ABSTRACT
Background Arsenic, cobalt, barium, and other individual metal exposure have been confirmed to be associated with the incidence of kidney stones. However, there are few studies on the association between mixed metal exposure and kidney stones, especially in occupational groups. Objective To investigate the association between mixed metal exposure and kidney stones in an occupational population from a metal smelting plant. Methods A questionnaire survey was conducted to collect sociodemographic characteristics, medical history, and lifestyle information of 1158 mixed metal-exposed workers in a metal smelting plant in Guangdong Province from July 2021 to January 2022. Midstream morning urine samples were collected from the workers, the concentrations of 18 metals including lithium, vanadium, chromium, manganese, cobalt, nickel, copper, zinc, arsenic, selenium, strontium, molybdenum, cadmium, cesium, barium, tungsten, titanium, and lead were measured by inductively coupled plasma mass spectrometry, and the urinary mercury levels were measured by cold atomic absorption spectroscopy. Based on predetermined inclusion criteria, a total of 919 mixed metal-exposed workers were included in the study, including 117 workers in the kidney stone group and 802 workers in the non-kidney stone group. With a detection rate of urinary metals greater than 80% as entry criterion, 16 eligible metals were finally included for further analysis. Parametric or non-parametric methods were used to compare the differences between continuous or categorical variables of the non-kidney stone group and the kidney stone group. Logistic regression models were constructed to explore the association between individual metal exposures and kidney stones. Weighted quantile sum (WQS) regression models were used to evaluate the association between mixed metal exposure and kidney stones, as well as the weights of each metal on kidney stones. Then Bayesian kernel machine regression (BKMR) models were used to explore the overall effect of mixed metal exposure on renal calculi and the potential interactions between metals. Results We found that there were significant differences in sex, age, length of service, and body mass Index (BMI) between the non-kidney stone group and the kidney stone group (P<0.05). The urinary concentrations of molybdenum and barium in the kidney stone group were higher than those in the non-kidney stone group, and the differences were statistically significant (P<0.05). The logistic regression models demonstrated that urinary cobalt, arsenic, molybdenum, and barium were positively correlated with the risk of kidney stones (Ptrend<0.05). The WQS regression models showed that the mixed exposure to vanadium, cobalt, arsenic, molybdenum, and barium was positively associated with the risk of kidney stones (P<0.05). Among them, molybdenum, arsenic, and barium accounted for 0.391, 0.337, and 0.154, respectively. The BKMR results revealed a positive association between metal mixture exposure and the risk of kidney stones (P<0.05). When other metals were fixed at the 25th, 50th, or 75th percentile, arsenic, molybdenum, cobalt, and barium exhibited significant positive effects on the risk of kidney stones (P<0.05), while vanadium showed a significant negative effect (P<0.05). The interaction analysis demonstrated interactions between barium and cobalt, as well as between vanadium and cobalt (P<0.05). Conclusion In the occupational population of this smelter, occupational mixed metal exposure could increase the risk of kidney stones, and the main metals are molybdenum, arsenic, barium, and cobalt.
ABSTRACT
The global emergence of SARS-CoV-2 urgently requires an in-depth understanding of molecular functions of viral proteins and their interactions with the host proteome. Several individual omics studies have extended our knowledge of COVID-19 pathophysiology1-10. Integration of such datasets to obtain a holistic view of virus-host interactions and to define the pathogenic properties of SARS-CoV-2 is limited by the heterogeneity of the experimental systems. We therefore conducted a concurrent multi-omics study of SARS-CoV-2 and SARS-CoV. Using state-of-the-art proteomics, we profiled the interactome of both viruses, as well as their influence on transcriptome, proteome, ubiquitinome and phosphoproteome in a lung-derived human cell line. Projecting these data onto the global network of cellular interactions revealed crosstalk between the perturbations taking place upon SARS-CoV-2 and SARS-CoV infections at different layers and identified unique and common molecular mechanisms of these closely related coronaviruses. The TGF-{beta} pathway, known for its involvement in tissue fibrosis, was specifically dysregulated by SARS-CoV-2 ORF8 and autophagy by SARS-CoV-2 ORF3. The extensive dataset (available at https://covinet.innatelab.org) highlights many hotspots that can be targeted by existing drugs and it can guide rational design of virus- and host-directed therapies, which we exemplify by identifying kinase and MMPs inhibitors with potent antiviral effects against SARS-CoV-2.