Circadian regulator BMAL1::CLOCK promotes cell proliferation in hepatocellular carcinoma by controlling apoptosis and cell cycle.

Hepatocellular carcinoma (HCC) remains a global health challenge whose incidence is growing worldwide. Previous evidence strongly supported the notion that the circadian clock controls physiological homeostasis of the liver and plays a key role in hepatocarcinogenesis. Despite the progress, cellular and molecular mechanisms underpinning this HCC-clock crosstalk remain unknown. Addressing this knowledge gap, we show here that although the human HCC cells Hep3B, HepG2, and Huh7 displayed variations in circadian rhythm profiles, all cells relied on the master circadian clock transcription factors, BMAL1 and CLOCK, for sustained cell growth. Down-regulating Bmal1 or Clock in the HCC cells induced apoptosis and arrested cell cycle at the G2/M phase. Mechanistically, we found that inhibiting Bmal1/Clock induced dysregulation of the cell cycle regulators Wee1 and p21 which cooperatively contribute to tumor cell death. Bmal1/Clock knockdown caused downregulation of Wee1 that led to apoptosis activation and upregulation of p21 which arrested the cell cycle at the G2/M phase. Collectively, our results suggest that the circadian clock regulators BMAL1 and CLOCK promote HCC cell proliferation by controlling Wee1 and p21 levels, thereby preventing apoptosis and cell cycle arrest. Our findings shed light on cellular impact of the clock proteins for maintaining HCC oncogenesis and provide proof-of-principle for developing cancer therapy based on modulation of the circadian clock.

CancerMIRNome: an interactive analysis and visualization database for miRNome profiles of human cancer.

MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising diagnostic and prognostic biomarkers for human cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive biomarkers for early cancer detection. Novel and user-friendly tools are desperately needed to facilitate data mining of the vast amount of miRNA expression data from The Cancer Genome Atlas (TCGA) and large-scale circulating miRNA profiling studies. To fill this void, we developed CancerMIRNome, a comprehensive database for the interactive analysis and visualization of miRNA expression profiles based on 10 554 samples from 33 TCGA projects and 28 633 samples from 40 public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and machine learning algorithms have been packaged in CancerMIRNome, allowing for the pan-cancer analysis of a miRNA of interest across multiple cancer types and the comprehensive analysis of miRNome profiles to identify dysregulated miRNAs and develop diagnostic or prognostic signatures. The data analysis and visualization modules will greatly facilitate the exploit of the valuable resources and promote translational application of miRNA biomarkers in cancer. The CancerMIRNome database is publicly available at http://bioinfo.jialab-ucr.org/CancerMIRNome.

Boosting predictabilities of agronomic traits in rice using bivariate genomic selection.

The multivariate genomic selection (GS) models have not been adequately studied and their potential remains unclear. In this study, we developed a highly efficient bivariate (2D) GS method and demonstrated its significant advantages over the univariate (1D) rival methods using a rice dataset, where four traditional traits (i.e. yield, 1000-grain weight, grain number and tiller number) as well as 1000 metabolomic traits were analyzed. The novelty of the method is the incorporation of the HAT methodology in the 2D BLUP GS model such that the computational efficiency has been dramatically increased by avoiding the conventional cross-validation. The results indicated that (1) the 2D BLUP-HAT GS analysis generally produces higher predictabilities for two traits than those achieved by the analysis of individual traits using 1D GS model, and (2) selected metabolites may be utilized as ancillary traits in the new 2D BLUP-HAT GS method to further boost the predictability of traditional traits, especially for agronomically important traits with low 1D predictabilities.

Extended application of genomic selection to screen multiomics data for prognostic signatures of prostate cancer.

Prognostic tests using expression profiles of several dozen genes help provide treatment choices for prostate cancer (PCa). However, these tests require improvement to meet the clinical need for resolving overtreatment, which continues to be a pervasive problem in PCa management. Genomic selection (GS) methodology, which utilizes whole-genome markers to predict agronomic traits, was adopted in this study for PCa prognosis. We leveraged The Cancer Genome Atlas (TCGA) database to evaluate the prediction performance of six GS methods and seven omics data combinations, which showed that the Best Linear Unbiased Prediction (BLUP) model outperformed the other methods regarding predictability and computational efficiency. Leveraging the BLUP-HAT method, an accelerated version of BLUP, we demonstrated that using expression data of a large number of disease-relevant genes and with an integration of other omics data (i.e. miRNAs) significantly increased outcome predictability when compared with panels consisting of a small number of genes. Finally, we developed a novel stepwise forward selection BLUP-HAT method to facilitate searching multiomics data for predictor variables with prognostic potential. The new method was applied to the TCGA data to derive mRNA and miRNA expression signatures for predicting relapse-free survival of PCa, which were validated in six independent cohorts. This is a transdisciplinary adoption of the highly efficient BLUP-HAT method and its derived algorithms to analyze multiomics data for PCa prognosis. The results demonstrated the efficacy and robustness of the new methodology in developing prognostic models in PCa, suggesting a potential utility in managing other types of cancer.

Synthesis of an Environmentally Friendly Modified Mulberry Branch-Derived Biochar Composite: High Degradation Efficiency of BPA and Mitigation of Toxicity in Silkworm Larvae.

In the present study, mulberry branch-derived biochar CuO (MBC/CuO) composite was successfully synthesized and used as a catalyst to activate persulfate (PS) for the degradation of bisphenol A (BPA). The MBC/CuO/PS system exhibited a high degradation efficiency (93%) of BPA, under the conditions of 0.1 g/L MBC/CuO, 1.0 mM PS, 10 mg/L BPA. Free radical quenching and electron spin-resonance spectroscopy (ESR) experiments confirmed that both free radicals •OH, SO4•- and O2•- and non-radicals 1O2 were involved in the MBC/CuO reaction system. Cl- and NOM displayed negligible influence on the degradation of BPA, while HCO3- promoted the removal of BPA. In addition, the toxicity tests of BPA, MBC/CuO and the degraded BPA solution were conducted by the 5th instar silkworm larvae. The toxicity of BPA was reduced after the treatment in the MBC/CuO/PS system, and no obvious toxicity of the synthesized MBC/CuO composite was found in the toxicity evaluation experiments. This work provides a new value-added utilization of mulberry branches as a cost-effective and environmentally friendly PS activator.

A systematic comparison of anti-angiogenesis efficacy and cardiotoxicity of receptor tyrosine kinase inhibitors in zebrafish model.

Pathological angiogenesis is fundamental to progression of cancerous tumors and blinding eye diseases. Anti-angiogenic receptor tyrosine kinase inhibitors (TKIs) are in broad use for the treatment of these diseases. With more and more TKIs available, it is a challenge to make an optimal choice. It remains unclear whether TKIs demonstrate similar anti-angiogenesis activities in different tissues. Many TKIs have shown varying degrees of toxic effects that should also be considered in clinical use. This study investigates the anti-angiogenic effects of 13 FDA-approved TKIs on the intersegmental vessels (ISVs), subintestinal vessels (SIVs) and retinal vasculature in zebrafish embryos. The results show that vascular endothelial growth factor receptor TKIs (VEGFR-TKIs) exhibit anti-angiogenic abilities similarly on ISVs and SIVs, and their efficacy is consistent with their IC50 values against VEGFR2. In addition, VEGFR-TKIs selectively induces the apoptosis of endothelial cells in immature vessels. Among all TKIs tested, axitinib demonstrates a strong inhibition on retinal neovascularization at a low dose that do not strongly affect ISVs and SIVs, supporting its potential application for retinal diseases. Zebrafish embryos demonstrate cardiotoxicity after VEGFR-TKIs treatment, and ponatinib and sorafenib show a narrow therapeutic window, suggesting that these two drugs may need to be dosed more carefully in patients. We propose that zebrafish is an ideal model for studying in vivo antiangiogenic efficacy and cardiotoxicity of TKIs.

The Role and Mechanism of Hyperoside against Depression-like Behavior in Mice via the NLRP1 Inflammasome.

BACKGROUND AND OBJECTIVES: Hypericum perforatum (HP) is widely used for depressive therapy. Nevertheless, the antidepressant effect and potential mechanism of hyperoside (Hyp), the main active component of HP, have not been determined. MATERIALS AND METHODS: We performed ultra-performance liquid chromatography-quadrupole-time-of-flight-tandem mass spectrometry (UPLC-Q-TOF-MS/MS) technology to analyze the components in HP. Using data mining and network pharmacology methods, combined with Cytoscape v3.7.1 and other software, the active components, drug-disease targets, and key pathways of HP in the treatment of depression were evaluated. Finally, the antidepressant effects of Hyp and the mechanism involved were verified in chronic-stress-induced mice. RESULTS: We identified 12 compounds from HP. Hyp, isoquercetin, and quercetin are the main active components of HP. The Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), the Analysis Platform, DrugBank, and other databases were analyzed using data mining, and the results show that the active components of HP and depression are linked to targets such as TNF-, IL-2, TLR4, and so on. A potential signaling pathway that was most relevant to the antidepressant effects of Hyp is the C-type lectin receptor signaling pathway. Furthermore, the antidepressant effects of Hyp were examined, and it is verified for the first time that Hyp significantly alleviated depressive-like behaviors in chronic-stress-induced mice, which may be mediated by inhibiting the NLRP1 inflammasome through the CXCL1/CXCR2/BDNF signaling pathway. CONCLUSION: Hyp is one of the main active components of HP, and Hyp has antidepressant effects through the NLRP1 inflammasome, which may be connected with the CXCL1/CXCR2/BDNF signaling pathway.

Inference of Chromosome-Length Haplotypes Using Genomic Data of Three or a Few More Single Gametes.

Compared with genomic data of individual markers, haplotype data provide higher resolution for DNA variants, advancing our knowledge in genetics and evolution. Although many computational and experimental phasing methods have been developed for analyzing diploid genomes, it remains challenging to reconstruct chromosome-scale haplotypes at low cost, which constrains the utility of this valuable genetic resource. Gamete cells, the natural packaging of haploid complements, are ideal materials for phasing entire chromosomes because the majority of the haplotypic allele combinations has been preserved. Therefore, compared with the current diploid-based phasing methods, using haploid genomic data of single gametes may substantially reduce the complexity in inferring the donor's chromosomal haplotypes. In this study, we developed the first easy-to-use R package, Hapi, for inferring chromosome-length haplotypes of individual diploid genomes with only a few gametes. Hapi outperformed other phasing methods when analyzing both simulated and real single gamete cell sequencing data sets. The results also suggested that chromosome-scale haplotypes may be inferred by using as few as three gametes, which has pushed the boundary to its possible limit. The single gamete cell sequencing technology allied with the cost-effective Hapi method will make large-scale haplotype-based genetic studies feasible and affordable, promoting the use of haplotype data in a wide range of research.

Analysis of trait heritability in functionally partitioned rice genomes.

Knowledge of the genetic architecture of importantly agronomical traits can speed up genetic improvement in cultivated rice (Oryza sativa L.). Many recent investigations have leveraged genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs), associated with agronomic traits in various rice populations. The reported trait-relevant SNPs appear to be arbitrarily distributed along the genome, including genic and nongenic regions. Whether the SNPs in different genomic regions play different roles in trait heritability and which region is more responsible for phenotypic variation remains opaque. We analyzed a natural rice population of 524 accessions with 3,616,597 SNPs to compare the genetic contributions of functionally distinct genomic regions for five agronomic traits, i.e., yield, heading date, plant height, grain length, and grain width. An analysis of heritability in the functionally partitioned rice genome showed that regulatory or intergenic regions account for the most trait heritability. A close look at the trait-associated SNPs (TASs) indicated that the majority of the TASs are located in nongenic regions, and the genetic effects of the TASs in nongenic regions are generally greater than those in genic regions. We further compared the predictabilities using the genetic variants from genic regions with those using nongenic regions. The results revealed that nongenic regions play a more important role than genic regions in trait heritability in rice, which is consistent with findings in humans and maize. This conclusion not only offers clues for basic research to disclose genetics behind these agronomic traits, but also provides a new perspective to facilitate genomic selection in rice.

GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, miRNA and mRNA data in GDC.

Motivation: The large-scale multidimensional omics data in the Genomic Data Commons (GDC) provides opportunities to investigate the crosstalk among different RNA species and their regulatory mechanisms in cancers. Easy-to-use bioinformatics pipelines are needed to facilitate such studies. Results: We have developed a user-friendly R/Bioconductor package, named GDCRNATools, for downloading, organizing and analyzing RNA data in GDC with an emphasis on deciphering the lncRNA-mRNA related competing endogenous RNAs regulatory network in cancers. Many widely used bioinformatics tools and databases are utilized in our package. Users can easily pack preferred downstream analysis pipelines or integrate their own pipelines into the workflow. Interactive shiny web apps built in GDCRNATools greatly improve visualization of results from the analysis. Availability and implementation: GDCRNATools is an R/Bioconductor package that is freely available at Bioconductor (http://bioconductor.org/packages/devel/bioc/html/GDCRNATools.html). Detailed instructions, manual and example code are also available in Github (https://github.com/Jialab-UCR/GDCRNATools).

Use of capillary electrophoresis to select a DNA aptamer that recognizes swine anaphylatoxin C5a.

Complement factor 5a is a potent proinflammatory mediator that contributes to the pathogenesis of numerous inflammatory diseases. Protein-based C5a inhibitors have proven to be clinically valuable. Aptamers, which are oligonucleic acid chains or polypeptides, can bind to target molecules and hence have the potential to be used for detection and blockade of targets. Here, we describe the discovery that the single-stranded DNA aptamer S1 can bind specifically to swine C5a, which can then be quickly selected for with capillary electrophoresis for high-throughput sequencing. Aptamer S1 bound specifically to swine C5a with a dissociation constant of 4 µM as measured by surface plasmon resonance (SPR). Moreover, aptamer S1 inhibited C5a-induced chemotaxis of neutrophils in vitro. Our study suggests that the S1 aptamer has great potential to be a key structure in the development of effective therapeutic agents against inflammatory diseases.

Identification of optimal prediction models using multi-omic data for selecting hybrid rice.

Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available for predicting breeding values for agronomically important traits. In this study, the best prediction strategies were determined for yield, 1000 grain weight, number of grains per panicle, and number of tillers per plant of hybrid rice (derived from recombinant inbred lines) by comprehensively evaluating all possible combinations of omic datasets with different prediction methods. It was demonstrated that, in rice, the predictions using a combination of genomic and metabolomic data generally produce better results than single-omics predictions or predictions based on other combined omic data. Best linear unbiased prediction (BLUP) appears to be the most efficient prediction method compared to the other commonly used approaches, including least absolute shrinkage and selection operator (LASSO), stochastic search variable selection (SSVS), support vector machines with radial basis function and epsilon regression (SVM-R(EPS)), support vector machines with radial basis function and nu regression (SVM-R(NU)), support vector machines with polynomial kernel and epsilon regression (SVM-P(EPS)), support vector machines with polynomial kernel and nu regression (SVM-P(NU)) and partial least squares regression (PLS). This study has provided guidelines for selection of hybrid rice in terms of which types of omic datasets and which method should be used to achieve higher trait predictability. The answer to these questions will benefit academic research and will also greatly reduce the operative cost for the industry which specializes in breeding and selection.

Simultaneous enantiomeric analysis of non-steroidal anti-inflammatory drugs in environment by chiral LC-MS/MS: A pilot study in Beijing, China.

A simple, sensitive and quick method for direct simultaneous chiral analysis of frequently used non-steroidal anti-inflammatory drugs (NSAIDs) (ibuprofen, naproxen and flurbiprofen) in river water by HPLC-MS/MS was established and validated. Chromatographic parameters including the mobile phase composition, pH values, temperature and flow rates were optimized to obtain both satisfactory sensitivity and enantiomeric resolution (Rs≥ 1.0), which suggested the composition and pH values of mobile phase played crucial influence on enantioseparations. The method demonstrated its superiority compared with previous studies regarding to the low MQLs (1.1-37.1 ng/L) and short runtime (< 20 min), enabling quantitative enantiomeric determination of trace level of emerging contaminants in water. The environmental monitoring of receiving water (34 sites along rivers) in Beijing revealed ibuprofen was the most abundant, with mean concentration of 114.9 ng/L and detection frequency of 91%, naproxen was also detectable at all sites from < MQL-43.2 ng/L, both presenting an excess of the S-(+)-enantiomer. Therefore to better understand the ecological risk posed from the trace organic contaminants on the aquatic organisms, chiral pollutants need analyzed at the enantiomeric levels. This is the first to profile the enantiospecific occurrence of NSAIDs in surface water in Beijing, China. It could provide useful information on environmental behaviors of chiral pollutants and facilitate more accurate environmental risk assessment.

Spatial Distribution and Congener Profiles of Polybrominated Diphenyl Ethers in Surface Sediment from Sanmen Bay and Xiamen Bay, Southeast China.

Coastal areas are influenced by anthropogenic input of a variety of organic pollutants, among which polybrominated diphenyl ethers (PBDEs) represent an important group. In the present study, we investigated the contamination status of PBDEs in surface sediment from two economically important Bays in Southeast China, Sanmen Bay (SMB; n = 29) and Xiamen Bay (XMB; n = 10). Concentrations of ∑PBDEs ranged from 2.2 to 78.5 ng/g dw (median 5.7 ng/g dw) in SMB and 7.9-276.0 ng/g dw (median 43.5 ng/g dw) in XMB, respectively. A nearshore-offshore decreasing trend was observed for both ∑PBDEs and BDE-209 concentrations, indicating strong urban influences. Although the current levels would not produce any significant impact on benthos and aquatic ecosystems of the studied regions, continuous monitoring is needed to understand the temporal trends of contamination in the important coastal waters and whether sediment-associated PBDEs constitute a potential source to aquatic ecosystems.

Polyhalogenated Carbazoles in Surface Sediment from Sanmen Bay, East China Sea: Spatial Distribution and Congener Profile.

Polyhalogenated carbazoles (PHCZs) have recently emerged as a group of halogenated pollutants with broad occurrences and bioaccumulation potential in aquatic systems. However, investigations on their occurrences in coastal waters remain very limited. In the present study we investigated PHCZs in surface sediment collected from 29 sites in Sanmen Bay, East China Sea. The results demonstrated a universal presence of PHCZs in sediment, with concentrations of ∑PHCZs (including all congeners) ranging from 7.7 to 17.5 ng/g dry weight (median: 11.3 ng/g dw). The PHCZ congener composition profile revealed a dominance of 3,6-dichlorocarbazole (36-CCZ) with comparable concentration with that of carbazole. Given that PHCZs are widely distributed in Sanmen Bay sediment and their concentrations rivaled other well-known persistent organic pollutants in the same area, this group of halogenated pollutants merits additional investigations of their potential risks to the studied aquatic system, as well as other important watersheds.

Metabolome-wide association studies for agronomic traits of rice.

Identification of trait-associated metabolites will advance the knowledge and understanding of the biosynthetic and catabolic pathways that are relevant to the complex traits of interest. In the past, the association between metabolites (treated as quantitative traits) and genetic variants (e.g., SNPs) has been extensively studied using metabolomic quantitative trait locus (mQTL) mapping. Nevertheless, the research on the association between metabolites with agronomic traits has been inadequate. In practice, the regular approaches for QTL mapping analysis may be adopted for metabolites-phenotypes association analysis due to the similarity in data structure of these two types of researches. In the study, we compared four regular QTL mapping approaches, i.e., simple linear regression (LR), linear mixed model (LMM), Bayesian analysis with spike-slab priors (Bayes B) and least absolute shrinkage and selection operator (LASSO), by testing their performances on the analysis of metabolome-phenotype associations. Simulation studies showed that LASSO had the higher power and lower false positive rate than the other three methods. We investigated the associations of 839 metobolites with five agronomic traits in a collection of 533 rice varieties. The results implied that a total of 25 metabolites were significantly associated with five agronomic traits. Literature search and bioinformatics analysis indicated that the identified 25 metabolites are significantly involved in some growth and development processes potentially related to agronomic traits. We also explored the predictability of agronomic traits based on the 839 metabolites through cross-validation, which showed that metabolomic prediction was efficient and its application in plant breeding has been justified.

Intuitive Label-Free SERS Detection of Bacteria Using Aptamer-Based in Situ Silver Nanoparticles Synthesis.

The characteristic of an ideal bacteria-detection method should have high sensitivity and specificity, be easy to operate, and not have a time-consuming culture process. In this study, we report a new bacteria-detection strategy that can recognize bacteria quickly and directly by surface-enhanced Raman scattering (SERS) with the formation of well-defined bacteria-aptamer@AgNPs. SERS signals generated by bacteria-aptamer@AgNPs exhibited a linear dependence on bacteria (R2 = 0.9671) concentration ranging from 101 to 107 cfu/mL. The detection limit is sensitive down to 1.5 cfu/mL. Meanwhile, the bacteria SERS signal was dramatically enhanced by its specifically recognized aptamer, and the bacteria could be identified directly and visually through the SERS spectrum. This strategy eliminates the puzzling data analysis of previous studies and offers significant advantages over existing approaches, getting a critical step toward the creation of SERS-based biochips for rapid in situ bacteria detection in mixture samples.

Direct chiral separations of the enantiomers of phenylpyrazole pesticides and the metabolites by HPLC.

The enantiomeric separation of the enantiomers of three phenylpyrazole pesticides (fipronil, flufiprole, ethiprole) and two fipronil metabolites (amide-fipronil and acid-fipronil) were investigated by high-performance liquid chromatography (HPLC) with a CHIRALPAK® IB chiral column. The mobile phase was n-hexane or petroleum ether with 2-propanol or ethanol as modifier at a flow rate of 1.0 mL/min. The influences of mobile phase composition and column temperature between 15 and 35°C on the separations were studied. All the analytes except ethiprole obtained complete enantiomeric separation after chromatographic condition optimization. Fipronil, flufiprole, amide-fipronil, and acid-fipronil obtained complete separation with the best resolution factors of 2.40, 3.40, 1.67, and 16.82, respectively, but ethiprole showed no enantioselectivity under the optimized conditions. In general, n-hexane with 2-propanol gave better separations in most cases. The results showed decreasing temperature and content of modifier in the mobile phase resulted in better separation and longer analysis time as well. The thermodynamic parameters calculated according to linear the Van't Hoff equation indicated the chiral separations in the study were enthalpy-driven. Fipronil and its two chiral hydrolyzed metabolites obtained baseline separation simultaneously under optimized conditions.

Enantioselective accumulation, metabolism and phytoremediation of lactofen by aquatic macrophyte Lemna minor.

Pesticides are frequently detected in water bodies due to the agricultural application, which may pose impacts on aquatic organisms. The enantioselective bioaccumulation and metabolism of the herbicide lactofen in aquatic floating macrophyte Lemna minor (L. minor) were studied and the potential L. minor phytoremediation was investigated. Ultra-high performance liquid chromatography - tandem mass spectrometry (UHPLC-MS-MS) analysis for lactofen and its two known metabolites in L. minor was performed. The initial concentrations of racemic lactofen, R-lactofen and S-lactofen were all 30µgL-1 in the growth solution. The distribution of lactofen and its metabolites in growth solution and L. minor was determined throughout a 5-d laboratory trial. It was observed that S-lactofen was preferentially taken up and metabolized in L. minor. After rac-lactofen exposure, the accumulation amount of S-lactofen was approximately 3-fold more than that of R-lactofen in L. minor and the metabolism rate of S-lactofen (T1/2=0.92 d) was significantly faster than R-lactofen (T1/2=1.55 d). L. minor could only slightly accelerate the metabolism and removal of lactofen in the growth solution. As for the metabolites, desethyl lactofen was found to be the major metabolite in L. minor and the growth solution, whereas the metabolite acifluorfene was undetectable. No interconversion of the two enantiomers was observed after individual enantiomer exposure, indicating they were configurationally stable. The findings of this work represented that the accumulation and metabolism of lactofen in L. minor were enantioselective, and L. minor had limited capacity for the removal of lactofen and its metabolite in water.

Highly efficient and stable adsorption of lithium from brine with microcapsules containing 1-phenylazo-2-naphthol and trioctylphosphine oxide.

Lithium extraction from salt lake brine is still challenging due to the existence of similar elements, e.g. sodium. In the present work, polysulfone (PSF) microcapsules containing 1-phenylazo-2-naphthol (HS) and trioctylphosphine oxide (TOPO) as extractants were successfully prepared by microfluidic technology for the separation of Li+ from brine with Li+ and Na+. The morphology, composition, and structure of HS-TOPO-based microcapsules were characterized systematically. The results showed that microcapsules consisting of 20 wt% (m m-1) polysulfone and 80 wt% (m m-1) 1-phenylazo-2-naphthol-trioctylphosphine oxide as the extractant, which was labeled as PSF/HS-TOPO-2/8, exhibited the best performance for Li+ adsorption. The separation factor (SF) of Li+ over Na+ is up to 653 and the adsorption capacity for Li+ in the simulated brine could reach 3.67 mg g-1 for microcapsules PSF/HS-TOPO-2/8, which demonstrated that Li+ can be separated with high selectivity. Besides, the kinetic results demonstrated that the adsorption followed quasi-secondary adsorption kinetic models, indicating that the adsorption mechanism of lithium by microcapsules involved chemisorption. After ten cycles of adsorption-elution, the maximum equilibrium adsorption capacity still remained at 87%. All these results demonstrate that PSF/HS-TOPO-2/8 microcapsules can be used as an efficient adsorber for the adsorption of Li+ from brine with high selectivity and stability.