Macaque monkeys are good sentinel to humans for environmental pollutions because their similarities in genetic and physiological characteristics. So, their reference values about exposures to heavy metals are required for proper data interpretation. Here, we report several heavy metals concentrations in the hair of rhesus monkeys which are widely used in biomedical research. The hair of 28 imported rhesus monkeys from an animal farm in southwest China were examined for the presence of eight heavy metals (Arsenic, Beryllium, Cadmium, Chromium, Iron, Lead, Mercury, and Selenium). The analyzed data in parts per million (ppm) for hair concentrations of heavy metals in rhesus monkeys were as follow: As (0.654+/-0.331), Be (0.005+/-0.003), Cd (0.034+/-0.022), Cr (11.329+/-4.259), Fe (87.106+/-30.114), Pb (0.656+/-0.613), Hg (0.916+/-0.619), and Se (3.200+/-0.735). The concentrations of Be, Cr, and As showed significant higher in females than in males (P<0.05). We present here the reference values of several heavy metals in healthy China-origin rhesus monkeys. These data may provide valuable information for veterinarians and investigators using rhesus monkeys in experimental studies.
Animals ; Beryllium ; Cadmium ; China ; Chromium ; Female ; Hair ; Haplorhini ; Humans ; Iron ; Macaca ; Macaca mulatta ; Male ; Metals, Heavy ; Nitriles ; Pyrethrins ; Reference Values ; Research Personnel ; Veterinarians

Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.