Anticancer drug discovery by targeting cullin neddylation

Protein neddylation is a post-translational modification which transfers the ubiquitin-like protein NEDD8 to a lysine residue of the target substrate through a three-step enzymatic cascade.The bestknown substrates of neddylation are cullin family proteins,which are the core component of Cullin-RING E3 ubiquitin ligases(CRLs).Given that cullin neddylation is required for CRL activity,and CRLs control the turn-over of a variety of key signal proteins and are often abnormally activated in cancers,targeting neddylation becomes a promising approach for discovery of novel anti-cancer therapeutics.In the past decade,we have witnessed significant progress in the field of protein neddylation from preclinical target validation,to drug screening,then to the clinical trials of neddylation inhibitors.In this review,we first briefly introduced the nature of protein neddylation and the regulation of neddylation cascade,followed by a summary of all reported chemical inhibitors of neddylation enzymes.We then discussed the structure-based targeting of protein-protein interaction in neddylation cascade,and finally the available approaches for the discovery of new neddylation inhibitors.This review will provide a focused,up-to-date and yet comprehensive overview on the discovery effort of neddylation inhibitors.

Functional Near-Infrared Spectroscopy Neurofeedback of Cortical Target Enhances Hippocampal Activation and Memory Performance

Dear Editor,Neuromodulation,a rapidly expanding field attracting wide attention over recent decades,facilitates human cognition,behavior,and pathology by modifying the activity of specific neural targets.Human brain functions can be modified by exogenous brain neuromodulation techniques that deliver physical energy(e.g.,electrical current or magnetic pulses)into the brain[1],such as deep brain stimulation,transcranial magnetic stimulation,and tran-scranial direct current stimulation.

Risk factors and a prediction model for sepsis: A multicenter retrospective study in China

Background:Sepsis is typically associated with poor outcomes.There are various risk factors and predictive mod-els for sepsis based on clinical indicators.However,these models are usually predictive of all critical patients.This study explored the risk factors for 28-day outcomes of patients with sepsis and developed a prognosis prediction model.Methods:This was a multicenter retrospective analysis of sepsis patients hospitalized in three intensive care units(ICUs)from September 1st 2015,to June 30th 2020.Demographic,clinical history,and laboratory test data were extracted from patient records.Investigators explored the risk factors affecting 28-day sepsis prognosis by univariate analysis.The effects of confounding factors were excluded by multivariate logistic regression analysis,and new joint predictive factors were calculated.A model predicting 28-day sepsis prognosis was constructed through data processing analysis.Results:A total of 545 patients with sepsis were included.The 28-day mortality rate was 32.3%.Risk factors including age,D-dimer,albumin,creatinine,and prothrombin time(PT)were predictive of death from sepsis.The goodness-of-fit value for this prediction model was 0.534,and the area under the receiver operating characteristic curve was 0.7207.Further analysis of the immune subgroups(n=140)revealed a significant decrease in CD3+,CD4+CD8-,and CD4+CD29+memory effector T lymphocytes and an increase in CD56+natural killer(NK)cells in the hypoalbuminemia group compared with the normal albumin group(65.5 vs.58.3,P=0.005;41.2 vs.32.4,P=0.005;21.8 vs.17.1,P=0.029;12.6 vs.17.6,P=0.004).Conclusions:Risk factors for 28-day sepsis mortality include age,D-dimer,creatinine,PT,and albumin.A decrease in albumin level may exacerbate immunosuppression in patients with sepsis.This study establishes a prediction model based on these indicators,which shows a good degree of calibration and differentiation.This model may provide good predictive value for clinical sepsis prognosis.