Potential mechanisms of non-suicidal self-injury(NSSI)in major depressive disorder:a systematic review

Background Non-suicidal self-injury(NSSI)is a frequent and prominent phenomenon in major depressive disorder(MDD).Even though its prevalence and risk factors are relatively well understood,the potential mechanisms of NSSI in MDD remain elusive.Aims To review present evidence related to the potential mechanisms of NSSI in MDD.Methods According to Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines,articles for this systematic review were searched on Medline(through PubMed),Embase(through Elsevier),PsycINFO(through OVID)and Web of Science databases for English articles,as well as China National Knowledge Infrastructure(CNKI),SinoMed,Wanfang Data,and the Chongqing VIP Chinese Science and Technology Periodical(VIP)Databases for Chinese articles published from the date of inception to 2 August 2022.Two researchers(BW,HZ)independently screened studies based on inclusion and exclusion criteria and assessed their quality.Results A total of 25157 studies were searched.Only 25 of them were ultimately included,containing 3336 subjects(1535 patients with MDD and NSSI,1403 patients with MDD without NSSI and 398 HCs).Included studies were divided into 6 categories:psychosocial factors(11 studies),neuroimaging(8 studies),stress and hypothalamic-pituitary-adrenal(HPA)axis(2 studies),pain perception(1 study),electroencephalogram(EEG)(2 studies)and epigenetics(1 study).Conclusions This systematic review indicates that patients with MDD and NSSI might have specific psychosocial factors,aberrant brain functions and neurochemical metabolisms,HPA axis dysfunctions,abnormal pain perceptions and epigenetic alterations.

Single-cell and spatially resolved omics:Advances and limitations

Recent advances in experimental and computational single-cell and spatially resolved omics have opened new avenues for research in biology and medicine.These technologies allow for the study of individual cells in unprecedented detail,providing insights into the heterogeneity within tissues and organs,and how different cells interact with each other.Humans and other eukaryotes are composed of billions of cells,each with vastly heterogeneous cell types and functional cell states determined by intrinsic and extrinsic factors.

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation

Targeted protein degradation(TPD)has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell’s endogenous protein degradation machinery.However,the susceptibility of proteins for targeting by TPD approaches,termed“degradability”,is largely unknown.Here,we developed a machine learning model,model-free analysis of protein degradability(MAPD),to predict degradability from features intrinsic to protein targets.MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds[with an area under the precision–recall curve(AUPRC)of 0.759 and an area under the receiver operating characteristic curve(AUROC)of 0.775]and is likely generalizable to independent non-kinase proteins.We found five features with statistical significance to achieve optimal prediction,with ubiquitination potential being the most predictive.By structural modeling,we found that E2-accessible ubiquitination sites,but not lysine residues in general,are particularly associated with kinase degradability.Finally,we extended MAPD predictions to the entire proteome to find964 disease-causing proteins(including proteins encoded by 278 cancer genes)that may be tractable to TPD drug development.