ACS Publications最新文献

Mitochondria are essential organelles that govern energy metabolism, redox balance, and cell survival; their dysfunction is implicated in a wide range of pathologies, including neurodegenerative disorders, cardiovascular diseases, metabolic syndromes, and cancer. Despite their significance as therapeutic targets, the unique structural and electrochemical properties of mitochondria, particularly the impermeable inner mitochondrial membrane and high membrane potential pose major challenges for the targeted delivery of therapeutic agents. Recent advances in biomaterials have spotlighted peptide-polymer conjugates as versatile platforms, capable of navigating intracellular barriers and achieving precise mitochondrial localization. These hybrid systems combine the physicochemical tunability of polymers with the biofunctionality of peptides, enhancing cellular uptake, endosomal escape, and suborganelle trafficking. The incorporation of stimuli-responsive elements further enables spatiotemporal control of cargo release in response to intracellular cues such as pH shifts, thermal fluctuations, redox gradients, or enzymatic activity. Such systems are especially promising for mitochondrial gene and protein delivery, offering improved selectivity, reduced systemic toxicity, and the potential to restore mitochondrial function under pathological conditions. This review showcases advanced strategies in stimuli-responsive peptide-polymer systems for mitochondria-targeted delivery, highlighting how their smart, responsive functions enable precise, controllable therapeutic interventions and drive the development of next-generation, transformative biomaterials in precision nanomedicine.

In an effort to discover a dual-functional and eco-friendly platform to address the challenges of halide entrapment and removal of toxic metal ions from wastewater concurrently, this work delineates a novel approach of fishing out a potential weapon compound I, from a pool of three constructs, harnessing the concept of hydrophobic orchestration. We propose that the chloride-palmitic acid derivative, formed through nucleophilic substitution of the palmitic acid's alcoholic hydroxy group, plays a crucial role in driving self-assembly, which ultimately leads to hydrogel formation and halide entrapment. Furthermore, the resulting chloride-palmitic acid derivative undergoes heavy metal ion (Pb²⁺/Cd²⁺)-induced syneresis, likely due to the formation of metal-ligand complexes under the given experimental conditions, as supported by extensive experimental evidence. This dual-responsive behavior of compound I, along with its reusability for up to three cycles, represents a promising and effective strategy for environment management.

Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic interactions, polarization, and Born effective charges (BECs), just by learning from energy and force training data. Here, we present LES as a standalone library, compatible with any short-range MLIP, and demonstrate its integration with methods such as MACE, NequIP, Allegro, CACE, CHGNet, and UMA. We benchmark LES-enhanced models on distinct systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates, and show that LES not only captures correct electrostatics but also improves accuracy. Additionally, we scale LES to large and chemically diverse data by training MACELES-OFF on the SPICE set containing molecules and clusters, making a universal MLIP with electrostatics for organic systems, including biomolecules. MACELES-OFF is more accurate than its short-range counterpart (MACE-OFF) trained on the same data set, predicts dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling efficient long-range electrostatics without directly training on electrical properties, LES paves the way for electrostatic foundation MLIPs.

Enzyme-mediated resistance is among the main strategies that bacteria use to evade antibiotic action. S-Adenosylmethionine-dependent erythromycin resistance methyltransferases catalyze the methylation of 23S rRNA in bacteria, causing resistance to macrolides, lincosamides, and streptogramin type-B antibiotics. Given the diversity and number of identified variants of these enzymes, it is vital to devise ways of inhibiting their activity to rescue affected antibiotics. Here, we use computer-aided solvent mapping and virtual screening techniques to identify inhibitors of Erms displaying promising adjuvant properties. We further demonstrate that an E. coli model expressing a recombinant S. aureus ErmC (SaErmC) variant causes substantial resistance to representative macrolide and lincosamide antibiotics. Assessment of test compounds using this resistance model revealed candidates that displayed promising adjuvant activity when combined with erythromycin or clindamycin. Antibiotic combinations with a principal candidate oxadiazole, JNAL-016, completely blocked SaErmC-mediated resistance against erythromycin, resulting in an antibiotic-sensitive phenotype in broth microdilution screening assays. This compound also suppressed ErmC activity, allowing erythromycin to regain its bactericidal properties when assessed in actively growing cultures using time-kill assays. JNAL-016 displayed a noncompetitive mode of inhibition against SaErmC activity in vitro and bound the purified enzyme with high affinity (K_d = 1.8 ± 0.7 μM) based on microscale thermophoresis data. Competition experiments suggested that JNAL-016 competes with SAM for its binding pocket on the enzyme, and this compound exhibited no toxicity against human embryonic kidney cells. These findings establish a practical strategy for targeting Erm-mediated resistance, which could lead to a viable adjuvant-based therapy against bacterial pathogens that weaponize variants of this class of methyltransferases.

Chemical neuroscience wields tools to uncover the molecular mysteries of the brain. Sensors can be fabricated with properties tailored to the scales needed to decode neurochemical information. Current instrumentation is capable of measurement rates that exceed neurochemical release rates. Modern machine learning models are approaching parameterization near the number of brain synapses. Fast voltammetry has remained a neuroanalytical workhorse technique for nearly half a century and has undergone significant transformations in many aspects due to advances in hardware and computation. Here, we review current and future uses of machine learning coupled with fast voltammetry to quantify neurochemical dynamics in the brains of behaving animal and human subjects. We focus on the advances that machine learning offers to pervasive problems in fast voltammetry. We identify current challenges and limitations for in vivo studies and delineate several routes for future development.

Pathological changes in the knee joint are reflected in the protein composition of synovial fluid (SF), which is altered in osteoarthritis (OA) and may serve as a source of biomarkers. This study used label-free quantification proteomics to analyze SF protein profiles from individuals with varying grades of cartilage damage and healthy controls. SF samples (n = 61) from healthy knees (grade 0) and OA-affected joints (grades I-IV, Outerbridge score) were analyzed using LC-MS/MS. Feature selection was performed with the Jonckheere-Terpstra nonparametric test. Candidate biomarkers were validated by ELISA in an independent cohort (n = 51), with OA severity graded according to the Kellgren-Lawrence (K/L) scale. Using this approach, nine proteins were significantly differentially expressed between OA and control samples (p < 0.01), showing higher levels in early OA stages compared to moderate and late disease (p < 0.05). Among these, aldo-keto reductase family 1 member C1 (AKR1C1), a protein involved in oxidative stress and autophagy, positively correlated with OA severity in both cohorts. These findings highlight several protein biomarkers with potential utility in early OA diagnosis and monitoring disease progression. Notably, AKR1C1 emerges as a promising diagnostic and prognostic biomarker, warranting further investigation.

Bioactive glass (BG) has emerged as a promising material in bone tissue engineering due to its unique ability to actively participate in the healing process. The present review introduces human natural bone and its properties, and the challenges posed in artificial bone material development. While much of the existing literature emphasizes its structural and compositional design, this review offers a novel perspective by focusing exclusively on the biological properties of BG that drive tissue regeneration. Key mechanisms, including osteoconduction, osteoinduction, angiogenesis, antibacterial activity, and immunomodulation, are critically examined to highlight how BG influences cellular behavior and the healing microenvironment. The review further presents recent in vitro and in vivo findings, compares the biological efficacy of different glass compositions, and discusses current clinical applications. By concentrating on the biological interface rather than fabrication strategies, this work provides an updated and focused framework for understanding the regenerative potential of BG and identifies future directions for enhancing its therapeutic performance. However, challenges such as controlling ion release kinetics, improving mechanical reliability and porosity balance, aligning degradation rates with tissue healing, ensuring predictable in vivo performance across diverse patient conditions, and overcoming barriers to large-scale clinical translation remain to be addressed. Addressing these limitations will be critical to fully realize the clinical potential of BG in bone regeneration.

Multidrug-resistant pulmonary infections pose significant therapeutic challenges as treatment options continue to dwindle in the face of rising antimicrobial resistance. Similar challenges arise in the management of wound infections such as those resulting from burn and blast injuries, where resistant pathogens severely limit treatment options. These wounds are further complicated by high microbial loads that exacerbate tissue damage, delay healing, and increase the risk of systemic infection. The escalating threat of antimicrobial resistance highlights the urgent need for innovative therapeutic strategies. This study evaluates the therapeutic potential of a novel topical formulation, azithromycin-bicarbonate (AZM-BIC), for addressing drug-resistant infections in both pulmonary and wound settings. Using murine models of infection in bicarbonate-depleted environments, including lung, blast injury, and burn wound models, topical administration of AZM-BIC enabled the localized delivery of therapeutic concentrations of bicarbonate. In the pulmonary model, AZM-BIC significantly reduced the bacterial burden. In vitro and ex vivo studies revealed AZM-BIC's ability to inhibit biofilm formation, a critical factor in managing chronic infections. In wound infection models, AZM-BIC reduced the bacterial burden and enhanced wound healing. These findings establish AZM-BIC as a promising therapeutic approach, offering a targeted, effective solution for pulmonary infection management and wound care amid the growing threat of antimicrobial resistance. Furthermore, given that azithromycin is a well-established antibiotic and bicarbonate is a physiological component that is safe and well-tolerated, AZM-BIC represents a readily translatable strategy for clinical implementation.

The coordination along a reaction sequence from a triazole precursor to a copper(II) complex was investigated with accurate electron density by means of high-resolution X-ray diffraction experiments and density functional theory calculations. The analysis of electron density distributions enables visualization of the electron polarization occurring upon the oxidation of the triazole alcohol and subsequently induced by coordination of the triazole carboxylic acid to the metal. This series of experimental measurements allows the direct visualization of the modified properties, with implications for molecular recognition and crystal engineering purposes.

Metal ion binding to amino acid residues is an important interaction motif that controls the tertiary structures of oligopeptides. Analyses of the vibrational band patterns displayed by the amino acid scaffolds are commonly used to characterize the local docking motifs. Here we carry out two-color, IR-IR photobleaching measurements to obtain isomer-selective vibrational spectra of the Cs⁺Gly ion-molecule complex isolated in a cryogenically cooled, radiofrequency ion trap. The distinct band patterns of two noninterconverting isomers are observed and traced to different bidentate binding motifs between Cs⁺ and the glycine scaffold. In one isomer, the ion attaches to the oxygen atoms of the carboxyl group whereas in the other it docks to the amino nitrogen and the carbonyl oxygen. Attachment to the acid headgroup yields a very diffuse absorption associated the OH group engaged in a strong intramolecular H-bond that closes a 5 membered ring. The band assignments, rearrangement pathways and electrostatic distortion of the electron density distributions in the glycine scaffold by the proximal ion are explored with electronic structure calculations and anharmonic theory.