Assessing the Perturbing Effects of Drugs on Lipid Bilayers Using Gramicidin Channel-Based In Silico and In Vitro Assays
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Nanodiamonds (NDs), tetrahedral carbon frameworks with size ranging from 1 to 100 nanometers, have gained growing attention in recent years due to their distinct optical, thermal, and mechanical properties compared to other carbon nanomaterials (e.g., graphene, carbon nanotubes, carbon dots). Combined with a high surface-to-volume ratio and tunable and chemically versatile surfaces, these support broad applications across catalysis, electronics, and life sciences. Moreover, the biocompatible characteristics of NDs enable their controllable interfacial interactions with biological systems, positioning them as excellent candidates for advancing cutting-edge biomedical sciences, particularly through the engineering of efficient material-biointerfaces that facilitate optimal interactions with biological systems. Among various forms of NDs, fluorescent nanodiamonds (FNDs) have emerged as some of the most impactful and rapidly advancing materials, demonstrating strong potential in ultrasensitive spin-enhanced bioimaging, high-precision biosensing, traceable drug delivery, and quantum-enabled biomedical technologies. This Perspective introduces the key principles underlying NDs and FNDs, including their structural properties, synthesis methods, and surface functionalization strategies. It also highlights emerging biomedical applications of NDs and FNDs, with particular emphasis on neurological disorders. Last, the article discusses current challenges in advancing NDs as a multifunctional platform for neural therapies with translational potential toward clinical trials.
The electrochemical reduction of CO 2 (CO 2 RR) into value-added chemicals offers a promising route toward a circular carbon economy and reduced reliance on fossil fuels. A detailed understanding of the structural and electronic factors governing the performance of molecular CO 2 RR electrocatalysts is essential for the design of efficient, tunable systems. Here, in this study, we report a series of rhenium(I) complexes, fac-[Re I (6,6′-(R) 2 -bpy)(CO) 3 Cl] (bpy = 2,2′-bipyridine; R = mesityl (mes), 2,4,6-triisopropylphenyl (trip), or isophthalic acid (phth)) and evaluate their electrocatalytic activity. Among these, fac-[Re I (6,6′-(mes) 2 -bpy)(CO) 3 Cl] exhibited the highest performance, enabling selective CO 2 -to-CO conversion for 1 hour with Faradaic efficiency (FE) > 97%, representing an unprecedented activity level for a Re-bpy catalysts. Single-crystal X-ray diffraction and density functional theory (DFT) calculations indicated that favorable CO 2 binding could be promoted by the tilting of the 6,6′-(mes)2-bpy ligand (from the Re-CO coordination plane), providing mechanistic insight into the observed enhancement. The study consequently demonstrates a rational correlation between the CO 2 electrocatalytic performance of Re-bpy catalysts and their structural variations, as derived from X-ray data and corroborated by computational modeling.
Lipid membranes are central to cellular life. Complementing experiments, computational modeling has been essential in unraveling complex lipid-biomolecule interactions, crucial in both academia and industry. The Martini model, a coarse-grained force field for efficient molecular dynamics simulations, is widely used to study membrane phenomena but has faced limitations, particularly in capturing realistic lipid phase behavior. Here, we present refined Martini 3 lipid models with a mapping scheme that distinguishes lipid tails that differ by just two carbon atoms, enhancing the structural resolution and thermodynamic accuracy of model membrane systems including ternary mixtures. The expanded Martini lipid library includes thousands of models, enabling simulations of complex and biologically relevant systems. These advancements establish Martini as a robust platform for lipid-based simulations across diverse fields.
We report a tunable chemical genetics approach for enhancing genetic code expansion in different wild-type bacterial strains that employs apidaecin-like, anti-microbial peptides observed to temporarily sequester and thereby inhibit Release Factor 1 (RF1). In a concentration-dependent matter, these peptides granted a conditional lambda phage resistance to a recoded Escherichia coli strain with non-essential RF1 activity and promoted multi-site non-standard amino acid (nsAA) incorporation at in-frame amber stop codons in vivo and in vitro. When exogenously added, the peptides stimulated specific nsAA incorporation in a variety of sensitive, wild-type (RF1+) strains including Agrobacterium tumefaciens, a species in which nsAA incorporation has not been previously reported. Improvement in nsAA incorporation was typically 2–15-fold in E. coli BL21, MG1655, DH10B strains and A. tumefaciens with the >20-fold improvement observed in probiotic E. coli Nissle 1917. In-cell expression of these peptides promoted multi-site nsAA incorporation in transcripts with up to 6 amber codons, with a >35-fold increase in BL21 showing moderate toxicity. Leveraging this RF1 sensitivity allowed multiplexed partial recoding of MG1655 and DH10B that rapidly resulted in resistant strains that showed an additional ~2 fold boost to nsAA incorporation independent of the peptide. Lastly, in-cell expression of an apidaecin-like peptide library allowed the discovery of new peptide variants with reduced toxicity that still improved multi-site nsAA incorporation >25-fold. In parallel to genetic reprogramming efforts, these new approaches can facilitate genetic code expansion technologies in a variety of wild-type bacterial strains.
The molecular composition of organic aerosols, especially for day/nighttime variations of organosulfates above agricultural fields, is not well understood despite profound impacts on regional climate, crop production, air quality, and human health. Here, nanospray desorption electrospray ionization with high-resolution mass spectrometry (nano-DESI-HRMS) is used to interrogate the molecular composition of organic aerosols collected at the Southern Great Plains, located in an agricultural region of Oklahoma. Identified molecular formulae featured carbon, hydrogen, oxygen (CHO), nitrogen (CHNO), and/or sulfur (CHOS, CHNOS), with higher organosulfate proportions during daytime (41%) compared to nighttime (30%). In this work, nighttime aerosols featured increases in CHO, CHNO, and extremely low volatility organic carbon (ELVOC) species. However, due to high relative humidity, the nighttime aerosols phase state was found to be more liquid-like than daytime aerosols using parametrized glass transition temperatures. Aerosol molecular composition from an anthropogenically influenced plume (southerly winds) showed significant increases in CHOS and ELVOC species. By comparison with chamber studies, CHOS species are suspected to be of mixed biogenic and anthropogenic origin, whereas CHNOS species (not identified in the southerly winds) are suggested to predominately be of biogenic origin. Overall, this study provides key insight into organosulfates above agricultural fields, demonstrating dependence upon day/night cycles and episodic anthropogenic emissions.
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A key challenge in synthetic biology is the successful utilization of characterized parts, such as promoters, in different biological contexts. We report the evaluation of the media robustness of a small library of E. coli PhoB regulated promoters that enable heterologous protein production in two-stage cultures. Expression levels were measured both in a rich Autoinduction Broth as well as a minimal mineral salts media. Expression was both media and promoter dependent. Of the 16 promoters tested, 4 were identified to have tightly controlled expression, which was also robust to media formulation. Improved promoter robustness led to more predictable scale up and consistent expression in instrumented bioreactors. Furthermore, this subset of PhoB activated promoters, useful for two-stage autoinduction, highlights the impact of the environment on the performance of biological parts and the importance of robustness testing in synthetic biology.
CRISPR-based interference has become common in various applications from genetic circuits to dynamic metabolic control. In E. coli, the native CRISPR Cascade system can be utilized for silencing by deletion of the cas3 nuclease along with expression of guide RNA arrays, where multiple genes can be silenced from a single transcript. We notice the loss of spacer sequences from guide arrays utilized for dynamic silencing. Here, we report that unstable guide arrays are due to expression of the Cas1/2 endonuclease complex. We propose a model wherein basal Cas1/2 endonuclease activity results in the loss of spacers from guide arrays. Subsequently, mutant guide arrays can be amplified through selection. Replacing a constitutive promoter driving Cascade complex expression with a tightly controlled inducible promoter improves guide array stability, while minimizing leaky gene silencing. Additionally, these results demonstrate the potential of Cas1/2 mediated guide deletion as a mechanism to avoid CRISPR based autoimmunity.
Solid-state electrolytes (SSEs) require ionic conductivities that are competitive with liquid electrolytes to realize applications in all-solid-state batteries. Although candidate SSEs have been discovered, the underlying mechanisms enabling superionic conduction (>1 mS cm –1 ) remain elusive. In particular, the role of ultrafast lattice dynamics in mediating ion migration, which involves couplings between ions, phonons, and electrons, is rarely explored experimentally at their corresponding time scales. To investigate the complex contributions of coupled lattice dynamics on ion migration, we modulate the charge density occupations within the crystal framework and then measure the time-resolved change in impedance on picosecond time scales for a candidate SSE, Li 0.5 La 0.5 TiO 3 (LLTO). Upon perturbation, we observe enhanced ion migration at ultrafast time scales. The respective transients match the time scales of optical and acoustic phonon vibrations, suggesting their involvement in ion migration. We further computationally evaluate the effect of a charge transfer from the O 2p to the Ti 3d band on the electronic and physical structure of LLTO. We hypothesize that the charge-transfer excitation distorts the TiO 6 polyhedra by altering the local charge density occupancy of the hopping site at the migration pathway saddle point, thereby causing a reduction in the migration barrier for the Li + hop. We rule out the contribution of photogenerated electron carriers and laser heating. Overall, our investigation introduces a new spectroscopic tool to probe fundamental ion hopping mechanisms transiently at ultrafast time scales, which has previously only been achieved in a time-averaged manner or solely via computational methods.
Dynamic axial focusing functionality has recently experienced widespread incorporation in microscopy, augmented/virtual reality (AR/VR), adaptive optics and material processing. However, the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such functionality. The varifocal tools that are the least burdensome to operate (e.g. liquid crystal, elastomeric or optofluidic lenses) suffer from low (≈100 Hz) refresh rates. Conversely, the fastest devices sacrifice either critical capabilities such as their dwelling capacity (e.g. acoustic gradient lenses or monolithic micromechanical mirrors) or low operating overhead (e.g. deformable mirrors). Here, we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed, dwelling capacity and lightweight drive by employing low-rigidity micromirrors that exploit the robustness of defocusing phase profiles. Geometrically, the device consists of an 8.2 mm diameter array of piston-motion and 48-μm-pitch micromirror pixels that provide 2π phase shifting for wavelengths shorter than 1100 nm with 10–90% settling in 64.8 μs (i.e., 15.44 kHz refresh rate). The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires <30 V per channel. Optical experiments demonstrated the array’s wide focusing range with a measured ability to target 29 distinct resolvable depth planes. Overall, the features of the proposed array offer the potential for compact, straightforward methods of tackling bottlenecked applications, including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR.
The tetrahydroisoquinoline (THIQ) moiety is a privileged substructure of many bioactive natural products and semi-synthetic analogs. Plants manufacture more than 3,000 THIQ alkaloids, including the opioids morphine and codeine. While microbial species have been engineered to synthesize a few compounds from the benzylisoquinoline alkaloid (BIA) family of THIQs, low product titers impede industrial viability and limit access to the full chemical space. Here we report a yeast THIQ platform by increasing production of the central BIA intermediate ( S )-reticuline to 4.6 g L –1 , a 57,000-fold improvement over our first-generation strain. We show that gains in BIA output coincide with the formation of several substituted THIQs derived from amino acid catabolism. We use these insights to repurpose the Ehrlich pathway and synthesize an array of THIQ structures. This work provides a blueprint for building diverse alkaloid scaffolds and enables the targeted overproduction of thousands of THIQ products, including natural and semi-synthetic opioids.
Abstract Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.
Abstract High titer, rate, yield (TRY), and scalability are challenging metrics to achieve due to trade-offs between carbon use for growth and production. To achieve these metrics, we take the minimal cut set (MCS) approach that predicts metabolic reactions for elimination to couple metabolite production strongly with growth. We compute MCS solution-sets for a non-native product indigoidine, a sustainable pigment, in Pseudomonas putida KT2440, an emerging industrial microbe. From the 63 solution-sets, our omics guided process identifies one experimentally feasible solution requiring 14 simultaneous reaction interventions. We implement a total of 14 genes knockdowns using multiplex-CRISPRi. MCS-based solution shifts production from stationary to exponential phase. We achieve 25.6 g/L, 0.22 g/l/h, and ~50% maximum theoretical yield (0.33 g indigoidine/g glucose). These phenotypes are maintained from batch to fed-batch mode, and across scales (100-ml shake flasks, 250-ml ambr®, and 2-L bioreactors).
Naturally-occurring thermal materials usually possess specific thermal conductivity ($\textit{κ}$), forming a digital set of $\textit{κ}$ values. Emerging thermal metamaterials have been deployed to realize effective thermal conductivities unattainable in natural materials. However, the effective thermal conductivities of such mixing-based thermal metamaterials are still in digital fashion, i.e., the effective conductivity remains discrete and static. Here, we report an analog thermal material whose effective conductivity can be in-situ tuned from near-zero to near-infinity $\textit{κ}$. The proof-of-concept scheme consists of a spinning core made of uncured polydimethylsiloxane (PDMS) and fixed bilayer rings made of silicone grease and steel. Thanks to the spinning PDMS and its induced convective effects, we can mold the heat flow robustly with continuously changing and anisotropic $\textit{κ}$. Our work enables a single functional thermal material to meet the challenging demands of flexible thermal manipulation. It also provides platforms to investigate heat transfer in systems with moving components.
Influenza A virus infection in swine impacts the agricultural industry in addition to its zoonotic potential. Here, we utilize epigraph, a computational algorithm, to design a universal swine H3 influenza vaccine. The epigraph hemagglutinin proteins are delivered using an Adenovirus type 5 vector and are compared to a wild type hemagglutinin and the commercial inactivated vaccine, FluSure. In mice, epigraph vaccination leads to significant cross-reactive antibody and T-cell responses against a diverse panel of swH3 isolates. Epigraph vaccination also reduces weight loss and lung viral titers in mice after challenge with three divergent swH3 viruses. Vaccination studies in swine, the target species for this vaccine, show stronger levels of cross-reactive antibodies and T-cell responses after immunization with the epigraph vaccine compared to the wild type and FluSure vaccines. In both murine and swine models, epigraph vaccination shows superior cross-reactive immunity that should be further investigated as a universal swH3 vaccine.
Abstract Expanding the portfolio of products that can be made from lignin will be critical to enabling a viable bio-based economy. Here, we engineer Pseudomonas putida for high-yield production of the tricarboxylic acid cycle-derived building block chemical, itaconic acid, from model aromatic compounds and aromatics derived from lignin. We develop a nitrogen starvation-detecting biosensor for dynamic two-stage bioproduction in which itaconic acid is produced during a non-growth associated production phase. Through the use of two distinct itaconic acid production pathways, the tuning of TCA cycle gene expression, deletion of competing pathways, and dynamic regulation, we achieve an overall maximum yield of 56% (mol/mol) and titer of 1.3 g/L from p -coumarate, and 1.4 g/L titer from monomeric aromatic compounds produced from alkali-treated lignin. This work illustrates a proof-of-principle that using dynamic metabolic control to reroute carbon after it enters central metabolism enables production of valuable chemicals from lignin at high yields by relieving the burden of constitutively expressing toxic heterologous pathways.
Abstract Branched-chain amino acid (BCAA) metabolism fulfills numerous physiological roles and can be harnessed to produce valuable chemicals. However, the lack of eukaryotic biosensors specific for BCAA-derived products has limited the ability to develop high-throughput screens for strain engineering and metabolic studies. Here, we harness the transcriptional regulator Leu3p from Saccharomyces cerevisiae to develop a genetically encoded biosensor for BCAA metabolism. In one configuration, we use the biosensor to monitor yeast production of isobutanol, an alcohol derived from valine degradation. Small modifications allow us to redeploy Leu3p in another biosensor configuration that monitors production of the leucine-derived alcohol, isopentanol. These biosensor configurations are effective at isolating high-producing strains and identifying enzymes with enhanced activity from screens for branched-chain higher alcohol (BCHA) biosynthesis in mitochondria as well as cytosol. Furthermore, this biosensor has the potential to assist in metabolic studies involving BCAA pathways, and offers a blueprint to develop biosensors for other products derived from BCAA metabolism.