1
|
Marjanović Čermak AM, Mustać S, Cvjetko P, Pavičić I, Kifer D, Bešić E, Domijan AM. Thallium Toxicity and its Interference with Potassium Pathways Tested on Various Cell Lines. Biol Trace Elem Res 2024; 202:5025-5035. [PMID: 38349487 DOI: 10.1007/s12011-024-04086-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/28/2024] [Indexed: 10/01/2024]
Abstract
Thallium (Tl) is a highly toxic heavy metal whose mechanism of toxicity is still not completely understood. The aim of this study was to test Tl cytotoxicity on several cell lines of different tissue origin in order to clarify specific Tl toxicity to a particular organ. In addition, possible interference of Tl with cell potassium (K) transport was examined. Human keratinocytes (HaCaT), human hepatocellular carcinoma (HepG2), porcine kidney epithelial cells (PK15), human neuroblastoma (SH-SY5Y) and Chinese hamster lung fibroblast cells (V79) were treated with thallium (I) acetate in a wide concentration range (3.9-500 µg/mL) for 24 h, 48 and 72 h. To assess competitive interaction between Tl and K, the cells were treated with four Tl concentrations close to IC50 (15.63, 31.25, 62.50, 125 µg/mL) in combination with/or without potassium (I) acetate (500 µg/mL). The cells' morphology was monitored, and cytotoxic effect was assessed by 3-(4, 5-dimethylthiazole-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) test. The most sensitive to Tl exposure were SH-SY5Y cells, while HepG2 were the most resistant. The combined exposure to thallium (I) acetate and potassium (I) acetate for every cell line, except V79 cells, resulted in higher cell viability compared to thallium (I) acetate alone. The results of our study indicate that cell sensitivity to Tl treatment is largely affected by tissue culture origin, its function, and Na+/K+-ATPase activity.
Collapse
Affiliation(s)
| | - Stipe Mustać
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, 10000, Croatia
| | - Petra Cvjetko
- Faculty of Science, Department of Biology, University of Zagreb, Zagreb, 10000, Croatia
| | - Ivan Pavičić
- Institute for Medical Research and Occupational Health, Ksaverska cesta 2, Zagreb, 10000, Croatia
| | - Domagoj Kifer
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, 10000, Croatia
| | - Erim Bešić
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, 10000, Croatia
| | - Ana-Marija Domijan
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, 10000, Croatia
| |
Collapse
|
2
|
Zuberi S, Rafi H, Hussain A, Hashmi S. Upregulation of Nrf2 in myocardial infarction and ischemia-reperfusion injury of the heart. PLoS One 2024; 19:e0299503. [PMID: 38489253 PMCID: PMC10942075 DOI: 10.1371/journal.pone.0299503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 02/09/2024] [Indexed: 03/17/2024] Open
Abstract
Myocardial infarction (MI) is a leading cause of morbidity and mortality in the world and is characterized by ischemic necrosis of an area of the myocardium permanently devoid of blood supply. During reperfusion, reactive oxygen species are released and this causes further insult to the myocardium, resulting in ischemia-reperfusion (IR) injury. Since Nrf2 is a key regulator of redox balance, it is essential to determine its contribution to these two disease processes. Conventionally Nrf2 levels have been shown to rise immediately after ischemia and reperfusion but its contribution to disease process a week after the injury remains uncertain. Mice were divided into MI, IR injury, and sham surgery groups and were sacrificed 1 week after surgery. Infarct was visualized using H&E and trichrome staining and expression of Nrf2 was assessed using immunohistochemistry, Western blot, and ELISA. MI displayed a higher infarct size than the IR group (MI: 31.02 ± 1.45%, IR: 13.03 ± 2.57%; p < 0.01). We observed a significantly higher expression of Nrf2 in the MI group compared to the IR model using immunohistochemistry, spot densitometry of Western blot (MI: 2.22 ± 0.16, IR: 1.81 ± 0.10, Sham: 1.52 ± 0.13; p = 0.001) and ELISA (MI: 80.78 ± 27.08, IR: 31.97 ± 4.35; p < 0.01). There is a significantly higher expression of Nrf2 in MI compared to the IR injury group. Modulation of Nrf2 could be a potential target for therapeutics in the future, and its role in cardioprotection can be further investigated.
Collapse
Affiliation(s)
- Sahar Zuberi
- Department of Biological and Biomedical Sciences, Aga Khan University, Karachi Pakistan
- Department of Physiology, Rashid Latif Khan University Medical College, Lahore, Pakistan
| | - Hira Rafi
- Department of Biological and Biomedical Sciences, Aga Khan University, Karachi Pakistan
- Postdoctoral Fellow Northwestern University Feinberg School of Medicine Chicago, Illinois, United States of America
| | - Azhar Hussain
- Department of Biological and Biomedical Sciences, Aga Khan University, Karachi Pakistan
| | - Satwat Hashmi
- Department of Biological and Biomedical Sciences, Aga Khan University, Karachi Pakistan
| |
Collapse
|
3
|
Fujihara J, Nishimoto N. Thallium - poisoner's poison: An overview and review of current knowledge on the toxicological effects and mechanisms. Curr Res Toxicol 2024; 6:100157. [PMID: 38420185 PMCID: PMC10899033 DOI: 10.1016/j.crtox.2024.100157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/13/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
Thallium (Tl) is one of the most toxic metals and its historic use in homicides has led it to be known as "the poisoner's poison." This review summarizes the methods for identifying Tl and determining its concentrations in biological samples in recently reported poisoning cases, as well as the toxicokinetics, toxicological effects, toxicity mechanisms, and detoxication methods of Tl. Recent findings regarding Tl neurotoxicological pathways and toxicological effects of Tl during pregnancy are also presented. Confirmation of elevated Tl concentrations in blood, urine, or hair is indispensable for diagnosing Tl poisoning. The kidneys show the highest Tl concentration within 24 h after ingestion, while the brain shows the highest concentration thereafter. Tl has a very slow excretion rate due to its large distribution volume. Following acute exposure, gastrointestinal symptoms are observed at an early stage, and neurological dysfunction is observed later: Tl causes the most severe damage in the central nervous system. Alopecia and Mees' lines in the nails are observed within 1 month after Tl poisoning. The toxicological mechanism of Tl is considered to be interference of vital potassium-dependent processes with Tl+ because its ionic radius is similar to that of K+, as well as inhibition of enzyme reactions by the binding of Tl to -SH groups, which disturbs vital metabolic processes. Tl toxicity is also related to reactive oxygen species generation and mitochondrial dysfunction. Prussian blue is the most effective antidote, and metallothionein alone or in combination with Prussian blue was recently reported to have cytoprotective effects after Tl exposure. Because Tl poisoning cases are still reported, early determination of Tl in biological samples and treatment with an antidote are essential.
Collapse
Affiliation(s)
- Junko Fujihara
- Department of Legal Medicine, Shimane University Faculty of Medicine, 89-1 Enya, Izumo, Shimane 693-8501, Japan
| | - Naoki Nishimoto
- Shimane Institute for Industrial Technology, 1 Hokuryo, Matsue, Shimane 690-0816, Japan
| |
Collapse
|
4
|
Nagel A, Cuss CW, Goss GG, Shotyk W, Glover CN. Effects of Acute and Subchronic Waterborne Thallium Exposure on Ionoregulatory Enzyme Activity and Oxidative Stress in Rainbow Trout (Oncorhynchus mykiss). ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2024; 43:87-96. [PMID: 37750573 DOI: 10.1002/etc.5756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/04/2023] [Accepted: 09/22/2023] [Indexed: 09/27/2023]
Abstract
The mechanisms of acute (96-hour) and subchronic (28-day) toxicity of the waterborne trace metal thallium (Tl) to rainbow trout (Oncorhynchus mykiss) were investigated. Specifically, effects on branchial and renal ionoregulatory enzymes (sodium/potassium adenosine triphosphatase [ATPase; NKA] and proton ATPase) and hepatic oxidative stress endpoints (protein carbonylation, glutathione content, and activities of catalase and glutathione peroxidase) were examined. Fish (19-55 g) were acutely exposed to 0 (control), 0.9 (regulatory limit), 2004 (half the acute median lethal concentration), or 4200 (acute median lethal concentration) µg Tl L-1 or subchronically exposed to 0, 0.9, or 141 (an elevated environmental concentration) µg Tl L-1 . The only effect following acute exposure was a stimulation of renal H+ -ATPase activity at the highest Tl exposure concentration. Similarly, the only significant effect of subchronic Tl exposure was an inhibition of branchial NKA activity at 141 µg Tl L-1 , an effect that may reflect the interaction of Tl with potassium ion handling. Despite significant literature evidence for effects of Tl on oxidative stress, there were no effects of Tl on any such endpoint in rainbow trout, regardless of exposure duration or exposure concentration. Elevated basal levels of antioxidant defenses may explain this finding. These data suggest that ionoregulatory perturbance is a more likely mechanism of Tl toxicity than oxidative stress in rainbow trout but is an endpoint of relevance only at elevated environmental Tl concentrations. Environ Toxicol Chem 2024;43:87-96. © 2023 SETAC.
Collapse
Affiliation(s)
- Andrew Nagel
- Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Chad W Cuss
- Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
- School of Science and the Environment, Memorial University Newfoundland-Grenfell Campus, Corner Brook, Newfoundland, Canada
| | - Greg G Goss
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - William Shotyk
- Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
| | - Chris N Glover
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
- Faculty of Science and Technology and Athabasca River Basin Research Institute, Athabasca University, Athabasca, Alberta, Canada
| |
Collapse
|
5
|
Wang G, Fang L, Chen Y, Ma Y, Zhao H, Wu Y, Xu S, Cai G, Pan F. Association between exposure to mixture of heavy metals and hyperlipidemia risk among U.S. adults: A cross-sectional study. CHEMOSPHERE 2023; 344:140334. [PMID: 37788750 DOI: 10.1016/j.chemosphere.2023.140334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 09/20/2023] [Accepted: 09/28/2023] [Indexed: 10/05/2023]
Abstract
Previous studies have suggested that exposure to heavy metals might increase the risk of hyperlipidemia. However, limited research has investigated the association between exposure to mixture of heavy metals and hyperlipidemia risk. To explore the independent and combined effects of heavy metal exposure on hyperlipidemia risk, this study involved 3293 participants from the National Health and Nutrition Examination Survey (NHANES), including 2327 with hyperlipidemia and the remaining without. In the individual metal analysis, the logistic regression model confirmed the positive effects of barium (Ba), cadmium (Cd), mercury (Hg), Lead (Pb), and uranium (U) on hyperlipidemia risk, Ba, Cd, Hg and Pb were further validated in restricted cubic splines (RCS) regression model and identified as positive linear relationships. In the metal mixture analysis, weighted quantile sum (WQS) regression, Bayesian kernel machine regression (BKMR), and quantile-based g computation (qgcomp) models consistently revealed a positive correlation between exposure to metal mixture and hyperlipidemia risk, with Ba, Cd, Hg, Pb, and U having significant positive driving roles in the overall effects. These associations were more prominent in young/middle-aged individuals. Moreover, the BKMR model uncovered some interactions between specific heavy metals. In conclusion, this study offers new evidence supporting the link between combined exposure to multiple heavy metals and hyperlipidemia risk, but considering the limitations of this study, further prospective research is required.
Collapse
Affiliation(s)
- Guosheng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China; The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Lanlan Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China; The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Yuting Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China; The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Yubo Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China; The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Hui Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China; The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Ye Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China; The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Shengqian Xu
- Department of Rheumatism and Immunity, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, China
| | - Guoqi Cai
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China; The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Faming Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China; The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China.
| |
Collapse
|
6
|
Li W, Huang G, Tang N, Lu P, Jiang L, Lv J, Qin Y, Lin Y, Xu F, Lei D. Effects of heavy metal exposure on hypertension: A machine learning modeling approach. CHEMOSPHERE 2023; 337:139435. [PMID: 37422210 DOI: 10.1016/j.chemosphere.2023.139435] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/10/2023]
Abstract
Heavy metal exposure is a common risk factor for hypertension. To develop an interpretable predictive machine learning (ML) model for hypertension based on levels of heavy metal exposure, data from the NHANES (2003-2016) were employed. Random forest (RF), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), ridge regression (RR), AdaBoost (AB), gradient boosting decision tree (GBDT), voting classifier (VC), and K-nearest neighbour (KNN) algorithms were utilized to generate an optimal predictive model for hypertension. Three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) methods, were integrated into a pipeline and embedded in ML for model interpretation. A total of 9005 eligible individuals were randomly allocated into two distinct sets for predictive model training and validation. The results showed that among the predictive models, the RF model demonstrated the highest performance, achieving an accuracy rate of 77.40% in the validation set. The AUC and F1 score for the model were 0.84 and 0.76, respectively. Blood Pb, urinary Cd, urinary Tl, and urinary Co levels were identified as the main influencers of hypertension, and their contribution weights were 0.0504 ± 0.0482, 0.0389 ± 0.0256, 0.0307 ± 0.0179, and 0.0296 ± 0.0162, respectively. Blood Pb (0.55-2.93 μg/dL) and urinary Cd (0.06-0.15 μg/L) levels exhibited the most pronounced upwards trend with the risk of hypertension within a specific value range, while urinary Tl (0.06-0.26 μg/L) and urinary Co (0.02-0.32 μg/L) levels demonstrated a declining trend with hypertension. The findings on the synergistic effects indicated that Pb and Cd were the primary determinants of hypertension. Our findings underscore the predictive value of heavy metals for hypertension. By utilizing interpretable methods, we discerned that Pb, Cd, Tl, and Co emerged as noteworthy contributors within the predictive model.
Collapse
Affiliation(s)
- Wenxiang Li
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China.
| | - Guangyi Huang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Ningning Tang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Peng Lu
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Li Jiang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Jian Lv
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Yuanjun Qin
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Yunru Lin
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Fan Xu
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China.
| | - Daizai Lei
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China.
| |
Collapse
|