Biological Systems Research Articles

A Siderophore Mimicking Gelation Component for Capturing and Self-Separation of Fe(III) from an Aqueous Solution of Mixture of Metal Ions.

The carbohydrazide-based gelation component N2,N4,N6-(1,3,5-triazine-2,4,6-triyl)tris(benzene-1,3,5-tricarbohydrazide) (CBTC) was synthesized and characterized using various spectroscopic tools. CBTC and trimesic acid (TMA) get self-assembled to form metallogel with Fe3+, specifically through various noncovalent interactions in a DMSO and H2O mixture. The self-assembly shows remarkable specificity toward Fe(III) among different transition metal salts. It is pertinent to point out that the binding specificity for Fe3+ can also be found in nature in the form of siderophores, as they are mainly involved in scavenging iron selectively from the surroundings. DFT studies have been used to investigate the possible interaction between the different components of the iron metallogel. To determine the selectivity of CBTC for iron, CBTC, along with trimesic acid, is used to interact with other metal ions, including Fe(III) ions, in a single system. The gelation components CBTC and TMA selectively bind with iron(III), which leads to the formation of metallogel and gets separated as a discrete layer, leaving the other metal ions in the solution. Therefore, CBTC and TMA together show iron-scavenging properties. This selective scavenging property is explored through FE-SEM, XPS, PXRD, IR, and ICP-AES analysis. The FE-SEM analysis shows a flower-petal-like morphology for the Fe(III) metallogel. The resemblance in the CBTC-TMA-Fe metallogel and metallogel obtained from the mixture of different metal salts is established through FE-SEM images and XPS analysis. The release of iron from the metallogel is achieved with the help of ascorbic acid, which converts Fe3+ to Fe2+. In biological systems, iron also gets released similarly from siderophores. This is the first report where the synthesized gelation component CBTC molecule is capable of scavenging out iron in the form of metallogel and self-separating from the aqueous mixture in the presence of various other metal ions.

Physics-informed and data-driven discovery of governing equations for complex phenomena in heterogeneous media.

Rapid evolution of sensor technology, advances in instrumentation, and progress in devising data-acquisition software and hardware are providing vast amounts of data for various complex phenomena that occur in heterogeneous media, ranging from those in atmospheric environment, to large-scale porous formations, and biological systems. The tremendous increase in the speed of scientific computing has also made it possible to emulate diverse multiscale and multiphysics phenomena that contain elements of stochasticity or heterogeneity, and to generate large volumes of numerical data for them. Thus, given a heterogeneous system with annealed or quenched disorder in which a complex phenomenon occurs, how should one analyze and model the system and phenomenon, explain the data, and make predictions for length and time scales much larger than those over which the data were collected? We divide such systems into three distinct classes. (i) Those for which the governing equations for the physical phenomena of interest, as well as data, are known, but solving the equations over large length scales and long times is very difficult. (ii) Those for which data are available, but the governing equations are only partially known, in the sense that they either contain various coefficients that must be evaluated based on the data, or that the number of degrees of freedom of the system is so large that deriving the complete equations is very difficult, if not impossible, as a result of which one must develop the governing equations with reduced dimensionality. (iii) In the third class are systems for which large amounts of data are available, but the governing equations for the phenomena of interest are not known. Several classes of physics-informed and data-driven approaches for analyzing and modeling of the three classes of systems have been emerging, which are based on machine learning, symbolic regression, the Koopman operator, the Mori-Zwanzig projection operator formulation, sparse identification of nonlinear dynamics, data assimilation combined with a neural network, and stochastic optimization and analysis. This perspective describes such methods and the latest developments in this highly important and rapidly expanding area and discusses possible future directions.

Visualization of the hepatic and renal cell uptake and trafficking of tetrahedral DNA origami in tumour.

DNA nanostructures, known for their programmability, ease of modification, and favourable biocompatibility, have gained widespread application in the biomedical field. Among them, Tetrahedral DNA Origami (TDOs), as a novel DNA nanostructure, possesses well-defined structures, multiple modification sites, and large cavities, making it a promising drug carrier. However, current understanding of TDOs' interactions with biological systems, particularly with target cells and organs, remains unexplored, limiting its further applications in biomedicine. In this work, we prepared TDOs with an average particle size of 40 nm and labelled them with Cy5 fluorescent molecules. Following intravenous injection in mice, the uptake of TDOs by different types of liver and kidney cells was observed. Results indicated that TDOs accumulate in renal tubules and are metabolized by Kupffer cells, epithelial cells, and hepatocytes in the liver. Additionally, in a tumour-bearing mouse model, TDOs passively targeted tumour tissues and exhibited excellent tumour penetration and retention after rapid metabolism in hepatocytes. Our findings provide crucial insights for the development of TDO-based drug delivery systems.

Tailbeat perturbations improve swimming efficiency in self-propelled flapping foils

Recent studies have shown that superimposing rhythmic perturbations to oscillating tailbeats could simultaneously enhance both the thrust and efficiency (Lehn et al., Phys. Rev. Fluids, vol. 2, 2017, p. 023101; Chao et al., PNAS Nexus, vol. 3, 2024, p. 073). However, these investigations were conducted with a tethered flapping foil, overlooking the self-propulsion intrinsic to real swimming fish. Here, we investigate how the high-frequency, low-amplitude superimposed rhythmic perturbations impact the self-propelled pitching and heaving swimming of a rigid foil. The swimming-speed-based Reynolds number ranges from 1400 to 2700 in our study, depending on superimposed perturbations and swimming modes. Numerical results reveal that perturbations significantly increase swimming speeds in both pitching and heaving motions, while enhancing efficiency exclusively in the heaving motion. Further derived scaling laws elucidate the relationships of perturbations with speeds, power costs and efficiency, respectively. These findings not only hypothesise the potential advantages of perturbations in biological systems, but also inspire designs and controls in biomimetic propulsion and manoeuvring within aquatic environments.

Edge-based relative entropy as a sensitive indicator of critical transitions in biological systems

BackgroundDisease progression in biosystems is not always a steady process but is occasionally abrupt. It is important but challenging to signal critical transitions in complex biosystems.MethodsIn this study, based on the theoretical framework of dynamic network biomarkers (DNBs), we propose a model-free method, edge-based relative entropy (ERE), to identify temporal key biomolecular associations/networks that may serve as DNBs and detect early-warning signals of the drastic state transition during disease progression in complex biological systems. Specifically, by combining gene‒gene interaction (edge) information with the relative entropy, the ERE method converts gene expression values into network entropy values, quantifying the dynamic change in a biomolecular network and indicating the qualitative shift in the system state.ResultsThe proposed method was validated using simulated data and real biological datasets of complex diseases. The applications show that for certain diseases, the ERE method helps to reveal so-called “dark genes” that are non-differentially expressed but with high ERE values and of essential importance in both gene regulation and prognosis.ConclusionsThe proposed method effectively identified the critical transition states of complex diseases at the network level. Our study not only identified the critical transition states of various cancers but also provided two types of new prognostic biomarkers, positive and negative edge biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

Root-Applied Cerium Oxide Nanoparticles and Their Specific Effects on Plants: A Review.

With the pronounced increase in nanotechnology, it is likely that biological systems will be exposed to excess nanoparticles (NPs). Cerium oxide nanoparticles (CeO2 NPs) are among the most abundantly produced nanomaterials in the world. Their widespread use raises fundamental questions related to the accumulation in the environment and further interactions with living organisms, especially plants. NPs present in either soil or soilless environments are absorbed by the plant root systems and further transported to the aboveground parts. After entering the cytoplasm, NPs interact with chloroplast, nucleus, and other structures responsible for metabolic processes at the cellular level. In recent years, several studies have shown the impact of nanoceria on plant growth and metabolic processes. Research performed on different plants has shown a dual role for CeO2 NPs. The observed effects can be positive or negative and strongly depend on the plant species, characterization, and concentrations of NPs. This review describes the impact of root-applied CeO2 NPs on plant growth, photosynthesis, metal homeostasis, and parameters of induced oxidative stress.

The influence of cation exchange on the possible mechanism of erionite toxicity: A synchrotron-based micro-X-ray fluorescence study on THP-1-derived macrophages exposed to erionite-Na

Fibrous erionite is the only zeolite classified as Group 1 carcinogen by the International Agency for Research on Cancer (IARC). Carcinogenesis induced by erionite is thought to involve several factors as biopersistence, the iron role and cation exchange processes. To better understand these mechanisms, a detailed investigation at the micro scale was performed, collecting elemental information on iron and cation release and their distribution in biological systems by synchrotron micro-X-ray fluorescence mapping (SR-micro-XRF) and synchrotron micro-X-ray absorption spectroscopy (SR-micro-XANES) at the TwinMic beamline (Elettra synchrotron) and at the ID21 beamline of the European Synchrotron Radiation Facility (ESRF). By microscopy and chemical mapping, highly detailed maps of the chemical and morphological interaction of biological systems with fibres could be produced. In detail, THP-1 cell line derived macrophages, used as in vitro model, were analysed during erionite-Na phagocytosis at different time intervals, after single dose exposure. For comparison, cellular fluorescent probes were also used to evaluate the intracellular free sodium and calcium concentrations. Synchrotron analyses visualised the spatial distribution of both fibre and mineral particle associated metals during the phagocytosis, describing the mechanism of internalisation of erionite-Na and its accessory mineral phases. The intracellular distribution of metals and other cations was mapped to evaluate metal release, speciation changes and/or cation exchange during phagocytosis. The fluorescent probes complemented microchemical data clarifying, and confirming, the cation distribution observed in the SR-micro-XRF maps. The significant cytoplasmic calcium decrease, and the concomitant sodium increase, after the fibre phagocytosis seemed due to activation of plasma membrane cations exchangers triggered by the internalisation while, surprisingly, the ion-exchange capacity of erionite-Na could play a minor role in the disruption of the two cations intracellular homeostasis. These results help to elucidate the role of cations in the toxicity of erionite-treated THP-1 macrophages and add knowledge to its carcinogenicity process.

Ultrastructural Analysis of Large Japanese Field Mouse (Apodemus speciosus) Testes Exposed to Low-Dose-Rate (LDR) Radiation after the Fukushima Nuclear Power Plant Accident.

Since the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, great attention has been paid to the impact of chronic low-dose-rate (LDR) radiation exposure on biological systems. The reproductive system is sensitive to radiation, with implications connected to infertility. We investigated the testis ultrastructure of the wild large Japanese field mouse (Apodemus speciosus) from three areas contaminated after the FDNPP accident, with different levels of LDR radiation (0.29 µSv/h, 5.11 µSv/h, and 11.80 µSv/h). Results showed good preservation of the seminiferous tubules, comparable to the unexposed animals (controls), except for some ultrastructural modifications. Increases in the numerical density of lipid droplet clusters in spermatogenic cells were found at high levels of LDR radiation, indicating an antioxidant activity rising due to radiation recovery. In all groups, wide intercellular spaces were found between spermatogenic cells, and cytoplasmic vacuolization increased at intermediate and high levels and vacuolated mitochondria at the high-level. However, these findings were also related to the physiological dynamics of spermatogenesis. In conclusion, the testes of A. speciosus exposed to LDR radiation associated with the FDNPP accident showed a normal spermatogenesis, with some ultrastructural changes. These outcomes may add information on the reproductive potential of mammals chronically exposed to LDR radiation.

Integration and holistic analysis of multiple multidimensional soil data sets

Complex matrices such as soil have a range of measurable characteristics, and thus data to describe them can be considered multidimensional. These characteristics can be strongly influenced by factors that introduce confounding effects that hinder analyses. Traditional statistical approaches lack the flexibility and granularity required to adequately evaluate such matrices, particularly those with large dataset of varying data types (i.e. quantitative non-compositional, quantitative compositional). We present a statistical workflow designed to effectively analyse complex, multidimensional systems, even in the presence of confounding variables. The developed methodology involves exploratory analysis to identify the presence of confounding variables, followed by data decomposition (including strategies for both compositional and non-compositional quantitative data) to minimise the influence of these confounding factors such as sampling site/location. These data processing methods then allow for common patterns to be highlighted in the data, including the identification of biomarkers and determination of non-trivial associations between variables. We demonstrate the utility of this statistical workflow by jointly analysing the chemical composition and fungal biodiversity of New Zealand vineyard soils that have been managed with either organic low-input or conventional input approaches. By applying this pipeline, we were able to identify biomarkers that distinguish viticultural soil from both approaches and also unearth links and associations between the chemical and metagenomic profiles. While soil is an example of a system that can require this type of statistical methodology, there are a range of biological and ecological systems that are challenging to analyse due to the complex interplay of global and local effects. Utilising our developed pipeline will greatly enhance the way that these systems can be studied and the quality and impact of insight gained from their analysis.

Evaluation of some selected antibiotics and dyes removal by fungi isolated from wastewater sludge

In developing countries, stabilization ponds are the most common facilities wastewater treatment, where microbial consortia are the workhorse for removing organic contaminants. Fungi play an important role in the removal of organic matter. However, their efficiency in removing recalcitrant pollutants such as antibiotics and dyes still needs to be determined. The present work evaluates four fungal strains isolated from wastewater treatment lagoon systems (aerobic-anaerobic-anoxic) to remove persistent organic pollutants: Tilmicosin, Enrofloxacin, and Methylthionine chloride in Malt medium rich in carbon and in Mineral medium with only mineral nutrients. Fungi were isolated from sludge obtained from lagoons and identified based on their morphology. Removal assays were conducted in synthetic wastewater, with variations in operating conditions such as fungus type, temperature, and residence time. The quantification of contaminants was performed using UV-V spectrometry, and data on pH, conductivity, and spore concentration were obtained. The results were analyzed using R software, version 4.0.2. The results indicate that adsorption is the primary pathway in all cases. It was found that the greater the amount of fungal biomass produced during the test, the greater the absorption of the mode; that was the case with the Malt medium and dyes tests compared to Mineral medium and antibiotics tests. The studied fungi poorly removed antibiotics, around 20% in Malta medium and less than 10% in mineral medium, but are more capable of adsorbing Methylene blue, around 70% in Malta medium and less than 20% in the mineral medium. These results indicate natural fungi role in removing persistent organic pollutants (POPs). This shows that this type of plant needs the support of ternary treatments to eliminate POPs such as antibiotics. Limiting the discharge of this type of POPs is recommended as they could risk the biological remediation system’s health. Nevertheless, Fungal species from these lagoon systems can be used in dye removal processes.

The treatment of petrochemical wastewater via ozone-persulfate coupled catalytic oxidation: mechanism of removal of soluble organic matter.

Petrochemical wastewater contains a variety of organic pollutants. Advanced oxidation processes (AOPs) are used for deep petrochemical wastewater treatment with distinct advantages, including the complete mineralization of organic substances, minimal residual byproducts, and compatibility with biological treatment systems. This work evaluates the effectiveness of three methods, namely, ozone, persulfate, and O3-PMS (ozone-persulfate) processes, which were compared to remove soluble organic matter. The O3-PMS process offered significant advantages in terms of organic matter removal efficiency. This process involves ozone dissolution in an aqueous persulfate solution, producing a more significant amount of hydroxyl radicals in comparison to single AOPs. The production of hydroxyl radicals and the synergistic effect of hydroxyl radicals and persulfate radicals were investigated. In the O3-PMS process, transition metal ions were added to understand the mechanism of the O3-PMS coupled catalytic oxidation system. The results showed that when the ozone concentration was in the range of 5 ~ 25mg/L, the dosage of persulfate was in the range of 0.01 ~ 0.05mol/L, the dosage of metal compounds was in the range of 0:0 ~ 2:1, and the reaction time was in the range of 0 ~ 2h; the optimum chemical oxygen demand (CODCr) and total organic content (TOC) removal effect was achieved under the coupled system with an ozone concentration of 10mg/L, a persulfate dosage of 0.02mol/L, a 1:2 dosage ratio of between Fe2+ and Cu2+ compounds, and a reaction time of 2h. Under optimal reaction conditions, the rates of CODCr and TOC removal reached 70% and 79.3%, respectively. Furthermore, the removal kinetics of the O3-PMS coupled catalytic oxidation system was analyzed to optimize the removal conditions of COD and TOC, and the mechanism regulating the degradation of dissolved organic matter was explored by three-dimensional fluorescence and GC-MS technology. Thus, O3-PMS coupled catalytic oxidation is an effective process for the deep treatment of wastewater. The careful selection of transition metal ions serves as a theoretical foundation for the subsequent preparation of catalysts for the ozone persulfate oxidation system, and this study provides a suitable reference for removing organic matter from petrochemical wastewater.

Effect of the Photoexcitation Wavelength and Polarization on the Generated Heat by a Nd-Doped Microspinner at the Microscale.

Thermal control at small scales is critical for studying temperature-dependent biological systems and microfluidic processes. Concerning this, optical trapping provides a contactless method to remotely study microsized heating sources. This work introduces a birefringent luminescent microparticle of NaLuF4:Nd3+ as a local heater in a liquid system. When optically trapped with a circularly polarized laser beam, the microparticle rotates and heating is induced through multiphonon relaxation of the Nd3+ ions. The temperature increment in the surrounding medium is investigated, reaching a maximum heating of ≈5 °C within a 30 µm radius around the static particle under 51 mW laser excitation at 790 nm. Surprisingly, this study reveals that the particle's rotation minimally affects the temperature distribution, contrary to the intuitive expectation of liquid stirring. The influence of the microparticle rotation on the reduction of heating transfer is analyzed. Numerical simulations confirm that the thermal distribution remains consistent regardless of spinning. Instead, the orientation-dependence of the luminescence process emerges as a key factor responsible for the reduction in heating. The anisotropy in particle absorption and the lag between the orientation of the particle and the laser polarization angle contribute to this effect. Therefore, caution must be exercised when employing spinning polarization-dependent luminescent particles for microscale thermal analysis using rotation dynamics.

Suppressing the background of LC-ESI-MS analysis of permethylated glycans using the active background ion reduction device.

Mass spectrometry (MS) has revolutionized analytical chemistry, enabling precise identification and quantification of chemical species, which is pivotal for biomarker discovery and understanding complex biological systems. Despite its versatility, the presence of background ions in MS analysis hinders the sensitive detection of low-abundance analytes. Therefore, studies aimed at lowering background ion levels have become increasingly important. Here, we utilized the commercially available Active Background Ion Reduction Device (ABIRD) to suppress background ions and assess its effect on the liquid chromatography-electrospray ionization (LC-ESI)-MS analyses of N-glycans on the Q Exactive HF mass spectrometer. We also investigated the effect of different solvent vapors in the ESI source on N-glycan analysis by MS. ABIRD generally had no effect on high-mannose and neutral structures but reduced the intensity of some structures that contained sialic acid, fucose, or both when methanol vapor filled the ESI source. Based on our findings on the highest number of identified N-glycans from human serum, methanol vapor in the ion source compartment may enhance N-glycan LC-ESI-MS analyses by improving the desolvation of droplets formed during the ESI process due to its high volatility. This protocol may be further validated and extended to advanced bottom-up proteomic/glycoproteomic studies for the analysis of peptide/glycopeptide ions by MS.

Multiple phenotype association tests based on sliced inverse regression

BackgroundJoint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in dimensionality has become a very challenging problem in the widespread use of joint analysis. To handle the excessiveness of variables, we consider the sliced inverse regression (SIR) method. Specifically, we propose a novel SIR-based association test that is robust and powerful in testing the association between multiple predictors and multiple outcomes.ResultsWe conduct simulation studies in both low- and high-dimensional settings with various numbers of Single-Nucleotide Polymorphisms and consider the correlation structure of traits. Simulation results show that the proposed method outperforms the existing methods. We also successfully apply our method to the genetic association study of ADNI dataset. Both the simulation studies and real data analysis show that the SIR-based association test is valid and achieves a higher efficiency compared with its competitors.ConclusionSeveral scenarios with low- and high-dimensional responses and genotypes are considered in this paper. Our SIR-based method controls the estimated type I error at the pre-specified level α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document}.

A comprehensive approach to characterize navigation instruments for magnetic guidance in biological systems

Achieving non-invasive spatiotemporal control over cellular functions, tissue organization, and behavior is a desirable aim for advanced therapies. Magnetic fields, due to their negligible interaction with biological matter, are promising for in vitro and in vivo applications, even in deep tissues. Particularly, the remote manipulation of paramagnetic (including superparamagnetic and ferromagnetic, all with a positive magnetic susceptibility) entities through magnetic instruments has emerged as a promising approach across various biological contexts. However, variations in the properties and descriptions of these instruments have led to a lack of reproducibility and comparability among studies. This article addresses the need for standardizing the characterization of magnetic instruments, with a specific focus on their ability to control the movement of paramagnetic objects within organisms. While it is well known that the force exerted on magnetic particles depends on the spatial variation (gradient) of the magnetic field, the magnitude of the field is often overlooked in the literature. Therefore, we comprehensively analyze and discuss both actors and propose a novel descriptor, termed ‘effective gradient’, which combines both dependencies. To illustrate the importance of both factors, we characterize different magnet systems and relate them to experiments involving superparamagnetic nanoparticles. This standardization effort aims to enhance the reproducibility and comparability of studies utilizing magnetic instruments for biological applications.

Colorimetric Sensor Array for Simultaneous Assay of Catecholamine Neurotransmitters Using Various-Sized Citrate-Capped Gold Nanoparticles

Driven by the need to in situ monitoring of the endogenous substances in biological matrices, a simple, rapid and sensitive optical nanoarray system has been designed for determination and visual discrimination of four important catecholamine neurotransmitters: dopamine (DA), epinephrine (EP), norepinephrine (NE) and levodopa (LD). To fabricate this platform, spherical citrate-capped gold nanoparticles (AuNPs) in the size range of 13–42 nm have been employed as sensing elements. For each catecholamine different responses toward the AuNPs was obtained indicating unique aggregation kinetics. When the sensor array exposed to different catecholamines (CAs), both the color and UV–Vis absorbance spectra changed due to the interaction of analytes and AuNPs, creating a unique fingerprint-like pattern for each analyte which can be visually detected by naked eye. To analyze response profiles, we employed unsupervised chemometric algorithms including hierarchical cluster analysis (HCA) and principal component analysis (PCA) and satisfactory class separations were achieved. Furthermore, the sensitive RGB based color difference patterns were generated providing easily monitored visual output. Based on this sensor array, the set of four catecholamine neurotransmitters have been correctly identified and classified in the spiked real urine samples. The target analytes have been well distinguished at low concentrations of 0.3 − 4 μM DA, 0.8 − 10 μM EP, 0.8 − 8 μM NE, and 0.3 − 10 μM LD using the proposed “chemical nose” strategy. We believe that the established optical sensor array can be potentially employed as an efficient discrimination tool for diagnostic and therapeutic applications in the future.

Oscillatory Coupling Between Neural and Cardiac Rhythms.

Oscillations serve a critical role in organizing biological systems. In the brain, oscillatory coupling is a fundamental mechanism of communication. The possibility that neural oscillations interact directly with slower physiological rhythms (e.g., heart rate, respiration) is largely unexplored and may have important implications for psychological functioning. Oscillations in heart rate, an aspect of heart rate variability (HRV), show remarkably robust associations with psychological health. Mather and Thayer proposed coupling between high-frequency HRV (HF-HRV) and neural oscillations as a mechanism that partially accounts for such relationships. We tested this hypothesis by measuring phase-amplitude coupling between HF-HRV and neural oscillations in 37 healthy adults at rest. Robust coupling was detected in all frequency bands. Granger causality analyses indicated stronger heart-to-brain than brain-to-heart effects in all frequency bands except gamma. These findings suggest that cardiac rhythms play a causal role in modulating neural oscillations, which may have important implications for mental health.

Proteomic Ligandability Maps of Spirocycle Acrylamide Stereoprobes Identify Covalent ERCC3 Degraders.

Covalent chemistry coupled with activity-based protein profiling (ABPP) offers a versatile way to discover ligands for proteins in native biological systems. Here, we describe a set of stereo- and regiochemically defined spirocycle acrylamides and the analysis of these electrophilic "stereoprobes" in human cancer cells by cysteine-directed ABPP. Despite showing attenuated reactivity compared to structurally related azetidine acrylamide stereoprobes, the spirocycle acrylamides preferentially liganded specific cysteines on diverse protein classes. One compound termed ZL-12A promoted the degradation of the TFIIH helicase ERCC3. Interestingly, ZL-12A reacts with the same cysteine (C342) in ERCC3 as the natural product triptolide, which did not lead to ERCC3 degradation but instead causes collateral loss of RNA polymerases. ZL-12A and triptolide cross-antagonized one another's protein degradation profiles. Finally, we provide evidence that the antihypertension drug spironolactone─previously found to promote ERCC3 degradation through an enigmatic mechanism─also reacts with ERCC3_C342. Our findings thus describe monofunctional degraders of ERCC3 and highlight how covalent ligands targeting the same cysteine can produce strikingly different functional outcomes.

Multiplexed in-situ mutagenesis driven by a dCas12a-based dual-function base editor.

Mutagenesis driving genetic diversity is vital for understanding and engineering biological systems. However, the lack of effective methods to generate in-situ mutagenesis in multiple genomic loci combinatorially limits the study of complex biological functions. Here, we design and construct MultiduBE, a dCas12a-based multiplexed dual-function base editor, in an all-in-one plasmid for performing combinatorial in-situ mutagenesis. Two synthetic effectors, duBE-1a and duBE-2b, are created by amalgamating the functionalities of cytosine deaminase (fromhAPOBEC3A orhAID*Δ ), adenine deaminase (fromTadA9), and crRNA array processing (fromdCas12a). Furthermore, introducing the synthetic separator Sp4 minimizes interference in the crRNA array, thereby facilitating multiplexed in-situ mutagenesis in both Escherichia coli and Bacillus subtilis. Guided by the corresponding crRNA arrays, MultiduBE is successfully employed for cell physiology reprogramming and metabolic regulation. A novel mutation conferring streptomycin resistance has been identified in B. subtilis and incorporated into the mutant strains with multiple antibiotic resistance. Moreover, surfactin and riboflavin titers of the combinatorially mutant strains improved by 42% and 15-fold, respectively, compared with the control strains with single gene mutation. Overall, MultiduBE provides a convenient and efficient way to perform multiplexed in-situ mutagenesis.

17O Solid-State NMR Spectroscopy of Lipid Membranes.

Despite the limitations posed by poor sensitivity, studies have reported the unique advantages of 17O based NMR spectroscopy to study systems existing in liquid, solid, or semisolid states. 17O NMR studies have exploited the remarkable sensitivity of quadrupole coupling and chemical shift anisotropy tensors to the local environment in the characterization of a variety of intra- and intermolecular interactions and motion. Recent studies have considerably expanded the use of 17O NMR to study dynamic intermolecular interactions associated with some of the challenging biological systems under magic angle spinning (MAS) and aligned conditions. The very fast relaxing nature of 17O has been well utilized in cellular and in vivo MRS (magnetic resonance spectroscopy) and MRI (magnetic resonance imaging) applications. The main focus of this Review is to highlight the new developments in the biological solids with a detailed discussion for a few selected examples including membrane proteins and nanodiscs. In addition to the unique benefits and limitations, the remaining challenges to overcome, and the impacts of higher magnetic fields and sensitivity enhancement techniques are discussed.