorimetry reveals multi-state thermal denaturation of G. stearothermophilus tryptophanyl-tRNA synthetase.

Mechanistic studies of Geobacillus stearothermophilus tryptophanyl-tRNA synthetase (TrpRS) afford an unusually detailed description-the escapement mechanism-for the distinct steps coupling catalysis to domain motion, efficiently converting the free energy of ATP hydrolysis into biologically useful alternative forms of information and work. Further elucidation of the escapement mechanism requires understanding thermodynamic linkages between domain configuration and conformational stability. To that end, we compare experimental thermal melting of fully liganded and apo TrpRS with a computational simulation of the melting of its fully liganded form. The simulation also provides important structural cameos at successively higher temperatures, enabling more confident interpretation. Experimental and simulated melting both proceed through a succession of three transitions at successively higher temperature. The low-temperature transition occurs at approximately the growth temperature of the organism and so may be functionally relevant but remains too subtle to characterize structurally. Structural metrics from the simulation imply that the two higher-temperature transitions entail forming a molten globular state followed by unfolding of secondary structures. Ligands that stabilize the enzyme in a pre-transition (PreTS) state compress the temperature range over which these transitions occur and sharpen the transitions to the molten globule and fully denatured states, while broadening the low-temperature transition. The experimental enthalpy changes provide a key parameter necessary to convert changes in melting temperature of combinatorial mutants into mutationally induced conformational free energy changes. The TrpRS urzyme, an excerpted model representing an early ancestral form, containing virtually the entire catalytic apparatus, remains largely intact at the highest simulated temperatures.

Quantifying network behavior in the rat prefrontal cortex: a reproducibility crisis.

The question of how consciousness and behavior arise from neural activity is fundamental to understanding the brain, and to improving the diagnosis and treatment of neurological and psychiatric disorders. There is significant murine and primate literature on how behavior is related to the electrophysiological activity of the medial prefrontal cortex and its role in working memory processes such as planning and decision-making. Existing experimental designs, however, have insufficient statistical power to unravel the complex processes of the prefrontal cortex. We therefore examined the theoretical limitations of such experiments, providing concrete guidelines for robust and reproducible science. We piloted the use of dynamic time warping and associated statistical tests to data from neuron spike trains and local field potentials, to quantify neural network synchronicity and correlate neuroelectrophysiology with rat behavior. Our results indicate the statistical limitations of existing data, making meaningful comparison between dynamic time warping with traditional Fourier and wavelet analysis currently impossible until larger and cleaner datasets are available. Significance Statement: The prefrontal cortex is important in decision-making, yet no robust method currently exists to correlate neuron firing in the PFC to behavior. We argue that existing experimental designs are ill-suited to addressing these scientific questions, and we propose a potential method using dynamic time warping to analyze PFC neural electrical activity. We conclude that careful curation of experimental controls is needed to separate true neural signals from noise accurately.

Break to Build: Isothermal Assembly of Nucleic Acid Nanoparticles (NANPs) via Enzymatic Degradation.

The intrinsic properties of RNA and DNA biopolymers emphasized by engineered nucleic acid nanoparticles (NANPs) offer accelerated development of next-generation therapies. The rational design of NANPs facilitates programmable architectures intended for regulated molecular and cellular interactions. The conventional bottom-up assembly of NANPs relies on the thermal annealing of individual strands. Here, we introduce a concept of nuclease-driven production of NANPs where selective digestion of functionally inert structures leads to isothermal self-assembly of liberated constituents. The working principles, morphological changes, assembly kinetics, and the retention of structural integrity for system components subjected to anhydrous processing and storage are assessed. We show that the assembly of precursors into a single structure improves stoichiometry and enhances the functionality of nuclease-driven products. Furthermore, the experiments with immune reporting cell lines show that the developed protocols retain the immunostimulatory functionality of tested NANPs. The presented approach enables exploitation of the advantages of conditionally produced NANPs and demonstrates that NANPs' stability, immunorecognition, and assembly can be regulated to allow for a more robust functional system.

A noncommutative combinatorial protein logic circuit controls cell orientation in nanoenvironments.

Single-protein-based devices that integrate signal sensing with logical operations to generate functional outputs offer exceptional promise for monitoring and modulating biological systems. Engineering such intelligent nanoscale computing agents is challenging, as it requires the integration of sensor domains into a functional protein via intricate allosteric networks. We incorporate a rapamycin-sensitive sensor (uniRapR) and a blue light-responsive LOV2 domain into human Src kinase, creating a protein device that functions as a noncommutative combinatorial logic circuit. In our design, rapamycin activates Src kinase, causing protein localization to focal adhesions, whereas blue light exerts the reverse effect that inactivates Src translocation. Focal adhesion maturation induced by Src activation reduces cell migration dynamics and shifts cell orientation to align along collagen nanolane fibers. Using this protein device, we reversibly control cell orientation by applying the appropriate input signals, a framework that may be useful in tissue engineering and regenerative medicine.

Big versus small: The impact of aggregate size in disease.

Protein aggregation results in an array of different size soluble oligomers and larger insoluble fibrils. Insoluble fibrils were originally thought to cause neuronal cell deaths in neurodegenerative diseases due to their prevalence in tissue samples and disease models. Despite recent studies demonstrating the toxicity associated with soluble oligomers, many therapeutic strategies still focus on fibrils or consider all types of aggregates as one group. Oligomers and fibrils require different modeling and therapeutic strategies, targeting the toxic species is crucial for successful study and therapeutic development. Here, we review the role of different-size aggregates in disease, and how factors contributing to aggregation (mutations, metals, post-translational modifications, and lipid interactions) may promote oligomers opposed to fibrils. We review two different computational modeling strategies (molecular dynamics and kinetic modeling) and how they are used to model both oligomers and fibrils. Finally, we outline the current therapeutic strategies targeting aggregating proteins and their strengths and weaknesses for targeting oligomers versus fibrils. Altogether, we aim to highlight the importance of distinguishing the difference between oligomers and fibrils and determining which species is toxic when modeling and creating therapeutics for protein aggregation in disease.

Deciphering the Mechanistic Basis for Perfluoroalkyl-Protein Interactions.

Although rarely used in nature, fluorine has emerged as an important elemental ingredient in the design of proteins with altered folding, stability, oligomerization propensities, and bioactivity. Adding to the molecular modification toolbox, here we report the ability of privileged perfluorinated amphiphiles to noncovalently decorate proteins to alter their conformational plasticity and potentiate their dispersion into fluorous phases. Employing a complementary suite of biophysical, in-silico and in-vitro approaches, we establish structure-activity relationships defining these phenomena and investigate their impact on protein structural dynamics and intracellular trafficking. Notably, we show that the lead compound, perfluorononanoic acid, is 106 times more potent in inducing non-native protein secondary structure in select proteins than is the well-known helix inducer trifluoroethanol, and also significantly enhances the cellular uptake of complexed proteins. These findings could advance the rational design of fluorinated proteins, inform on potential modes of toxicity for perfluoroalkyl substances, and guide the development of fluorine-modified biologics with desirable functional properties for drug discovery and delivery applications.

Urolithin A analog inhibits castration-resistant prostate cancer by targeting the androgen receptor and its variant, androgen receptor-variant 7.

We investigated the efficacy of a small molecule ASR-600, an analog of Urolithin A (Uro A), on blocking androgen receptor (AR) and its splice variant AR-variant 7 (AR-V7) signaling in castration-resistant prostate cancer (CRPC). ASR-600 effectively suppressed the growth of AR+ CRPC cells by inhibiting AR and AR-V7 expressions; no effect was seen in AR- CRPC and normal prostate epithelial cells. Biomolecular interaction assays revealed ASR-600 binds to the N-terminal domain of AR, which was further confirmed by immunoblot and subcellular localization studies. Molecular studies suggested that ASR-600 promotes the ubiquitination of AR and AR-V7 resulting in the inhibition of AR signaling. Microsomal and plasma stability studies suggest that ASR-600 is stable, and its oral administration inhibits tumor growth in CRPC xenografted castrated and non-castrated mice. In conclusion, our data suggest that ASR-600 enhances AR ubiquitination in both AR+ and AR-V7 CRPC cells and inhibits their growth in vitro and in vivo models.