Structure and Chemical Reactivity of Y-Stabilized ZrO2 Surfaces: Importance for the Water-Gas Shift Reaction.

The surface structure and chemical properties of Y-stabilized zirconia (YSZ) have been subjects of intense debate over the past three decades. However, a thorough understanding of chemical processes occurring at YSZ powders faces significant challenges due to the absence of reliable reference data acquired for well-controlled model systems. Here, we present results from polarization-resolved infrared reflection absorption spectroscopy (IRRAS) obtained for differently oriented, Y-doped ZrO2 single-crystal surfaces after exposure to CO and D2O. The IRRAS data reveal that the polar YSZ(100) surface undergoes reconstruction, characterized by an unusual, red-shifted CO band at 2132 cm-1. Density functional theory calculations allowed to relate this unexpected observation to under-coordinated Zr4+ cations in the vicinity of doping-induced O vacancies. This reconstruction leads to a strongly increased chemical reactivity and water spontaneously dissociates on YSZ(100). The latter, which is an important requirement for catalysing the water-gas-shift (WGS) reaction, is absent for YSZ(111), where only associative adsorption was observed. Together with a novel analysis Scheme these reference data allowed for an operando characterisation of YSZ powders using DRIFTS (diffuse reflectance infrared Fourier transform spectroscopy). These findings facilitate rational design and tuning of YSZ-based powder materials for catalytic applications, in particular CO oxidation and the WGS reaction.

Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals.

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce Activity Quantification and Analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine learning techniques. It decomposes complex live imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, and imaging modalities. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, and distinct sensorimotor signal propagation patterns in the mouse spinal cord.

MMR vaccination induces trained immunity via functional and metabolic reprogramming of γδ T cells.

The measles, mumps, and rubella (MMR) vaccine protects against all-cause mortality in children, but the immunological mechanisms mediating these effects are poorly known. We systematically investigated whether MMR can induce long-term functional changes in innate immune cells, a process termed trained immunity, that could at least partially mediate this heterologous protection. In a randomized, placebo-controlled trial, 39 healthy adults received either the MMR vaccine or a placebo. Using single-cell RNA-Seq, we found that MMR caused transcriptomic changes in CD14+ monocytes and NK cells, but most profoundly in γδ T cells. Monocyte function was not altered by MMR vaccination. In contrast, the function of γδ T cells was markedly enhanced by MMR vaccination, with higher production of TNF and IFN-γ, as well as upregulation of cellular metabolic pathways. In conclusion, we describe a trained immunity program characterized by modulation of γδ T cell function induced by MMR vaccination.

Cellular rejuvenation to combat HIV-1-related neurocognitive impairment.

HIV-1-related neurocognitive impairment affects a significant proportion of people living with HIV, and accelerated brain aging has been implicated in its pathogenesis. This forum explores the application of cellular rejuvenation strategies to target molecular mechanisms of brain aging, promote neuronal health, and combat cognitive decline.

Measuring protein-membrane interaction through radial fluorescence correlation in 2 dimensions.

The cell membrane has a fundamental role in the cell life cycle but there's still much to be learned about its heterogeneous structure, regulation, and protein interaction. Additionally, the protein-membrane interaction is often overlooked when studying specific protein dynamics. In this work, we present a new tool for a better understanding of protein dynamics and membrane function using live cells and fast non-invasive techniques without the need for individual particle tracking. To this end, we used the 2D-pair correlation function (2D-pCF) to study protein interactions across cellular membranes. We performed numerical simulations and confocal experiments using a GAP-mEGFP fusion construct known to interact with the plasmatic membrane. Our results demonstrate that based on a quantitative correlation analysis as the 2D pair correlation of the signal intensities, is possible to characterize protein-membrane interactions in live systems and real-time. Combining experimental and numerical results this work presents a new powerful approach to the study of the dynamic protein-membrane interaction.

Hydroxyproline metabolism enhances IFN-γ-induced PD-L1 expression and inhibits autophagic flux.

The immune checkpoint protein PD-L1 plays critical roles in both immune system homeostasis and tumor progression. Impaired PD-1/PD-L1 function promotes autoimmunity and PD-L1 expression within tumors promotes immune evasion. If and how changes in metabolism or defined metabolites regulate PD-L1 expression is not fully understood. Here, using a metabolomics activity screening-based approach, we have determined that hydroxyproline (Hyp) significantly and directly enhances adaptive (i.e., IFN-γ-induced) PD-L1 expression in multiple relevant myeloid and cancer cell types. Mechanistic studies reveal that Hyp acts as an inhibitor of autophagic flux, which allows it to regulate this negative feedback mechanism, thereby contributing to its overall effect on PD-L1 expression. Due to its prevalence in fibrotic tumors, these findings suggest that hydroxyproline could contribute to the establishment of an immunosuppressive tumor microenvironment and that Hyp metabolism could be targeted to pharmacologically control PD-L1 expression for the treatment of cancer or autoimmune diseases.

Adapter CAR T cells to counteract T-cell exhaustion and enable flexible targeting in AML.

Although the landscape for treating acute myeloid leukemia (AML) patients has changed substantially in recent years, the majority of patients will eventually relapse and succumb to their disease. Allogeneic stem cell transplantation provides the best anti-AML treatment strategy, but is only suitable in a minority of patients. In contrast to B-cell neoplasias, chimeric antigen receptor (CAR) T-cell therapy in AML has encountered challenges in target antigen heterogeneity, safety, and T-cell dysfunction. We established a Fab-based adapter CAR (AdCAR) T-cell platform with flexibility of targeting and control of AdCAR T-cell activation. Utilizing AML cell lines and a long-term culture assay for primary AML cells, we were able to demonstrate AML-specific cytotoxicity using anti-CD33, anti-CD123, and anti-CLL1 adapter molecules in vitro and in vivo. Notably, we show for the first time the feasibility of sequential application of adapter molecules of different specificity in primary AML co-cultures. Importantly, using the AML platform, we were able to demonstrate that chronic T-cell stimulation and exhaustion can be counteracted through introduction of treatment-free intervals. As T-cell exhaustion and target antigen heterogeneity are well-known causes of resistance, the AdCAR platform might offer effective strategies to ameliorate these limitations.

Anharmonic Correction to Free Energy Barriers from DFT-Based Molecular Dynamics Using Constrained Thermodynamic Integration.

For the calculation of anharmonic contributions to free energy barriers, constrained thermodynamic λ-path integration (λ-TI) from a harmonic reference force field to density functional theory is presented as an alternative to the established Blue Moon ensemble method (ξ-TI), in which free energy gradients along the reaction coordinate ξ are integrated. With good agreement in all cases, the λ-TI method is benchmarked against the ξ-TI method for several reactions, including the internal CH3 group rotation in ethane, a nucleophilic substitution of CH3Cl, a retro-Diels-Alder reaction, and a proton transfer in zeolite H-SSZ-13. An advantage of λ-TI is that one can use virtually any reference state to compute anharmonic contributions to reaction free energies or free energy barriers. This is particularly relevant for catalysis, where it is now possible to compute anharmonic corrections to the free energy of a transition state relative to any reference, for example, the most stable state of the active site and the reactants in the gas phase. This is in contrast to ξ-TI, where free energy barriers can only be computed relative to an initial state with all reactants coadsorbed. Finally, the Bennett acceptance ratio method combined with λ-TI is demonstrated to reduce the number of required integration grid points with tolerable accuracy, favoring thus λ-TI over ξ-TI in terms of computational efficiency.

Metal-Support Interactions in Heterogeneous Catalysis: DFT Calculations on the Interaction of Copper Nanoparticles with Magnesium Oxide.

Oxide supports play an important role in enhancing the catalytic properties of transition metal nanoparticles in heterogeneous catalysis. How extensively interactions between the oxide support and the nanoparticles impact the electronic structure as well as the surface properties of the nanoparticles is hence of high interest. In this study, the influence of a magnesium oxide support on the properties of copper nanoparticles with different size, shape, and adsorption sites is investigated using density functional theory (DFT) calculations. By proposing simple models to reduce the cost of the calculations while maintaining the accuracy of the results, we show using the nonreducible oxide support MgO as an example that there is no significant influence of the MgO support on the electronic structure of the copper nanoparticles, with the exception of adsorption directly at the Cu-MgO interface. We also propose a simplified methodology that allows us to reduce the cost of the calculations, while the accuracy of the results is maintained. We demonstrate in addition that the Cu nanowire model corresponds well to the nanoparticle model, which reduces the computational cost even further.

Rhodium Single-Atom Catalyst Design through Oxide Support Modulation for Selective Gas-Phase Ethylene Hydroformylation.

A frontier challenge in single-atom (SA) catalysis is the design of fully inorganic sites capable of emulating the high reaction selectivity traditionally exclusive of organometallic counterparts in homogeneous catalysis. Modulating the direct coordination environment in SA sites, via the exploitation of the oxide support's surface chemistry, stands as a powerful albeit underexplored strategy. We report that isolated Rh atoms stabilized on oxygen-defective SnO2 uniquely unite excellent TOF with essentially full selectivity in the gas-phase hydroformylation of ethylene, inhibiting the thermodynamically favored olefin hydrogenation. Density Functional Theory calculations and surface characterization suggest that substantial depletion of the catalyst surface in lattice oxygen, energetically facile on SnO2 , is key to unlock a high coordination pliability at the mononuclear Rh centers, leading to an exceptional performance which is on par with that of molecular catalysts in liquid media.

Templated encapsulation of platinum-based catalysts promotes high-temperature stability to 1,100 °C.

Stable catalysts are essential to address energy and environmental challenges, especially for applications in harsh environments (for example, high temperature, oxidizing atmosphere and steam). In such conditions, supported metal catalysts deactivate due to sintering-a process where initially small nanoparticles grow into larger ones with reduced active surface area-but strategies to stabilize them can lead to decreased performance. Here we report stable catalysts prepared through the encapsulation of platinum nanoparticles inside an alumina framework, which was formed by depositing an alumina precursor within a separately prepared porous organic framework impregnated with platinum nanoparticles. These catalysts do not sinter at 800 °C in the presence of oxygen and steam, conditions in which conventional catalysts sinter to a large extent, while showing similar reaction rates. Extending this approach to Pd-Pt bimetallic catalysts led to the small particle size being maintained at temperatures as high as 1,100 °C in air and 10% steam. This strategy can be broadly applied to other metal and metal oxides for applications where sintering is a major cause of material deactivation.

In Vivo Detection of Cyclic-di-AMP in Staphylococcus aureus.

Cyclic-di-AMP (CDA) is a signaling molecule that controls various cellular functions including antibiotic tolerance and osmoregulation in Staphylococcus aureus (S. aureus). In this study, we developed a novel biosensor (bsuO P6-4) for in vivo detection of CDA in S. aureus. The fluorescent biosensor is based on a natural CDA riboswitch from Bacillus subtilis connected at its P6 stem to the dye-binding aptamer Spinach. Our study showed that bsuO P6-4 could detect a wide concentration range of CDA in both laboratory and clinical strains, making it suitable for use in both basic and clinical research applications.

Competition between reverse water gas shift reaction and methanol synthesis from CO2: influence of copper particle size.

Converting CO2 into value-added chemicals and fuels, such as methanol, is a promising approach to limit the environmental impact of human activities. Conventional methanol synthesis catalysts have shown limited efficiency and poor stability in a CO2/H2 mixture. To design improved catalysts, crucial for the effective utilization of CO2, an in-depth understanding of the active sites and reaction mechanism is desired. The catalytic performance of a series of carbon-supported Cu catalysts, with Cu particle sizes in the range of 5 to 20 nm, was evaluated under industrially relevant temperature and pressure, i.e. 260 °C and 40 bar(g). The CO2 hydrogenation reaction exhibited clear particle size effects up to 13 nm particles, with small nanoparticles having the lower activity, but higher methanol selectivity. MeOH and CO formation showed a different size-dependence. The TOFCO increased from 1.9 × 10-3 s-1 to 9.4 × 10-3 s-1 with Cu size increasing from 5 nm to 20 nm, while the TOFMeOH was size-independent (8.4 × 10-4 s-1 on average). The apparent activation energies for MeOH and CO formation were size-independent with values of 63 ± 7 kJ mol-1 and 118 ± 6 kJ mol-1, respectively. Hence the size dependence was ascribed to a decrease in the fraction of active sites suitable for CO formation with decreasing particle size. Theoretical models and DFT calculations showed that the origin of the particle size effect is most likely related to the differences in formate coverage for different Cu facets whose abundancy depends on particle size. Hence, the CO2 hydrogenation reaction is intrinsically sensitive to the Cu particle size.

Design of D-Amino Acids SARS-CoV-2 Main Protease Inhibitors Using the Cationic Peptide from Rattlesnake Venom as a Scaffold.

The C30 endopeptidase (3C-like protease; 3CLpro) is essential for the life cycle of SARS-CoV-2 (severe acute respiratory syndrome-coronavirus-2) since it plays a pivotal role in viral replication and transcription and, hence, is a promising drug target. Molecules isolated from animals, insects, plants, or microorganisms can serve as a scaffold for the design of novel biopharmaceutical products. Crotamine, a small cationic peptide from the venom of the rattlesnake Crotalus durissus terrificus, has been the focus of many studies since it exhibits activities such as analgesic, in vitro antibacterial, and hemolytic activities. The crotamine derivative L-peptides (L-CDP) that inhibit the 3CL protease in the low µM range were examined since they are susceptible to proteolytic degradation; we explored the utility of their D-enantiomers form. Comparative uptake inhibition analysis showed D-CDP as a promising prototype for a D-peptide-based drug. We also found that the D-peptides can impair SARS-CoV-2 replication in vivo, probably targeting the viral protease 3CLpro.

Behavioral Training Related Neurotransmitter Receptor Expression Dynamics in the Nidopallium Caudolaterale and the Hippocampal Formation of Pigeons.

Learning and memory are linked to dynamic changes at the level of synapses in brain areas that are involved in cognitive tasks. For example, changes in neurotransmitter receptors are prerequisite for tuning signals along local circuits and long-range networks. However, it is still unclear how a series of learning events promotes plasticity within the system of neurotransmitter receptors and their subunits to shape information processing at the neuronal level. Therefore, we investigated the expression of different glutamatergic NMDA (GRIN) and AMPA (GRIA) receptor subunits, the GABAergic GABARG2 subunit, dopaminergic DRD1, serotonergic 5HTR1A and noradrenergic ADRA1A receptors in the pigeon's brain. We studied the nidopallium caudolaterale, the avian analogue of the prefrontal cortex, and the hippocampal formation, after training the birds in a rewarded stimulus-response association (SR) task and in a simultaneous-matching-to-sample (SMTS) task. The results show that receptor expression changed differentially after behavioral training compared to an untrained control group. In the nidopallium caudolaterale, GRIN2B, GRIA3, GRIA4, DRD1D, and ADRA1A receptor expression was altered after SR training and remained constantly decreased after the SMTS training protocol, while GRIA2 and DRD1A decreased only under the SR condition. In the hippocampal formation, GRIN2B decreased and GABARG2 receptor expression increased after SR training. After SMTS sessions, GRIN2B remained decreased, GABARG2 remained increased if compared to the control group. None of the investigated receptors differed directly between both conditions, although differentially altered. The changes in both regions mostly occur in favor of the stimulus response task. Thus, the present data provide evidence that neurotransmitter receptor expression dynamics play a role in the avian prefrontal cortex and the hippocampal formation for behavioral training and is uniquely, regionally and functionally associated to cognitive processes including learning and memory.

Multi-Omics Integration Reveals Only Minor Long-Term Molecular and Functional Sequelae in Immune Cells of Individuals Recovered From COVID-19.

The majority of COVID-19 patients experience mild to moderate disease course and recover within a few weeks. An increasing number of studies characterized the long-term changes in the specific anti-SARS-CoV-2 immune responses, but how COVID-19 shapes the innate and heterologous adaptive immune system after recovery is less well known. To comprehensively investigate the post-SARS-CoV-2 infection sequelae on the immune system, we performed a multi-omics study by integrating single-cell RNA-sequencing, single-cell ATAC-sequencing, genome-wide DNA methylation profiling, and functional validation experiments in 14 convalescent COVID-19 and 15 healthy individuals. We showed that immune responses generally recover without major sequelae after COVID-19. However, subtle differences persist at the transcriptomic level in monocytes, with downregulation of the interferon pathway, while DNA methylation also displays minor changes in convalescent COVID-19 individuals. However, these differences did not affect the cytokine production capacity of PBMCs upon different bacterial, viral, and fungal stimuli, although baseline release of IL-1Ra and IFN-γ was higher in convalescent individuals. In conclusion, we propose that despite minor differences in epigenetic and transcriptional programs, the immune system of convalescent COVID-19 patients largely recovers to the homeostatic level of healthy individuals.

Mechanistic differences between methanol and dimethyl ether in zeolite-catalyzed hydrocarbon synthesis.

Water influences critically the kinetics of the autocatalytic conversion of methanol to hydrocarbons in acid zeolites. At very low conversions but otherwise typical reaction conditions, the initiation of the reaction is delayed in presence of H2O. In absence of hydrocarbons, the main reactions are the methanol and dimethyl ether (DME) interconversion and the formation of a C1 reactive mixture-which in turn initiates the formation of first hydrocarbons in the zeolite pores. We conclude that the dominant reactions for the formation of a reactive C1 pool at this stage involve hydrogen transfer from both MeOH and DME to surface methoxy groups, leading to methane and formaldehyde in a 1:1 stoichiometry. While formaldehyde reacts further to other C1 intermediates and initiates the formation of first C-C bonds, CH4 is not reacting. The hydride transfer to methoxy groups is the rate-determining step in the initiation of the conversion of methanol and DME to hydrocarbons. Thus, CH4 formation rates at very low conversions, i.e., in the initiation stage before autocatalysis starts, are used to gauge the formation rates of first hydrocarbons. Kinetics, in good agreement with theoretical calculations, show surprisingly that hydrogen transfer from DME to methoxy species is 10 times faster than hydrogen transfer from methanol. This difference in reactivity causes the observed faster formation of hydrocarbons in dry feeds, when the concentration of methanol is lower than in presence of water. Importantly, the kinetic analysis of CH4 formation rates provides a unique quantitative parameter to characterize the activity of catalysts in the methanol-to-hydrocarbon process.

Toward Computing Accurate Free Energies in Heterogeneous Catalysis: a Case Study for Adsorbed Isobutene in H-ZSM-5.

Herein, we propose a novel computational protocol that enables calculating free energies with improved accuracy by combining the best available techniques for enthalpy and entropy calculation. While the entropy is described by enhanced sampling molecular dynamics techniques, the energy is calculated using ab initio methods. We apply the method to assess the stability of isobutene adsorption intermediates in the zeolite H-SSZ-13, a prototypical problem that is computationally extremely challenging in terms of calculating enthalpy and entropy. We find that at typical operating conditions for zeolite catalysis (400 °C), the physisorbed π-complex, and not the tertiary carbenium ion as often reported, is the most stable intermediate. This method paves the way for sampling-based techniques to calculate the accurate free energies in a broad range of chemistry-related disciplines, thus presenting a big step forward toward predictive modeling.

Humoral response to SARS-CoV-2 and seasonal coronaviruses in COVID-19 patients.

We used enzyme-linked immunoassay methods to measure the prevalence and the levels of antibody responses to the nucleocapsid (N) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and four seasonal human coronaviruses (HCoV-OC43, HCoV-HKU1, HCoV 229E, and HCoV-NL63) in a cohort of 115 convalescent plasma donors infected with SARS-CoV-2 (1-61 days after symptom onset) compared to antibody levels in 114 individuals with no evidence of a recent infection with SARS-CoV-2. In the humoral response to the four seasonal coronaviruses, only HCoV-HKU1- and HCoV-229E-assays showed slightly elevated antibody levels in the COVID group compared to the control group. While in the COVID-group the levels of SARS-CoV-2 antibodies correlated significantly with disease severity, no association was found in the levels of antibodies against the seasonal coronaviruses. The most striking result in both groups was that the levels of antibodies against all tested coronaviruses, including the new SARS-CoV-2 showed a highly significant correlation with each other. There seems to be an individual predisposition to a weaker or stronger humoral immune response against all known seasonal human coronaviruses including the new SARS-CoV-2, which could lead to a definition of low and high responders against human coronaviruses with potential impact on the assessment of postinfection antibody levels and protection.