Solar-Powered AEM Electrolyzer via PGM-Free (Oxy)hydroxide Anode with Solar to Hydrogen Conversion Efficiency of 12.44.

Water electrolyzers powered by renewable energy are emerging as clean and sustainable technology for producing hydrogen without carbon emissions. Specifically, anion exchange membrane (AEM) electrolyzers utilizing non-platinum group metal (non-PGM) catalysts have garnered attention as a cost-effective method for hydrogen production, especially when integrated with solar cells. Nonetheless, the progress of such integrated systems is hindered by inadequate water electrolysis efficiency, primarily caused by poor oxygen evolution reaction (OER) electrodes. To address this issue, a NiFeCo─OOH has developed as an OER electrocatalyst and successfully demonstrated its efficacy in an AEM electrolyzer, which is powered by renewable electricity and integrated with a silicon solar cell.

Synergistic Effects in LaNiO3 Perovskites between Nickel and Iron Heterostructures for Improving Durability in Oxygen Evolution Reaction for AEMWE.

Perovskite materials that aren't stable during the oxygen evolution reaction (OER) are unsuitable for anion-exchange membrane water electrolyzers (AEMWE). But through manipulating their electronic structures, their performance can further increase. Among the first-row transition metals, nickel and iron are widely recognized as prominent electrocatalysts; thus, the researchers are looking into how combining them can improve the OER. Recent research has actively explored the design and study of heterostructures in this field, showcasing the dynamic exploration of innovative catalyst configurations. In this study, a heterostructure is used to manipulate the electronic structure of LaNiO3 (LNO) to improve both OER properties and durability. Through adsorbing iron onto the LNO (LNO@Fe) as γ iron oxyhydroxide (γ-FeOOH), the binding energy of nickel in the LNO exhibited negative shifts, inferring nickel movement toward the metallic state. Consequently, the electrochemical properties of LNO@Fe are further improved. LNO@Fe showed excellent performance (1.98 A cm-2, 1 m KOH, 50 °C at 1.85 V) with 84.1% cell efficiency in AEMWE single cells, demonstrating great improvement relative to LNO. The degradation for the 850 h durability analysis of LNO@Fe is ≈68 mV kh-1, which is ≈58 times less than that of LNO.

Induced degradation of lineage-specific oncoproteins drives the therapeutic vulnerability of small cell lung cancer to PARP inhibitors.

Although BRCA1/2 mutations are not commonly found in small cell lung cancer (SCLC), a substantial fraction of SCLC shows clinically relevant response to PARP inhibitors (PARPis). However, the underlying mechanism(s) of PARPi sensitivity in SCLC is poorly understood. We performed quantitative proteomic analyses and identified proteomic changes that signify PARPi responses in SCLC cells. We found that the vulnerability of SCLC to PARPi could be explained by the degradation of lineage-specific oncoproteins (e.g., ASCL1). PARPi-induced activation of the E3 ligase HUWE1 mediated the ubiquitin-proteasome system (UPS)-dependent ASCL1 degradation. Although PARPi induced a general DNA damage response in SCLC cells, this signal generated a cell-specific response in ASCL1 degradation, leading to the identification of HUWE1 expression as a predictive biomarker for PARPi. Combining PARPi with agents targeting these pathways markedly improved therapeutic response in SCLC. The degradation of lineage-specific oncoproteins therefore represents a previously unidentified mechanism for PARPi efficacy in SCLC.

Nimbolide targets RNF114 to induce the trapping of PARP1 and synthetic lethality in BRCA-mutated cancer.

Recent studies have pointed to PARP1 trapping as a key determinant of the anticancer effects of PARP1 inhibitors (PARPi). We identified RNF114, as a PARylation-dependent, E3 ubiquitin ligase involved in DNA damage response. Upon sensing genotoxicity, RNF114 was recruited, in a PAR-dependent manner, to DNA lesions, where it targeted PARP1 for degradation. The blockade of this pathway interfered with the removal of PARP1 from DNA lesions, leading to profound PARP1 trapping. We showed that a natural product, nimbolide, inhibited the E3 ligase activity of RNF114 and thus caused PARP1 trapping. However, unlike conventional PARPi, nimbolide treatment induced the trapping of both PARP1 and PARylation-dependent DNA repair factors. Nimbolide showed synthetic lethality with BRCA mutations, and it overcame intrinsic and acquired resistance to PARPi, both in vitro and in vivo. These results point to the exciting possibility of targeting the RNF114-PARP1 pathway for the treatment of homologous recombination-deficient cancers.

PARP1 inhibitors induce pyroptosis via caspase 3-mediated gasdermin E cleavage.

The identification of PARP1 as a therapeutic target for BRCA1/2-deficient cells has led to a paradigm shift for the treatment of human malignancies with BRCA1/2 mutations. However, our understanding of the mechanism of action of PARP1 inhibitors (PARPi) is still evolving. It is being increasingly appreciated that the immunomodulatory function of PARPi is a critical contributor of the anti-tumor effects of these compounds. Here, we identify a novel cell death effector pathway for PARPi where PARPi induces inflammatory pyroptosis that is mediated by caspase 3-dependent cleavage of GSDME. Caspase 3 is activated upon PARPi treatment which directly cleaves GSDME and, subsequently induces pyroptosis. Genetic and pharmacological experiments show that the presence of the PARP1 protein with uncompromised DNA binding capability is required for PARPi-induced pyroptosis, suggesting that PARP1 trapping is a key driver of this phenomenon. Importantly, we show that PARPi-induced GSDME cleavage and pyroptosis occurred only in the BRCA1-deficient cells, but not in those reconstituted with BRCA1 wild-type (WT). These findings suggest that pyroptosis could be a novel aspect of the immunomodulatory function of PARPi. Our studies could also offer new insights to the potential biomarkers and therapeutic strategies to achieve better anti-tumor effects of PARPi for BRCA-deficient tumors with low GSDME expression.

Impact of Asp/Glu-ADP-ribosylation on protein-protein interaction and protein function.

PARylation plays critical role in regulating multiple cellular processes such as DNA damage response and repair, transcription, RNA processing, and stress response. More than 300 human proteins have been found to be modified by PARylation on acidic residues, that is, Asp (D) and Glu (E). We used the deep-learning tool AlphaFold to predict protein-protein interactions (PPIs) and their interfaces for these proteins based on coevolution signals from joint multiple sequence alignments (MSAs). AlphaFold predicted 260 confident PPIs involving PARylated proteins, and about one quarter of these PPIs have D/E-PARylation sites in their predicted PPI interfaces. AlphaFold predictions offer novel insights into the mechanisms of PARylation regulations by providing structural details of the PPI interfaces. D/E-PARylation sites have a preference to occur in coil regions and disordered regions, and PPI interfaces containing D/E-PARylation sites tend to occur between short linear sequence motifs in disordered regions and globular domains. The hub protein PCNA is predicted to interact with more than 20 proteins via the common PIP box motif and the structurally variable flanking regions. D/E-PARylation sites were found in the interfaces of key components of the RNA transcription and export complex, the SF3a spliceosome complex, and H/ACA and C/D small nucleolar ribonucleoprotein complexes, suggesting that systematic PARylation have a profound effect in regulating multiple RNA-related processes such as RNA nuclear export, splicing, and modification. Finally, PARylation of SUMO2 could modulate its interaction with CHAF1A, thereby representing a potential mechanism for the cross-talk between PARylation and SUMOylation in regulation of chromatin remodeling.

Quantitative phosphoproteomic analyses identify STK11IP as a lysosome-specific substrate of mTORC1 that regulates lysosomal acidification.

The evolutionarily conserved serine/threonine kinase mTORC1 is a central regulator of cell growth and proliferation. mTORC1 is activated on the lysosome surface. However, once mTORC1 is activated, it is unclear whether mTORC1 phosphorylates local lysosomal proteins to regulate specific aspects of lysosomal biology. Through cross-reference analyses of the lysosome proteome with the mTORC1-regulated phosphoproteome, we identify STK11IP as a lysosome-specific substrate of mTORC1. mTORC1 phosphorylates STK11IP at Ser404. Knockout of STK11IP leads to a robust increase of autophagy flux. Dephosphorylation of STK11IP at Ser404 represses the role of STK11IP as an autophagy inhibitor. Mechanistically, STK11IP binds to V-ATPase, and regulates the activity of V-ATPase. Knockout of STK11IP protects mice from fasting or Methionine/Choline-Deficient Diet (MCD)-induced fatty liver. Thus, our study demonstrates that STK11IP phosphorylation represents a mechanism for mTORC1 to regulate lysosomal acidification and autophagy, and points to STK11IP as a promising therapeutic target for the amelioration of diseases with aberrant autophagy signaling.

Cobalt-Iron-Phosphate Hydrogen Evolution Reaction Electrocatalyst for Solar-Driven Alkaline Seawater Electrolyzer.

Seawater splitting represents an inexpensive and attractive route for producing hydrogen, which does not require a desalination process. Highly active and durable electrocatalysts are required to sustain seawater splitting. Herein we report the phosphidation-based synthesis of a cobalt-iron-phosphate ((Co,Fe)PO4) electrocatalyst for hydrogen evolution reaction (HER) toward alkaline seawater splitting. (Co,Fe)PO4 demonstrates high HER activity and durability in alkaline natural seawater (1 M KOH + seawater), delivering a current density of 10 mA/cm2 at an overpotential of 137 mV. Furthermore, the measured potential of the electrocatalyst ((Co,Fe)PO4) at a constant current density of -100 mA/cm2 remains very stable without noticeable degradation for 72 h during the continuous operation in alkaline natural seawater, demonstrating its suitability for seawater applications. Furthermore, an alkaline seawater electrolyzer employing the non-precious-metal catalysts demonstrates better performance (1.625 V at 10 mA/cm2) than one employing precious metal ones (1.653 V at 10 mA/cm2). The non-precious-metal-based alkaline seawater electrolyzer exhibits a high solar-to-hydrogen (STH) efficiency (12.8%) in a commercial silicon solar cell.

Cholesterol Regulates the Tumor Adaptive Resistance to MAPK Pathway Inhibition.

Although targeted MAPK pathway inhibition has achieved remarkable patient responses in many cancers, the development of resistance has remained a critical challenge. Adaptive tumor response underlies the drug resistance. Furthermore, such bypass mechanisms often lead to the activation of many pro-survival kinases, which complicates the rational design of combination therapies. Here, we performed global tyrosine phosphoproteomic (pTyr) analyses and demonstrated that targeted MAPK signaling inhibition in melanoma leads to a profound remodeling of the pTyr proteome. Intriguingly, altered cholesterol metabolism might drive, in a coordinated fashion, the activation of these kinases. Indeed, we found an accumulation of intracellular cholesterol in melanoma cells (with BRAFV600E mutations) and non-small cell lung cancer cells (with KRASG12C mutations) treated with MAPK and KRASG12C inhibitors, respectively. Importantly, depletion of cholesterol not only prevents the feedback activation of pTyr signaling but also enhances the cytotoxic effects of MAPK pathway inhibitors, both in vitro and in vivo. Together, our findings suggest that cholesterol contributes to the tumor adaptive response upon targeted MAPK pathway inhibitors. These results also suggest that MAPK pathway inhibitors could be combined with cholesterol-lowering agents to achieve a more complete and durable response in tumors with hyperactive MAPK signaling.

Novel high voltage polymer insulators using computational and data-driven techniques.

One of the key bottlenecks in the development of high voltage electrical systems is the identification of suitable insulating materials capable of supporting high voltages. Under high voltage scenarios, conventional polymer based insulators, which are one of the popular choices of insulators, suffer from the drawback of space charge accumulation, which leads to degradation in desirable electronic properties and facilitates dielectric breakdown. In this work, we aid the development of novel polymers for high voltage insulation applications by enabling the rapid prediction of properties that are correlated with dielectric breakdown, i.e.,the bandgap (Egap) of the polymer and electron injection barrier (Φe) at the electrode-insulator interface. To accomplish this, density functional theory based methods are used to develop large, chemically diverse datasets of Φe and Egap. The deviation of the computed properties from experimental observations is addressed using a statistical technique called Bayesian calibration. Furthermore, to enable rapid estimation of these properties for a large set of polymers, machine learning models are developed using the created dataset. These models are further used to predict Egap and Φe for a set of 13k previously known polymers. Polymers with high values of these properties are selected as potential high voltage insulators and are recommended for synthesis. Finally, the models developed here are deployed at www.polymergenome.org to enable the community use.

An Informatics Approach for Designing Conducting Polymers.

Doping conjugated polymers, which are potential candidates for the next generation of organic electronics, is an effective strategy for manipulating their electrical conductivity. However, selecting a suitable polymer-dopant combination is exceptionally challenging because of the vastness of the chemical, configurational, and morphological spaces one needs to search. In this work, high-performance surrogate models, trained on available experimentally measured data, are developed to predict the p-type electrical conductivity and are used to screen a large candidate hypothetical data set of more than 800 000 polymer-dopant combinations. Promising candidates are identified for synthesis and device fabrication. Additionally, new design guidelines are extracted that verify and extend knowledge on important molecular fragments that correlate to high conductivity. Conductivity prediction models are also deployed at www.polymergenome.org for broader open-access community use.

Polymer informatics with multi-task learning.

Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict properties of polymers are becoming commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge available in datasets, which are oftentimes sparse; inherent correlations between different property datasets are disregarded. Here, we demonstrate the potency of multi-task learning approaches that exploit such inherent correlations effectively. Data pertaining to 36 different properties of over 13,000 polymers are supplied to deep-learning multi-task architectures. Compared to conventional single-task learning models, the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available. Moreover, these models are interpretable. Chemical rules, that explain how certain features control trends in property values, emerge from the present work, paving the way for the rational design of application specific polymers meeting desired property or performance objectives.

Avoid the trap: Targeting PARP1 beyond human malignancy.

PARP1 is a poly(ADP-ribose) polymerase (PARP) enzyme that plays a critical role in regulating DNA damage response. The main enzymatic function of PARP1 is to catalyze a protein post-translational modification known as poly(ADP-ribosyl)ation (PARylation). Human cancers with homologous recombination deficiency are highly sensitive to PARP1 inhibitors. PARP1 is aberrantly activated in many non-oncological diseases, leading to the excessive NAD+ depletion and PAR formation, thus causing cell death and tissue damage. PARP1 deletion offers a profound protective effect in the relevant animal models. However, many of the current PARP1 inhibitors also induce PARP1 trapping, which drives subsequent DNA damage, innate immune response and cytotoxicity. This minireview provides an overview of the basic biology of PARP1 trapping, and its implications in disease. Furthermore, we also discuss the recent development of PARP1 PROTAC compounds, and their utility as "non-trapping" PARP1 degraders for the potential amelioration of non-oncological diseases driven by aberrant PARP1 activation.

Promoting electrocatalytic overall water splitting by sulfur incorporation into CoFe-(oxy)hydroxide.

The design and fabrication of highly cost-effective electrocatalysts with high activity, and stability to enhance the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) has been considered to be one of the most promising approaches toward overall water splitting. In this study, sulfur-incorporated cobalt-iron (oxy)hydroxide (S-(Co,Fe)OOH) nanosheets were directly grown on commercial iron foam via galvanic corrosion and hydrothermal methods. The incorporation of sulfur into (Co,Fe)OOH results in superior catalytic performance and high stability in both the HER and OER conducted in 1 M KOH. The incorporation of sulfur enhanced the electrocatalytic activity by modifying the electronic structure and chemical states of (Co,Fe)OOH. An alkaline water electrolyzer for overall water splitting was fabricated using a two-electrode configuration utilizing the S-(Co,Fe)OOH bifunctional electrocatalyst in both the HER and OER. The fabricated electrolyzer outperformed a precious metal-based electrolyzer using Pt/C as the HER electrocatalyst and IrO2 as the OER electrocatalyst, which are the benchmark catalysts. This electrolyzer provides a lower potential of 1.641 V at 10 mA cm-2 and maintains 98.4% of its performance after 50 h of durability testing. In addition, the S-(Co,Fe)OOH-based electrolyzer successfully generated hydrogen under natural illumination upon its combination with a commercial silicon solar cell and exhibited a solar to hydrogen (STH) efficiency of up to 13.0%. This study shows that S-(Co,Fe)OOH is a promising candidate for application in the future renewable energy industry due to its high cost-effectiveness, activity, and stability during overall water splitting. In addition, the combination of a commercial silicon solar cell with an alkaline water electrolyzer has great potential for the production of hydrogen.

PARP1 inhibitors trigger innate immunity via PARP1 trapping-induced DNA damage response.

It is being increasingly appreciated that the immunomodulatory functions of PARP1 inhibitors (PARPi) underlie their clinical activities in various BRCA-mutated tumors. PARPi possess both PARP1 inhibition and PARP1 trapping activities. The relative contribution of these two mechanisms toward PARPi-induced innate immune signaling, however, is poorly understood. We find that the presence of the PARP1 protein with uncompromised DNA-binding activities is required for PARPi-induced innate immune response. The activation of cGAS-STING signaling induced by various PARPi closely depends on their PARP1 trapping activities. Finally, we show that a small molecule PARP1 degrader blocks the enzymatic activity of PARP1 without eliciting PARP1 trapping or cGAS-STING activation. Our findings thus identify PARP1 trapping as a major contributor of the immunomodulatory functions of PARPi. Although PARPi-induced innate immunity is highly desirable in human malignancies, the ability of 'non-trapping' PARP1 degraders to avoid the activation of innate immune response could be useful in non-oncological diseases.

Predicting Crystallization Tendency of Polymers Using Multifidelity Information Fusion and Machine Learning.

The degree of crystallinity of a polymer is a critical parameter that controls a variety of polymer properties. A high degree of crystallinity is associated with excellent mechanical properties crucial for high-performing applications like composites. Low crystallinity promotes ion and gas mobility critical for battery and membrane applications. Experimental determination of the crystallinity for new polymers is time and cost intensive. A data-driven machine learning-based method capable of rapidly predicting the crystallinity could counter these disadvantages and be used to screen polymers for a myriad of applications in a fast, inexpensive fashion. In this work, we developed the first-of-its-kind, data-driven machine learning model to predict the most-likely polymer crystallinity trained on experimental data and theoretical group contribution methods. Since polymer data under consistent processing conditions are unavailable, we tackled process variability by using the "most-likely" polymer values which we refer to as the polymer's tendency to crystallize. Experimental data for polymers' tendency to crystallize is limited by number and diversity, and to tackle this, we augmented experimentation-based data with data using group contribution methods. Therefore, this work utilized two data sets, viz., a high-fidelity, experimental data set for 107 polymers and a more diverse, less accurate low-fidelity data set for 429 polymers which used group contribution methods. We used a multifidelity information fusion strategy to utilize all the information captured in the low-fidelity data set while still predicting at the high-fidelity accuracy. Although this model inherently assumed "typical" processing conditions and estimated the "most-likely" percent crystallinity value, it can help in the estimation of a polymer's tendency to crystallize in a far more cost-effective and efficient manner.

Critical Assessment of the Hildebrand and Hansen Solubility Parameters for Polymers.

Solubility parameter models are widely used to select suitable solvents/nonsolvents for polymers in a variety of processing and engineering applications. In this study, we focus on two well-established models, namely, the Hildebrand and Hansen solubility parameter models. Both models are built on the basis of the notion of "like dissolves like" and identify a liquid as a good solvent for a polymer if the solubility parameters of the liquid and the polymer are close to each other. Here we make a critical and quantitative assessment of the accuracy/utility of these two models by comparing their predictions against actual experimental data. Using a data set of 75 polymers, we find that the Hildebrand model displays a predictive accuracy of 60% for solvents and 76% for nonsolvents. The Hansen model leads to a similar performance; on the basis of a data set of 25 polymers for which Hansen parameters are available, we find that it has an accuracy of 67% for solvents and 76% for nonsolvents. The availability of the Hildebrand parameters for a large polymer data set makes it a widely applicable capability, as the Hildebrand parameter for a new polymer may be determined using this data set and machine learning methods as we have done before; the predicted Hildebrand parameter for a new polymer may then be used to determine suitable solvents and nonsolvents. Such predictions are difficult to make with the Hansen model, as the data set of Hansen parameters for polymers is rather small. Nevertheless, the Hildebrand approach must be used with caution. Our analysis shows that while the Hildebrand model has a predictive accuracy of 70-75% for nonpolar polymers, it performs rather poorly for polar polymers (with an accuracy of 57%). Going forward, determination of solvents and nonsolvents for polymers may benefit by developing classification models built directly on the basis of available experimental data sets rather than utilizing the solubility parameter approach, which is limited in versatility and accuracy.

Site-specific phosphorylation of Fbxw7 by Cdk5/p25 and its resulting decreased stability are linked to glutamate-induced excitotoxicity.

Cyclin-dependent kinase 5 (Cdk5) is a serine/threonine protein kinase that regulates brain development and neurodegeneration. Cdk5 is activated by p25 that is generated from calpain-dependent cleavage of p35. The generation of p25 is responsible for the aberrant hyper-activation of Cdk5, which causes neurodegeneration. Using in vitro assays, we discovered that F-box/WD repeat-containing protein 7 (Fbxw7) is a new substrate of Cdk5. Additionally, Cdk5-dependent phosphorylation of Fbxw7 was detected in the presence of p25, and two amino acid residues (S349 and S372) were determined to be major phosphorylation sites. This phosphorylation was eventually linked to decreased stability of Fbxw7. Using a culture model of cortical neurons challenged with glutamate, we confirmed that decreased stability of Fbxw7 was indeed Cdk5-dependent. Furthermore, diminished levels of Fbxw7 led to increased levels of transcription factor AP-1 (c-Jun), a known substrate of Fbxw7. Given that previous reports demonstrate that c-Jun plays a role in accelerating neuronal apoptosis in these pathological models, our data support the concepts of a molecular cascade in which Cdk5-mediated phosphorylation of Fbxw7 negatively regulates Fbxw7 expression, thereby contributing to neuronal cell death following glutamate-mediated excitotoxicity.

Hot-Carrier Dynamics and Chemistry in Dielectric Polymers.

Dielectric polymers are widely used in electronics and energy technologies, but their performance is severely limited by the electrical breakdown under a high electric field. Dielectric breakdown is commonly understood as an avalanche of processes such as carrier multiplication and defect generation that are triggered by field-accelerated hot electrons and holes. However, how these processes are initiated remains elusive. Here, nonadiabatic quantum molecular dynamics simulations reveal microscopic processes induced by hot electrons and holes in a slab of an archetypal dielectric polymer, polyethylene, under an electric field of 600 MV/m. We found that electronic-excitation energy is rapidly dissipated within tens of femtoseconds because of strong electron-phonon scattering, which is consistent with quantum-mechanical perturbation calculations. This in turn excites other electron-hole pairs to cause carrier multiplication. We also found that the key to chemical damage is localization of holes that travel to a slab surface and weaken carbon-carbon bonds on the surface. Such quantitative information can be incorporated into first-principles-informed, predictive modeling of dielectric breakdown.