Opioid-induced fragile-like regulatory T cells contribute to withdrawal.

Dysregulation of the immune system is a cardinal feature of opioid addiction. Here, we characterize the landscape of peripheral immune cells from patients with opioid use disorder and from healthy controls. Opioid-associated blood exhibited an abnormal distribution of immune cells characterized by a significant expansion of fragile-like regulatory T cells (Tregs), which was positively correlated with the withdrawal score. Analogously, opioid-treated mice also showed enhanced Treg-derived interferon-γ (IFN-γ) expression. IFN-γ signaling reshaped synaptic morphology in nucleus accumbens (NAc) neurons, modulating subsequent withdrawal symptoms. We demonstrate that opioids increase the expression of neuron-derived C-C motif chemokine ligand 2 (Ccl2) and disrupted blood-brain barrier (BBB) integrity through the downregulation of astrocyte-derived fatty-acid-binding protein 7 (Fabp7), which both triggered peripheral Treg infiltration into NAc. Our study demonstrates that opioids drive the expansion of fragile-like Tregs and favor peripheral Treg diapedesis across the BBB, which leads to IFN-γ-mediated synaptic instability and subsequent withdrawal symptoms.

C2H2-zinc-finger transcription factors bind RNA and function in diverse post-transcriptional regulatory processes.

Cys2-His2 zinc-finger proteins (C2H2-ZNFs) constitute the largest class of DNA-binding transcription factors (TFs) yet remain largely uncharacterized. Although certain family members, e.g., GTF3A, have been shown to bind both DNA and RNA, the extent to which C2H2-ZNFs interact with-and regulate-RNA-associated processes is not known. Using UV crosslinking and immunoprecipitation (CLIP), we observe that 148 of 150 analyzed C2H2-ZNFs bind directly to RNA in human cells. By integrating CLIP sequencing (CLIP-seq) RNA-binding maps for 50 of these C2H2-ZNFs with data from chromatin immunoprecipitation sequencing (ChIP-seq), protein-protein interaction assays, and transcriptome profiling experiments, we observe that the RNA-binding profiles of C2H2-ZNFs are generally distinct from their DNA-binding preferences and that they regulate a variety of post-transcriptional processes, including pre-mRNA splicing, cleavage and polyadenylation, and m6A modification of mRNA. Our results thus define a substantially expanded repertoire of C2H2-ZNFs that bind RNA and provide an important resource for elucidating post-transcriptional regulatory programs.

Transcriptome variation in human tissues revealed by long-read sequencing.

Regulation of transcript structure generates transcript diversity and plays an important role in human disease1-7. The advent of long-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure8-16. In this Article, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from Genotype-Tissue Expression (GTEx) tissues and cell lines, complementing the GTEx resource. We identified just over 70,000 novel transcripts for annotated genes, and validated the protein expression of 10% of novel transcripts. We developed a new computational package, LORALS, to analyse the genetic effects of rare and common variants on the transcriptome by allele-specific analysis of long reads. We characterized allele-specific expression and transcript structure events, providing new insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data. We were able to perturb the transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we used this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns.

Personal omics profiling reveals dynamic molecular and medical phenotypes.

Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.

A computational framework for the inference of protein complex remodeling from whole-proteome measurements.

Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets.

Computational Mechanisms Underlying Motivation to Earn Symbolic Reinforcers.

Reinforcement learning is a theoretical framework that describes how agents learn to select options that maximize rewards and minimize punishments over time. We often make choices, however, to obtain symbolic reinforcers (e.g., money, points) that are later exchanged for primary reinforcers (e.g., food, drink). Although symbolic reinforcers are ubiquitous in our daily lives, widely used in laboratory tasks because they can be motivating, mechanisms by which they become motivating are less understood. In the present study, we examined how monkeys learn to make choices that maximize fluid rewards through reinforcement with tokens. The question addressed here is how the value of a state, which is a function of multiple task features (e.g., the current number of accumulated tokens, choice options, task epoch, trials since the last delivery of primary reinforcer, etc.), drives value and affects motivation. We constructed a Markov decision process model that computes the value of task states given task features to then correlate with the motivational state of the animal. Fixation times, choice reaction times, and abort frequency were all significantly related to values of task states during the tokens task (n = 5 monkeys, three males and two females). Furthermore, the model makes predictions for how neural responses could change on a moment-by-moment basis relative to changes in the state value. Together, this task and model allow us to capture learning and behavior related to symbolic reinforcement.

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study.

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

Mechanistic insights into the C-type lectin receptor CLEC12A-mediated immune recognition of monosodium urate crystal.

CLEC12A, a member of the C-type lectin receptor family involved in immune homeostasis, recognizes MSU crystals released from dying cells. However, the molecular mechanism underlying the CLEC12A-mediated recognition of MSU crystals remains unclear. Herein, we reported the crystal structure of the human CLEC12A-C-type lectin-like domain (CTLD) and identified a unique "basic patch" site on CLEC12A-CTLD that is necessary for the binding of MSU crystals. Meanwhile, we determined the interaction strength between CLEC12A-CTLD and MSU crystals using single-molecule force spectroscopy. Furthermore, we found that CLEC12A clusters at the cell membrane and seems to serve as an internalizing receptor of MSU crystals. Altogether, these findings provide mechanistic insights for understanding the molecular mechanisms underlying the interplay between CLEC12A and MSU crystals.

Integrated bulk and single-cell transcriptomes reveal pyroptotic signature in prognosis and therapeutic options of hepatocellular carcinoma by combining deep learning.

Although some pyroptosis-related (PR) prognostic models for cancers have been reported, pyroptosis-based features have not been fully discovered at the single-cell level in hepatocellular carcinoma (HCC). In this study, by deeply integrating single-cell and bulk transcriptome data, we systematically investigated significance of the shared pyroptotic signature at both single-cell and bulk levels in HCC prognosis. Based on the pyroptotic signature, a robust PR risk system was constructed to quantify the prognostic risk of individual patient. To further verify capacity of the pyroptotic signature on predicting patients' prognosis, an attention mechanism-based deep neural network classification model was constructed. The mechanisms of prognostic difference in the patients with distinct PR risk were dissected on tumor stemness, cancer pathways, transcriptional regulation, immune infiltration and cell communications. A nomogram model combining PR risk with clinicopathologic data was constructed to evaluate the prognosis of individual patients in clinic. The PR risk could also evaluate therapeutic response to neoadjuvant therapies in HCC patients. In conclusion, the constructed PR risk system enables a comprehensive assessment of tumor microenvironment characteristics, accurate prognosis prediction and rational therapeutic options in HCC.

DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax.

Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST's capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.

A Prism Vote method for individualized risk prediction of traits in genotype data of Multi-population.

Multi-population cohorts offer unprecedented opportunities for profiling disease risk in large samples, however, heterogeneous risk effects underlying complex traits across populations make integrative prediction challenging. In this study, we propose a novel Bayesian probability framework, the Prism Vote (PV), to construct risk predictions in heterogeneous genetic data. The PV views the trait of an individual as a composite risk from subpopulations, in which stratum-specific predictors can be formed in data of more homogeneous genetic structure. Since each individual is described by a composition of subpopulation memberships, the framework enables individualized risk characterization. Simulations demonstrated that the PV framework applied with alternative prediction methods significantly improved prediction accuracy in mixed and admixed populations. The advantage of PV enlarges as genetic heterogeneity and sample size increase. In two real genome-wide association data consists of multiple populations, we showed that the framework considerably enhanced prediction accuracy of the linear mixed model in five-group cross validations. The proposed method offers a new aspect to analyze individual's disease risk and improve accuracy for predicting complex traits in genotype data.

CSP7 Protects Alveolar Epithelial Cells by Targeting p53-Fibrinolytic Pathways During Lung Injuries.

Impaired alveolar epithelial regeneration in patients with idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) is attributed to telomere dysfunction in type II alveolar epithelial cells (A2Cs). Genetic susceptibility, aging, and toxicant exposures, including tobacco smoke (TS), contribute to telomere dysfunction in A2Cs. Here we investigated whether improvement of telomere function plays a role in CSP7-mediated protection of A2Cs against ongoing senescence and apoptosis during bleomycin (BLM)-induced pulmonary fibrosis (PF) as well as alveolar injury caused by chronic TS exposure. We found a significant telomere shortening in A2Cs isolated from IPF and COPD lungs in line with other studies. These cells showed increased p53 in addition to its post-translational modification with induction of activated caspase-3 and ß-galactosidase, suggesting a p53-mediated loss of A2C renewal. Further, we found increased expression of SIAH-1, a p53-inducible E3 ubiquitin ligase known to down-regulate telomere repeats binding factor 2 (TRF2). Consistent with the loss of TRF2 and upregulation of TRF1, telomerase reverse transcriptase (TERT) was downregulated in A2Cs. A2Cs from fibrotic lungs of mice either repeatedly instilled with BLM or isolated from chronic TS exposure-induced lung injury model showed reduced telomere length along with induction of p53, PAI-1, SIAH1 and TRF1 as well as loss of TRF2 and TERT, which were reversed in wild-type mice after treatment with CSP7. Interestingly, PAI-1-/- mice, or those lacking microRNA-34a expression in A2Cs, resisted telomere dysfunction, while uPA-/- mice failed to respond to CSP7 treatment, suggesting p53-microRNA-34a feed-forward induction and p53-uPA pathway contributes to telomere dysfunction.

Genome-wide analysis of the PYL-PP2C-SnRK2s family in the ABA signaling pathway of pitaya reveals its expression profiles under canker disease stress.

BACKGROUND: Abscisic acid (ABA) plays a crucial role in seed dormancy, germination, and growth, as well as in regulating plant responses to environmental stresses during plant growth and development. However, detailed information about the PYL-PP2C-SnRK2s family, a central component of the ABA signaling pathway, is not known in pitaya. RESULTS: In this study, we identified 19 pyrabactin resistance-likes (PYLs), 70 type 2 C protein phosphatases (PP2Cs), and 14 SNF1-related protein kinase 2s (SnRK2s) from pitaya. In pitaya, tandem duplication was the primary mechanism for amplifying the PYL-PP2C-SnRK2s family. Co-linearity analysis revealed more homologous PYL-PP2C-SnRK2s gene pairs located in collinear blocks between pitaya and Beta vulgaris L. than that between pitaya and Arabidopsis. Transcriptome analysis showed that the PYL-PP2C-SnRK2s gene family plays a role in pitaya's response to infection by N. dimidiatum. By spraying ABA on pitaya and subsequently inoculating it with N. dimidiatum, we conducted qRT-PCR experiments to observe the response of the PYL-PP2C-SnRK2s gene family and disease resistance-related genes to ABA. These treatments significantly enhanced pitaya's resistance to pitaya canker. Further protein interaction network analysis helped us identify five key PYLs genes that were upregulated during the interaction between pitaya and N. dimidiatum, and their expression patterns were verified by qRT-PCR. Subcellular localization analysis revealed that the PYL (Hp1879) gene is primarily distributed in the nucleus. CONCLUSION: This study enhances our understanding of the response of PYL-PP2C-SnRK2s to ABA and also offers a new perspective on pitaya disease resistance.

The risk of treatment-related toxicities with PD-1/PD-L1 inhibitors in patients with lung cancer.

The risk of treatment-related toxicities with programmed cell death 1 and its ligand (PD-1/PD-L1) inhibitors in patients with lung cancer is unclear and inconclusive. PubMed, EMBASE, and the Cochrane Library databases were systematically searched without language restrictions from inception to May 31, 2024 to identify Phase 3 randomized controlled trials of lung cancer comparing PD-1/PD-L1 inhibitors versus placebo/best supportive care (alone or in combination with nontargeted chemotherapy) that had available data regarding treatment-related adverse events (TRAEs) or incidence and sample size. Random-effect models were employed to study the pooled relative risk (RR) and 95% confidence intervals (CIs). Finally, 36 trials, involving 19,693 participants, fulfilled the inclusion criteria. PD-1/PD-L1 inhibitors significantly augmented the likelihood of developing all-grade (RR, 1.03; 95% CI, 1.01-1.04, p < .01) and grade ≥3 TRAEs (RR, 1.16; 95% CI, 1.10 to 1.23, p < .01). PD-1/PD-L1 inhibitors substantially augmented the odds of developing treatment-related serious adverse events (SAEs) (RR, 1.48; 95% CI, 1.27-1.71, p < .01) and fatal adverse events (FAEs) (RR, 1.42; 95% CI, 1.11-1.82, p < .01). Subgroup analyses indicated that the RR of SAEs and FAEs were generally consistent, regardless of treatment type, tumor type, treatment setting, PD-1/PD-L1 inhibitors type and study design. The most common causes of FAEs were respiratory failure/insufficiency (33.3%), cardiac events (16.1%), and hematological disorders (10.1%). We demonstrated that PD-1/PD-L1 inhibitors were significantly correlated with higher possibility of developing treatment-related toxicities, especially SAEs and FAEs, compared with placebo/best supportive care controls.

Genome-wide analysis of common and rare variants via multiple knockoffs at biobank scale, with an application to Alzheimer disease genetics.

Knockoff-based methods have become increasingly popular due to their enhanced power for locus discovery and their ability to prioritize putative causal variants in a genome-wide analysis. However, because of the substantial computational cost for generating knockoffs, existing knockoff approaches cannot analyze millions of rare genetic variants in biobank-scale whole-genome sequencing and whole-genome imputed datasets. We propose a scalable knockoff-based method for the analysis of common and rare variants across the genome, KnockoffScreen-AL, that is applicable to biobank-scale studies with hundreds of thousands of samples and millions of genetic variants. The application of KnockoffScreen-AL to the analysis of Alzheimer disease (AD) in 388,051 WG-imputed samples from the UK Biobank resulted in 31 significant loci, including 14 loci that are missed by conventional association tests on these data. We perform replication studies in an independent meta-analysis of clinically diagnosed AD with 94,437 samples, and additionally leverage single-cell RNA-sequencing data with 143,793 single-nucleus transcriptomes from 17 control subjects and AD-affected individuals, and proteomics data from 735 control subjects and affected indviduals with AD and related disorders to validate the genes at these significant loci. These multi-omics analyses show that 79.1% of the proximal genes at these loci and 76.2% of the genes at loci identified only by KnockoffScreen-AL exhibit at least suggestive signal (p < 0.05) in the scRNA-seq or proteomics analyses. We highlight a potentially causal gene in AD progression, EGFR, that shows significant differences in expression and protein levels between AD-affected individuals and healthy control subjects.

Host-pathogen interaction between pitaya and Neoscytalidium dimidiatum reveals the mechanisms of immune response associated with defense regulators and metabolic pathways.

BACKGROUND: Understanding how plants and pathogens regulate each other's gene expression during their interactions is key to revealing the mechanisms of disease resistance and controlling the development of pathogens. Despite extensive studies on the molecular and genetic basis of plant immunity against pathogens, the influence of pitaya immunity on N. dimidiatum metabolism to restrict pathogen growth is poorly understood, and how N. dimidiatum breaks through pitaya defenses. In this study, we used the RNA-seq method to assess the expression profiles of pitaya and N. dimidiatum at 4 time periods after interactions to capture the early effects of N. dimidiatum on pitaya processes. RESULTS: The study defined the establishment of an effective method for analyzing transcriptome interactions between pitaya and N. dimidiatum and to obtain global expression profiles. We identified gene expression clusters in both the host pitaya and the pathogen N. dimidiatum. The analysis showed that numerous differentially expressed genes (DEGs) involved in the recognition and defense of pitaya against N. dimidiatum, as well as N. dimidiatum's evasion of recognition and inhibition of pitaya. The major functional groups identified by GO and KEGG enrichment were responsible for plant and pathogen recognition, phytohormone signaling (such as salicylic acid, abscisic acid). Furthermore, the gene expression of 13 candidate genes involved in phytopathogen recognition, phytohormone receptors, and the plant resistance gene (PG), as well as 7 effector genes of N. dimidiatum, including glycoside hydrolases, pectinase, and putative genes, were validated by qPCR. By focusing on gene expression changes during interactions between pitaya and N. dimidiatum, we were able to observe the infection of N. dimidiatum and its effects on the expression of various defense components and host immune receptors. CONCLUSION: Our data show that various regulators of the immune response are modified during interactions between pitaya and N. dimidiatum. Furthermore, the activation and repression of these genes are temporally coordinated. These findings provide a framework for better understanding the pathogenicity of N. dimidiatum and its role as an opportunistic pathogen. This offers the potential for a more effective defense against N. dimidiatum.

Development of Phenyl-substituted Isoindolinone- and Benzimidazole-type Cereblon Ligands for Targeted Protein Degradation.

Thalidomide, pomalidomide and lenalidomide, collectively referred to as immunomodulatory imide drugs (IMiDs), are frequently employed in proteolysis-targeting chimeras (PROTACs) as cereblon (CRBN) E3 ligase-recruiting ligands. However, their molecular glue properties that co-opt the CRL4CRBN to degrade its non-natural substrates may lead to undesired off-target effects for the IMiD-based PROTAC degraders. Herein, we reported a small library of potent and cell-permeable CRBN ligands, which exert high selectivity over the well-known CRBN neo-substrates of IMiDs by structure-based design. They were further utilized to construct bromodomain-containing protein 4 (BRD4) degraders, which successfully depleted BRD4 in the tested cells. Overall, we reported a series of functionalized CRBN recruiters that circumvent the promiscuity from traditional IMiDs, and this study is informative to the development of selective CRBN-recruiting PROTACs for many other therapeutic targets.

Self-Assembly via Condensation of Imine or Its N-Substituted Derivatives.

ConspectusCompared to traditionally used irreversible chemical reactions, dynamic covalent chemistry (DCC) including imine formation represents a more advanced technique in the preparation of molecules with complex structures and topologies, whose syntheses require the formation of many bonds. By allowing the occurrence of error checking and self-correcting, it is likely that the target molecules with high enough thermodynamic stability could be self-assembled in high or even quantitative yield. Two questions are raised herein. First, it becomes a central problem in self-assembly that how to endow a target product with high enough thermodynamic stability so that it can be produced as the major or the only product within the self-assembly library. Second, the reversible nature of dynamic bonds jeopardizes the intrinsic stability of the products. More specifically, the imine bond which represents the mostly used dynamic covalent bond, is apt to undergo hydrolysis in the presence of water. Developing new approaches to make imine more robust and compatible with water is thus of importance. In this account, we summarized the progress made in our group in the field of self-assembly based on C═N bond formation. In organic solvent where an imine bond is relatively robust, we focus on studying how to enhance the thermodynamic stability of a target molecule by introducing intramolecular forces. These noncovalent interactions either release enthalpy to favor the formation of the target molecule or preorganize the building blocks into specific conformations that mimic the product, so that the entropy loss of the formation of the latter is thus suppressed. In water, which often leads to imine hydrolysis, we developed two strategies to enhance the water-compatibility. By taking advantage of multivalency, namely, multiple bonds are often more robust than a single bond, self-assembly via condensation of imine was performed successfully in water, a solvent that is considered as forbidden zone of imine. Another approach is to replace typical imine with its more robust and water compatible derivatives, namely, either hydrazone or oxime, whose C═N bonds are generally less electrophilic compared to typical imine. With the water-compatible dynamic bonds in hand, a variety topological nontrivial molecules such as catenanes and knots was self-assembled successfully in aqueous media, driven by hydrophobic effect. When the self-assembled molecules in the form of rings and cages were designed for supramolecular purposes, water-compatibility endows a merit that allows the hosts to take advantage of hydrophobic effect to drive host-guest recognition, enabling various tasks to be accomplished, such as separation of guest isomers with similar physical properties, recognition of highly hydrated anions, as well as stabilization of guest dimers.

Li Decorated Penta-BCN as a Competitive Reversible Hydrogen Storage Media: A First-Principles Study.

Efficient storage media are crucial for practical applications of hydrogen, which is the most promising clean energy resource. In addition to possessing a highly reversible gravimetric capacity, the stability and superlight mass of potential storage media should not be underestimated. In this study, we exploit the light mass and unique puckered structure of penta-BCNs to design Li-decorated penta-BCNs for hydrogen storage via a series of first-principles calculations. Our results reveal that Li atoms can form stable chemical complexes with the surface of penta-BCNs with an average binding energy of -2.21 eV without causing deformation. Each Li@penta-BCN unit can physically adsorb up to 27H2 molecules, and the highest hydrogen storage capacity can reach 7.44 wt %, with an average adsorption energy of -0.16 eV/H2, surpassing the target value of 5.5 wt % set by the U.S. Department of Energy. Further elaborate analysis of the electronic structure shows the polarization enhancement mechanism, which is caused by charge transfer from Li atoms to the penta-BCN surface. Our results indicate that Li-decorated penta-BCN could be a promising hydrogen storage material for further application and inspire the theoretical or experimental design of novel materials for clean energy.

Photothermal Synergistic Catalysis over Defective Zn3In2S6 for CO2 Fixation.

Carbon dioxide (CO2) cycloaddition not only produces highly valued cyclic carbonate but also utilizes CO2 as C1 resources with 100% atomic efficiency. However, traditional catalytic routes still suffer from inferior catalytic efficiency and harsh reaction conditions. Developing multienergy-field catalytic technology with expected efficiency offers great opportunity for satisfied yield under mild conditions. Herein, Zn3In2S6 with sulfur vacancies (Sv) was fabricated with the assistance of cetyltrimethylammonium bromide (CTAB), which is further employed for photothermally driven CO2 cycloaddition first. Photoluminescence spectroscopy and photoelectrochemical characterization demonstrated its superior separation kinetics of photoinduced carriers induced by defect engineering. The temperature-programmed desorption (TPD) technique indicated its excellent Lewis acidity-basicity characters. Due to the combination of above merits from photocatalysis and thermal catalysis, defective Zn3In2S6-Sv achieved a yield as high as 73.2% for cyclic carbonate at 80 °C under blue LED illumination within 2 h (apparent quantum yield of 0.468% under illumination of 380 nm monochromatic light at 36 mW·cm-2), which is 2.9, 2.0, and 6.9 times higher than that in dark conditions and those of pristine Zn3In2S6 and industrial representative tetrabutylammonium bromide (TBAB) thermal-catalysis process under the same conditions, respectively. The synergistic reaction path of photocatalysis and thermal catalysis was discriminated by theoretical calculation. This work provides new insights into the photothermal synergistic catalysis CO2 cycloaddition with defective ternary metal sulfides.