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BackgroundLupus nephritis (LN) is a common complication of systemic lupus erythematosus that presents a high risk of end-stage renal disease. In the present study, we used CIBERSORT and gene set enrichment analysis (GSEA) of gene expression profiles to identify immune cell infiltration characteristics and related core genes in LN.ResultsDatasets from the Gene Expression Omnibus, GSE32591 and GSE113342, were downloaded for further analysis. The GSE32591 dataset, which included 32 LN glomerular biopsy tissues and 14 glomerular tissues from living donors, was analyzed by CIBERSORT. Different immune cell types in LN were analyzed by the Limma software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis based on GSEA were performed by clusterProfiler software. Lists of core genes were derived from Spearman correlation between the most significant GO term and differentially expressed immune cell gene from CIBERSORT. GSE113342 was employed to validate the association between selected core genes and clinical manifestation. Five types of immune cells revealed important associations with LN, and monocytes emerged as having the most prominent differences. GO and KEGG analyses indicated that immune response pathways are significantly enriched in LN. The Spearman correlation indicated that 15 genes, including FCER1G, CLEC7A, MARCO, CLEC7A, PSMB9, and PSMB8, were closely related to clinical features.ConclusionsThis study is the first to identify immune cell infiltration with microarray data of glomeruli in LN by using CIBERSORT analysis and provides novel evidence and clues for further research of the molecular mechanisms of LN.

Many phthalate esters (PAEs) are chemicals of high production volume and of toxicological concern. The second-order rate constant for base-catalyzed hydrolysis (k B) is a key parameter for assessing environmental persistence of PAEs. However, the k B values for most PAEs are lacking, and the experimental determination of k B encounters various difficulties. Herein, density functional theory (DFT) methods were selected by comparing empirical k B values of five PAEs and five carboxylic acid esters with the DFT-calculated ones. Results indicate that PAEs with cyclic side chains are more vulnerable to base-catalyzed hydrolysis than PAEs with linear alkyl side chains, followed by PAEs with branched alkyl side chains. By combining experimental and DFT-calculated second-order rate constants for base-catalyzed hydrolysis of one side chain in PAEs (k B_side chain), quantitative structure–activity relationship models were developed. The models can differentiate PAEs with the departure of the leaving group (or the nucleophilic attack of OH–) as the rate-determining step in the hydrolysis and estimate k B values, which provides a promising way to predict hydrolysis kinetics of PAEs. The half-lives of the investigated PAEs were calculated and vary from 0.001 h to 558 years (pH = 7∼9), further illustrating the necessity of prediction models for hydrolysis kinetics in assessing the environmental persistence of chemicals.

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Machine learning (ML) models for screening endocrine-disrupting chemicals (EDCs), such as thyroid stimulating hormone receptor (TSHR) agonists, are essential for sound management of chemicals. Previous models for screening TSHR agonists were built on imbalanced datasets and lacked applicability domain (AD) characterization essential for regulatory application. Herein, an updated TSHR agonist dataset was built, for which the ratio of active to inactive compounds greatly increased to 1:2.6, and chemical spaces of structure–activity landscapes (SALs) were enhanced. Resulting models based on 7 molecular representations and 4 ML algorithms were proven to outperform previous ones. Weighted similarity density (ρs) and weighted inconsistency of activities (I A) were proposed to characterize the SALs, and a state-of-the-art AD characterization methodology ADSAL{ρs, I A} was established. An optimal classifier developed with PubChem fingerprints and the random forest algorithm, coupled with ADSAL{ρs ≥ 0.15, I A ≤ 0.65}, exhibited good performance on the validation set with the area under the receiver operating characteristic curve being 0.984 and balanced accuracy being 0.941 and identified 90 TSHR agonist classes that could not be found previously. The classifier together with the ADSAL{ρs, I A} may serve as efficient tools for screening EDCs, and the AD characterization methodology may be applied to other ML models.