Regulation of biofilm gene expression by DNA replication in Bacillus subtilis.

Bacillus subtilis relies on biofilms for survival in harsh environments. Extracellular polymeric substance (EPS) is a crucial component of biofilms, yet the dynamics of EPS production in single cells remain elusive. To unveil the modulation of EPS synthesis, we built a minimal network model comprising the SinI-SinR-SlrR module, Spo0A, and EPS. Stochastic simulations revealed that antagonistic interplay between SinI and SinR enables EPS production in bursts. SlrR widens these bursts and increases their frequency by stabilizing SinR-SlrR complexes and depleting free SinR. DNA replication and chromosomal positioning of key genes dictate pulsatile changes in the slrR:sinR gene dosage ratio (gr) and Spo0A-P levels, each promoting EPS production in distinct phases of the cell cycle. As the cell cycle lengthens with nutrient stress, the duty cycle of gr pulsing decreases, whereas the amplitude of Spo0A-P pulses elevates. This coordinated response facilitates keeping a constant proportion of EPS-secreting cells within colonies across diverse nutrient conditions. Our results suggest that bacteria may 'encode' eps expression through strategic chromosomal organization. This work illuminates how stochastic protein interactions, gene copy number imbalance, and cell-cycle dynamics orchestrate EPS synthesis, offering a deeper understanding of biofilm formation.

SUMOylation of Smad2 mediates TGF-ß-regulated endothelial-mesenchymal transition.

Endothelial-mesenchymal transition (EndoMT) is a complex biological process in which endothelial cells are transformed into mesenchymal cells, and dysregulated EndoMT causes a variety of pathological processes. Transforming growth factor beta (TGF-ß) signaling effectively induces the EndoMT process in endothelial cells, and Smad2 is the critical protein of the TGF-ß signaling pathway. However, whether small ubiquitin-like modifier modification (SUMOylation) is involved in EndoMT remains unclear. Here, we show that Smad2 is predominantly modified by SUMO1 at two major SUMOylation sites with PIAS2α as the primary E3 ligase, whereas SENP1 (sentrin/SUMO-specific protease 1) mediates the deSUMOylation of Smad2. In addition, we identified that SUMOylation significantly enhances the transcriptional activity and protein stability of Smad2, regulating the expression of downstream target genes. SUMOylation increases the phosphorylation of Smad2 and the formation of the Smad2-Smad4 complex, thus promoting the nuclear translocation of Smad2. Ultimately, the wildtype, but not SUMOylation site mutant Smad2 facilitated the EndoMT process. More importantly, TGF-ß enhances the nuclear translocation of Smad2 by enhancing its SUMOylation and promoting the EndoMT process. These results demonstrate that SUMOylation of Smad2 plays a critical role in the TGF-ß-mediated EndoMT process, providing a new theoretical basis for the treatment and potential drug targets of EndoMT-related clinical diseases.

Protectin D1 ameliorates non-compressive lumbar disc herniation through SIRT1-mediated CGRP signaling.

Background. Neuro-inflammatory response promotes the initiation and sustenance of lumbar disc herniation (LDH). Protectin D1 (PD1), as a new type of specialized pro-resolving mediator (SPM), can improve the prognosis of various inflammatory diseases. Recent studies have shown that over representation of calcitonin gene-related peptides (CGRP) may activate nociceptive signaling following nerve injury. Silent information regulator 1 (SIRT1) is ubiquitously expressed in the dorsal horn of the spinal cord and plays a role in the pathogenesis of LDH. In this study, we investigated the analgesic effects of PD1 and elucidated the impact of neurogenic inflammation in the pathogenesis of neuropathic pain induced by non-compressive lumbar disc herniation (NCLDH) in a rat model. Methods. NCLDH models were established by applying protruding autologous nucleus pulposus to the L5 Dorsal root ganglion (DRG). PD1, SIRT1 antagonist or agonist, CGRP or antagonist were administered as daily intrathecal injections for three consecutive days postoperatively. Behavioral tests were conducted to assess mechanical and thermal hyperalgesia. The ipsilateral lumbar (L4-6) segment of the spinal dorsal horn was isolated for further analysis. Alterations in the release of SIRT1 and CGRP were explored using western blot and immunofluorescence. Results. Application of protruded nucleus (NP) materials to the DRG induced mechanical and thermal allodynia symptoms, and deregulated the expression of pro-inflammatory and anti-inflammatory cytokines in rats. Intrathecal delivery of PD1 significantly reversed the NCLDH-induced imbalance in neuro-inflammatory response and alleviated the symptoms of mechanical and thermal hyperalgesia. In addition, NP application to the DGRs resulted the spinal upregulation of CGRP and SIRT1 expression, which was almost restored by intrathecal injection of PD1 in a dose-dependent manner. SIRT1 antagonist or agonist and CGRP or antagonist treatment further confirmed the result. Conclusion. Our findings indicate PD1 has a potent analgesic effect, and can modulate neuro-inflammation by regulating SIRT1-mediated CGRP signaling in NCLDH.

Harnessing Cooperative Multivalency in Thioguanine for SERS Differentiation of Polyfunctional Analytes Differing by Single Functional Group.

Small-molecule receptors are increasingly employed to probe various functional groups for (bio)chemical analysis. However, differentiation of polyfunctional analogs sharing multiple functional groups remains challenging for conventional mono- and bidentate receptors because their insufficient number of binding sites limits interactions with the least reactive yet property-determining functional group. Herein, we introduce 6-thioguanine (TG) as a supramolecular receptor for unique tridentate receptor-analyte complexation,achieving ≥ 95% identification accuracy among 16 polyfunctional analogs across three scenarios: glycerol derivatives, disubstituted propanes, and vicinal diols. Crucially, we demonstrate distinct spectral changes induced by the tridentate interaction between TG's three anchoring points and all the analyte's functional groups, even the least reactive ones. Notably, H-bond networks formed in the TG-analyte complexes demonstrate additive effect in binding strength originating from good bond linearity, cooperativity, and resonance, thus strengthens complexation events and amplifies the differences in spectral changes induced among analytes. It also enhances spectral consistency by selectively form a sole configuration that is stronger than the respective analyte-analyte interaction. Finally, we achieve 95.4% accuracy for multiplex identification of a mixture consisting of multiple polyfunctional analogs. We envisage that extension to other multidentate non-covalent interactions enables the development of interference-free small molecule-based sensors for various (bio)chemical analysis applications.

Creating 3D Nanoparticle Structural Space via Data Augmentation to Bidirectionally Predict Nanoparticle Mixture's Purity, Size, and Shape from Extinction Spectra.

Nanoparticle (NP) characterization is essential because diverse shapes, sizes, and morphologies inevitably occur in as-synthesized NP mixtures, profoundly impacting their properties and applications. Currently, the only technique to concurrently determine these structural parameters is electron microscopy, but it is time-intensive and tedious. Here, we create a three-dimensional (3D) NP structural space to concurrently determine the purity, size, and shape of 1000 sets of as-synthesized Ag nanocubes mixtures containing interfering nanospheres and nanowires from their extinction spectra, attaining low predictive errors at 2.7-7.9 %. We first use plasmonically-driven feature enrichment to extract localized surface plasmon resonance attributes from spectra and establish a lasso regressor (LR) model to predict purity, size, and shape. Leveraging the learned LR, we artificially generate 425,592 augmented extinction spectra to overcome data scarcity and create a comprehensive NP structural space to bidirectionally predict extinction spectra from structural parameters with <4 % error. Our interpretable NP structural space further elucidates the two higher-order combined electric dipole, quadrupole, and magnetic dipole as the critical structural parameter predictors. By incorporating other NP shapes and mixtures' extinction spectra, we anticipate our approach, especially the data augmentation, can create a fully generalizable NP structural space to drive on-demand, autonomous synthesis-characterization platforms.

Modular Click Assembly DNA-Encoded Glycoconjugate Libraries with on-DNA Functional Group Transformations.

Carbohydrates are an important class of naturally active products and play vital roles in regulating various physiological activities. To meet the demand for carbohydrate-based libraries used for the identification of potential drug candidates for pharmaceutical-related targets, we developed a set of on-DNA protocols to construct the DNA-encoded glycoconjugates, including Seyferth-Gilbert homologation, anomeric azidation, and CuAAC cyclization. These on-DNA chemistries enable the generation and modification of DNA-linked glycosyl compounds with good conversions and broad substrate scope. Finally, three DNA-linked glycoconjugate libraries were successfully generated to demonstrate their applicability and feasibility in library preparation.

A method using deep learning to discover new predictors from left-ventricular mechanical dyssynchrony for CRT response.

BACKGROUND: Studies have shown that the conventional parameters characterizing left ventricular mechanical dyssynchrony (LVMD) measured on gated SPECT myocardial perfusion imaging (MPI) have their own statistical limitations in predicting cardiac resynchronization therapy (CRT) response. The purpose of this study is to discover new predictors from the polarmaps of LVMD by deep learning to help select heart failure patients with a high likelihood of response to CRT. METHODS: One hundred and fifty-seven patients who underwent rest gated SPECT MPI were enrolled in this study. CRT response was defined as an increase in left ventricular ejection fraction (LVEF) > 5% at 6 [Formula: see text] 1 month follow up. The autoencoder (AE) technique, an unsupervised deep learning method, was applied to the polarmaps of LVMD to extract new predictors characterizing LVMD. Pearson correlation analysis was used to explain the relationships between new predictors and existing clinical parameters. Patients from the IAEA VISION-CRT trial were used for an external validation. Heatmaps were used to interpret the AE-extracted feature. RESULTS: Complete data were obtained in 130 patients, and 68.5% of them were classified as CRT responders. After variable selection by feature importance ranking and correlation analysis, one AE-extracted LVMD predictor was included in the statistical analysis. This new AE-extracted LVMD predictor showed statistical significance in the univariate (OR 2.00, P = .026) and multivariate (OR 1.11, P = .021) analyses, respectively. Moreover, the new AE-extracted LVMD predictor not only had incremental value over PBW and significant clinical variables, including QRS duration and left ventricular end-systolic volume (AUC 0.74 vs 0.72, LH 7.33, P = .007), but also showed encouraging predictive value in the 165 patients from the IAEA VISION-CRT trial (P < .1). The heatmaps for calculation of the AE-extracted predictor showed higher weights on the anterior, lateral, and inferior myocardial walls, which are recommended as LV pacing sites in clinical practice. CONCLUSIONS: AE techniques have significant value in the discovery of new clinical predictors. The new AE-extracted LVMD predictor extracted from the baseline gated SPECT MPI has the potential to improve the prediction of CRT response.

Achieving Molecular Recognition of Structural Analogues in Surface-Enhanced Raman Spectroscopy: Inducing Charge and Geometry Complementarity to Mimic Molecular Docking.

Molecular recognition of complex isomeric biomolecules remains challenging in surface-enhanced Raman scattering (SERS) spectroscopy due to their small Raman cross-sections and/or poor surface affinities. To date, the use of molecular probes has achieved excellent molecular sensitivities but still suffers from poor spectral specificity. Here, we induce "charge and geometry complementarity" between probe and analyte as a key strategy to achieve high spectral specificity for effective SERS molecular recognition of structural analogues. We employ 4-mercaptopyridine (MPY) as the probe, and chondroitin sulfate (CS) disaccharides with isomeric sulfation patterns as our proof-of-concept study. Our experimental and in silico studies reveal that "charge and geometry complementarity" between MPY's binding pocket and the CS sulfation patterns drives the formation of site-specific, multidentate interactions at the respective CS isomerism sites, which "locks" each CS in its analogue-specific complex geometry, akin to molecular docking events. Leveraging the resultant spectral fingerprints, we achieve > 97 % classification accuracy for 4 CSs and 5 potential structural interferences, as well as attain multiplex CS quantification with < 3 % prediction error. These insights could enable practical SERS differentiation of biologically important isomers to meet the burgeoning demand for fast-responding applications across various fields such as biodiagnostics, food and environmental surveillance.

Divergent On-DNA Transformations from DNA-Linked Piperidones.

A group of highly efficient and divergent transformations for constructing multiple DNA-linked chemotypes based on a piperidone core were successfully developed. We reported the first procedure for the synthesis of a DNA-conjugated piperidine intermediate under basic conditions. Subsequently, this substructure was subjected to additional reactions to generate several privileged scaffolds, including 4-aminopiperidine, fused [1,2,4]triazolo[1,5-a]pyrimidine, and a quinoline derivative. These transformations paved the way for constructing focused scaffold-based DNA-encoded libraries with druglike properties.

Mapping micrometer-scale wetting properties of superhydrophobic surfaces.

There is a huge interest in developing superrepellent surfaces for antifouling and heat-transfer applications. To characterize the wetting properties of such surfaces, the most common approach is to place a millimetric-sized droplet and measure its contact angles. The adhesion and friction forces can then be inferred indirectly using Furmidge's relation. While easy to implement, contact angle measurements are semiquantitative and cannot resolve wetting variations on a surface. Here, we attach a micrometric-sized droplet to an atomic force microscope cantilever to directly measure adhesion and friction forces with nanonewton force resolutions. We spatially map the micrometer-scale wetting properties of superhydrophobic surfaces and observe the time-resolved pinning-depinning dynamics as the droplet detaches from or moves across the surface.

Surface-Enhanced Raman Scattering (SERS) Taster: A Machine-Learning-Driven Multireceptor Platform for Multiplex Profiling of Wine Flavors.

Integrating machine learning with surface-enhanced Raman scattering (SERS) accelerates the development of practical sensing devices. Such integration, in combination with direct detection or indirect analyte capturing strategies, is key to achieving high predictive accuracies even in complex matrices. However, in-depth understanding of spectral variations arising from specific chemical interactions is essential to prevent model overfit. Herein, we design a machine-learning-driven "SERS taster" to simultaneously harness useful vibrational information from multiple receptors for enhanced multiplex profiling of five wine flavor molecules at parts-per-million levels. Our receptors employ numerous noncovalent interactions to capture chemical functionalities within flavor molecules. By strategically combining all receptor-flavor SERS spectra, we construct comprehensive "SERS superprofiles" for predictive analytics using chemometrics. We elucidate crucial molecular-level interactions in flavor identification and further demonstrate the differentiation of primary, secondary, and tertiary alcohol functionalities. Our SERS taster also achieves perfect accuracies in multiplex flavor quantification in an artificial wine matrix.

Inducing Ring Complexation for Efficient Capture and Detection of Small Gaseous Molecules Using SERS for Environmental Surveillance.

Gas-phase surface-enhanced Raman scattering (SERS) remains challenging due to poor analyte affinity to SERS substrates. The reported use of capturing probes suffers from concurrent inconsistent signals and long response time due to the formation of multiple potential probe-analyte interaction orientations. Here, we demonstrate the use of multiple non-covalent interactions for ring complexation to boost the affinity of small gas molecules, SO2 and NO2 , to our SERS platform, achieving rapid capture and multiplex detection down to 100 ppm. Experimental and in-silico studies affirm stable ring complex formation, and kinetic investigations reveal a 4-fold faster response time compared to probes without stable ring complexation capability. By synergizing spectral concatenation and support vector machine regression, we achieve 91.7 % accuracy for multiplex quantification of SO2 and NO2 in excess CO2 , mimicking real-life exhausts. Our platform shows immense potential for on-site exhaust and air quality surveillance.

DNA-encoded CH functionality via photoredox-mediated hydrogen atom transformation catalysis.

We described a mode of catalytic activation that accomplished the α-alkylation of N-Boc saturated heterocycles with DNA-linked acrylamide via photoredox-mediated hydrogen atom transfer (HAT) catalysis. This C(sp3)-C(sp3) bond formation reaction tolerated five-, six- and seven-membered cyclic substrates, substantially streamline synthetic efforts to functionalize the α-position of heterocycles with native CH functional handle. This photoredox catalyzed CH functionalization proceeded in mild DNA-compatible condition, and suited for the construction of DNA-encoded libraries.

Applying a Nanoparticle@MOF Interface To Activate an Unconventional Regioselectivity of an Inert Reaction at Ambient Conditions.

Here we design an interface between a metal nanoparticle (NP) and a metal-organic framework (MOF) to activate an inert CO2 carboxylation reaction and in situ monitor its unconventional regioselectivity at the molecular level. Using a Kolbe-Schmitt reaction as model, our strategy exploits the NP@MOF interface to create a pseudo high-pressure CO2 microenvironment over the phenolic substrate to drive its direct C-H carboxylation at ambient conditions. Conversely, Kolbe-Schmitt reactions usually demand high reaction temperature (>125 °C) and pressure (>80 atm). Notably, we observe an unprecedented CO2 meta-carboxylation of an arene that was previously deemed impossible in traditional Kolbe-Schmitt reactions. While the phenolic substrate in this study is fixed at the NP@MOF interface to facilitate spectroscopic investigations, free reactants could be activated the same way by the local pressurized CO2 microenvironment. These valuable insights create enormous opportunities in diverse applications including synthetic chemistry, gas valorization, and greenhouse gas remediation.

Three-Dimensional Surface-Enhanced Raman Scattering Platforms: Large-Scale Plasmonic Hotspots for New Applications in Sensing, Microreaction, and Data Storage.

Surface-enhanced Raman scattering (SERS) is a molecular-specific spectroscopic technique that provides up to 1010-fold enhancement of signature Raman fingerprints using nanometer-scale 0D to 2D platforms. Over the past decades, 3D SERS platforms with additional plasmonic materials in the z-axis have been fabricated at sub-micrometer to centimeter scale, achieving higher hotspot density in all x, y, and z spatial directions and higher tolerance to laser misalignment. Moreover, the flexibility to construct platforms in arbitrary sizes and 3D shapes creates attractive applications besides traditional SERS sensing. In this Account, we introduce our library of substrate-based and substrate-less 3D plasmonic platforms, with an emphasis on their non-sensing applications as microlaboratories and data storage labels. We aim to provide a scientific synopsis on these high-potential yet currently overlooked applications of SERS and ignite new scientific discoveries and technology development in 3D SERS platforms to tackle real-world issues. One highlight of our substrate-based SERS platforms is multilayered platforms built from micrometer-thick assemblies of plasmonic particles, which can achieve up to 1011 enhancement factor. As an alternative, constructing 3D hotspots on non-plasmonic supports significantly reduces waste of plasmonic materials while allowing high flexibility in structural design. We then introduce our emerging substrate-less plasmonic capsules including liquid marbles and colloidosomes, which we further incorporate the latter within an aerosol to form centimeter-scale SERS-active plasmonic cloud, the world's largest 3D SERS platform to date. We then discuss the various emerging applications arising only from these 3D platforms, in the fields of sensing, microreactions, and data storage. An important novel sensing application is the stand-off detection of airborne analytes that are several meters away, made feasible with aerosolized plasmonic clouds. We also describe plasmonic capsules as excellent miniature lab-in-droplets that can simultaneously provide in situ monitoring at the molecular level during reaction, owing to their ultrasensitive 3D plasmonic shells. We highlight the emergence of 3D SERS-based data storage platforms with 10-100-fold higher storage density than 2D platforms, featuring a new approach in the development of level 3 security (L3S) anti-counterfeiting labels. Ultimately, we recognize that 3D SERS research can only be developed further when its sensing capabilities are concurrently strengthened. With this vision, we foresee the creation of highly applicable 3D SERS platforms that excel in both sensing and non-sensing areas, providing modern solutions in the ongoing Fourth Industrial Revolution.

Designing surface-enhanced Raman scattering (SERS) platforms beyond hotspot engineering: emerging opportunities in analyte manipulations and hybrid materials.

Surface-enhanced Raman scattering (SERS) is a molecule-specific spectroscopic technique with diverse applications in (bio)chemistry, clinical diagnosis and toxin sensing. While hotspot engineering has expedited SERS development, it is still challenging to detect molecules with no specific affinity to plasmonic surfaces. With the aim of improving detection performances, we venture beyond hotspot engineering in this tutorial review and focus on emerging material design strategies to capture and confine analytes near SERS-active surfaces as well as various promising hybrid SERS platforms. We outline five major approaches to enhance SERS performance: (1) enlarging Raman scattering cross-sections of non-resonant molecules via chemical coupling reactions; (2) targeted chemical capturing of analytes through surface-grafted agents to localize them on plasmonic surfaces; (3) physically confining liquid analytes on non-wetting SERS-active surfaces and (4) confining gaseous analytes using porous materials over SERS hotspots; (5) synergizing conventional metal-based SERS platforms with functional materials such as graphene, semiconducting materials, and piezoelectric polymers. These approaches can be integrated with engineered hotspots as a multifaceted strategy to further boost SERS sensitivities that are unachievable using hotspot engineering alone. Finally, we highlight current challenges in this research area and suggest new research directions towards efficient SERS designs critical for real-world applications.

Modulating Orientational Order to Organize Polyhedral Nanoparticles into Plastic Crystals and Uniform Metacrystals.

In nanoparticle self-assembly, the current lack of strategy to modulate orientational order creates challenges in isolating large-area plastic crystals. Here, we achieve two orientationally distinct supercrystals using one nanoparticle shape, including plastic crystals and uniform metacrystals. Our approach integrates multi-faceted Archimedean polyhedra with molecular-level surface polymeric interactions to tune nanoparticle orientational order during self-assembly. Experiments and simulations show that coiled surface polymer chains limit interparticle interactions, creating various geometrical configurations among Archimedean polyhedra to form plastic crystals. In contrast, brush-like polymer chains enable molecular interdigitation between neighboring particles, favoring consistent particle configurations and result in uniform metacrystals. Our strategy enhances supercrystal diversity for polyhedra comprising multiple nondegenerate facets.

ZIF-Induced d-Band Modification in a Bimetallic Nanocatalyst: Achieving Over 44 % Efficiency in the Ambient Nitrogen Reduction Reaction.

The electrochemical nitrogen reduction reaction (NRR) offers a sustainable solution towards ammonia production but suffers poor reaction performance owing to preferential catalyst-H formation and the consequential hydrogen evolution reaction (HER). Now, the Pt/Au electrocatalyst d-band structure is electronically modified using zeolitic imidazole framework (ZIF) to achieve a Faradaic efficiency (FE) of >44 % with high ammonia yield rate of >161 µg mgcat -1 h-1 under ambient conditions. The strategy lowers electrocatalyst d-band position to weaken H adsorption and concurrently creates electron-deficient sites to kinetically drive NRR by promoting catalyst-N2 interaction. The ZIF coating on the electrocatalyst doubles as a hydrophobic layer to suppress HER, further improving FE by >44-fold compared to without ZIF (ca. 1 %). The Pt/Au-NZIF interaction is key to enable strong N2 adsorption over H atom.

Plasmonic-induced overgrowth of amorphous molybdenum sulfide on nanoporous gold: An ambient synthesis method of hybrid nanoparticles with enhanced electrocatalytic activity.

Hybrid materials of earth abundant transition metal dichalcogenides and noble metal nanoparticles, such as molybdenum sulfide (MoSx) and gold nanoparticles, exhibit synergistic effects that can enhance electrocatalytic reactions. However, most current hybrid MoSx-gold synthesis requires an energy intensive heat source of >500 °C or chemical plating to achieve deposition of MoSx on the gold surface. Herein, we demonstrate the direct overgrowth of MoSx over colloidal nanoporous gold (NPG), conducted feasibly under ambient conditions, to form hybrid particles with enhanced electrocatalytic performance toward hydrogen evolution reaction. Our strategy exploits the localized surface plasmon resonance-mediated photothermal heating of NPG to achieve >230 °C surface temperature, which induces the decomposition of the (NH4)2MoS4 precursor and direct overgrowth of MoSx over NPG. By tuning the concentration ratio between the precursor and NPG, the amount of MoSx particles deposited can be systematically controlled from 0.5% to 2% of the Mo/(Au + Mo) ratio. Importantly, we find that the hybrid particles exhibit higher bridging and an apical S to terminal S atomic ratio than pure molybdenum sulfide, which gives rise to their enhanced electrocatalytic performance for hydrogen evolution reaction. We demonstrate that hybrid MoSx-NPG exhibits >30 mV lower onset potential and a 1.7-fold lower Tafel slope as compared to pure MoSx. Our methodology provides an energy- and cost-efficient synthesis pathway, which can be extended to the synthesis of various functional hybrid structures with unique properties for catalysis and sensing applications.