De novo protein design with a denoising diffusion network independent of pretrained structure prediction models.

The recent success of RFdiffusion, a method for protein structure design with a denoising diffusion probabilistic model, has relied on fine-tuning the RoseTTAFold structure prediction network for protein backbone denoising. Here, we introduce SCUBA-diffusion (SCUBA-D), a protein backbone denoising diffusion probabilistic model freshly trained by considering co-diffusion of sequence representation to enhance model regularization and adversarial losses to minimize data-out-of-distribution errors. While matching the performance of the pretrained RoseTTAFold-based RFdiffusion in generating experimentally realizable protein structures, SCUBA-D readily generates protein structures with not-yet-observed overall folds that are different from those predictable with RoseTTAFold. The accuracy of SCUBA-D was confirmed by the X-ray structures of 16 designed proteins and a protein complex, and by experiments validating designed heme-binding proteins and Ras-binding proteins. Our work shows that deep generative models of images or texts can be fruitfully extended to complex physical objects like protein structures by addressing outstanding issues such as the data-out-of-distribution errors.

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

Unveiling promising drug targets for autism spectrum disorder: insights from genetics, transcriptomics, and proteomics.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder for which current treatments are limited and drug development costs are prohibitive. Identifying drug targets for ASD is crucial for the development of targeted therapies. Summary-level data of expression quantitative trait loci obtained from GTEx, protein quantitative trait loci data from the ROSMAP project, and two ASD genome-wide association studies datasets were utilized for discovery and replication. We conducted a combined analysis using Mendelian randomization (MR), transcriptome-wide association studies, Bayesian colocalization, and summary-data-based MR to identify potential therapeutic targets associated with ASD and examine whether there are shared causal variants among them. Furthermore, pathway and drug enrichment analyses were performed to further explore the underlying mechanisms and summarize the current status of pharmacological targets for developing drugs to treat ASD. The protein-protein interaction (PPI) network and mouse knockout models were performed to estimate the effect of therapeutic targets. A total of 17 genes revealed causal associations with ASD and were identified as potential targets for ASD patients. Cathepsin B (CTSB) [odd ratio (OR) = 2.66 95, confidence interval (CI): 1.28-5.52, P = 8.84 × 10-3], gamma-aminobutyric acid type B receptor subunit 1 (GABBR1) (OR = 1.99, 95CI: 1.06-3.75, P = 3.24 × 10-2), and formin like 1 (FMNL1) (OR = 0.15, 95CI: 0.04-0.58, P = 5.59 × 10-3) were replicated in the proteome-wide MR analyses. In Drugbank, two potential therapeutic drugs, Acamprosate (GABBR1 inhibitor) and Bryostatin 1 (CASP8 inhibitor), were inferred as potential influencers of autism. Knockout mouse models suggested the involvement of the CASP8, GABBR1, and PLEKHM1 genes in neurological processes. Our findings suggest 17 candidate therapeutic targets for ASD and provide novel drug targets for therapy development and critical drug repurposing opportunities.

A guide to the BRAIN Initiative Cell Census Network data ecosystem.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Combined prediction and design reveals the target recognition mechanism of an intrinsically disordered protein interaction domain.

An increasing number of protein interaction domains and their targets are being found to be intrinsically disordered proteins (IDPs). The corresponding target recognition mechanisms are mostly elusive because of challenges in performing detailed structural analysis of highly dynamic IDP-IDP complexes. Here, we show that by combining recently developed computational approaches with experiments, the structure of the complex between the intrinsically disordered C-terminal domain (CTD) of protein 4.1G and its target IDP region in NuMA can be dissected at high resolution. First, we carry out systematic mutational scanning using dihydrofolate reductase-based protein complementarity analysis to identify essential interaction regions and key residues. The results are found to be highly consistent with an α/ß-type complex structure predicted by AlphaFold2 (AF2). We then design mutants based on the predicted structure using a deep learning protein sequence design method. The solved crystal structure of one mutant presents the same core structure as predicted by AF2. Further computational prediction and experimental assessment indicate that the well-defined core structure is conserved across complexes of 4.1G CTD with other potential targets. Thus, we reveal that an intrinsically disordered protein interaction domain uses an α/ß-type structure module formed through synergistic folding to recognize broad IDP targets. Moreover, we show that computational prediction and experiment can be jointly applied to segregate true IDP regions from the core structural domains of IDP-IDP complexes and to uncover the structure-dependent mechanisms of some otherwise elusive IDP-IDP interactions.

NIEND: neuronal image enhancement through noise disentanglement.

MOTIVATION: The full automation of digital neuronal reconstruction from light microscopic images has long been impeded by noisy neuronal images. Previous endeavors to improve image quality can hardly get a good compromise between robustness and computational efficiency. RESULTS: We present the image enhancement pipeline named Neuronal Image Enhancement through Noise Disentanglement (NIEND). Through extensive benchmarking on 863 mouse neuronal images with manually annotated gold standards, NIEND achieves remarkable improvements in image quality such as signal-background contrast (40-fold) and background uniformity (10-fold), compared to raw images. Furthermore, automatic reconstructions on NIEND-enhanced images have shown significant improvements compared to both raw images and images enhanced using other methods. Specifically, the average F1 score of NIEND-enhanced reconstructions is 0.88, surpassing the original 0.78 and the second-ranking method, which achieved 0.84. Up to 52% of reconstructions from NIEND-enhanced images outperform all other four methods in F1 scores. In addition, NIEND requires only 1.6 s on average for processing 256 × 256 × 256-sized images, and images after NIEND attain a substantial average compression rate of 1% by LZMA. NIEND improves image quality and neuron reconstruction, providing potential for significant advancements in automated neuron morphology reconstruction of petascale. AVAILABILITY AND IMPLEMENTATION: The study is conducted based on Vaa3D and Python 3.10. Vaa3D is available on GitHub (https://github.com/Vaa3D). The proposed NIEND method is implemented in Python, and hosted on GitHub along with the testing code and data (https://github.com/zzhmark/NIEND). The raw neuronal images of mouse brains can be found at the BICCN's Brain Image Library (BIL) (https://www.brainimagelibrary.org). The detailed list and associated meta information are summarized in Supplementary Table S3.

Apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1) promotes stress granule formation via YBX1 phosphorylation in ovarian cancer.

APE1 is an essential gene involved in DNA damage repair, the redox regulation of transcriptional factors (TFs) and RNA processing. APE1 overexpression is common in cancers and correlates with poor patient survival. Stress granules (SGs) are phase-separated cytoplasmic assemblies that cells form in response to environmental stresses. Precise regulation of SGs is pivotal to cell survival, whereas their dysregulation is increasingly linked to diseases. Whether APE1 engages in modulating SG dynamics is worthy of investigation. In this study, we demonstrate that APE1 colocalizes with SGs and promotes their formation. Through phosphoproteome profiling, we discover that APE1 significantly alters the phosphorylation landscape of ovarian cancer cells, particularly the phosphoprofile of SG proteins. Notably, APE1 promotes the phosphorylation of Y-Box binding protein 1 (YBX1) at S174 and S176, leading to enhanced SG formation and cell survival. Moreover, expression of the phosphomutant YBX1 S174/176E mimicking hyperphosphorylation in APE1-knockdown cells recovered the impaired SG formation. These findings shed light on the functional importance of APE1 in SG regulation and highlight the importance of YBX1 phosphorylation in SG dynamics.

Abnormal global alternative RNA splicing in COVID-19 patients.

Viral infections can alter host transcriptomes by manipulating host splicing machinery. Despite intensive transcriptomic studies on SARS-CoV-2, a systematic analysis of alternative splicing (AS) in severe COVID-19 patients remains largely elusive. Here we integrated proteomic and transcriptomic sequencing data to study AS changes in COVID-19 patients. We discovered that RNA splicing is among the major down-regulated proteomic signatures in COVID-19 patients. The transcriptome analysis showed that SARS-CoV-2 infection induces widespread dysregulation of transcript usage and expression, affecting blood coagulation, neutrophil activation, and cytokine production. Notably, CD74 and LRRFIP1 had increased skipping of an exon in COVID-19 patients that disrupts a functional domain, which correlated with reduced antiviral immunity. Furthermore, the dysregulation of transcripts was strongly correlated with clinical severity of COVID-19, and splice-variants may contribute to unexpected therapeutic activity. In summary, our data highlight that a better understanding of the AS landscape may aid in COVID-19 diagnosis and therapy.

POLM variant G312R promotes ovarian tumorigenesis through genomic instability and COL11A1-NF-κB axis.

In ovarian cancer (OC), identifying key molecular players in disease escalation and chemoresistance remains critical. Our investigation elucidates the role of the DNA polymerase mu (POLM), especially G312R mutation, in propelling oncogenesis through dual pathways. POLMG312R markedly augments the ribonucleotide insertion capability of POLM, precipitating genomic instability. In addition, our research reveals that POLMG312R perturbs collagen alpha-1 (XI) chain (COL11A1) expression-a gene that plays a key role in oncogenesis-and modulates the NF-κB signaling pathway, alters the secretion of downstream inflammatory cytokines, and promotes tumor-macrophage interactions. We illustrate a bidirectional regulatory interaction between POLM, particularly its G312R variant, and COL11A1. This interaction regulates NF-κB signaling, culminating in heightened malignancy and resistance to chemotherapy in OC cells. These insights position the POLM as a potential molecular target for OC therapy, shedding light on the intricate pathways underpinning POLM variant disease progression.NEW & NOTEWORTHY Our research reveals that POLM plays an important role in ovarian cancer development, especially the mutation G312R. We uncover the POLMG312R mutation as a driver of genomic instability in ovarian cancer via aberrant ribonucleotide incorporation. We reveal that POLMG312R upregulates COL11A1 and activates NF-κB signaling, contributing to tumor progression and chemoresistance. This study identifies the POLM-COL11A1-NF-κB axis as a novel oncogenic pathway.

Disentangling sex-dependent effects of APOE on diverse trajectories of cognitive decline in Alzheimer's disease.

Current diagnostic systems for Alzheimer's disease (AD) rely upon clinical signs and symptoms, despite the fact that the multiplicity of clinical symptoms renders various neuropsychological assessments inadequate to reflect the underlying pathophysiological mechanisms. Since putative neuroimaging biomarkers play a crucial role in understanding the etiology of AD, we sought to stratify the diverse relationships between AD biomarkers and cognitive decline in the aging population and uncover risk factors contributing to the diversities in AD. To do so, we capitalized on a large amount of neuroimaging data from the ADNI study to examine the inflection points along the dynamic relationship between cognitive decline trajectories and whole-brain neuroimaging biomarkers, using a state-of-the-art statistical model of change point detection. Our findings indicated that the temporal relationship between AD biomarkers and cognitive decline may differ depending on the synergistic effect of genetic risk and biological sex. Specifically, tauopathy-PET biomarkers exhibit a more dynamic and age-dependent association with Mini-Mental State Examination scores (p<0.05), with inflection points at 72, 78, and 83 years old, compared with amyloid-PET and neurodegeneration (cortical thickness from MRI) biomarkers. In the landscape of health disparities in AD, our analysis indicated that biological sex moderates the rate of cognitive decline associated with APOE4 genotype. Meanwhile, we found that higher education levels may moderate the effect of APOE4, acting as a marker of cognitive reserve.

Spin-Polarized Charge Separation at Two-Dimensional Semiconductor/Molecule Interfaces.

Spin-polarized electrons can improve the efficiency and selectivity of photo- and electro-catalytic reactions, as demonstrated in the past with magnetic or magnetized catalysts. Here, we present a scheme in which spin-polarized charge separation occurs at the interfaces of nonmagnetic semiconductors and molecular films in the absence of a magnetic field. We take advantage of the spin-valley-locked band structure and valley-dependent optical selection rule in group VI transition metal dichalcogenide (TMDC) monolayers to generate spin-polarized electron-hole pairs. Photoinduced electron transfer from WS2 to fullerene (C60) and hole transfer from MoSe2 to phthalocyanine (H2Pc) are found to result in spin polarization lifetimes that are 1 order of magnitude longer than those in the TMDC monolayers alone. Our findings connect valleytronic properties of TMDC monolayers to spin-polarized interfacial charge transfer and suggest a viable route toward spin-selective photocatalysis.

Wogonin upregulates SOCS3 to alleviate the injury in Diabetic Nephropathy by inhibiting TLR4-mediated JAK/STAT/AIM2 signaling pathway.

BACKGROUND: Diabetic nephropathy (DN) is a life-threatening renal disease and needs urgent therapies. Wogonin is renoprotective in DN. This study aimed to explore the mechanism of how wogonin regulated high glucose (HG)-induced renal cell injury. METHODS: Diabetic mice (db/db), control db/m mice, and normal glucose (NG)- or HG-treated human tubule epithelial cells (HK-2) were used to evaluate the levels of suppressor of cytokine signaling 3 (SOCS3), Toll-like receptor 4 (TLR4), inflammation and fibrosis. Lentivirus was used to regulate SOCS3 and TLR4 expressions. After oral gavage of wogonin (10 mg/kg) or vehicle in db/db mice, histological morphologies, blood glucose, urinary protein, serum creatinine values (Scr), blood urea nitrogen (BUN), superoxide dismutase (SOD), glutathione (GSH), and reactive oxygen species (ROS) were assessed. RT-qPCR and Western blot evaluated inflammation and fibrosis-related molecules. RESULTS: HG exposure induced high blood glucose, severe renal injuries, high serumal Src and BUN, low SOD and GSH, and increased ROS. HG downregulated SOCS3 but upregulated TLR4 and JAK/STAT, fibrosis, and inflammasome-related proteins. Wogonin alleviated HG-induced renal injuries by decreasing cytokines, ROS, Src, and MDA and increasing SOD and GSH. Meanwhile, wogonin upregulated SOCS3 and downregulated TLR4 under HG conditions. Wogonin-induced SOCS3 overexpression directly decreased TLR4 levels and attenuated JAK/STAT signaling pathway-related inflammation and fibrosis, but SOCS3 knockdown significantly antagonized the protective effects of wogonin. However, TLR4 knockdown diminished SOCS3 knockdown-induced renal injuries. CONCLUSION: Wogonin attenuates renal inflammation and fibrosis by upregulating SOCS3 to inhibit TLR4 and JAK/STAT pathway.

Label-Free Multiple Reaction Monitoring-Mass Spectrometry for Quantifying Phosphopeptides from Extracellular Vesicles.

Increasing efforts have been made to develop proteins in circulating extracellular vesicles (EVs) as potential disease markers. It is in particular intriguing to measure post-translational modifications (PTMs) such as phosphorylation, preserved and stable in EVs. To facilitate the quantitative measurement of EV protein phosphorylation for potential clinical use, a label-free (LF) multiple reaction monitoring (MRM) strategy is introduced by utilizing a synthetic phosphopeptide set (phos-iRT) as the internal standards and a local normalization method. The quantitation method was investigated in terms of its linear dynamic range, sensitivity, accuracy, precision, and matrix effect, with a dynamic range spanning from 10 to 1000 ng/mL and an accuracy ranging from 82.4 to 116.8% for EV samples. Then, the LF-MRM-based local normalization method was utilized to evaluate and optimize our recently developed EVTOP method for the enrichment of phosphopeptides from EVs. Finally, we applied the optimized EV enrichment approach and the LF-MRM-based local normalization method to quantify phosphopeptides in urine EVs from patients with prostate cancer (PCa) and healthy individuals, showcasing the strategy's superiority in quantifying phosphopeptides without isotopic internal standards and validating that the method is generally applicable in MRM-based EV phosphopeptide quantification.

Targeted Phosphoproteomics of Human Saliva Extracellular Vesicles via Multiple Reaction Monitoring Cubed (MRM3).

Oral squamous cell carcinoma (OSCC) has become a global health problem due to its increasing incidence and high mortality rate. Early intervention through monitoring of the diagnostic biomarker levels during OSCC treatment is critical. Extracellular vesicles (EVs) are emerging surrogates in intercellular communication through transporting biomolecule cargo and have recently been identified as a potential source of biomarkers such as phosphoproteins for many diseases. Here, we developed a multiple reaction monitoring cubed (MRM3) method coupled with a novel sample preparation strategy, extracellular vesicles to phosphoproteins (EVTOP), to quantify phosphoproteins using a minimal amount of saliva (50 µL) samples from OSCC patients with high specificity and sensitivity. Our results established differential patterns in the phosphopeptide content of healthy, presurgery, and postsurgery OSCC patient groups. Notably, we discovered significantly increased salivary phosphorylated alpha-amylase (AMY) in the postsurgery group compared to the presurgery group. We hereby present the first targeted MS method with extremely high sensitivity for measuring endogenous phosphoproteins in human saliva EVs.

Tracing weak neuron fibers.

MOTIVATION: Precise reconstruction of neuronal arbors is important for circuitry mapping. Many auto-tracing algorithms have been developed toward full reconstruction. However, it is still challenging to trace the weak signals of neurite fibers that often correspond to axons. RESULTS: We proposed a method, named the NeuMiner, for tracing weak fibers by combining two strategies: an online sample mining strategy and a modified gamma transformation. NeuMiner improved the recall of weak signals (voxel values <20) by a large margin, from 5.1 to 27.8%. This is prominent for axons, which increased by 6.4 times, compared to 2.0 times for dendrites. Both strategies were shown to be beneficial for weak fiber recognition, and they reduced the average axonal spatial distances to gold standards by 46 and 13%, respectively. The improvement was observed on two prevalent automatic tracing algorithms and can be applied to any other tracers and image types. AVAILABILITY AND IMPLEMENTATION: Source codes of NeuMiner are freely available on GitHub (https://github.com/crazylyf/neuronet/tree/semantic_fnm). Image visualization, preprocessing and tracing are conducted on the Vaa3D platform, which is accessible at the Vaa3D GitHub repository (https://github.com/Vaa3D). All training and testing images are cropped from high-resolution fMOST mouse brains downloaded from the Brain Image Library (https://www.brainimagelibrary.org/), and the corresponding gold standards are available at https://doi.brainimagelibrary.org/doi/10.35077/g.25. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Tetratricopeptide repeat protein SlREC2 positively regulates cold tolerance in tomato.

Cold stress is a key environmental constraint that dramatically affects the growth, productivity, and quality of tomato (Solanum lycopersicum); however, the underlying molecular mechanisms of cold tolerance remain poorly understood. In this study, we identified REDUCED CHLOROPLAST COVERAGE 2 (SlREC2) encoding a tetratricopeptide repeat protein that positively regulates tomato cold tolerance. Disruption of SlREC2 largely reduced abscisic acid (ABA) levels, photoprotection, and the expression of C-REPEAT BINDING FACTOR (CBF)-pathway genes in tomato plants under cold stress. ABA deficiency in the notabilis (not) mutant, which carries a mutation in 9-CIS-EPOXYCAROTENOID DIOXYGENASE 1 (SlNCED1), strongly inhibited the cold tolerance of SlREC2-silenced plants and empty vector control plants and resulted in a similar phenotype. In addition, foliar application of ABA rescued the cold tolerance of SlREC2-silenced plants, which confirms that SlNCED1-mediated ABA accumulation is required for SlREC2-regulated cold tolerance. Strikingly, SlREC2 physically interacted with ß-RING CAROTENE HYDROXYLASE 1b (SlBCH1b), a key regulatory enzyme in the xanthophyll cycle. Disruption of SlBCH1b severely impaired photoprotection, ABA accumulation, and CBF-pathway gene expression in tomato plants under cold stress. Taken together, this study reveals that SlREC2 interacts with SlBCH1b to enhance cold tolerance in tomato via integration of SlNCED1-mediated ABA accumulation, photoprotection, and the CBF-pathway, thus providing further genetic knowledge for breeding cold-resistant tomato varieties.

Assessment of urine sample collection and processing variables for extracellular vesicle-based proteomics.

Extracellular vesicles (EVs) in urine are a promising source for developing non-invasive biomarkers. However, urine concentration and content are highly variable and dynamic, and actual urine collection and handling often is nonideal. Furthermore, patients such as those with prostate diseases have challenges in sample collection due to difficulties in holding urine at designated time points. Here, we simulated the actual situation of clinical sample collection to examine the stability of EVs in urine under different circumstances, including urine collection time and temporary storage temperature, as well as daily urine sampling under different diet conditions. EVs were isolated using functionalized EVtrap magnetic beads and characterized by nanoparticle tracking analysis (NTA), western blotting, electron microscopy, and mass spectrometry (MS). EVs in urine remained relatively stable during temporary storage for 6 hours at room temperature and for 12 hours at 4 °C, while significant fluctuations were observed in EV amounts from urine samples collected at different time points from the same individuals, especially under certain diets. Sample normalization with creatinine reduced the coefficient of variation (CV) values among EV samples from 17% to approximately 6% and facilitated downstream MS analyses. Finally, based on the results, we applied them to evaluate potential biomarker panels in prostate cancer by data-independent acquisition (DIA) MS, presenting the recommendation that can facilitate biomarker discovery with nonideal handling conditions.

The Assembly of Cerium(III)-Containing Silicotungstate with Lewis Acid-Base Sites Enables the Selective C3-Alkenylation of Oxindole.

A cerium(III)-containing silicotungstate, [H2N(CH3)2]10NaK[KCe(SiW11O39)2(H2O)]·18.5H2O (CeSiW), was successfully synthesized and characterized. Structure analysis reveals that CeSiW is composed of two {SiW11O39} units connected by one cerium(III) cation to form a typical 1:2 sandwich structure, which is further expanding into a 1D chain linked by K+ ions. The oxygen-enriched surfaces of {SiW11O39} units and open cerium sites provide abundant Lewis base and acid sites in CeSiW. As a result, CeSiW efficiently catalyzed the C3-alkenylation of oxindoles with aldehydes through the simultaneous activation of both reaction substrates on its crystal framework. Various 3-benzylidene-oxindoles are synthesized with excellent yields and high E-selectivity under solvent-free conditions.

A Meso Butterfly-Like Ce(III)-Containing Antimonotungstate with Catalytic Activity for Synthesizing Isoindolinones.

A unique meso Ce(III)-containing antimonotungstate, {Na(OAc)(H2O)2[Ce4(tar)(Htar)2(Sb2W21O72)2(H2O)7]}244- (Ce4tar3; H4tar = tartaric acid), consisting of two enantiomeric parts with a butterfly-like configuration, was successfully synthesized by a one-pot in situ method and characterized. The coordination of d- or l-tar ligands induced the formation of Dawson-like {Ce2Sb2W21} with right or left configurations, thereby determining the d/l configurations of {Na(OAc)(H2O)2[Ce4(tar)(Htar)2(Sb2W21O72)2(H2O)7]}22-. Carboxyl groups link these two enantiomeric parts with Ce(III) ions from each other around the symmetric center of the P1̅ space group. The three types of tar ligands exhibit distinct coordination modes, and all coordinate with at least one W(VI) atom using one carboxylate oxygen atom and one α-OH. Ce4tar3 represents the largest case among those meso-dl-tar-functionalized polyoxometalates. Furthermore, Ce4tar3 exhibits excellent catalytic activity for synthesizing isoindolinones via the three-component reaction of 2-acetylbenzoic acids, amines, and phosphine oxides.

An Unusual Crystalline Porous Framework Constructed from Multitypes of Cages with Proton Conduction Property and Heterogeneous Catalytic Activity for Pyrazole Synthesis.

An unusual crystalline porous framework constructed from four types of cages, including all-inorganic Keggin-type polyoxometalate (POM) cages [H3W12O40]5-, organic hexamethylenetetramine (Hmt) cages, nanosized silver-Hmt coordination cages, and giant POM-silver-Hmt cages, was hydrothermally synthesized and structurally characterized. The framework features a highly symmetrical structure with one-dimensional nanoscale channels and holds good thermal/solvent stability, which endow it with proton conduction properties and heterogeneous catalytic activity for pyrazole. This paper not only contributes to broadening the structural diversity of cage-based crystalline porous framework materials but also sheds new light on the design of new functional framework materials.