Development of a New High-throughput Screening Model for Human High Density Lipoprotein Receptor (CLA-1) Agonists

To develop a new high-throughput screening model for human high-density lipoprotein （HDL） receptor （CD36 and LIMPⅡ analogous-1, CLA-1） agonists using CLA-1-expressing insect cells. Methods With the total RNA of human hepatoma cells BEL-7402 as template, the complementary DNA （cDNA） of CLA-1 was amplified by reverse transcription-polymerase chain reaction （RT-PCR）. Bac-to-Bac baculovirus expression system was used to express CLA-1 in insect cells. CLA-1 cDNA was cloned downstream of polyhedrin promoter of Autographa californica nuclear polyhedrosis virus （AcNPV） into donor vector pFastBacl and recombinant pFastBacl-CLA-1 was transformed into E. coli DH10Bac to transpose CLA-1 cDNA to bacrnid DNA. Recombinant bacrnid-CLA-1 was transfected into Spodopterafrugiperda Sf9 insect cells to produce recombinant baculovirus particles. Recombinant CLA- 1 was expressed on the membrane of Sf9 cells infected with the recombinant baculoviruses. A series of parameters of DiI-lipoprotein binding assays of CLA-1-expressing Sf9 cells in 96-well plates were optimized. Results Western blot analysis and DiI-lipoprotein binding assays confirmed that CLA-1 expressed in insect cells had similar immunoreactivity and ligand binding activity as its native counterpart. A reliable and sensitive in vitro cell-based assay was established to assess the activity of CLA-1 and used to screen agonists from different sample libraries. Conclusion Human HDL receptor CLA-1 was successfully expressed in Sf9 insect cells and a novel high-throughput screening model for CLA-1 agonists was developed. Utilization of this model allows us to identify potent and selective CLA-1 agonists which might possibly be used as therapeutics for atherosclerosis.

Development and validation of a prediction model for early screening of people at high risk for colorectal cancer

BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.

From the perspective of experimental practice: High-throughput computational screening in photocatalysis

Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is now generating widespread interest in boosting the conversion effi-ciency of solar energy.In the past decade,computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts.In this viewpoint,we started with introducing the challenges of photocatalysis from the view of experimental practice,especially the inefficiency of the traditional“trial and error”method.Sub-sequently,a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail.On the basis of the current experimental progress in photocatalysis,we also exemplified the various challenges associated with high-throughput computational screening strategies.Finally,we offered a preferred high-throughput computational screening procedure for pho-tocatalysts from an experimental practice perspective(model construction and screening,standardized experiments,assessment and revision),with the aim of a better correlation of high-throughput simulations and experimental practices,motivating to search for better descriptors.

High-throughput computational screening and in vitro evaluation identifies 5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione(C3),as a novel EGFR—HER2 dual inhibitor in gastric tumors

Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation,adhesion,angiogenesis,and metastasis.Conventional therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic mutations.Hence,dual inhibition strategies are recommended to increase potency and reduce cytotoxicity.In this study,we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase activities.Diversity-based High-throughput Virtual Screening(D-HTVS)was used to screen the whole ChemBridge small molecular library against EGFR and HER2.The atomistic molecular dynamic simulation was conducted to understand the dynamics and stability of the protein-ligand complexes.EGFR/HER2 kinase enzymes,KATOIII,and Snu-5 cells were used for in vitro validations.The atomistic Molecular Dynamics simulations followed by solvent-based Gibbs binding free energy calculation of top molecules,identified compound C3(5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione)to have a good affinity for both EGFR and HER2.The predicted compound,C3,was promising with better binding energy,good binding pose,and optimum interactions with the EGFR and HER2 residues.C3 inhibited EGFR and HER2 kinases with IC50 values of 37.24 and 45.83 nM,respectively.The GI50 values of C3 to inhibit KATOIII and Snu-5 cells were 84.76 and 48.26 nM,respectively.Based on these findings,we conclude that the identified compound C3 showed a conceivable dual inhibitory activity on EGFR/HER2 kinase,and therefore can be considered as a plausible lead-like molecule for treating gastric cancers with minimal side effects,though testing in higher models with pharmacokinetic approach is required.

High-throughput screening system of citrus bacterial cankerassociated transcription factors and its application to the regulation of citrus canker resistance

One of the main diseases that adversely impacts the global citrus industry is citrus bacterial canker(CBC),caused by the bacteria Xanthomonas citri subsp.citri(Xcc).Response to CBC is a complex process,with both proteinDNA as well as protein–protein interactions for the regulatory network.To detect such interactions in CBC resistant regulation,a citrus high-throughput screening system with 203 CBC-inducible transcription factors(TFs),were developed.Screening the upstream regulators of target by yeast-one hybrid(Y1H)methods was also performed.A regulatory module of CBC resistance was identified based on this system.One TF(CsDOF5.8)was explored due to its interactions with the 1-kb promoter fragment of CsPrx25,a resistant gene of CBC involved in reactive oxygen species(ROS)homeostasis regulation.Electrophoretic mobility shift assay(EMSA),dual-LUC assays,as well as transient overexpression of CsDOF5.8,further validated the interactions and transcriptional regulation.The CsDOF5.8–CsPrx25 promoter interaction revealed a complex pathway that governs the regulation of CBC resistance via H2O2homeostasis.The high-throughput Y1H/Y2H screening system could be an efficient tool for studying regulatory pathways or network of CBC resistance regulation.In addition,it could highlight the potential of these candidate genes as targets for efforts to breed CBC-resistant citrus varieties.

An integrated microfluidics platform with high-throughput single-cell cloning array and concentration gradient generator for efficient cancer drug effect screening

Background:Tumor cell heterogeneity mediated drug resistance has been recognized as the stumbling block of cancer treatment.Elucidating the cytotoxicity of anticancer drugs at single-cell level in a high-throughput way is thus of great value for developing precision therapy.However,current techniques suffer from limitations in dynamically characterizing the responses of thousands of single cells or cell clones presented to multiple drug conditions.Methods:We developed a new microfluidics-based“SMART”platform that is Simple to operate,able to generate a Massive single-cell array and Multiplex drug concentrations,capable of keeping cells Alive,Retainable and Trackable in the microchambers.These features are achieved by integrating a Microfluidic chamber Array(4320 units)and a sixConcentration gradient generator(MAC),which enables highly efficient analysis of leukemia drug effects on single cells and cell clones in a high-throughput way.Results:A simple procedure produces 6 on-chip drug gradients to treat more than 3000 single cells or single-cell derived clones and thus allows an efficient and precise analysis of cell heterogeneity.The statistic results reveal that Imatinib(Ima)and Resveratrol(Res)combination treatment on single cells or clones is much more efficient than Ima or Res single drug treatment,indicated by the markedly reduced half maximal inhibitory concentration(IC50).Additionally,single-cell derived clones demonstrate a higher IC_(50) in each drug treatment compared to single cells.Moreover,primary cells isolated from two leukemia patients are also found with apparent heterogeneity upon drug treatment on MAC.Conclusions:This microfluidics-based“SMART”platform allows high-throughput single-cell capture and culture,dynamic drug-gradient treatment and cell response monitoring,which represents a new approach to efficiently investigate anticancer drug effects and should benefit drug discovery for leukemia and other cancers.

Model-Free Feature Screening via Maximal Information Coefficient (MIC) for Ultrahigh-Dimensional Multiclass Classification

It is common for datasets to contain both categorical and continuous variables. However, many feature screening methods designed for high-dimensional classification assume that the variables are continuous. This limits the applicability of existing methods in handling this complex scenario. To address this issue, we propose a model-free feature screening approach for ultra-high-dimensional multi-classification that can handle both categorical and continuous variables. Our proposed feature screening method utilizes the Maximal Information Coefficient to assess the predictive power of the variables. By satisfying certain regularity conditions, we have proven that our screening procedure possesses the sure screening property and ranking consistency properties. To validate the effectiveness of our approach, we conduct simulation studies and provide real data analysis examples to demonstrate its performance in finite samples. In summary, our proposed method offers a solution for effectively screening features in ultra-high-dimensional datasets with a mixture of categorical and continuous covariates.

Model-Free Ultra-High-Dimensional Feature Screening for Multi-Classified Response Data Based on Weighted Jensen-Shannon Divergence

In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified from the point of view of introducing Jensen-Shannon divergence to measure the importance of covariates. The idea of the method is to calculate the Jensen-Shannon divergence between the conditional probability distribution of the covariates on a given response variable and the unconditional probability distribution of the covariates, and then use the probabilities of the response variables as weights to calculate the weighted Jensen-Shannon divergence, where a larger weighted Jensen-Shannon divergence means that the covariates are more important. Additionally, we also investigated an adapted version of the method, which is to measure the relationship between the covariates and the response variable using the weighted Jensen-Shannon divergence adjusted by the logarithmic factor of the number of categories when the number of categories in each covariate varies. Then, through both theoretical and simulation experiments, it was demonstrated that the proposed methods have sure screening and ranking consistency properties. Finally, the results from simulation and real-dataset experiments show that in feature screening, the proposed methods investigated are robust in performance and faster in computational speed compared with an existing method.

Brain organoids are new tool for drug screening of neurological diseases

At the level of in vitro drug screening,the development of a phenotypic analysis system with highcontent screening at the core provides a strong platform to support high-throughput drug screening.There are few systematic reports on brain organoids,as a new three-dimensional in vitro model,in terms of model stability,key phenotypic fingerprint,and drug screening schemes,and particula rly rega rding the development of screening strategies for massive numbers of traditional Chinese medicine monomers.This paper reviews the development of brain organoids and the advantages of brain organoids over induced neurons or cells in simulated diseases.The paper also highlights the prospects from model stability,induction criteria of brain organoids,and the screening schemes of brain organoids based on the characteristics of brain organoids and the application and development of a high-content screening system.

Study of a Novel Antiosteoporosis Screening Model Targeted on Cathepsin K

Objective To establish an effective assay to access the effects of natural products on cathepsin K for screening antiosteoporosis drugs. Methods To obtain the purified cathepsin K, we cloned the target fragment from the mRNA of human osteosacoma cell line MG63 and demonstrated its correctness through DNA sequencing. Cathepsin K was expressed in a high amount in E.coli after IPTG induction, and was purified to near homogenetity through resolution and column purification. The specificity of the protein was shown by Western blotting experiment. The biological activity of the components in the fermentation broth was assayed by their inhibitory effects on cathepsin K and its analog papain. Results With the inhibition of papain activity as a screen index, the fermentation samples of one thousand strains of fungi were tested and 9 strains among them showed strong inhibitory effects. The crude products of the fermentation broth were tested for their specific inhibitory effects on the purified human cathepsin K, the product of fungi 2358 shows the highest specificity against cathepsin K. Conclusions The compounds isolated from fungi 2358 show the highest biological activity and are worth further structure elucidation and function characterization.

Modeling screening efficiency with vibrational parameters based on DEM 3D simulation

The efficiency of particle screening was studied over a range of vibrational parameters including amplitude, frequency and vibrational direction. The Discrete Element Method (DEM) was used to simulate the screening process. A functional relationship between efficiency and the parameters, both singly and combined, is established. The function is a complicated exponential. Optimal amplitude and frequency values are smaller for particles near the mesh and larger for other particles. The optimum vibration angle is 45° for nearly all kinds of particles. A transverse velocity, V⊥, was defined and V⊥=0.2 m/s was identified to be the most efficient operating point by both simulation and experimental observation. Comparison of these results with those reported by others is included.

Machine Learning-Assisted High-Throughput Virtual Screening for On-Demand Customization of Advanced Energetic Materials

Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error.Herein,a methodology combining domain knowledge,a machine learning algorithm,and experiments is presented for accelerating the discovery of novel energetic materials.A high-throughput virtual screening(HTVS)system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established.With the proposed HTVS system,candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25112 molecules.Furthermore,a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results,thus verifying the effectiveness of the proposed methodology.This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.

High-throughput screening of mouse gene knockouts identifies established and novel skeletal phenotypes

Screening gene function in vivo is a powerful approach to discover novel drug targets. We present high-throughput screening （HTS） data for 3 762 distinct global gene knockout （KO） mouse lines with viable adult homozygous mice generated using either gene-trap or homologous recombination technologies. Bone mass was determined from DEXA scans of male and female mice at 14 weeks of age and by microCT analyses of bones from male mice at 16 weeks of age. Wild-type （WT） cagemates/littermates were examined for each gene KO. Lethality was observed in an additional 850 KO lines. Since primary HTS are susceptible to false positive findings, additional cohorts of mice from KO lines with intriguing HTS bone data were examined. Aging, ovariectomy, histomorphometry and bone strength studies were performed and possible non-skeletal phenotypes were explored. Together, these screens identified multiple genes affecting bone mass： 23 previously reported genes （Calcr, Cebpb, Crtap, Dcstamp, Dkkl, Duoxa2, Enppl, Fgf23, Kissl/Kisslr, Kl （Klotho）, Lrp5, Mstn, Neol, Npr2, Ostml, Postn, Sfrp4, S1c30a5, Sic39a13, Sost, Sumf1, Src, Wnt10b）, five novel genes extensively characterized （Cldn18, Fam20c, Lrrkl, Sgpll, Wnt16）, five novel genes with preliminary characterization （Agpat2, RassfS, Slc10a7, Stc26a7, Slc30a10） and three novel undisclosed genes coding for potential osteoporosis drug targets.

A Cell-based High-throughput Screening Assay for Farnesoid X Receptor Agonists

Objective To develop a high-throughput screening assay for Farnesoid X receptor （FXR） agonists based on mammalian one-hybrid system （a chimera receptor gene system） for the purpose of identifying new lead compounds for dyslipidaemia drug from the chemical library. Methods cDNA encoding the human FXR ligand binding domain （LBD） was amplified by RT-PCR from a human liver total mRNA and fused to the DNA binding domain （DBD） of yeast GAL4 of pBIND to construct a GAL4-FXR （LBD） chimera expression plasmid. Five copies of the GAL4 DNA binding site were synthesized and inserted into upstream of the SV40 promoter of pGL3-promoter vector to construct a reporter plasmid pG5-SV40 Luc. The assay was developed by transient co-transfection with pG5-SV40 Luc reporter plasmid and pBIND-FXR-LBD （189-472） chimera expression plasmid. Results After optimization, CDCA, a FXR natural agonist, could induce expression of the luciferase gene in a dose-dependent manner, and had a signal/noise ratio of 10 and Z＇ factor value of 0.65, Conclusion A stable and sensitive cell-based high-throughput screening model can be used in high-throughput screening for FXR agonists from the synthetic and natural compound library.

High-throughput computational screening and design of nanoporous materials for methane storage and carbon dioxide capture

The globally increasing concentrations of greenhouse gases in atmosphere after combustion of coal-or petroleum-based fuels give rise to tremendous interest in searching for porous materials to efficiently capture carbon dioxide(CO_2) and store methane(CH4), where the latter is a kind of clean energy source with abundant reserves and lower CO_2 emission. Hundreds of thousands of porous materials can be enrolled on the candidate list, but how to quickly identify the really promising ones, or even evolve materials(namely, rational design high-performing candidates) based on the large database of present porous materials? In this context, high-throughput computational techniques, which have emerged in the past few years as powerful tools, make the targets of fast evaluation of adsorbents and evolving materials for CO_2 capture and CH_4 storage feasible. This review provides an overview of the recent computational efforts on such related topics and discusses the further development in this field.

High-throughput Screening of the Enantioselectivity of Hyperthermophilic Mutant Esterases from Archaeon Aeropyrum pernix K1 for Resolution of (R,S)-2-Octanol Acetate

To identify the desired hypertherrnophilic variants within a mutant esterase library for the resolution of （R, S）-2- octanol acetate, a simple, reliable, and versatile method was developed in this study. We built a screening strategy including two steps, first we selected agar plate with substrate to screen the enzymatic activity; secondly we used a pH indicator to screen the enantioselectivity. This method could rapidly detect favorable mutants with high activity and enantioselectivity. A total of 96. 2% of tedious screening work can be precluded using this screening strategy. It is an effective screening for alkyl ester and can be applied to relative screening researches. The four improved mutants were screened from the mutant esterase library. Their enantioselectivities, activities, and structures were investigated at different temperatures.

3D bioprinting of in vitro porous hepatoma models:establishment,evaluation,and anticancer drug testing

Traditional tumor models do not tend to accurately simulate tumor growth in vitro or enable personalized treatment and are particularly unable to discover more beneficial targeted drugs.To address this,this study describes the use of threedimensional(3D)bioprinting technology to construct a 3D model with human hepatocarcinoma SMMC-7721 cells(3DP-7721)by combining gelatin methacrylate(GelMA)and poly(ethylene oxide)(PEO)as two immiscible aqueous phases to form a bioink and innovatively applying fluorescent carbon quantum dots for long-term tracking of cells.The GelMA(10%,mass fraction)and PEO(1.6%,mass fraction)hydrogel with 3:1 volume ratio offered distinct pore-forming characteristics,satisfactorymechanical properties,and biocompatibility for the creation of the 3DP-7721 model.Immunofluorescence analysis and quantitative real-time fluorescence polymerase chain reaction(PCR)were used to evaluate the biological properties of the model.Compared with the two-dimensional culture cell model(2D-7721)and the 3D mixed culture cell model(3DM-7721),3DP-7721 significantly improved the proliferation of cells and expression of tumor-related proteins and genes.Moreover,we evaluated the differences between the three culture models and the effectiveness of antitumor drugs in the three models and discovered that the efficacy of antitumor drugs varied because of significant differences in resistance proteins and genes between the three models.In addition,the comparison of tumor formation in the three models found that the cells cultured by the 3DP-7721 model had strong tumorigenicity in nude mice.Immunohistochemical evaluation of the levels of biochemical indicators related to the formation of solid tumors showed that the 3DP-7721 model group exhibited pathological characteristics of malignant tumors,the generated solid tumors were similar to actual tumors,and the deterioration was higher.This research therefore acts as a foundation for the application of 3DP-7721 models in drug development research.

Preliminary Research on a Mesenchymal Stem Cell-model for Screening Traditional Chinese Medicines

The authors focused their attention on the establishment of a mesenchymal stem cell（MSC） model for screening traditional Chinese medicines（TCMs） so as to investigate the effects of Shuanglong Formula（SLF） components（Ginsenosides and salvianolic acids） and ingredients（ginsenoside Rb1 and salvianolic acid B） on cardiomyocyte differentiation from MSCs.The SLF components were analyzed and quantified by HPLC-TOF-MS.Cardiomyocyte differentiation was induced by culturing MSCs in the induction medium supplemented with SLF ingredients,SLF components,5-azacytidine（5-aza）,5-aza＋SLF ingredients and 5-aza＋SLF components,respectively,for up to 30 d,and evulated by the expression of Cardiac-specific myosin heavy chain（MHC） and troponin I（TnI） via immunofluoresent staining.Slow growth rate and changed morphology were observed during cardiomyocyte differentiation.After 20 d of induction,differentiating MSCs were positive for MHC and TnI staining.The effects of SLF components were better than those of SLF ingredients.Taken together,SLF can induce the differentiation of MSCs into cardiomyogenic cells in vitro,and MSCs can be used as a powerful tool for screening TCMs.

A high-throughput screening method for breeding Aspergillus niger with 12C6+ ion beam-improved cellulase

In this study,a high-throughput screening method was established through the 24-square deep-well microliter plate(MTP) fermentation and micro-plate detection for large-scale screening of the mutants.It was suitable for screening a large number of mutants and improving the breeding efficiency after heavy-ion beam irradiation.Seventeen strains showed higher cellulase activity compared with the initial strain after the screening of plate and MTP fermentation.The filter paper activity and β-glucosidase activity of Aspergillus niger H11201 had increased 38.74 and 63.23%separately compared with A.niger H11 by shaking flask fermentation,and it was genetically stable after being passaged to nine generations.The results indicate that the high-throughput screening method can be used for the quick breeding of A.niger with high cellulase activity.