Pollution has been recognised as a major global health threat. Although exposure to various pollutants, including toxic metals or mixtures of environmental stressors is widespread, the development of diseases caused by direct environmental exposure is, luckily, limited. Whether the disease develops will depend on the causative agent, exposure levels and duration, the period of life when exposure occurs, age, and sex. Other factors that may contribute to the development and progression of a disease include other condition or disease, dietary habits, physical activity, medications taken, and variation in genetic susceptibility (1, 2, 3).
In the course of our continuing study of the exposure, health risks, and effects of the main toxic and essential elements lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), zinc (Zn), copper (Cu), iron (Fe), and selenium (Se), we recently came across increasing evidence of a link between the levels of these elements in the body of healthy and diseased persons and specific gene polymorphisms of metallothioneins (MTs). This motivated us to prepare an overview of the relationships between element levels and three most studied single nucleotide polymorphisms (SNPs) of the
Metallothioneins are a superfamily of cysteine-rich, intracellular, metal-binding proteins present in plants, vertebrates, invertebrates, eukaryotes, and prokaryotes. Historically, the discovery of MTs has closely been related to the study of Cd. The earliest work in this area was reported in 1941 by Maliuga, whereas the data on Cd-binding protein isolated from equine renal cortex, later named
In mammals, including humans, there are four main groups of MTs with different sequences, expression, and characteristics: MT1, MT2, MT3, and MT4. Isoforms MT1 and MT2 are expressed in almost all tissues. MT3 is expressed mainly in the brain, and to a lesser extent in the heart, kidneys, and reproductive organs (reviewed in 8–10, 13–15). MT4 is expressed in the epithelial cells of the skin and mucosa (16). MT molecules are single-chain polypeptides, which contain 61 to 68 amino acids, and 20 of them are cysteine, ordered in the sequences Cys-Cys, Cys-x-Cys, and Cys-x-y-Cys (x and y are amino acid which are not cysteine) (8, 9, 10, 17). Cysteine sulphur atoms are responsible for binding divalent metals in two clusters of MTs, connected with a sequence which does not contain cysteine. Amino acids 1 to 30 form a stable α-cluster (C-terminal) with four metal binding sites, whereas amino acids 31 to 68 form a reactive β-cluster (N-terminal) with three binding sites for divalent metals ions. Therefore, each MT molecule can bind up to seven divalent ions of Zn, Cd, Hg, and Pb, 12 of Cu, and 18 of Ag. Four metal ions first fill the α-cluster, and remaining three ions enter the β-cluster. Metals bound to the β-cluster are released more easily than metals bound to the α-cluster (8, 10, 13, 15, 18). The order of metal-binding affinities was tested by
The synthesis of MTs is induced by numerous factors such as metals and metalloids, various chemical agents, including acetaminophen (paracetamol), cytokines, and many other stress-producing conditions, including oxidative stress, infection and inflammation. The peculiar chemical structure of MTs gives them their molecular stability and specificity and defines their role in various physiological and pathological conditions (8, 9, 10, 11, 29, 30, 31, 32, 33). Their main biological function is to maintain the homeostasis of essential metals Zn and Cu (reviewed in 12). Studies conducted
Metallothioneins have multiple roles. Besides its main function to keep essential elements in balance, they protect the body against free radicals and toxic effects of metal ions (reviewed in 8–10, 13, 24, 42−46). High levels of MTs can be found in foetal and neonatal liver, but these drop to the levels found in adults during the postnatal period. Increased liver MT levels during prenatal period in all mammalian species are believed to protect against potentially toxic Zn and Cu ions before the intestinal control mechanisms develop (46, 47, 48, 49). Another important role of MTs is to protect against oxidative stress caused by various environmental stressors, including toxic metals. Experimental studies showed lower acute hepatotoxicity of Cd due to induced MT synthesis and high Cd binding to cytosolic MT, which reduces exposure of target organelles to Cd (reviewed in 32). Studies conducted on knock out mice showed that those without MT expression were more sensitive to Cd toxicity than control mice. The protective effects of MTs are generally clear against acute metal toxicity and carcinogenicity but not as much against chronic metal toxicity, to be addressed later in the text (8, 50, 51, 52). In general, large amounts of –SH groups in MT molecule enable reaction with numerous electrophilic chemicals, as they catch free radicals such as hydroxyl, superoxide or nitric oxide radicals produced during metabolism of xenobiotics (33, 53, 54, 55, 56, 57).
Other important roles of MTs involve cell survival, inhibition of apoptosis, angiogenesis and vascular remodelling, and immunomodulation. Studies on human umbilical vascular endothelial cells (HUVECs) have shown that a homedomain protein HMBOX1, which acts as a transcription factor and is abundantly expressed in the cytoplasm of the endothelial cells, maintains cell survival by promoting autophagy and inhibiting apoptosis by interaction with MT2, which increases intracellular free Zn (58). This role of MTs in vascular remodelling is important in the development of atherosclerosis and malignant tumours. Furthermore, MTs seem to inhibit pro-inflammatory cytokines, such as interleukins IL-6 and IL- 12 and tumour necrosis factor TNF-α, and can therefore supress inflammation (59). Investigations on MT-null mice showed higher susceptibility to the hepatotoxic effects of the anti-inflammatory drug paracetamol (acetaminophen), which points to the protective role of MTs against chemically induced hepatotoxicity (60, 61). The protective antioxidant role of MTs against radiation-mediated immunosuppression and cell damage was confirmed in experiments on MT-null mice (62, 63).
Synthesis of MTs in humans is encoded by a cluster of genes located in the q13 locus of chromosome 16 (16q13). Until now, 17 genes have been identified in this cluster, and at least 11 of them are functional; eight among MT1 isoforms (
Metal ions other than Zn can induce MT synthesis by mechanisms different than the one described above. Toxic metals cannot activate MTF-1 and, due to high binding affinity for MT, they replace Zn ions in MT molecules and thus increase intracellular Zn levels (reviewed in 13–15, 24). Free Zn then stimulates further synthesis of MT by binding to MTF- 1, which then binds to MRE and ultimately has impact on metal toxicity. In other words, under conditions of acute exposure to high doses of toxic metals such as Cd or Hg, higher MT expression may reduce their toxicity. However, in chronic exposure to either of the toxic metals (Cd or Hg), increased MT synthesis leads to prolonged retention of that metal in the body, which increases the risk of toxic effects. In addition, increased MT may capture essential elements in internal organs, primarily Zn in the liver, making them less available for their physiological roles such as transfer to the developing foetus through placenta during pregnancy (reviewed in 8, 73).
Metallothionein expression can be induced by oxidative stress when generated hydrogen peroxide (H2O2) radicals oxidise MT, and Zn is released, which then activates MTF-1 (64). Glucocorticoids also regulate MT transcription by binding to their response elements (GREs) in the promoter region of the MT genes (74). MT expression can be also induced also by tissue hypoxia (75), catecholamines (76), or hypothermia (77).
Single nucleotide polymorphisms (SNPs) are genetic variations characterised by the replacement of one nucleotide with another in a certain stretch of DNA, which occurs in at least 1 % of the population and differs between population groups. Given their location, SNPs can either be in the coding or non-coding gene region. Those in the coding region may affect amino acid arrangement or influence protein kinetics, mRNA structure, and stability, while SNPs in the promoter region or other regulatory gene regions affect protein production (reviewed in 67, 78). According to the National Center for Biotechnology Information (NCBI) database on polymorphism, dbSNP (79), 24 polymorphisms in the
Below we describe these and other information about
The rs28366003 (
Studies of the rs28366003 SNP were mostly conducted in Turkish and Polish populations, but several were also done in Japan and the United States, China, Thailand, Spain, and Croatia. Table 1 shows the frequencies of AA, AG, and GG genotypes reported in these studies. According to the literature data, the frequency of the AA genotype ranges from 84.0 % to 95.5 % in healthy Polish (80, 81, 82, 84, 85, 86, 87, 88), 86.0 % to 90.4 % in Turkish (89, 90, 91, 92, 93, 94), and about 82 % in Japanese population (95, 96). The highest frequencies were found in a healthy Spanish population (97.9 %) (97), US black women (97.9 %) (98), Croatian women (93–94.0 %) (4, 99), and a healthy Chinese population (92.5 %) (100). The lowest frequency of 57.8 % and 53.4 % was found in healthy Iranian and Columbian populations, respectively (101, 102). The frequency of the AG ranged from 2.1 % in healthy Spanish population (97) and black US women (98) to 37.8 % and 43.6 % in Iranian and Columbian population, respectively (101, 102). We conducted the first study of that kind in Croatia and found that nearly 6 % of the healthy postpartum women were G allele carriers (4). Several authors reported higher percentages of G allele carriers in case study groups than controls (80, 84, 97, 100), and others reported no differences (88, 96). Higher frequency of AG genotype was reported among white (12.8 %) than black (2.1 %) women in the USA (98).
Genotype frequencies of the rs28366003 (
Authors and year of publication (reference No.) | Genotype frequencies (%) |
|||||
---|---|---|---|---|---|---|
Ethnicity | n | Study participants | AA | AG | GG | |
Stajnko et al., 2019 (99) | Croatian | 136 | Pregnant women | 93.0# | 7.0$ | |
Slovenian | 176 | Non-pregnant women | 95.0# | 5.0$ | ||
Shokrzadeh et al., 2019 (101) | Iranian | 95 | Men and women with gastric cancer | 46.4 | 41.0 | 12.6 |
90 | Control healthy men and women | 57.8 | 37.8 | 4.4 | ||
Sekovanić et al., 2018 (4) | Croatian | 268 | Mother-newborn pairs | 94.0 | 6.0 | § |
González-al., 2018 (Martínez 102) et | Colombian | 101 | Men and women | 53.4 | 43.6 | 3.0 |
Białkowska et al., 2018 | Polish | 197 | Men with prostate cancer | 90.9 | 9.1§ | |
(88) | 197 | Control men without prostate cancer | 89.3 | 10.7§ | ||
Yang et al., 2017 (105) | Thai | 677 | Men and women | 79.5 | 20.5 | 0.0 |
Liu et al., 2017 (100) | Chinese | 459 | Women with breast cancer (various types) | 82.3 | 15.3 | 2.4 |
549 | Control healthy women | 92.5 | 7.5 | 0.0 | ||
130 | Men and women with AMD | 88.5 | 11.5 | 0.0 | ||
García et al., 2017 (97) | Spanish | 96 | Control healthy men and women | 97.9 | 2.1 | 0.0 |
Raudenska et al., 2017 (103) | 70 | Men and women with type 2 diabetes mellitus | 88.6 | 8.6 | 0.0 | |
Czech | 80 | Control healthy men and women | 86.3 | 13.7 | 0.0 | |
Hattori et al., 2016 (95) | Japanese | 2774 | Men and women | 81.8 | 17.4 | 0.8 |
Adams et al., 2015 (104) | US | 170 | Premenopausal women | 88.0 | 12.0 | 0.0 |
151 | Men and women | 84.0 | 15.0 | 1.0 | ||
130 | Men and women with SIP | 75.4 | 23.8 | 0.8 | ||
Starska et al., 2015 (80) | Polish | 418 | Control men and women without head or neck tumour | 95.5 | 4.1 | 0.0 |
117 | Men and women with SIP | 76.1 | 23.1 | 0.8 | ||
Starska et al., 2015 (84) | Polish | 132 | Control men and women with normal sinonasal mucosa | 87.9 | 12.1 | 0.0 |
323 | Men and women with SCC | 89.2 | 9.9 | 0.9 | ||
Starska et al., 2014 (85) | Polish | 116 | Control men and women with normal laryngeal mucosa | 84.5 | 14.6 | 0.9 |
323 | Men and women with laryngeal cancer | 89.2 | 9.9 | 0.9 | ||
Starska et al., 2014 (81) | Polish | 418 | Control healthy men and women | 84.0 | 16.0 | 0.0 |
Krześlak et al., 2014 (82) | 534 | Women with ductal breast cancer | 87.1 | 12.3 | 0.6 | |
Polish | 556 | Control healthy women | 92.8 | 7.2 | 0.0 | |
Krześlak et al., 2013 (86) | 412 | Men with prostate cancer | 76.0 | 21.1 | 2.9 | |
Polish | 67 | Control men without prostate cancer | 88.0 | 12.0 | 0.0 | |
Wang et al., 2012 (106) | US | 239 | Men and women | 89.1 | 10.1 | 0.8 |
358 | Men with prostate cancer | 76.8 | 20.9 | 2.3 | ||
Forma et al., 2012 (87) | Polish | 406 | Control men without prostate cancer | 88.9 | 10.6 | 0.5 |
Tekin et al., 2012 (89) | Turkish | 95 | Mother-newborn pairs | 87.4 | 12.6 | 0.0 |
Tekin et al., 2012 (90) | Turkish | 91 | Mother-newborn pairs | 86.8 | 13.2 | 0.0 |
Kayaalti et al., 2011 (91) | Turkish | 616 | Men and women | 86.6 | 12.8 | 0.6 |
Kayaalti et al., 2011 (92) | Turkish | 354 | Men and women | 90.4 | 9.0 | 0.6 |
McElroy et al., 2010 (98) | US | 142 | Black women | 97.9 | 2.1 | - |
149 | White women | 87.3 | 12.8 | - | ||
Kayaalti et al., 2010 (93) | Turkish | 122 | Men and women (kidney samples) | 88.5 | 10.7 | 0.8 |
186 | Men and women (blood samples) | 86.0 | 13.4 | 0.6 | ||
Kayaalti et al., 2010 (94) | Turkish | 114 | Men and women (kidney samples) | 87.7 | 11.4 | 0.9 |
Hayashi et al., 2006 (96) | 37 | Patients with SALS | 75.7 | 24.3 | 0.0 | |
Japanese | 206 | Control healthy men and women | 82.5 | 17.0 | 0.5 |
n– sample size; AA – typical homozygote; AG – heterozygote; GG – atypical homozygote; AMD – age-related macular degeneration; SIP – sinonasal inverted papilloma (Schneiderian papilloma); SCC – squamous cell laryngeal carcinoma; SALS – sporadic amyotrophic lateral sclerosis; #A allele frequency; $G allele frequency; §G allele carriers (AG plus GG genotype)
Table 2 summarises associations between the rs28366003 SNP and various clinical entities reported in literature. The associations were found for different types of cancers in the breast, prostate, paranasal sinus, larynx and stomach (31, 80, 81, 82, 84, 85, 86, 87, 100, 101) and chronic diseases, such as type 2 diabetes mellitus, chronic kidney disease (95), and neovascular and dry forms of age-related macular degeneration (97). Several studies reported no association between rs28366003 SNP and prostate cancer (88), type 2 diabetes mellitus (103), or sporadic amyotrophic lateral sclerosis (96).
Table 3 summarises association between rs28366003 SNP and element levels in various healthy population groups or subjects with defined disease. These findings are controversial, as a number of studies found correlations with element concentrations in the human organism (84, 85, 86, 89, 90, 91, 104) and others did not (95, 99, 102, 105, 106).
In our recent study in healthy Croatian postpartum women (4) we found no significant association between rs28366003 and either Cd or Pb concentrations in the placenta and maternal and cord blood, although stepwise multiple regression analysis showed marginal contribution of this SNP to higher placental Cd and Pb, maternal Pb, and cord blood Cd concentrations. We did find lower placental Fe in non-smoking G allele carriers (persons with AG and GG genotype) than non-smoking persons with the wild AA genotype, which surprised us at first, as Fe-MT binding has mostly been underestimated in literature (reviewed in 107). This result can be at least partly explained by the links between MT and Fe. Conditions of an acidic lysosomal-like environment created
Association between the rs28366003
Authors and year of publication (reference No.) | Ethnicity | n | Study participants | Sample type | Findings |
---|---|---|---|---|---|
Shokrzadeh et al., 2019 (101) | Iranian | 95 | Men and women with gastric cancer | Leukocytes | SNP |
90 | Control healthy men and women | adenocarcinoma | |||
Bialkowska et al., 2018 (88) | 197 | Men with prostate cancer | Whole blood | No association was found between SNP |
|
197 | Control men without prostate cancer | G and prostate cancer | |||
Liu et al., 2017 (100) | Chinese | 459 | Women with breast cancer | Whole blood | SNP |
549 | Control healthy women | types of breast cancer | |||
130 | Men and women with AMD | AG genotype subjects had 5.5-fold higher risk for | |||
Garcia et al., 2017 (97) | Spanish | 96 | Control healthy men and women | Whole blood | AMD; G allele was associated with dry form of AMD |
Raudenska et al., 2017 (103) | 70 | Men and women with type 2 diabetes mellitus | No association was found between SNP |
||
80 | Control healthy men and women | G and type 2 diabetes mellitus | |||
165 | Men and women with DM | ||||
Hattori et al., 2016 (95) | Japanese | 417 | Men and women with CKD | Serum | GG genotype associated with CKD and AG genotype with DM; no association of |
2192 | Healthy men and women | ||||
130 | Men and women with SIP | SNP |
|||
Starska et al., 2015 (80) | 418 | Control men and women without head or neck tumour | papilloma); G allele increased 7.7-fold occurrence of SIP (Schneiderian papilloma); SNP |
||
Polish | 117 | Men and women with SIP | Tissue of nasal cavities or | Heterozygotes |
|
Starska et al., 2015 (84) | 132 | Control men and women with normal sinonasal mucosa | paranasal sinuses | of SIP | |
323 | Men and women with laryngeal cancer | AG genotype subjects had 1.6-fold higher risk for | |||
Starska et al., 2014 (81) | 418 | Control healthy men and women | laryngeal cancer development; Association between SNP |
||
Kizeslak et al., 2014 (82) | 534 | Women with ductal breast cancer | SNP |
||
Polish | 556 | Control healthy women | Whole blood | cancer | |
Kiześlak et al., 2013 (86) | Polish | 412 | Men with prostate cancer | AG genotype had higher risk for occurrence of | |
67 | Control men without prostate cancer | prostate cancer | |||
Forma et al., 2012 (87) | Polish | 358 | Men with prostate cancer | Prostate tissue | SNP |
406 | Control men without prostate cancer | cancer and Gleason score | |||
Kayaalty et al., 2011 (92) | Turkish | 354 | Healthy men and women | Whole blood | SNPMT24 -5A/G was associated with longevity |
Hayashi et al., 2006 (96) | Japanese | 37 | Patients with SALS | Whole blood | No association between SNP |
206 | Control healthy men and women | SALS and progression rate |
n - sample size; AG - heterozygote; GG - homozygote-atypical;
Association between the rs28366003
Authors and year of publication (reference No.) | ||||||||
---|---|---|---|---|---|---|---|---|
Ethnicity | n | Study participants | Sample type | AA | AG | GG | Findings | |
As concentrations (μg/g creatine) | ||||||||
Stajnko et al., 2019 (99) | Croatian | 136 | Pregnant women | Urine | 3.07* | 4.58*.§ | No differences between genotypes | |
Gonzalez-et al., 2018 Martinez, (102) | Colombian | 101 | Men and women | Urine | (Not available) | No and association SNP |
||
Maternal blood | 0.87±0.99* | 0.73±0.60*,§ | ||||||
Sekovanić et al., | Croatian | 268 | Mother-newborn pairs | Cord blood | 0.06±0.03* | 0.05±0.03*,§ | No difference in either | |
2018(4) | Placenta | 10.1±5.1 | 8.80±3.70§ | sample between genotypes | ||||
Hattori et al., 2016 (95) | Japanese | 2774 | Men and women | Serum | (Graphical illustration: Cd = 0.001*) | No differences between genotypes | ||
Adams et al, 2015 (104) | US | 321 | Men and women | Urine | (Graphical illustration) | ↓Cd in urine of G allele carriers | ||
Starska et al., 2015 (84) | Polish | 117 | Men and women with SIP | 116±79 | 376±126 | 393 | AG |
|
132 | Control men and women with normal sinonasal mucosa | Tissue of nasal or paranasal sinuses (dry) | 62.2±41.2 | 96.2±57.1 | - | |||
323 | Men and women with SCC | 198±87 | 369±128 | 509±57 | AG |
|||
Starska et al., 2014 (85) | Polish | 116 | Control men and women with normal laryngeal mucosa | Tissue of laryngeal mucosa (dry) | 87.2±32.2 | 113±26 | 117 | both sample types; GG |
Krzeslak et al., 2013 (86) | Polish | 412 | Men with prostate cancer | Prostate tissue (dry) | 720±330 | 970±460 | 1090±220 | AG |
Tekin et al, 2012 | Maternal blood | 1.60±0.94* | 2.54±2.72* | AG |
||||
Turkish | 95 | Mother-newborn pairs | Cord blood | 0.95±0.32* | 0.98±0.28* | maternal blood and ↓Cd in | ||
Placenta | 20.8±19.7 | 8.65±6.70 | placenta | |||||
Kayaalti et al, 2011 (91) | Turkish | 616 | Men and women | Whole blood | 1.60±1.44* | 2.09±1.85* | 5.98±4.38* | G allele carriers ↑Cd |
Kayaalti et al, 2010 (94) | Turkish | 114 | Men and women | Kidney samples (dry) | 87.7±62.9† | 151±60† | 153f | ↑Cd in G allele carriers |
Maternal blood | 13.7±6.6* | 12.0±3.6*,§ | ||||||
Sekovanić et al., 2018(4) | Croatian | 268 | Mother-newborn pairs | Cord blood | 8.3±5.5* | 7.1±4.0*-s | No difference in either sample between genotypes | |
Placenta | 6.9±4.9 | 5.5±2.8§ | ||||||
Yang et al, 2017 (105) | Thai | 677 | Men and women | Whole blood | 122±122* | 105±113* | - | No differences between genotype |
Maternal blood | 3.53±1.43* | 5.13±2.79* | - | |||||
Tekin et al, 2012 (901 | Turkish | 91 | Mother-newborn pairs | Cord blood | 2.42±1.00* | 2.94±1.49* | - | AG |
Placenta | 7.79±2.55 | 9.75±4.14 | - | |||||
Krzeslaketal, 2013 (86) | 412 | Men with prostate cancer | 3.11±1.27† | 4.66±1.82† | 5.11±2.52† | GG |
||
Polish | 67 | Control men without prostate cancer | Prostate tissue (dry) | 1.67±0.61f | 1.72±0.67f | - | prostate cancer | |
Kayaalti et al, 2011 (91) | Turkish | 616 | Men and women | Plasma | 30.1±13.9* | 32.9±14.9* | 50.4±11.5* | G allele carriers ↑Pb |
Maternal blood | 13.7±6.6* | 12.0±3.6*s | ||||||
Sekovanić et al., 2018(4) | Croatian | 268 | Mother-newborn pairs | Cord blood | 8.3±5.5* | 7.1±4.0*,§ | No difference in either sample between genotypes | |
Placenta | 6.9±4.9 | 5.5±2.8§ | ||||||
Wang et al., 2012 | TTC | 239 | Men and women | Urine | 1.03* | 0.76* | 0.34* | No difference between |
(106) | 247 | Men and women | Hair | 440 | 390 | 430 | genotypes | |
Fe |
||||||||
Maternal blood | 422±61* | 418±56*,§ | ||||||
Sekovanić et al., 2018(4) | Croatian | 268 | Mother-newborn pairs | Cord blood | 552±61* | 539±57*,§ | G allele carriers (AG+GG) |
|
Placenta | 83±22 | 74±18§ | ||||||
Maternal blood | 343±89* | 373±103* | - | |||||
Tekin et al, 2012 | Turkish | 95 | Mother-newborn pairs | Cord blood | 271±130* | 456±214* | - | AG |
Placenta | 527±194 | 624±162 | - | |||||
Zn |
||||||||
Maternal blood | 5.58±0.92* | 5.51±0.82*,§ | ||||||
Sekovanić et al., 2018(4) | Croatian | 268 | Mother-newborn pairs | Cord blood | 2.78±0.46* | 2.58±0.53*,§ | No difference in either sample between genotypes | |
Placenta | 13.7±3.0 | 13.4±1.8§ | ||||||
70 | Men and women with diabetics | (Graphical illustration: Zn= 3* in AG |
AG |
|||||
Raudenskaet al., 2017(103) | Czech | 80 | Control healthy men and women | Whole blood | (Graphical illustration: Zn= 5* in AG |
no association between Zn and SNP |
||
Hattori et al., 2016 (95) | Japanese | 2774 | Men and women | Serum | (Graphical illustration: Zn = 0.850*) | No differences between genotypes | ||
Adams et al, 2015 (104) | US | 321 | Men and women | Urine | (Graphical illustration) | ↓Zn in urine of G allele carriers | ||
117 | Men and women with SIP | Tissue of nasal cavities or paranasal sinuses (dry) | 52.2±41.2 | 127±76 | 136 | AG |
||
Starska et al., 2015 (84) | Polish | 132 | Control men and women with normal sinonasal mucosa | 199±44 | 204±52 | - | No association between Zn and SNPMT24 -5A/G in control samples | |
323 | Men and women with SCC | 86.4±38.1 | 184±57 | 194±74 | AG |
|||
Starska et al., 2014 (85) | Polish | 116 | Control men and women with normal laryngeal mucosa | Tissue of laryngeal mucosa | 97.6±30.0 | 133±27 | 129 | both sample types; GG |
Krzeslak et al., 2013 (86) | 412 | Men with prostate cancer | 135±48 | 239±80 | 243.7±64.4 | AG |
||
Polish | 67 | Control men without prostate cancer | Prostate tissue (dry) | 485±119 | 927±317 | AA genotype |Zn in cancer samples | ||
Maternal blood | 4.33±1.13* | 4.82±1.44* | - | |||||
Tekin et al, 2012 (89) | Turkish | 95 | Mother- newborn pairs | Cord blood | 1.32±0.55* | 1.48±0.53* | - | No difference between genotypes |
Placenta | 50.5±10.1 | 46.1±7 | - | |||||
Kayaaltietal,2011(91) | Turkish | 616 | Men and women | Plasma | 1.01±0.48* | 0.84±0.50* | 0.39±0.33* | G allele carriers ↓Zn |
Kayaalti et al., 2010 (94) | Turkish | 114 | Men and women | Kidney tissue (dry) | 180.2±84.6 | 192±115 | 142 | No difference between genotypes |
Maternal blood | 1.52±0.30* | 1.53±0.09*§ | ||||||
Sekovanić et al., 2018(4) | Croatian | 268 | Mother-newborn pairs | Cord blood | 0.59±0.09* | 0.58± 0.12*,§ | No difference in either sample between genotypes | |
Placenta | 0.78±0.18 | 0.74± 0.08§ | ||||||
Adams et al, 2015 (104) | US | 321 | Men and women | Urine | ( | Graphical illustration) | ↓Cu in urine of G allele carriers | |
117 | Men and women with SIP | Tissue of nasal cavities or paranasal sinuses (dry) | 24.2±13.6 | 27.1±11.6 | 26.2 | No differences between | ||
Starska et al., 2015 (84) | Polish | 132 | Control men and women with normal sinonasal mucosa | 11.0±2.98 | 17.2±5.2 | - | genotypes in SIP tissue samples; AG |
|
323 | Men and women with SCC | 14.4±7.83 | 26.6±12.5 | 29.7±0.72 | AG |
|||
Starska et al., 2014 (85) | Polish | 116 | Control men and women with normal laryngeal mucosa | Tissue of laryngeal mucosa | 9.85±4.10 | 12.7±3.56 | 11.5 | both sample types; GG |
412 | Men with prostate cancer | 10.3±4.2 | 21.1±9.6 | 25.6±5.8 | AG |
|||
Krzeslak et al., 2013 (86) | Polish | 67 | Control men without prostate cancer | Prostate tissue (dry) | 2.9±1.3 | 7.6±2.9 | - | ↑Cu in both sample types; GG |
Maternal blood | 1.67±0.34* | 1.84±0.50* | - | |||||
Tekin (89) et al, 2012 | Turkish | 95 | Mother-newborn pairs | Cord blood | 0.69±0.25* | 0.69±0.28* | - | No difference genotypes between |
Placenta | 5.90±2.59 | 6.63±1.73 | - | |||||
Kayaalti et al., 2011 (91) | Turkish | 616 | Men and women | Plasma | 1.04±0.44* | 1.02±0.52* | 0.91±0.37* | No difference genotypes between |
Kayaalti et al., 2010(94) | Turkish | 114 | Men and women | Kidney tissue (dry) | 17.2±16.9 | 15.3±10.6 | 31.9 | No difference genotypes between |
n- sample size;
The rs1610216 (
Genotype frequencies of the rs1610216 (
Authors and year of publication (reference No.) | Genotype frequencies (%) |
|||||
---|---|---|---|---|---|---|
Ethnicity | n | Study population | AA | AG | GG | |
Starska et al., 2015 | 130 | Men and women with SIP | 73.8 | 25.4 | 0.8 | |
(80) | Polish | 418 | Control men and women without head and neck tumours | 73.9 | 25.3 | 0.8 |
Starska et al., 2014 | Polish | 323 | Men and women with laryngeal cancer | 73.4 | 26.0 | 0.6 |
(81) | 418 | Control healthy men and women | 73.9 | 25.3 | 0.8 | |
Krześlak et al., | Polish | 534 | Women with breast cancer | 76.4 | 23.4 | 0.2 |
2014 (82) | 556 | Control healthy women | 72.3 | 27.5 | 0.2 | |
Forma et al., 2012 | Polish | 358 | Men with prostate cancer | 71.8 | 27.6 | 0.6 |
(87) | 406 | Control men without prostate cancer | 72.0 | 27.8 | 0.2 | |
142 | Patients with CAD | 89.2 | 9.4 | 1.4 | ||
Kozarova 2012 (114et ) al., | Bulgarian | 101 | Patients with DM | 69.7 | 28.3 | 2.0 |
61 | Control healthy volunteers | 90.5 | 0.0 | 9.5 | ||
100 | CS patients | 75.0 | 24.0 | 1.0 | ||
Giacconi et al., 2007 (113) | Italian | 188 | CS patients without cerebrovascular episodes | 73.0 | 25.0 | 2.0 |
218 | Control elderly volunteers | 71.0 | 26.0 | 3.0 | ||
Giacconi et al., | Italian | 91 | Men and women with carotid stenosis | 86.0 | 14.0 | 0.0 |
2005 (112) | 188 | Control elderly men and women | 70.2 | 26.1 | 3.7 |
n – sample size; AA – typical homozygote; AG – heterozygote; GG – atypical homozygote; SIP – sinonasal inverted papilloma (Schneiderian papilloma); CAD – coronary artery disease; DM – diabetes mellitus; CS – carotid artery stenosis
Association between the rs1610216 SNP
Authors and year of publication (reference No.) | Ethnicity | n | Study participants | Sample type | Findings |
---|---|---|---|---|---|
130 | Men and women with SIP | Tissue of nasal cavities or | No association between SNP |
||
Starska et al., 2015 (80) | Polish | 418 | Control men and women without head and neck tumours | paranasal sinuses | -209A/G and SIP |
323 | Men and women with laryngeal cancer | Tissue of squamous cell | No association between SNPMT24 | ||
Starska et al., 2014(81) | 418 | Control volunteers (men and women) | laryngeal cancer | -209A/G and development of laryngeal cancer | |
Krzeslak et al., 2014(82) | Polish | 534 | Women with breast cancer | Whole blood | No associations between SNP |
556 | Control healthy women | -209A/G and breast cancer | |||
Forma et al, 2012 (87) | Polish | 358 | Men with prostate cancer | Whole blood | No association between SNPMT24 |
406 | Control men without prostate cancer | -209 A/G and prostate cancer | |||
142 | Patients with CAD | Positive association between G allele carriers | |||
Kozarova et al., 2012 | Bulgarian | 101 | Patients with DM | Leukocytes | and DM; |
(114) | 61 | Control healthy volunteers | No association between |
||
100 | CS patients | ||||
Giacconi et al., 2007 (113) | Italian | 188 | CS patients without cerebrovascular episodes | Blood | No association between SNPMT24 -209 A/G and CS or cerebrovascular episodes |
218 | Control elderly volunteers | ||||
Giacconi et al., 2005 (112) | Italian | 91 | Men and women with carotid stenosis | Whole blood | No association between |
188 | Control elderly men and women | ischaemic cardiomyopathy and hyperglycaemia in AA genotype subjects |
n - sample size; AA - typical homozygote; AG - heterozygote; GG - atypical homozygote; MT2A - metallothionein 2A; DM - diabetes mellitus; CAD - coronary artery disease; SIP - sinonasal inverted papilloma (Schneiderian papilloma); CS - carotid artery stenosis
Table 5 shows the association between
The rs10636 (
Association between the rs1610216
Authors and year of publication (reference No.) | ||||||||
---|---|---|---|---|---|---|---|---|
Ethnicity | n | Study participants | Sample type | AA | AG | GG | Findings | |
Zn concentrations (mg/L) | ||||||||
Giacconi et al., 2005 (112) | Italian | 91 | Patients: elderly men and women with type 2 diabetes and carotid stenosis | Plasma | 0.77±0.15 | 0.88±0.18.§ | ↓Zn in AA vs. AG+GG in patients; in AA genotype subjects ↓Zn in patients 188 Control: healthy elderly men and vs. control | |
188 | Control: healthy elderly men and vs. control women | 0.87±0.25 | 0.88±0.15.§ |
n– sample size;
Genotype frequencies of the rs10636 (
Authors and year of publication (reference No.) | Ethnicity | n | Study participants | Genotype frequencies (%) |
||
---|---|---|---|---|---|---|
GG | GC | CC | ||||
Yang et al., 2017 (105) | Thai | 677 | Men and women | 52.4 | 41.6 | 6.0 |
Liu et al., 2017 (100) | Chinese | 459 | Women with breast cancer | 52.5 | 37.5 | 11.8 |
549 | Control healthy women | 52.8 | 40.8 | 6.4 | ||
García et al., 2017 | Spanish | 130 | Men and women with AMD | 56.9 | 36.9 | 6.2 |
(97) | 96 | Control healthy men and women | 67.7 | 27.1 | 5.2 | |
Fernandes (119) et al., 2016 | Brazilian | 221 | Workers in car battery factories | 62.0 | 32.0 | 6.0 |
Adams et al., 2015 | US | 170 | Premenopausal women | 54.0 | 42.0 | 4.0 |
(104) | 151 | Men and women | 62.0 | 34.0 | 4.0 | |
130 | Men and women with SIP | 44.6 | 43.1 | 12.3 | ||
Starska et al., 2015 (80) | Polish | 418 | Control men and women without head and neck tumours | 50.9 | 41.2 | 7.9 |
Starska et al., 2014 | Polish | 323 | Men and women with laryngeal cancer | 45.8 | 46.1 | 8.1 |
(81) | 418 | Control volunteers (men and women) | 50.9 | 41.2 | 7.9 | |
Yang et al., 2014 | Chinese | 287 | Men and women with CHD | 46.0 | 45.3 | 8.7 |
(117) | 226 | Control healthy men and women | 57.1 | 36.7 | 6.2 | |
Krześlak et al., 2014 | 534 | Women with breast cancer | 57.1 | 38.4 | 4.5 | |
(82) | Polish | 556 | Control healthy women | 50.3 | 45.5 | 4.2 |
Woods et al., 2013 | 163 | Boys average age 10 years | 61.3 | 30.7 | 8.0 | |
(116) | Portuguese | 167 | Girls average age 10 years | 59.9 | 33.5 | 6.6 |
Chen et al., 2012 (118) | Chinese | 465 | Men and women | 52.3 | 39.5 | 8.2 |
Wang et al., 2012 (106) | US | 464 | Men and women | 54.1 | 36.8 | 9.1 |
Forma et al., 2012 | Polish | 358 | Men with prostate cancer | 48.9 | 43.3 | 7.8 |
(87) | 406 | Control men without prostate cancer | 52.0 | 40.0 | 8.0 | |
Gundacker 2009 (120) et al., | Austrian | 180 | Men and women | 58.4 | 33.3 | 8.3 |
Yang et al., 2008 | Chinese | 182 | Men and women with DM | 46.7 | 42.9 | 10.4 |
(115) | 196 | Control volunteers (men and women) | 42.9 | 47.4 | 9.70 | |
100 | CS patients | 73.0 | 22.0 | 5.0 | ||
Giacconi et al., 2007 (113) | Italian | 188 | CS patients without cerebrovascular episodes | 66.0 | 30.0 | 4.0 |
218 | Control elderly volunteers | 56.0 | 37.0 | 7.0 |
n– sample size;
Association between the rs10636
Authors and year of publication (reference No.) | Ethnicity | n | Study participants | Sample type | Findings |
---|---|---|---|---|---|
Liu et al., 2017 (100) | Chinese | 459 | Women with breast cancer | Whole blood | SNP |
549 | Control healthy women | ||||
Garcia et al., 2017 (97) | Spanish | 130 | Men and women with AMD | Whole blood | No association between SNP |
96 | Control healthy men and women | ||||
Starska et al., 2015 (80) | Polish | 130 | Men and women with SIP | Tissue of nasal cavities or paranasal sinuses | No association between SNP |
418 | Control men and women without head and neck tumours | ||||
Starska et al., 2014 (81) | Polish | 323 | Men and women with laryngeal cancer | Tissue of squamous cell | No association between SNP |
418 | Control heathy men and women | laryngeal cancer | and development of laryngeal cancer | ||
Yang et al., 2014 (117) | Chinese | 287 | Men and women with CHD | Blood leukocytes | SNP |
226 | Control healthy men and women | ||||
Krzeslak et al., 2014 (82) | Polish | 534 | Women with breast cancer | Whole blood | No associations between SNPMT24 +838G/C and breast cancer |
556 | Control healthy women | ||||
Yang et al., 2008 (115) | Chinese | 397 | Men and women with DM | Whole blood | SNPMT24 +838G/C was associated with higher risk for hyperlipidemia and incidence of DM with neuropathy |
454 | Control men and women | ||||
Giacconi et al., 2007 (113) | Italian | 100 | CS patients | Blood | SNPMT24 +838G/C promote the progression of carotid artery disease to CS |
188 | CS patients without cerebrovascular episodes | ||||
218 | Control elderly volunteers |
n - sample size;
Association between the rs10636
Authors and year of publication (reference No.) | Ethnicity | n | Study participants | Sample type | Findings | |||
---|---|---|---|---|---|---|---|---|
GG | GC | CC | ||||||
Cd concentrations (μg/L) | ||||||||
Adams et al., 2015 (104) | US | 321 | Men and women | Urine | (Graphical illustration) | ↓Cd in urine of C allele carriers | ||
Chen et al, 2012 (118) | Chinese | 311 | Women exposed to Cd | Blood/ Urine | (Graphical illustration) | Trends of ↓Cd in blood of C allele carriers in highly polluted area; no difference of Cd in urine | ||
Pb concentrations (μg/L) | ||||||||
Yang et al., 2017 (105) | Thai | 677 | Men and women | Whole blood | 116±119 | 121±121 | 124±141 | No difference between genotypes |
Fernandes et al, 2016(119) | Brazilian | 221 | Workers in car battery factories | Whole blood | (Graphical illustration) | C allele carriers ↓Pb in blood | ||
Gundacker et al., 2009 (120) | Austrian | 122 | Men and women | Whole blood | 20.2 | 21.3 | 16.9 | CC genotype had ↓ Pb in blood |
Hg concentrations (μg/Lor μg/kg*) | ||||||||
Woods et al, 2013(116) | Portuguese | 96 | Boys of avg. age 10 years | Urine | 2.17±2.15 | 2.16±2.16§ | No difference between genotypes | |
Wang et al., 2012 (106) | US | 464 | Men and women | Urine | 1.04 | 1.04 | 1.22 | No difference between genotypes |
473 | Men and women | Hair | 500* | 430* | 570* | |||
Fe concentrations (mg/L) | ||||||||
Giacconi et al., 2007(113) | Italian | 288 | CS patients | Plasma | 1.11±0.47 | 0.99±0.32§ | C allele carriers had ↓Fe in plasma | |
Erythrocytes | 505±270 | 506±102§ | ||||||
Zn concentrations (mg/L) | ||||||||
Adams et al, 2015 (104) | US | 321 | Men and women | Urine | (Graphical illustration) | ↓Zn in urine of C allele carriers | ||
Giacconi et al, 2007 (113) | Italian | 288 | Patients with CS | Plasma | 0.71±0.17 | 0.74±0.15§ | C allele carriers ↑Zn in erythrocytes | |
Erythrocytes | 7.4±2.6 | 8.5±2.0§ | ||||||
Cu concentrations (mg/L) | ||||||||
Adams et al, 2015 (104) | US | 321 | Men and women | Urine | (Graphical illustration) | ↓Cu in urine of C allele carriers | ||
Giacconi et al, 2007 (113) | Italian | 288 | Patients with CS | Plasma | 1.07±0.29 | l.ll±0.24§ | C allele carriers ↑Cu in erythrocytes | |
Erythrocytes | 0.52±0.11 | 0.56±0.13§ |
n - sample size;
Table 8 summarises the findings on associations between rs 10636 and diseases. This polymorphism may be associated with higher incidence of neuropathy and hyperlipidaemia in patients with type 2 diabetes mellitus (115), coronary heart disease (117), and breast cancer (100). Krześlak et al. (82) found no association with ductal breast cancer. No association was also reported between rs10636 and macular degeneration related to age (97), Schneiderian papilloma, or laryngeal cancer (80, 81). Giacconi et al. (113) reported that in the C allele carriers carotid artery disease was more likely to progress to carotid artery stenosis.
The associations between this polymorphism and element concentrations in human organism are presented in Table 9. A weak association was reported for blood Cd in healthy women exposed to Cd (118). Although Hg was not associated with the CC genotype, a multivariate analysis indicated lower Hg in urine in subjects with the CC genotype than those with the GG genotype (106). C allele carriers were found to have lower concentrations of Cd, Cu and Zn in urine (104), Pb in blood (119, 120), and Fe in plasma (113) and higher Zn and Cu in red blood cells (113).
There is strong evidence that MTs participate in physiological and pathological processes in the human body which involve the homeostasis of intracellular essential element, primarily Zn and Cu. They may chelate divalent toxic metals, such as Cd, Pb, Hg, or Pt with the –SH groups in cysteine and thus detoxify cells, scavenge free radicals, and protect cells against oxidative stress. They also have a role in cell survival and proliferation, angiogenesis, and inhibition of apoptosis. Emerging evidence confirms that MT insufficiency may lead to pathogenic processes and carcinogenesis. Single gene polymorphisms of MTs may be responsible for individual differences in reactions to harmful effects of external chemical and physical stressors and reactive oxygen species in the body.
Identification of individual MT isoforms in human cells and tissues can be applied in prospective tissue, plasma, and urine analyses or retrospectively, using fixed paraffin-embedded tissue samples. In the future, MTs may serve as biomarkers of environmental exposure to toxic metals, such as Cd, as already reported in biomonitoring studies on occupational exposure in humans (121, 122, 123) or environmental exposure in animals (124, 125, 126). MTs are also intensively studied as potential clinical biomarkers to be used in the diagnosis, prognosis, and selection of efficient therapy/ies for a number of malignant tumours, such as breast, thyroid, head, neck, lung, gallbladder, pancreas, colon, kidney, ovary, prostate, bone, and skin cancers, childhood solid tumours, and various types of leukaemia (29, 30, 31, 32, 82, 86, 87, 94, 127, 128, 129, 130, 131, 132). Exogenous MTs are already being investigated for the treatment of pathological processes in the central nervous system (59).
To date, the rs28366003, rs10636, and rs1610216 SNPs in the