**3.4 Use of an alternative method to detect human gene polymorphisms without genotyping**

In the context of occupational and environmental exposure, the role of biotransformation enzymes is to ensure efficient detoxification of endogenous and exogenous compounds by specific biochemical pathways. These modify the dangerous substances into inactive compounds which once excreted into urine will avoid metabolite accumulation and harm the human organism. Although the screening of individual gene polymorphisms by the molecular biology laboratory is the ideal procedure to assess the susceptibility of each individual participating to the study, the availability of workers to donate the biosample is fundamental to proceed with the genetic analysis. One difficulty may be represented by the lack of workers' consent to venipuncture or in general to the biosample harvesting, either because they are simply not used to this procedure or because the venipuncture is perceived as too invasive and painful technique or due to the worker fear and insecurity of the potential analysis result. However the gene polymorphism assessment has no diagnostic value in terms of predisposition to develop a particular disease. In our experience we noted that workers of mixed ethnicities are employed in many industrial companies, and in this context, it might be difficult, if not impossible, to collect particularly the blood samples in comparison to urine. This may depend on several factors: the poor knowledge of the language, the difficulty of communication and the difference in culture, habit, diet and belief among workers. To bypass such critical issue and achieve the ethnic-specific genotype information without making use of the laboratory analysis, we took advantage of a public and online available database (http://grch37.ensembl.org/Homo\_sapiens/Variation) containing a provisional collection of the majority of genotype and allele frequencies of several ethnic groups. This resource is freely accessible and allowed us to obtain the genetic profile of different ethnicities which helped to predict and identify in silico specific susceptibilities within the population. This model, based on the statistical method of principal component analysis (PCA), has been designed to assess the relative risk of the homozygous variant and heterozygous genotype in four macro-groups, i.e. Africans, Eastern Asians, South Asians and Europeans, with respect to the worldwide population [34]. It has been conceived to identify the critical susceptibilities in the polymorphisms of genes involved in three main functional biochemical pathways, i.e. detoxification, oxidative stress and repair of damaged DNA, following exposure to the hazardous compounds. The SNPs have been selected on the basis of the exposure to toxic and carcinogenic substances commonly found in manufacturing factories and shipyards. Below we list the gene polymorphisms

**73**

**SNPs** Detoxification

GST-T1 GST-M1

GST-P1 GST-A1 CYP1A1\*2A

CYP2E1\*5B CYP2E1\*intron6

EPHX1 Ex\_3 EPHX1 Ex\_4

MPO

2333227

2234922

1051740

6413432

3813867

4646903

3957357

1695

366631

17856199

F: 5′ -TTC CTT ACT GGT CCT CAC ATC TC-3′

R: 5′ -TCA CCG GAT CAT GGC CAG CA-3′

F: 5′-GAA CTC CCT GAA AAG CTA AAG C-3′

R: 5′-GTT GGG CTC AAA TAT ACG GTG G-3′

F: 5′-ACC CCA GGG CTC TAT GGG AA-3′

R: 5′-TGA GGG CAC AAG AAG CCC CT-3′

F: 5′-GCA TCA GCT TGC CCT TCA-3′

R: 5′-AAA CGC TGT CAC CGT CCTG-3′

F: 5′-CAGTGAAGAGGTGTAGCCGCT3′

R: 5′-TAGGAGTCTTGTCTCATGCCT3′

F: 5′-CCA GTC GAG TCT ACA TTG TCA-3′

55

RsaI

R: 5′-TTC ATT CTG TCT TCT AAC TGG-3′

F: 5′-TCG TCA GTT CCT GAA AGC AGG-3′

62

DraI

R: 5′-GAG CTC TGA TGC AAG TAT CGC A-3′

F: 5′-GAT CGA TAA GTT CCG TTT CAC C-3′

52.6

EcoRV

R: 5′-ATC CTT AGT CTT GAA GTG AGG AT-3′

F: 5′-GGG GTA CCA GAG CCT GAC CGT-3′

58

RsaI

R: 5′-AAC ACC GGG CCC ACC CTT GGC-3′

F: 5′-CGG TAT AGG CAC ACA ATG GTG AG-3′

56

AciI

R: 5′-GCA ATG GTT CAA GCG ATT CTT C 3'

65

MspI

64

EarI

62

None

None

Teixeira et al. [22]

62

None

None

62

BsmAI

Wt: 176 Het: 176, 91, 85

Mut: 91, 85 Wt: 400 Het: 400, 308, 92

Ping et al. [23]

Mut: 308, 92 Wt: 340 Het: 340, 200, 140

Nie et al. [24]

Mut: 200, 140

Wt: 360, 50

Le Marchand et al.

[25]

Het: 410, 360, 50

Mut: 410

Wt: 572, 302, 121

Liu et al. [26]

Het: 874, 572, 302, 121,

Mut: 874, 121

Wt: 160

Erkisi et al. [27]

Het: 160, 140, 20

Mut: 140, 20

Wt: 295, 62

Hassett et al. [28]

Het: 295, 174, 62

Mut: 174, 121, 62

Wt: 168, 121, 61

Cascorbi et al. [29]

Het: 289, 168, 121, 61

Mut: 289, 61

**rs number**

**Primer sequences**

**Annealing temperature (°C)**

**Restriction enzyme**

**Restriction pattern (bp)**

**References**

*The Role of Genetic Polymorphisms in the Occupational Exposure*

*DOI: http://dx.doi.org/10.5772/intechopen.86975*


#### *The Role of Genetic Polymorphisms in the Occupational Exposure DOI: http://dx.doi.org/10.5772/intechopen.86975*

*The Recent Topics in Genetic Polymorphisms*

**without genotyping**

to be unreliable. In particular we applied this alternative method to detect two polymorphisms of one gene involved in response to oxidative stress and ototoxicity (NRF2 -617C/A and -653A/G), but the data were not completely satisfactory since CTPP-PCR produced contradictory results, particularly for the heterozygosis classification, requiring another orthogonal technique for confirming the data [20]. In the following section, we propose a list of the gene polymorphisms that we usually evaluate in the biological monitoring of the occupational exposure. They have been grouped on the basis of the enzyme function, i.e. detoxification, oxidative stress and DNA repair. The majority of them has been analyzed and reported in our previous published papers as biomarkers of susceptibility to the exposure of several organic compounds including styrene, toluene, ethylbenzene, benzene as well as biomarkers of genotoxic damage and of oxidative stress [14–17, 21]. **Table 1** shows a list of the analyzed susceptibility biomarkers together with the PCR-RFLP

protocols which have been used by our group with some modifications.

**3.4 Use of an alternative method to detect human gene polymorphisms** 

In the context of occupational and environmental exposure, the role of biotransformation enzymes is to ensure efficient detoxification of endogenous and exogenous compounds by specific biochemical pathways. These modify the dangerous substances into inactive compounds which once excreted into urine will avoid metabolite accumulation and harm the human organism. Although the screening of individual gene polymorphisms by the molecular biology laboratory is the ideal procedure to assess the susceptibility of each individual participating to the study, the availability of workers to donate the biosample is fundamental to proceed with the genetic analysis. One difficulty may be represented by the lack of workers' consent to venipuncture or in general to the biosample harvesting, either because they are simply not used to this procedure or because the venipuncture is perceived as too invasive and painful technique or due to the worker fear and insecurity of the potential analysis result. However the gene polymorphism assessment has no diagnostic value in terms of predisposition to develop a particular disease. In our experience we noted that workers of mixed ethnicities are employed in many industrial companies, and in this context, it might be difficult, if not impossible, to collect particularly the blood samples in comparison to urine. This may depend on several factors: the poor knowledge of the language, the difficulty of communication and the difference in culture, habit, diet and belief among workers. To bypass such critical issue and achieve the ethnic-specific genotype information without making use of the laboratory analysis, we took advantage of a public and online available database (http://grch37.ensembl.org/Homo\_sapiens/Variation) containing a provisional collection of the majority of genotype and allele frequencies of several ethnic groups. This resource is freely accessible and allowed us to obtain the genetic profile of different ethnicities which helped to predict and identify in silico specific susceptibilities within the population. This model, based on the statistical method of principal component analysis (PCA), has been designed to assess the relative risk of the homozygous variant and heterozygous genotype in four macro-groups, i.e. Africans, Eastern Asians, South Asians and Europeans, with respect to the worldwide population [34]. It has been conceived to identify the critical susceptibilities in the polymorphisms of genes involved in three main functional biochemical pathways, i.e. detoxification, oxidative stress and repair of damaged DNA, following exposure to the hazardous compounds. The SNPs have been selected on the basis of the exposure to toxic and carcinogenic substances commonly found in manufacturing factories and shipyards. Below we list the gene polymorphisms

**72**


**Table 1.**

**75**

**Figure 3.**

*The Role of Genetic Polymorphisms in the Occupational Exposure*

quinone dehydrogenase 1 (NQO1) rs1800566.

nine glycosylase (hOGG1) rs1052133.

*Calculation of the indicator risk in the four ethnic populations.*

reported in **Table 1** and commonly assessed in the laboratory genotyping during the biomonitoring campaigns that in our predictive model we integrated with other

(CYP2E1\*5B) rs3813867, myeloperoxidase (MPO) rs2333227.

1.Detoxification pathway genes: Glutathione S transferase (GST-A1) rs3957357, (GST-M1) rs366631, (GST-T1) rs17856199, (GST-P1) rs1695, epoxide hydrolase 1 (EPHX1 Ex\_3) rs1051740, (EPHX1 Ex\_4) rs2234922, cytochrome P450 1A1 (CYP1A1\_2A) rs4646903, cytochrome P450 2E1 (CYP2E1\*6) rs6413432,

2.Oxidative stress genes: Nuclear factor (erythroid-derived 2)-like 2 (NRF2) rs6721961, NRF2 rs35652124, heme oxygenase (HO-1) rs2071746, NAD(P)H

3.DNA repair pathway genes: X-ray repair cross-complementing 1 (XRCC1) rs25487, X-ray repair cross-complementing 3 (XRCC3) rs1799782, 8-oxogua

The model provides the relative risk (RR) for each ethnic group. RR has been calculated as the ratio between the variations of the gene frequency of the specific ethnic group with respect to the variation of the gene frequency of the worldwide population. If the variation of the gene frequency is >1, it means the susceptibility risk is higher in the ethnic group than in the general population. The most disadvan

tageous condition, unless specified, is generally associated with the frequency of the homozygous variant, namely, the mutant genotype, although in very few excep

tions it might associate to the homozygous wild-type genotype. The details of the rationale, method and elaboration of the susceptibility relative risk of the model are available in our previous paper [34]. Briefly, the predictive model allows to identify (1) ethnicity similarity in the susceptibility risk, (2) correlation of the ethnicity with specific metabolic genes and (3) estimation of the RR indicator in the four ethnic macro-populations. In **Figure 3** we report a quantification of the RR for all the four ethnic groups which has been worked out for the three gene polymorphism clusters (i.e. detoxification, oxidative stress and DNA repair). As seen in the graph, the




*DOI: http://dx.doi.org/10.5772/intechopen.86975*

relevant polymorphic genes [34].

 *List of susceptibility biomarkers analysed in our biomonitoring campaigns.* *The Role of Genetic Polymorphisms in the Occupational Exposure DOI: http://dx.doi.org/10.5772/intechopen.86975*

*The Recent Topics in Genetic Polymorphisms*

**74**

**SNPs** Oxidative stress

NRF2 – 617C/A NRF2 – 653A/G

NQO1

HO-1 DNA repair

XRCC1 399 G/A

XRCC1 194 hOGG1 326

1,052,133 *Wt, Homozygous wild type; Het, heterozygous; Mut, homozygous mutant.*

*List of susceptibility biomarkers analysed in our biomonitoring campaigns.*

**Table 1.**

1799782

25487

F: 5′-TTG TGC TTT CTC TGT GTC CA-3′

61

MspI

Wt: 374, 221

Kowalski et al. [32]

Het: 615, 374, 221

Mut: 615

Wt: 292, 174

Het: 313, 292, 174

Mut: 313, 174

Wt: 200

Le Marchand et al.

[33]

Het: 200, 100

Mut: 100

R: 5′-TCC TCC AGC CTT TTC TGA TA-3′

F: 5′-GCC CCG TCC CAG GTA-3′

61

MspI

R: 5'-AGC CCC AAG ACC CTT TCA TC-3'

F: 5′-GGA AGG TGC TTG GGG AAT-3′

58

Fnu4HI

R: 5′-ACT GTC ACT AGT CTC ACC AG-3'

2071746

1800566

35652124

6721961

F1 5′- CCC TGA TTT GGA GTT GCA GAA CC-3′

R2 5′- CTC CGT TTG CCT TTG ACG AC-3′

F1 5′-CTT TTA TCT CAC TTT ACC GCC CGA G-3' R2 5'-GGG

62

BseRI

GTT CCC GTT TTT CTC CC-3′

F: 5′-TCC TCA GAG TGG CAT TCT GC-3′

65

HinfI

R: 5′-TCT CCT CAT CCT GTA CCT CT-3′

5′-GTT CCT GAT GTT GCC CAC CAA GC-3′

60

HindIII

5′-CTG CAG GCT CTG GGT GTG ATT TTG-3′

**rs number**

**Primer sequences**

**Annealing** 

**Restriction** 

**Restriction pattern (bp)**

**References**

**enzyme**

**temperature (°C)**

62

NgoMIV

Wt: 191, 91

Chiarella et al. [20]

Het: 281, 191, 91

Mut: 282

Wt: 180, 138

Het: 318, 280,180,

Mut: 318

Wt: 195

Chen et al. [30]

Het: 195, 151,

Mut: 151

Wt: 131

Song et al. [31]

Het: 20, 131

Mut: 20

reported in **Table 1** and commonly assessed in the laboratory genotyping during the biomonitoring campaigns that in our predictive model we integrated with other relevant polymorphic genes [34].


The model provides the relative risk (RR) for each ethnic group. RR has been calculated as the ratio between the variations of the gene frequency of the specific ethnic group with respect to the variation of the gene frequency of the worldwide population. If the variation of the gene frequency is >1, it means the susceptibility risk is higher in the ethnic group than in the general population. The most disadvantageous condition, unless specified, is generally associated with the frequency of the homozygous variant, namely, the mutant genotype, although in very few exceptions it might associate to the homozygous wild-type genotype. The details of the rationale, method and elaboration of the susceptibility relative risk of the model are available in our previous paper [34]. Briefly, the predictive model allows to identify (1) ethnicity similarity in the susceptibility risk, (2) correlation of the ethnicity with specific metabolic genes and (3) estimation of the RR indicator in the four ethnic macro-populations. In **Figure 3** we report a quantification of the RR for all the four ethnic groups which has been worked out for the three gene polymorphism clusters (i.e. detoxification, oxidative stress and DNA repair). As seen in the graph, the

**Figure 3.** *Calculation of the indicator risk in the four ethnic populations.*

minimum risk for the three categories of gene polymorphisms is observed in the case of Africans, while South Asians are associated to the highest risk for detoxification and oxidative stress; Europeans show the highest risk in the DNA repair; and East Asians show a high risk in the oxidative stress. According to the following results, it seems that South Asians are associated with a cumulative highest susceptibility risk in comparison to the other populations, while Africans are associated to the lowest risk. The model cited here may represent a useful tool to predict the susceptibility risk associated to the occupational exposure and a potential alternative substituting the genotype screening of workers. However, it is still provisional, and it could be improved considering other ethnicities and including in the analysis a higher number of polymorphic genes involved in the biotransformation of the toxic and carcinogenic substances found in the workplace.
