The CARD8 inflammasome dictates HIV/SIV pathogenesis and disease progression.

While CD4+ T cell depletion is key to disease progression in people living with HIV and SIV-infected macaques, the mechanisms underlying this depletion remain incompletely understood, with most cell death involving uninfected cells. In contrast, SIV infection of "natural" hosts such as sooty mangabeys does not cause CD4+ depletion and AIDS despite high-level viremia. Here, we report that the CARD8 inflammasome is activated immediately after HIV entry by the viral protease encapsulated in incoming virions. Sensing of HIV protease activity by CARD8 leads to rapid pyroptosis of quiescent cells without productive infection, while T cell activation abolishes CARD8 function and increases permissiveness to infection. In humanized mice reconstituted with CARD8-deficient cells, CD4+ depletion is delayed despite high viremia. Finally, we discovered loss-of-function mutations in CARD8 from "natural hosts," which may explain the peculiarly non-pathogenic nature of these infections. Our study suggests that CARD8 drives CD4+ T cell depletion during pathogenic HIV/SIV infections.

Chemical inhibition of DPP9 sensitizes the CARD8 inflammasome in HIV-1-infected cells.

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) induce pyroptosis of HIV-1-infected CD4+ T cells through induction of intracellular HIV-1 protease activity, which activates the CARD8 inflammasome. Because high concentrations of NNRTIs are required for efficient elimination of HIV-1-infected cells, it is important to elucidate ways to sensitize the CARD8 inflammasome to NNRTI-induced activation. We show that this sensitization can be achieved through chemical inhibition of the CARD8 negative regulator DPP9. The DPP9 inhibitor Val-boroPro (VbP) can kill HIV-1-infected cells without the presence of NNRTIs and act synergistically with NNRTIs to promote clearance of HIV-1-infected cells in vitro and in humanized mice. More importantly, VbP is able to enhance clearance of residual HIV-1 in CD4+ T cells isolated from people living with HIV (PLWH). We also show that VbP can partially overcome NNRTI resistance. This offers a promising strategy for enhancing NNRTI efficacy in the elimination of HIV-1 reservoirs in PLWH.

MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.

One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available prediction methods can only predict whether two drugs interact or not, whereas few methods can predict interaction events between two drugs. Accurately predicting interaction events of two drugs is more useful for researchers to study the mechanism of the interaction of two drugs. In the present study, we propose a novel method, MDF-SA-DDI, which predicts drug-drug interaction (DDI) events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. MDF-SA-DDI is mainly composed of two parts: multi-source drug fusion and multi-source feature fusion. First, we combine two drugs in four different ways and input the combined drug feature representation into four different drug fusion networks (Siamese network, convolutional neural network and two auto-encoders) to obtain the latent feature vectors of the drug pairs, in which the two auto-encoders have the same structure, and their main difference is the number of neurons in the input layer of the two auto-encoders. Then, we use transformer blocks that include self-attention mechanism to perform latent feature fusion. We conducted experiments on three different tasks with two datasets. On the small dataset, the area under the precision-recall-curve (AUPR) and F1 scores of our method on task 1 reached 0.9737 and 0.8878, respectively, which were better than the state-of-the-art method. On the large dataset, the AUPR and F1 scores of our method on task 1 reached 0.9773 and 0.9117, respectively. In task 2 and task 3 of two datasets, our method also achieved the same or better performance as the state-of-the-art method. More importantly, the case studies on five DDI events are conducted and achieved satisfactory performance. The source codes and data are available at https://github.com/ShenggengLin/MDF-SA-DDI.

Comprehensive serum N-glycan profiling identifies a biomarker panel for early diagnosis of non-small-cell lung cancer.

Aberrant serum N-glycan profiles have been observed in multiple cancers including non-small-cell lung cancer (NSCLC), yet the potential of N-glycans in the early diagnosis of NSCLC remains to be determined. In this study, serum N-glycan profiles of 275 NSCLC patients and 309 healthy controls were characterized by MALDI-TOF-MS. The levels of serum N-glycans and N-glycosylation patterns were compared between NSCLC and control groups. In addition, a panel of N-glycan biomarkers for NSCLC diagnosis was established and validated using machine learning algorithms. As a result, a total of 54 N-glycan structures were identified in human serum. Compared with healthy controls, 29 serum N-glycans were increased or decreased in NSCLC patients. N-glycan abundance in different histological types or clinical stages of NSCLC presented differentiated changes. Furthermore, an optimal biomarker panel of eight N-glycans was constructed based on logistic regression, with an AUC of 0.86 in the validation set. Notably, this model also showed a desirable capacity in distinguishing early-stage patients from healthy controls (AUC = 0.88). In conclusion, our work highlights the abnormal N-glycan profiles in NSCLC and provides supports potential application of N-glycan biomarker panel in clinical NSCLC detection.

Gut microbiome variations in Rhinopithecus roxellanae caused by changes in the environment.

BACKGROUND: The snub-nosed monkey (Rhinopithecus roxellanae) is an endangered animal species mainly distributed in China and needs to be protected. Gut microbiome is an important determinant of animal health and population survival as it affects the adaptation of the animals to different foods and environments under kinetic changes of intrinsic and extrinsic factors. Therefore, this study aimed to elucidate gut fecal microbiome profiles of snub-nosed monkeys affected by several extrinsic and intrinsic factors, including raising patterns (captive vs. wild), age, sex, and diarrheal status to provide a reference for making protection strategies. RESULTS: The 16S rRNA gene sequencing was firstly used to pre-check clustering of 38 fecal samples from the monkeys including 30 wild and 8 captive (5 healthy and 3 diarrheal) from three Regions of Shennongjia Nature Reserve, Hubei Province, China. Then the 24 samples with high-quality DNA from 18 wild and 6 captive (4 healthy and 2 diarrheal) monkeys were subjected to shotgun metagenomic sequencing to characterize bacterial gut microbial communities. We discovered that the raising pattern (captive and wild) rather than age and sex was the predominant factor attributed to gut microbiome structure and proportionality. Wild monkeys had significantly higher bacterial diversity and lower Bacteroidetes/Firmicutes ratios than captive animals. Moreover, the gut microbiomes in wild healthy monkeys were enriched for the genes involved in fatty acid production, while in captive animals, genes were enriched for vitamin biosynthesis and metabolism and amino acid biosynthesis from carbohydrate intermediates. Additionally, a total of 37 antibiotic resistant genes (ARG) types were detected. Unlike the microbiome diversity, the captive monkeys have a higher diversity of ARG than the wild animals. CONCLUSION: Taken together, we highlight the importance of self-reprogramed metabolism in the snub-nosed monkey gut microbiome to help captive and wild monkeys adapt to different intrinsic and extrinsic environmental change.

Photo-Enhanced Chemo-Transistor Platform for Ultrasensitive Assay of Small Molecules.

Compared with traditional assay techniques, field-effect transistors (FETs) have advantages such as fast response, high sensitivity, being label-free, and point-of-care detection, while lacking generality to detect a wide range of small molecules since most of them are electrically neutral with a weak doping effect. Here, we demonstrate a photo-enhanced chemo-transistor platform based on a synergistic photo-chemical gating effect in order to overcome the aforementioned limitation. Under light irradiation, accumulated photoelectrons generated from covalent organic frameworks offer a photo-gating modulation, amplifying the response to small molecule adsorption including methylglyoxal, p-nitroaniline, nitrobenzene, aniline, and glyoxal when measuring the photocurrent. We perform testing in buffer, artificial urine, sweat, saliva, and diabetic mouse serum. The limit of detection is down to 10-19 M methylglyoxal, about 5 orders of magnitude lower than existing assay technologies. This work develops a photo-enhanced FET platform to detect small molecules or other neutral species with enhanced sensitivity for applications in fields such as biochemical research, health monitoring, and disease diagnosis.

DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method.

Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug repositioning. To reduce the experimental cost, a large number of computational approaches have been proposed for this task. The machine learning-based models, especially binary classification models, have been developed to predict whether a drug-target pair interacts or not. However, there is still much room for improvement in the performance of current methods. Multi-label learning can overcome some difficulties caused by single-label learning in order to improve the predictive performance. The key challenge faced by multi-label learning is the exponential-sized output space, and considering label correlations can help to overcome this challenge. In this paper, we facilitate multi-label classification by introducing community detection methods for DTI prediction, named DTI-MLCD. Moreover, we updated the gold standard data set by adding 15,000 more positive DTI samples in comparison to the data set, which has widely been used by most of previously published DTI prediction methods since 2008. The proposed DTI-MLCD is applied to both data sets, demonstrating its superiority over other machine learning methods and several existing methods. The data sets and source code of this study are freely available at https://github.com/a96123155/DTI-MLCD.

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph.

Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.

NeuroPpred-Fuse: an interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods.

Neuropeptides acting as signaling molecules in the nervous system of various animals play crucial roles in a wide range of physiological functions and hormone regulation behaviors. Neuropeptides offer many opportunities for the discovery of new drugs and targets for the treatment of neurological diseases. In recent years, there have been several data-driven computational predictors of various types of bioactive peptides, but the relevant work about neuropeptides is little at present. In this work, we developed an interpretable stacking model, named NeuroPpred-Fuse, for the prediction of neuropeptides through fusing a variety of sequence-derived features and feature selection methods. Specifically, we used six types of sequence-derived features to encode the peptide sequences and then combined them. In the first layer, we ensembled three base classifiers and four feature selection algorithms, which select non-redundant important features complementarily. In the second layer, the output of the first layer was merged and fed into logistic regression (LR) classifier to train the model. Moreover, we analyzed the selected features and explained the feasibility of the selected features. Experimental results show that our model achieved 90.6% accuracy and 95.8% AUC on the independent test set, outperforming the state-of-the-art models. In addition, we exhibited the distribution of selected features by these tree models and compared the results on the training set to that on the test set. These results fully showed that our model has a certain generalization ability. Therefore, we expect that our model would provide important advances in the discovery of neuropeptides as new drugs for the treatment of neurological diseases.

Future foods: Alternative proteins, food architecture, sustainable packaging, and precision nutrition.

There are numerous challenges facing the modern food and agriculture industry that urgently need to be addressed, including feeding a growing global population, mitigating and adapting to climate change, decreasing pollution, waste, and biodiversity loss, and ensuring that people remain healthy. At the same time, foods should be safe, affordable, convenient, and delicious. The latest developments in science and technology are being deployed to address these issues. Some of the most important elements within this modern food design approach are encapsulated by the MATCHING model: Meat-reduced; Automation; Technology-driven; Consumer-centric; Healthy; Intelligent; Novel; and Globalization. In this review article, we focus on four key aspects that will be important for the creation of a new generation of healthier and more sustainable foods: emerging raw materials; structural design principles for creating innovative products; developments in eco-friendly packaging; and precision nutrition and customized production of foods. We also highlight some of the most important new developments in science and technology that are being used to create future foods, including food architecture, synthetic biology, nanoscience, and sensory perception.Supplemental data for this article is available online at https://doi.org/10.1080/10408398.2022.2033683.

Ammonia-induced oxidative stress triggered proinflammatory response and apoptosis in pig lungs.

Ammonia, a common toxic gas, is not only one of the main causes of haze, but also can enter respiratory tract and directly affect the health of humans and animals. Pig was used as an animal model for exploring the molecular mechanism and dose effect of ammonia toxicity to lung. In this study, the apoptosis of type II alveolar epithelial cells was observed in high ammonia exposure group using transmission electron microscopy. Gene and protein expression analysis using transcriptome sequencing and western blot showed that low ammonia exposure induced T-cell-involved proinflammatory response, but high ammonia exposure repressed the expression of DNA repair-related genes and affected ion transport. Moreover, high ammonia exposure significantly increased 8-hydroxy-2-deoxyguanosine (8-OHdG) level, meaning DNA oxidative damage occurred. In addition, both low and high ammonia exposure caused oxidative stress in pig lungs. Integrated analysis of transcriptome and metabolome revealed that the up-regulation of LDHB and ND2 took part in high ammonia exposure-affected pyruvate metabolism and oxidative phosphorylation progress, respectively. Inclusion, oxidative stress mediated ammonia-induced proinflammatory response and apoptosis of porcine lungs. These findings may provide new insights for understanding the ammonia toxicity to workers in livestock farms and chemical fertilizer plants.

Evaluation of HIV-1 latency reversal and antibody-dependent viral clearance by quantification of singly spliced HIV-1 vpu/env mRNA.

The latent reservoir of HIV-1 is a major barrier for viral eradication. Potent HIV-1 broadly neutralizing antibodies (bNabs) have been used to prevent and treat HIV-1 infections in animal models and clinical trials. Combination of bNabs and latency-reversing agents (LRAs) is considered a promising approach for HIV-1 eradication. PCR-based assays that can rapidly and specifically measure singly spliced HIV-1 vpu/env mRNA are needed to evaluate the induction of the viral envelope production at the transcription level and bNab-mediated reservoir clearance. Here we reported a PCR-based method to accurately quantify the production of intracellular HIV-1 vpu/env mRNA. With the vpu/env assay, we determined the LRA combinations that could effectively induce vpu/env mRNA production in CD4+ T cells from ART-treated individuals. None of the tested LRAs were effective alone. A comparison between the quantitative viral outgrowth assay (Q-VOA) and the vpu/env assay showed that vpu/env mRNA production was closely associated with the reactivation of replication-competent HIV-1, suggesting that vpu/env mRNA was mainly produced by intact viruses. Finally, antibody-mediated in vitro killing in HIV-1-infected humanized mice demonstrated that the vpu/env assay could be used to measure the reduction of infected cells in tissues and was more accurate than the commonly used gag-based PCR assay which measured unspliced viral genomic RNA. In conclusion, the vpu/env assay allows convenient and accurate assessment of HIV-1 latency reversal and bNab-mediated therapeutic strategies.ImportanceHIV-1 persists in individuals on antiretroviral therapy (ART) due to the long-lived cellular reservoirs that contain dormant viruses. Recent discoveries of HIV-1-specific broadly neutralizing antibodies (bNabs) targeting HIV-1 Env protein rekindled the interest in antibody-mediated elimination of latent HIV-1. Latency-reversing agents (LRAs) together with HIV-1 bNabs is a possible strategy to clear residual viral reservoirs, which makes the evaluation of HIV-1 Env expression upon LRA treatment critical. We developed a PCR-based assay to quantify the production of intracellular HIV-1 vpu/env mRNA. Using patient CD4+ T cells, we found that induction of HIV-1 vpu/env mRNA required a combination of different LRAs. Using in vitro, ex vivo and humanized mouse models, we showed that the vpu/env assay could be used to measure antibody efficacy in clearing HIV-1 infection. These results suggest that the vpu/env assay can accurately evaluate HIV-1 reactivation and bNab-based therapeutic interventions.

Fortification of edible films with bioactive agents: a review of their formation, properties, and application in food preservation.

Biodegradable films constructed from food ingredients are being developed for food coating and packaging applications to create more sustainable and environmentally friendly alternatives to plastics and other synthetic film-forming materials. In particular, there is a focus on the creation of active packaging materials from natural ingredients, especially plant-based ones. The film matrix is typically constructed from film-forming food components, such as proteins, polysaccharides and lipids. These matrices can be fortified with active ingredients, such as antioxidants and antimicrobials, so as to enhance their functional properties. Edible active films must be carefully designed to have the required optical, mechanical, barrier, and preservative properties needed for commercial applications. This review focuses on the fabrication, properties, and functional performance of edible films constructed from natural active ingredients. It provides an overview of the type of active ingredients that can be used, how they interact with the film matrix, how they migrate through the films, and how they are released. It also discusses the potential application of these active films for food preservation. Finally, future trends are highlighted and areas where further research are required are discussed.

Chronic exposure to ammonia induces oxidative stress and enhanced glycolysis in lung of piglets.

Ammonia is one of the major environmental pollutants in the pig industry that seriously affects the airway health of pigs. In this study, we aimed to investigate the metabolic profiling changes of piglets' lung tissue after the exposure of 0 ppm (CG), 20 ppm (LG) and 50 ppm (HG) ammonia for 30 days. Compared with the control group, the obvious lung lesions were observed in HG, including interstitial thickening, inflammatory cell infiltration and focal hemorrhage. The significantly increased content of malondialdehyde in HG, combined with the significantly decreased mRNA expression of antioxidase and inflammatory-regulators in exposure groups, implied that ammonia exposure induced oxidative stress and diminished the anti-inflammatory response in lung tissues. Metabolomic analyses of lung tissues revealed 15 significantly altered metabolites among the three groups including multiple amino acids, carbohydrates and lipids. The accumulation of succinic acid, linoleic acid and phosphorylethanolamine and consumption of glucose, quinolinic acid and aspartic acid in ammonia exposure groups, indicated that energy supply from glucose aerobic oxidation was suppressed and the glycolysis and lipolysis were activated in lung tissues induced by chronic ammonia exposure.

Trait correlated expression combined with eQTL and ASE analyses identified novel candidate genes affecting intramuscular fat.

BACKGROUND: Intramuscular fat (IMF) content is a determining factor for meat taste. The Luchuan pig is a fat-type local breed in southern China that is famous for its desirable meat quality due to high IMF, however, the crossbred offspring of Luchuan sows and Duroc boars displayed within-population variation on meat quality, and the reason remains unknown. RESULTS: In the present study, we identified 212 IMF-correlated genes (FDR ≤ 0.01) using correlation analysis between gene expression level and the value of IMF content. The IMF-correlated genes were significantly enriched in the processes of lipid metabolism and mitochondrial energy metabolism, as well as the AMPK/PPAR signaling pathway. From the IMF-correlated genes, we identified 99 genes associated with expression quantitative trait locus (eQTL) or allele-specific expression (ASE) signals, including 21 genes identified by both cis-eQTL and ASE analyses and 12 genes identified by trans-eQTL analysis. Genome-wide association study (GWAS) of IMF identified a significant QTL on SSC14 (p-value = 2.51E-7), and the nearest IMF-correlated gene SFXN4 (r = 0.28, FDR = 4.00E-4) was proposed as the candidate gene. Furthermore, we highlighted another three novel IMF candidate genes, namely AGT, EMG1, and PCTP, by integrated analysis of GWAS, eQTL, and IMF-gene correlation analysis. CONCLUSIONS: The AMPK/PPAR signaling pathway together with the processes of lipid and mitochondrial energy metabolism plays a vital role in regulating porcine IMF content. Trait correlated expression combined with eQTL and ASE analysis highlighted a priority list of genes, which compensated for the shortcoming of GWAS, thereby accelerating the mining of causal genes of IMF.

Bringing Structural Implications and Deep Learning-Based Drug Identification for KRAS Mutants.

Colorectal cancer is considered one of the leading causes of death that is linked with the Kirsten Rat Sarcoma (KRAS) harboring codons 13 and 61 mutations. The objective for this study is to search for clinically important codon 61 mutations and analyze how they affect the protein structural dynamics. Additionally, a deep-learning approach is used to carry out a similarity search for potential compounds that might have a comparatively better affinity. Public databases like The Cancer Genome Atlas and Genomic Data Commons were accessed for obtaining the data regarding mutations that are associated with colon cancer. Multiple analysis such as genomic alteration landscape, survival analysis, and systems biology-based kinetic simulations were carried out to predict dynamic changes for the selected mutations. Additionally, a molecular dynamics simulation of 100 ns for all the seven shortlisted codon 61 mutations have been conducted, which revealed noticeable deviations. Finally, the deep learning-based predicted compounds were docked with the KRAS 3D conformer, showing better affinity and good docking scores as compared to the already existing drugs. Taking together the outcomes of systems biology and molecular dynamics, it is observed that the reported mutations in the SII region are highly detrimental as they have an immense impact on the protein sensitive sites' native conformation and overall stability. The drugs reported in this study show increased performance and are encouraged to be used for further evaluation regarding the situation that ascends as a result of KRAS mutations.

An empirical study on waste generation rates at different stages of construction projects in China.

Estimation of construction waste generation is critical to construction waste management decisions. However, current construction waste estimation methods have various limitations (e.g. small samples). To address those limitations, this research conducts an empirical study to evaluate the waste generation rate of different types of waste at different construction stages. In this study, construction waste from 148 new-built residential construction sites in China were sorted and weighted on site and their waste generation rates were estimated separately. The results indicated that the amount of inorganic nonmetallic waste with a generation rate of 16.59 kg m-2 was the highest among the five types of waste (i.e. inorganic nonmetallic waste, organic waste, metallic waste, composite waste, hazardous waste), while the waste generation rate for the underground construction stage, which was 27.57 kg m-2, was the highest among the three stages (i.e. underground stage, superstructure stage, finishing stage). Compared with previous data, the new waste generation rate proposed in this research can better estimate the actual waste generation situation in construction sites, which provides reliable information for proper decision-making. Furthermore, based on the result of the empirical study, some recommendations for construction waste reduction are proposed.

Comparison Between the Gut Microbiota in Different Gastrointestinal Segments of Large-Tailed Han and Small-Tailed Han Sheep Breeds with High-Throughput Sequencing.

Commensal microorganisms are essential to the normal development and function of many aspects of animal biology. However, the dynamic shift patterns of the microbiota of different gut segments in sheep and the correlation between fat type large-tailed phenotype and microbiota remain poorly unknown. This study therefore sought to assess the composition and distribution of the intestinal microbiome, and compared the difference of gut microbiota from different gastrointestinal segments within breeds and same intestinal sections between breeds. For these analyses, 16S rRNA V4 regions from 4 gut sections prepared from each of six individuals (3 from each breed) were sequenced to detect the microbiome composition in these samples. These analyses revealed the presence of 51,173 operational taxonomic units distributed across 24 phyla and 420 genera in these samples, with Firmicutes and Bacteroidetes being the most prevalent phyla of microbes present in these samples. Moreover, the bacterial composition showed distinct microbial communities in different gastrointestinal segments within breed, but showed similar and relative fixed bacterial abundance in the same intestinal segments from individuals of different breeds. We also found that only a few bacterial species (Lachnospiraceae, Akkermansia) were needed to distinguish between Small-tailed Han sheep (STH) and Large-tailed Han sheep (LTH) and their metabolic process maybe influence the fat type large-tailed phenotype formation in sheep. The functional profile analysis revealed that the environment information processing, genetic information processing, and metabolic pathways were enriched in all samples. The main functional roles of the gut microbiota were amino acid metabolism, replication and repair, carbohydrate metabolism, and membrane transport. Finally, our findings suggested that distinguished gut species between STH and LTH have relative fixed and the potential correlation is existing between the intestinal microorganisms and the large-tailed phenotype trait formation of sheep, which may offer clues for further investigation to detect the roles of intestinal microbiota in the metabolism and fat deposition in the tail of sheep.

Insights into Charge Transfer at an Atomically Precise Nanocluster/Semiconductor Interface.

The deposition of an atomically precise nanocluster, for example, Ag44 (SR)30 , onto a large-band-gap semiconductor such as TiO2 allows a clear interface to be obtained to study charge transfer at the interface. Changing the light source from visible light to simulated sunlight led to a three orders of magnitude enhancement in the photocatalytic H2 generation, with the H2 production rate reaching 7.4 mmol h-1 gcatalyst -1 . This is five times higher than that of TiO2 modified with Ag nanoparticles and even comparable to that of TiO2 modified with Pt nanoparticles under similar conditions. Energy band alignment and transient absorption spectroscopy reveal that the role of the metal clusters is different from that of both organometallic complexes and plasmonic nanoparticles: A type II heterojunction charge-transfer route is achieved under UV/Vis irradiation, with the cluster serving as a small-band-gap semiconductor. This results in the clusters acting as co-catalysts rather than merely photosensitizers.

Correction to: CCR5 editing by Staphylococcus aureus Cas9 in human primary CD4+ T cells and hematopoietic stem/progenitor cells promotes HIV-1 resistance and CD4+ T cell enrichment in humanized mice.

Following publication of their article [1], the authors realized that they inadvertently omitted the contribution of Dr. Li Wu (Ohio State University) who commented on the manuscript at the early stage of the manuscript preparation and provided one plasmid related to this work.