Single-cell transcriptome analysis reveals liver injury induced by glyphosate in mice.

BACKGROUND: Glyphosate (GLY), as the active ingredient of the most widely used herbicide worldwide, is commonly detected in the environment and living organisms, including humans. Its toxicity and carcinogenicity in mammals remain controversial. Several studies have demonstrated the hepatotoxicity of GLY; however, the underlying cellular and molecular mechanisms are still largely unknown. METHODS: Using single-cell RNA sequencing (scRNA-seq), immunofluorescent staining, and in vivo animal studies, we analyzed the liver tissues from untreated and GLY-treated mice. RESULTS: We generated the first scRNA-seq atlas of GLY-exposed mouse liver. GLY induced varied cell composition, shared or cell-type-specific transcriptional alterations, and dysregulated cell-cell communication and thus exerted hepatotoxicity effects. The oxidative stress and inflammatory response were commonly upregulated in several cell types. We also observed activation and upregulated phagocytosis in macrophages, as well as proliferation and extracellular matrix overproduction in hepatic stellate cells. CONCLUSIONS: Our study provides a comprehensive single-cell transcriptional picture of the toxic effect of GLY in the liver, which offers novel insights into the molecular mechanisms of the GLY-associated hepatotoxicity.

Development of a Signature Based on Eight Metastatic-Related Genes for Prognosis of GC Patients.

Gastric cancer (GC) has been a common tumor type with high mortality. Distal metastasis is one of the main causes of death in GC patients, which is also related to poor prognosis. The mRNA profiles and clinical information of GC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Univariate Cox and LASSO Cox analyses were used to screen the optimal metastasis-related genes (MRGs) to establish a prognostic Risk Score model for GC patients. The nomogram was used to visualize the Risk Score and predict the 1-, 3-, 5-year survival rate. The immune cell infiltration was analyzed by CIBERSORT and the ratio of immune-stromal component was calculated by the ESTIMATE algorithm. A total of 142 differentially expressed genes were identified between metastatic and non-metastatic GC samples. The optimal 8 genes, comprising GAMT (guanidinoacetate N-methyltransferase), ABCB5 (ATP-binding cassette subfamily B member 5), ITIH3 (inter-alpha-trypsin inhibitor heavy chain 3), GDF3 (growth differentiation factor 3), VSTM2L (V-set and transmembrane domain-containing 2 like), CIDEA (cell death inducing DFFA like effector a), NPTX1 (neuronal pentraxin-1), and UMOD (uromodulin), were further screened to establish a prognostic Risk Score, which proved to be an independent prognostic factor. Patients in high-risk group had a poor prognosis. There were significant differences in the proportion of 11 tumor-infiltrating immune cells between high-risk and low-risk subgroups. In addition, the StromalScore, ImmuneScore, and ESTIMATEScore in high-risk group were higher than those in low-risk group, indicating that the tumor microenvironment of the high-risk group was more complex. A Risk Score model based on eight metastasis-related genes could clearly distinguish the prognosis of GC patients. The poor prognosis of patients with high-Risk Score might be associated with the complex tumor microenvironments.