Experimental Challenge Research Articles

Enabling dynamic 3D coherent diffraction imaging via adaptive latent space tuning of generative autoencoders

Coherent diffraction imaging (CDI) is an advanced non-destructive 3D X-ray imaging technique for measuring a sample’s electron density. The main challenge of CDI is loss of phase information in diffraction intensity measurements, resulting in lengthy iterative reconstruction processes that can return non-unique solutions, which pose challenges for experiments attempting to track dynamic sample evolution through multiple states. As the increased brightness of fourth-generation light sources enables faster sample measurements and drives operando experiments with Bragg CDI, there is a growing need for faster reconstruction techniques that can keep pace. We have developed an adaptive generative autoencoder approach for uniquely tracking a sample’s electron density as it dynamically evolves. Our approach adaptively tunes the low-dimensional latent embedding of a generative autoencoder, enabling a computationally efficient manner to account for time-varying shifting distributions in real-time. Analytic proof of convergence is provided as well as numerical demonstration of sample tracking with noisy measurements.

Recombinant avian metapneumovirus subtype C expressing HA protein of H9N2 avian influenza virus are stable and induce protection

To prevent H9N2 avian influenza virus (AIV) and Avian metapneumonovirus/C (aMPV/C) infections, we constructed recombinant aMPV/C viruses expressing the HA protein of H9N2 AIV. In addition, EGFP was inserted into the intermediate non-coding region of P-M protein in the aMPV/C genome using a reverse genetic system. The conditions for rescuing the recombinant virus were enhanced followed by insertion of the H9N2 AIV HA gene into the same location in the aMPV/C. The constructed recombinant virus raMPV/C-HA expressed the H9N2 AIV HA protein and showed good stability. Immunization of chicks with raMPV/C-HA increased the generation of neutralizing antibodies against aMPV and H9N2 AIV for 21 days. In the late challenge experiment, raMPV/C-HA effectively inhibited the replication of the virus in vivo, decreased the incidence of infection and conferred protection effects.

Molecular Interactions and Responsive Locations of CO2-Responsive Surfactants in an Oil-Water-Surfactant System: Molecular Dynamics Simulation and Free Energy Perturbation.

Understanding the mechanism of a CO2-responsive surfactant is essential for enhancing its industrial applications. Conventional experimental methods face challenges in pinpointing the exact location of proton transfer within the system and in accurately describing the impact of intermolecular and intramolecular interactions on the CO2 responsiveness of such substances. To address this gap, this study employs molecular dynamics simulations and free energy perturbation methods to investigate the proton transfer process between a CO2-responsive cationic surfactant N'-dodecyl-N,N-dimethylacetamidinium (DMAAH+) and its counterion bicarbonate ion at the oil-water interface and micelle surface and in the bulk aqueous phase. Molecular dynamics simulations identified potential locations for the proton transfer process within the system and elucidated the types of interactions contributing to changes in Gibbs free energy. Subsequently, free energy perturbation was employed to calculate Gibbs free energy changes associated with proton transfer at different locations. The respective contributions of various intramolecular and intermolecular interactions were then compared and analyzed. It has been revealed that the deprotonation process is not thermodynamically spontaneous at all three responsive locations. The proton transfer occurs more frequently at the oil-water interface than at the micelle surface and is less common in the bulk aqueous phase. The findings enhance our understanding of the fundamental mechanisms governing the responsiveness of CO2-responsive surfactants and provide valuable insights for their practical application in industrial processes.

Navigating triplet repeats sequencing: concepts, methodological challenges and perspective for Huntington's disease.

The accurate characterization of triplet repeats, especially the overrepresented CAG repeats, is increasingly relevant for several reasons. First, germline expansion of CAG repeats above a gene-specific threshold causes multiple neurodegenerative disorders; for instance, Huntington's disease (HD) is triggered by >36 CAG repeats in the huntingtin (HTT) gene. Second, extreme expansions up to 800 CAG repeats have been found in specific cell types affected by the disease. Third, synonymous single nucleotide variants within the CAG repeat stretch influence the age of disease onset. Thus, new sequencing-based protocols that profile both the length and the exact nucleotide sequence of triplet repeats are crucial. Various strategies to enrich the target gene over the background, along with sequencing platforms and bioinformatic pipelines, are under development. This review discusses the concepts, challenges, and methodological opportunities for analyzing triplet repeats, using HD as a case study. Starting with traditional approaches, we will explore how sequencing-based methods have evolved to meet increasing scientific demands. We will also highlight experimental and bioinformatic challenges, aiming to provide a guide for accurate triplet repeat characterization for diagnostic and therapeutic purposes.

CACHE Challenge #1: Docking with GNINA Is All You Need.

We describe our winning submission to the first Critical Assessment of Computational Hit-Finding Experiments (CACHE) challenge. In this challenge, 23 participants employed a diverse array of structure-based methods to identify hits to a target with no known ligands. We utilized two methods, pharmacophore search and molecular docking, to identify our initial hit list and compounds for the hit expansion phase. Unlike many other participants, we limited ourselves to using docking scores in identifying and ranking hits. Our resulting best hit series tied for first place when evaluated by a panel of expert judges. Here, we report our top-performing open-source workflow and results.

Nuclear lipids in chromatin regulation: Biological roles, experimental approaches and existing challenges.

Lipids are crucial for various cellular functions. Besides the storage of energy equivalents, these include forming membrane bilayers and serving as signaling molecules. While significant progress has been made in the comprehension of the molecular and cellular biology of lipids, their functions in the cell nucleus remain poorly understood. The main role of the eukaryotic cell nucleus is to provide an environment for the storage and regulation of chromatin which is a complex of DNA, histones, and associated proteins. Recent studies suggest that nuclear lipids play a role in chromatin regulation and epigenetics. Here, we discuss various experimental methods in lipid-chromatin research, including biophysical, structural, and cell biology approaches, pointing out their strengths and weaknesses. We take the view that nuclear lipids have a far more widespread impact on chromatin than is currently acknowledged. This gap in comprehension is mostly due to existing experimental challenges in the study of lipid-chromatin biology. Several new, interdisciplinary approaches are discussed that could aid in elucidating the roles of nuclear lipids in chromatin regulation and gene expression.

Streptococcus pyogenes pharyngitis elicits diverse antibody responses to key vaccine antigens influenced by the imprint of past infections

Knowledge gaps regarding human immunity to Streptococcus pyogenes have impeded vaccine development. To address these gaps and evaluate vaccine candidates, we established a human challenge model of S. pyogenes pharyngitis. Here, we analyse antibody responses in serum and saliva against 19 antigens to identify characteristics distinguishing 19 participants who developed pharyngitis and 6 who did not. We show that pharyngitis elicits serum IgG responses to key vaccine antigens and a muted mucosal IgA response, whereas IgG responses are minimal and IgA responses more pronounced in participants without pharyngitis. Serum IgG responses to pharyngitis in adult participants resemble those in children and are inversely correlated with the magnitude of pre-existing responses. While a straightforward correlate of protection is not evident, baseline antibody signatures distinguish clinical and immunological outcomes following experimental challenge. This highlights the influence of a complex humoral imprint from previous exposure, relevant for interpreting immunogenicity in forthcoming vaccine trials.

Ketogenic diet modulates immune cell transcriptional landscape and ameliorates experimental autoimmune uveitis in mice

BackgroundUveitis manifests as immune-mediated inflammatory disorders within the eye, posing a serious threat to vision. The ketogenic diet (KD) has emerged as a promising dietary intervention, yet its impact on the immune microenvironments and role in uveitis remains unclear.MethodsUtilizing single-cell RNA sequencing (scRNA-seq) data from lymph node and retina of mice, we conduct a comprehensive investigation into the effects of KD on immune microenvironments. Flow cytometry is conducted to verify the potential mechanisms.ResultsThis study demonstrates that KD alters the composition and function of immune profiles. Specifically, KD promotes the differentiation of Treg cells and elevates its proportion in heathy mice. In response to experimental autoimmune uveitis challenges, KD alleviates the inflammatory symptoms, lowers CD4+ T cell pathogenicity, and corrects the Th17/Treg imbalance. Additionally, KD decreases the proportion of Th17 cell and increases Treg cells in the retina. Analysis of combined retinal and CDLN immune cells reveals that retinal immune cells, particularly CD4+ T cells, exhibit heightened inflammatory responses, which KD partially reverses.ConclusionsThe KD induces inhibitory structural and functional alterations in immune cells from lymph nodes to retina, suggesting its potential as a therapy for uveitis.

2-Aminobenzothiazole based adjuvant of polymyxin E against Gram-negative bacteria

Antibiotic resistance has emerged as a pressing global health issue. Polymyxin E, commonly regarded as a clinically important antibiotic, faces limitations due to its dose-dependent toxicity. A crucial strategy to combat this is the development of an antibiotic adjuvant to enhance efficacy while reducing dosage. Screening from our extensive in-house compound library, KJ1071 emerged as a promising adjuvant candidate for polymyxin E. Subsequent structure–activity relationship studies led to the discovery of compound A35, which demonstrated superior synergistic activity. However, its limited aqueous solubility posed challenges for biological experiments. To address this, we carried out a structural derivatization aimed at enhancing its solubility. The amino acid derivative A59 notably improved aqueous solubility to 3.237 mg/mL, a substantial 3237-fold increase compared to compound A35. Moreover, A59 amplified the efficacy of polymyxin E in eradicating a variety of Gram-negative bacteria, including certain clinical strains that are resistant to polymyxin E. The mechanism studies indicated that A59 might target the bacterial membrane. These findings suggest that the discovery of this innovative scaffold could provide critical insights for new adjuvant therapies.

Approximating Nucleation Rates of Glass Ceramics Using In-Situ X-ray Diffraction

Glass ceramics are ideal for applications ranging from the culinary to defense industries. The properties of glass ceramics are a strong function of their microstructure, which in turn is controlled by constitutive nucleation and growth treatments. Nucleation has been extensively studied but remains an experimental and theoretical challenge. Traditional isothermal methods for measuring nucleation rates require time-consuming measurements and careful statistics, leading to only a few material systems with nucleation data available, approximately one-hundred glass systems were studied in half a century. To overcome these challenges, we present a new non-isothermal technique utilizing in-situ X-Ray Diffraction (XRD) with data analyzed through a modified Johnson-Mehl-Avrami-Kolmogorov (JMAK) equation. Three homogenous nucleated glass systems were analyzed: Li2O•2SiO2 (lithium disilicate), Na2O•2CaO•3SiO2 (combeite), and Li2Oׅ•2B2O3 (lithium diborate). This method utilizes crystallized fractions through XRD, allowing resolution far beyond microscopy techniques. It was thus possible to compare the evolution of the crystallized volume fractions by X-ray diffraction with optical microscopy from literature. This method was successful in reproducing the experimental nucleation curve from the temporal development of the number density and crystal size within four orders of magnitude, while also achieving the correct peak position, leading to a new method to rapidly approximate the nucleation rate of complex glass-ceramics.

Efficacy of GPE− strain live attenuated vaccine and CP7_E2alf strain recombinant live vaccine (marker vaccine) against Japanese epidemic classical swine fever virus isolated in 2019 and DIVA discrimination ability of the marker vaccine

Classical swine fever (CSF) re-emerged in Japan in 2018, with the epidemic virus identified as genotype 2.1, which is moderately virulent and more difficult to detect and control than the highly virulent strain. Domestic pigs were administered with GPE− strain live attenuated vaccine (GPE− vaccine) for outbreak management. CP7_E2alf strain recombinant live vaccine (marker vaccine), approved for differentiation of infected from vaccinated animals (DIVA), was considered optional for obtaining CSF-free country certification issued by the World Organization for Animal Health. This study aimed to assess the efficacy of both vaccines in pigs through experimental challenge tests and evaluate the DIVA ability of the marker vaccine using two enzyme-linked immunosorbent assay (ELISA) antibody detection kits. Results showed that both GPE− and marker vaccines were effective against the Japanese epidemic strain; however, the ability of the ELISA antibody detection kits to discriminate the marker vaccine was limited. Therefore, to achieve CSF-free certification using vaccines with DIVA functionality, alternative detection methods and enhancement of the sensitivity and specificity of ELISA kits are needed.

Flight testing of laser-optical techniques for future optical air data sensors

AbstractOptical air data sensors, which can serve as alternatives or supplements to traditional air data systems, have been a subject of ongoing research. These remote sensing optical sensors offer the advantage of measuring primary air data parameters in undisturbed airflow. As active sensors, they can self-diagnose, associating each measurement with an uncertainty and accounting for signal loss due to factors such as icing or light blocking, thus avoiding silent false measurements. In this paper, we discuss a recent flight test campaign of optical measurement techniques for potential development into air data sensors for future aircraft. We used laser Doppler anemometry, which relies on aerosol scattering of laser light, to observe the Doppler shift of scattered light from multiple directions. This enables the reconstruction of the full wind vector and the retrieval of true air speed, angle of attack, and angle of sideslip. A second instrument used tunable diode laser absorption spectroscopy, which measures an individual transition of molecular oxygen in the A band, in order to extract the static pressure from the observed collisional broadening. The techniques were implemented as airborne research instruments on the DLR’s Dassault Falcon 20 aircraft and tested in two campaigns in 2022 under different atmospheric conditions and dynamic maneuvers. We present results from these campaigns, focusing on general sensor performance, measurement rates, accuracy, and experimental challenges that need to be addressed for future applications.

Second harmonic generation null angle polarization analysis for determining interfacial potential at charged interfaces.

Methods of quantifying the electrostatics of charged interfaces are important in a range of research areas. The surface-selective nonlinear optical technique second harmonic generation (SHG) is a sensitive probe of interfacial electrostatics. Recent work has shown that detection of the SHG phase in addition to its amplitude enables direct quantification of the interfacial potential. However, the experimental challenge of directly detecting the phase interferometrically with sufficient precision and stability has led to the proposal and development of alternative techniques to recover the same information, notably through wavelength scanning or angle scanning, each of which has their own associated experimental challenges. Here, we propose a new polarization-based approach to recover the required phase information, building upon the previously established nonlinear optical null ellipsometry (NONE) technique. Although NONE directly returns only relative phase information between different tensor elements of the second-order susceptibility, it is shown that a symmetry relation that connects the tensor elements of the potential-dependent third-order susceptibility can be used to generate the absolute phase reference required to calculate the interfacial potential. The sensitivity of the technique to potential at varying surface charge densities and ionic strengths is explored by means of simulated data of the silica:water interface. The error associated with the use of the linearized Poisson-Boltzmann approximation is discussed and compared to the error associated with the precision of the measured NONE null angles. Overall, the results suggest that NONE is a promising approach for performing phase-resolved SHG based quantification of interfacial potentials that experimentally requires only the addition of standard polarization optics to the basic single-wavelength, fixed-angle SHG apparatus.

Assessment of Protective Effect of Rutin Utilizing Advanced Intranasal Nanoparticulate System in Mice Model after Experimental Challenge with Pasteurella Multocida Isolated from Naturally Infected Sheep

Assessment of Protective Effect of Rutin Utilizing Advanced Intranasal Nanoparticulate System in Mice Model after Experimental Challenge with Pasteurella Multocida Isolated from Naturally Infected Sheep

Data-Driven Screening and Discovery of Metal-Organic Frameworks as C2 Adsorbents from over 900 Experimental Isotherms.

The separation of ethylene from ethane accounts for almost 100 million tons of CO2 emissions annually and 0.3% of global primary energy usage. Replacing current cryogenic distillation units with adsorption separation units, especially for the minor component of ethane, would enable significant efficiency gains. Metal-organic frameworks (MOFs) are well-suited for adsorption separation due to their high surface areas and tunable chemical properties. Exploring all possible MOFs is a daunting experimental challenge, motivating in silico screening with machine learning models. We present a database of 948 experimentally measured pure-component C2 isotherms from 192 MOFs gathered from the literature and use it to train machine learning models to predict MOF ethane and ethylene uptake across a range of temperature and pressure conditions. The models have high accuracy in interpolative tasks (mean absolute error ∼0.05 mmol/g) when trained on only 20% of available data. Performance on unseen structures was also reasonably accurate with a mean absolute error (MAE) ∼0.7 mmol/g. We apply the models to screen the CoRE MOF2019 ASR database and identify the most promising candidates. Several MOFs containing lanthanide metals were predicted to have high ethane selectivity, suggesting that this class of MOFs may merit further investigation. Feature importance analysis suggests that both optimizing MOF secondary building unit chemistry and the process conditions at which the sorbent will operate are critical for enabling ethane-selective separation. We synthesize a MOF predicted to exhibit high ethane selectivity and experimentally validate qualitative agreement with model predictions, highlighting the utility of both the data set and model in discovering unexplored C2 adsorbents.

The structural logic of dynamic signaling in the Escherichia coli serine chemoreceptor.

The experimental challenges posed by integral membrane proteins hinder molecular understanding of transmembrane signaling mechanisms. Here, we exploited protein crosslinking assays in living cells to follow conformational and dynamic stimulus signals in Tsr, the Escherichia coli serine chemoreceptor. Tsr mediates serine chemotaxis by integrating transmembrane serine-binding inputs with adaptational modifications of a methylation helix bundle to regulate a signaling kinase at the cytoplasmic tip of the receptor molecule. We created cysteine replacements at Tsr residues adjacent to hydrophobic packing faces of the bundle helices and crosslinked them with a cell-permeable, bifunctional thiol-reagent. We identified an extensively crosslinked dynamic junction midway through the methylation helix bundle that seemed uniquely poised to respond to serine signals. We explored its role in mediating signaling shifts between different packing arrangements of the bundle helices by measuring crosslinking in receptor molecules with apposed pairs of cysteine reporters in each subunit and assessing their signaling behaviors with an in vivo kinase assay. In the absence of serine, the bundle helices evinced compact kinase-ON packing arrangements; in the presence of serine, the dynamic junction destabilized adjacent bundle segments and shifted the bundle to an expanded, less stable kinase-OFF helix-packing arrangement. AlphaFold models of kinase-active Tsr showed a prominent bulge and kink at the dynamic junction that might antagonize stable structure at the receptor tip. Serine stimuli might inhibit kinase activity by shifting the bundle to a less stably-packed conformation that relaxes structural strain at the receptor tip, thereby allowing it to stabilize and freeze kinase activity.

Emergent biaxiality in chiral hybrid liquid crystals

Biaxial nematic liquid crystals are fascinating systems sometimes referred to as the Higgs boson of soft matter because of experimental observation challenges. Here we describe unexpected states of matter that feature biaxial orientational order of colloidal supercritical fluids and gases formed by sparse rodlike particles. Colloidal rods with perpendicular surface boundary conditions exhibit a strong biaxial symmetry breaking when doped into conventional chiral nematic fluids. Minimization of free energy prompts these particles to orient perpendicular to the local molecular director and the helical axis, thereby imparting biaxiality on the hybrid molecular-colloidal system. The ensuing phase diagram features colloidal gas and liquid and supercritical colloidal fluid states with long-range biaxial orientational symmetry, as supported by analytical and numerical modeling at all hierarchical levels of ordering. Unlike for nonchiral hybrid systems, dispersions in chiral nematic hosts display biaxial orientational order at vanishing colloid volume fractions, promising both technological and fundamental research utility.

Automatic classification of fungal-fungal interactions using deep leaning models

Fungi provide valuable solutions for diverse biotechnological applications, such as enzymes in the food industry, bioactive metabolites for healthcare, and biocontrol organisms in agriculture. Current workflows for identifying new biocontrol fungi often rely on subjective visual observations of strains’ performance in microbe-microbe interaction studies, making the process time-consuming and difficult to reproduce. To overcome these challenges, we developed an AI-automated image classification approach using machine learning algorithm based on deep neural network. Our method focuses on analyzing standardized images of 96-well microtiter plates with solid medium for fungal-fungal challenge experiments. We used our model to categorize the outcome of interactions between the plant pathogen Fusarium graminearum and individual isolates from a collection of 38,400 fungal strains. The authors trained multiple deep learning architectures and evaluated their performance. The results strongly support our approach, achieving a peak accuracy of 95.0 % with the DenseNet121 model and a maximum macro-averaged F1-Score of 93.1 across five folds. To the best of our knowledge, this paper introduces the first automated method for classifying fungal–fungal interactions using deep learning, which can easily be adapted for other fungal species.

The Electron‐Density Distribution of UCl4 and Its Topology from X‐ray Diffraction

AbstractThe chemistry of electrons in actinide complexes and materials is still poorly understood and represents a serious challenge and opportunity for experiment and theory. The study of the electron density distribution of the ground state of such systems through X‐ray diffraction represents a unique opportunity to quantitatively investigate different chemical bonding interactions at once, but was considered “almost impossible” on heavy‐atom systems, until very recently. Here, we present a combined experimental and theoretical investigation of the electron density distribution in UCL4 crystals and comparison with the previously reported spin density distribution from polarized neutron diffraction. All approaches provide a consistent picture in terms of electron and spin density distribution, and chemical bond characterization. More importantly, the synergy between experiments and quantum‐mechanical calculations allows to highlight the remarkable sensitivity of X‐ray diffraction to electrons in materials.

A protein vaccine of RBD integrated with immune evasion mutation shows broad protection against SARS-CoV-2.

Variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continue to emerge and evade immunity, resulting in breakthrough infections in vaccinated populations. There is an urgent need for the development of vaccines with broad protective effects. In this study, we selected hotspot mutations in the receptor-binding domain (RBD) that contribute to immune escape properties and integrated them into the original RBD protein to obtain a complex RBD protein (cRBD), and we found cRBDs have broad protective effects against SARS-CoV-2 variants. Three cRBDs were designed in our study. Compared with the BA.1 RBD protein, the cRBDs induced the production of higher levels of broader-spectrum neutralizing antibodies, suggesting stronger and broader protective efficacy. In viral challenge experiments, cRBDs were more effective than BA.1 RBD in attenuating lung pathologic injury. Among the three constructs, cRBD3 showed optimal broad-spectrum and protective effects and is a promising candidate for a broad-spectrum SARS-CoV-2 vaccine. In conclusion, immunization with cRBDs triggered immunity against a wide range of variants, including those that emerged after we had completed designing the cRBDs. This study preliminarily explores and validates the feasibility of incorporating hotspot mutations that contribute to immune evasion into the RBD to expand the activity spectrum of antigen-induced antibodies.