Although natural killer(NK)cells are endowed with intrinsic cytotoxicity,their therapeutic application often faces limitations because of their lack of tumor-specific targeting ability and limited ability to infiltrat...Although natural killer(NK)cells are endowed with intrinsic cytotoxicity,their therapeutic application often faces limitations because of their lack of tumor-specific targeting ability and limited ability to infiltrate solid tumors.To overcome these limitations,we developed anti-mesothelin(MSLN)uCAR-like NK cells,which are designed to enhance both the targeting specificity and tumor infiltration capacity,thereby improving the antitumor efficacy of NK cell-based therapies.We constructed,purified,and validated a tetravalent bispecific cell engager(MSLN×CD16A)via the SpyTag/SpyCatcher system.Cytokine-induced memory-like NK cells,induced by IL-12,IL-15,and IL-18,were precomplexed with MSLN×CD16A to generate anti-MSLN CAR-like NK cells.To further enhance tumor penetration,the tumor-penetrating peptide uCendR was integrated into the system to construct anti-MSLN uCARlike NK cells.In vitro,anti-MSLN CAR-like NK cells demonstrated selective cytotoxicity against MSLN-positive tumor cells through stable binding with MSLN×CD16A while sparing MSLN-negative cells.In xenograft models bearing MSLN-positive tumors,antiMSLN CAR-like NK cells exhibited significant antitumor activity,with favorable tolerability and no significant body weight loss or toxicity.Notably,anti-MSLN uCAR-like NK cells,which integrate a tumor-penetrating peptide,displayed enhanced intratumor penetration and superior therapeutic efficacy.Overall,this study establishes a modular,nongenetically engineered uCAR-like NK platform that couples targeted recognition with enhanced tissue access.These findings highlight the potential of anti-MSLN CARlike NK cells,particularly uCAR-like NK cells with enhanced tumor penetration,as promising therapeutic strategies for MSLN-positive solid tumors and lay the foundation for future clinical applications.展开更多
In the era of antibiotic resistance,in silico prediction of bacterial resistome pro-files,likely to be associated with inactivation of new potential antibiotics is of utmost impor-tance.Despite this,to the best of our...In the era of antibiotic resistance,in silico prediction of bacterial resistome pro-files,likely to be associated with inactivation of new potential antibiotics is of utmost impor-tance.Despite this,to the best of our knowledge,no tool exists for such prediction.Therefore,under the rationale that drugs with similar structures have similar resistome profiles,we devel-oped two models,a deterministic model and a stochastic model,to predict the bacterial re-sistome likely to neutralize uncharacterized but potential chemical structures.The current version of the tool involves the prediction of a resistome for Escherichia coli and Pseudomonas aeruginosa.The deterministic model on omitting two diverse but relatively less characterized drug classes,polyketides and polypeptides showed an accuracy of 87%,a sensitivity of 85%,and a precision of 89%,whereas the stochastic model predicted antibiotic classes of the test set compounds with an accuracy of 72%,a sensitivity of 75%,and a precision of 83%.The models have been implemented in both a standalone package and an online server,uCAREChemSuite-CLI and uCARE Chem Suite,respectively.In addition to resistome prediction,the online version of the suite enables the user to visualize the chemical structure,classify compounds in 19 pre-defined drug classes,perform pairwise alignment,and cluster with database compounds using a graphical user interface.展开更多
基金supported by the National Natural Science Foundation of China(82272811)the Jiangsu Province Key Research and Development Program(BE2023654).
文摘Although natural killer(NK)cells are endowed with intrinsic cytotoxicity,their therapeutic application often faces limitations because of their lack of tumor-specific targeting ability and limited ability to infiltrate solid tumors.To overcome these limitations,we developed anti-mesothelin(MSLN)uCAR-like NK cells,which are designed to enhance both the targeting specificity and tumor infiltration capacity,thereby improving the antitumor efficacy of NK cell-based therapies.We constructed,purified,and validated a tetravalent bispecific cell engager(MSLN×CD16A)via the SpyTag/SpyCatcher system.Cytokine-induced memory-like NK cells,induced by IL-12,IL-15,and IL-18,were precomplexed with MSLN×CD16A to generate anti-MSLN CAR-like NK cells.To further enhance tumor penetration,the tumor-penetrating peptide uCendR was integrated into the system to construct anti-MSLN uCARlike NK cells.In vitro,anti-MSLN CAR-like NK cells demonstrated selective cytotoxicity against MSLN-positive tumor cells through stable binding with MSLN×CD16A while sparing MSLN-negative cells.In xenograft models bearing MSLN-positive tumors,antiMSLN CAR-like NK cells exhibited significant antitumor activity,with favorable tolerability and no significant body weight loss or toxicity.Notably,anti-MSLN uCAR-like NK cells,which integrate a tumor-penetrating peptide,displayed enhanced intratumor penetration and superior therapeutic efficacy.Overall,this study establishes a modular,nongenetically engineered uCAR-like NK platform that couples targeted recognition with enhanced tissue access.These findings highlight the potential of anti-MSLN CARlike NK cells,particularly uCAR-like NK cells with enhanced tumor penetration,as promising therapeutic strategies for MSLN-positive solid tumors and lay the foundation for future clinical applications.
文摘In the era of antibiotic resistance,in silico prediction of bacterial resistome pro-files,likely to be associated with inactivation of new potential antibiotics is of utmost impor-tance.Despite this,to the best of our knowledge,no tool exists for such prediction.Therefore,under the rationale that drugs with similar structures have similar resistome profiles,we devel-oped two models,a deterministic model and a stochastic model,to predict the bacterial re-sistome likely to neutralize uncharacterized but potential chemical structures.The current version of the tool involves the prediction of a resistome for Escherichia coli and Pseudomonas aeruginosa.The deterministic model on omitting two diverse but relatively less characterized drug classes,polyketides and polypeptides showed an accuracy of 87%,a sensitivity of 85%,and a precision of 89%,whereas the stochastic model predicted antibiotic classes of the test set compounds with an accuracy of 72%,a sensitivity of 75%,and a precision of 83%.The models have been implemented in both a standalone package and an online server,uCAREChemSuite-CLI and uCARE Chem Suite,respectively.In addition to resistome prediction,the online version of the suite enables the user to visualize the chemical structure,classify compounds in 19 pre-defined drug classes,perform pairwise alignment,and cluster with database compounds using a graphical user interface.
