Future Perspectives

**51**

**Chapter 4**

**Abstract**

RhD antigen

**1. Introduction**

A New Alternative Approach

Determination Fetal RhD Status

*Ebru Dündar Yenilmez, Umut Kökbaş and Abdullah Tuli*

Prenatal detection of the fetal RHD status in early stage of pregnancy is observed to be useful in the management of RhD incompatibility to identify fetuses at risk of hemolytic disease. The routine use of antenatal and postnatal anti-D prophylaxis reduces the incidence of hemolytic disease of the fetus and newborn. Cell-free fetal DNA in maternal plasma is in use today for routine genotyping fetal RHD status. Fetal RhD antigens can be detected in the blood of RhDnegative pregnant women using a nanopolymer-coated biosensor and could be an alternative method for medical diagnosis. We detected RhD-positive fetal antibodies with biosensor in maternal blood of RhD-negative mothers. The electrochemical measurements were performed on a PalmSens potentiostat and corundum ceramic-based screen-printed gold electrode. The demonstrated method has a different view for the detection of fetal RhD status in early pregnancy. The biosensor technology is useful and can be carried out rapidly in clinical diagnosis. Biosensors are also reproducible methods which give results quickly compared to noninvasive fetal RHD genotyping with real-time PCR-based techniques. We suggest that this method could become an alternative part of fetal RHD genotyping from maternal plasma as a prenatal screening in the management of RhD incompatibility.

**Keywords:** RhD incompatibility, fetal RhD, biosensor, hemolytic disease,

that has a risk of hemolytic disease of the fetus and newborn (HDFN) [3].

The discovery of circulating fetal DNA by Lo et al. [1] has opened new possibilities for noninvasive prenatal diagnosis for investigators. It has been shown that this new source of fetal DNA also could be used for noninvasive prenatal determination of fetal RhD genotyping using the plasma of RhD-negative pregnant women [2]. RhD genotyping from maternal plasma is a valuable method to identify pregnancies

The HDFN is caused by IgG antibodies of the mother that cross the placenta to red cell surface antigens and facilitate destruction to the immune defense of fetal red cells or erythroid progenitors. This causes a significant rate of morbidity and mortality for the fetus. RhD antigen of the rhesus system is the most commonly

for RhD Incompatibility;

via Biosensor Technology

#### **Chapter 4**

## A New Alternative Approach for RhD Incompatibility; Determination Fetal RhD Status via Biosensor Technology

*Ebru Dündar Yenilmez, Umut Kökbaş and Abdullah Tuli* 

#### **Abstract**

 Prenatal detection of the fetal RHD status in early stage of pregnancy is observed to be useful in the management of RhD incompatibility to identify fetuses at risk of hemolytic disease. The routine use of antenatal and postnatal anti-D prophylaxis reduces the incidence of hemolytic disease of the fetus and newborn. Cell-free fetal DNA in maternal plasma is in use today for routine genotyping fetal RHD status. Fetal RhD antigens can be detected in the blood of RhDnegative pregnant women using a nanopolymer-coated biosensor and could be an alternative method for medical diagnosis. We detected RhD-positive fetal antibodies with biosensor in maternal blood of RhD-negative mothers. The electrochemical measurements were performed on a PalmSens potentiostat and corundum ceramic-based screen-printed gold electrode. The demonstrated method has a different view for the detection of fetal RhD status in early pregnancy. The biosensor technology is useful and can be carried out rapidly in clinical diagnosis. Biosensors are also reproducible methods which give results quickly compared to noninvasive fetal RHD genotyping with real-time PCR-based techniques. We suggest that this method could become an alternative part of fetal RHD genotyping from maternal plasma as a prenatal screening in the management of RhD incompatibility.

**Keywords:** RhD incompatibility, fetal RhD, biosensor, hemolytic disease, RhD antigen

#### **1. Introduction**

The discovery of circulating fetal DNA by Lo et al. [1] has opened new possibilities for noninvasive prenatal diagnosis for investigators. It has been shown that this new source of fetal DNA also could be used for noninvasive prenatal determination of fetal RhD genotyping using the plasma of RhD-negative pregnant women [2]. RhD genotyping from maternal plasma is a valuable method to identify pregnancies that has a risk of hemolytic disease of the fetus and newborn (HDFN) [3].

The HDFN is caused by IgG antibodies of the mother that cross the placenta to red cell surface antigens and facilitate destruction to the immune defense of fetal red cells or erythroid progenitors. This causes a significant rate of morbidity and mortality for the fetus. RhD antigen of the rhesus system is the most commonly

implicated antigen [4, 5]. Prophylaxis after delivery with anti-D immunoglobulin reduces the alloimmunization of RhD-negative women [4]. RhD alloimmunization has to be monitored for fetal anemia in complicated pregnancies for effective pre-/ postnatal transfusion treatment to prevent the baby from hydrops fetalis [3, 6].

Postnatal prophylaxis was used since the 1960s, with serology test used to identify the baby's RhD status [7]. The routine antenatal prophylaxis in the third trimester of pregnancy is now a standard implementation in many countries [4, 8, 9]. This application reduces the maternal sensitization and the HDFN in babies [7].

 Invasive procedures should be avoided in alloimmunized pregnant women because of the risk of transplacental hemorrhage (amniocentesis has the risk up to 17%), and the risk of pregnancy loss was found to be up to 2% after amniocentesis and chorionic villous sampling (CVS), respectively [10].

In this chapter we aimed to share our experiences about the determination of fetal RhD genotyping with cffDNA and detection of fetal RhD antigens from maternal blood using a new biosensor as a candidate method for management of RhD incompatibility.

#### **2. RhD incompatibility and management**

The knowledge about the fetal RhD type supports the management of alloimmunized pregnancies in RhD-negative women [11, 12].

 Prophylaxis after delivery is offered only to RhD-negative women who have given birth to an RhD-positive baby [9, 13]. This prevents babies from rhesus disease and reduces maternal sensitization. Routine antenatal anti-D prophylaxis use was first introduced in the mid-1990s. The sensitization rates were then reported to reduce from 1.2% for the earlier policy to 0.28% [7]. Commonly in white population, however, about 38% of these women would be carrying an RhD-negative fetus and thus receive anti-RhD immunoglobulin, a pooled human plasma product, unnecessarily [14, 15]. Fetal RHD genotyping with cell-free fetal DNA (cffDNA) is accepted as a useful method by obstetricians in early pregnancy for the management of RhD incompatibility. Since 2001, several European countries use cffDNA in maternal blood for noninvasive prenatal diagnosis of fetal RhD status [3]. There is also change in the measurement method in the hemolysis detection. This invasive method which detects the optical density at a wavelength of 450 nm in amnion fluid replaced by detect the fetal anemia by the Doppler measurement of the peak velocity of systolic blood flow in the middle cerebral artery [16].

#### **3. Fetal RhD genotyping with cell-free fetal DNA**

 Prenatal care strategies for the fetus with RhD have been changed significantly during the last few decades. Discovered cffDNA from plasma of pregnant women by Lo et al., in 1997, has been used for the noninvasive detection of fetal RhD status which avoids RhD-negative women from antenatal anti-RhD prophylaxis [17–20].

#### **3.1 Methods and sample preparation**

#### *3.1.1 Sample preparation*

Maternal blood (10 cc) was collected from each pregnant woman and placed into an EDTA tube. Centrifugation step was applied within 1 h (at 1600 × *g*, 10 min) after separating maternal plasma. After centrifugation the plasma was removed

#### *A New Alternative Approach for RhD Incompatibility; Determination Fetal RhD Status via... DOI: http://dx.doi.org/10.5772/intechopen.84878*

carefully from the collection tubes and transferred into polypropylene tubes. Another centrifugation step was done at 16,000 × *g* (10 min). The plasma supernatants removed to new polypropylene tubes and stored at −20°C until other processes. Collected plasmas were thawed, and the DNA was automatically extracted from 1 mL of plasma as reported previously [9, 21, 22].

#### *3.1.2 Fetal RhD genotyping*

 RHD genes (exons 5 and 7) were analyzed from isolated cffDNA samples. The oligonucleotide primers used to perform real-time quantitative PCR are reported in **Table 1** [9]. The gene of DYS14 was tested to confirm the presence of male fetal DNA, and the beta globin (β-globin) gene was used as a reference to confirm the presence of cffDNA [10]. Real-time PCR performed in a LightCycler 480 (Roche Applied Science, Basel, Switzerland) using 96-well plates. The PCR mixture was 50 μL in total volume that contains 300 nM of each primer, 50 nM probe, 2 × TaqMan Universal PCR master mix (Roche Diagnostics, Basel, Switzerland), and 15 μL of template DNA of plasma samples. The PCR cycling conditions were as follows. Incubation step was 50°C for 2 min and 95°C for 10 min. Amplification step was 95°C 15 s and 60°C 60 s (50 cycles). The β-globin gene protocol was the initialization step at 95°C for 10 min, followed by 95°C 15 s, 57°C 10 s, and 72°C 10 s (40 cycles). Samples were analyzed in triplicate. Calibration curves were run also for each analysis [21].

The clinical features of the subjects studied (mean age and week of pregnancy) are shown in **Table 2**. Fifteen fetuses were found to be RhD-negative females. The RhD status of the fetus was predicted in 70 pregnancies in our study. The gender determination of the fetuses was shown in **Table 3**.


We have shown that fetal RHD genotyping by multiplex real-time PCR is applicable and readily performed, with a high accuracy rate, as a routine clinical test in prenatal diagnostic laboratories in Turkey. This method avoids unnecessary

#### **Table 1.**

*Primer and probe sequences used in RHD genotyping [9].* 


#### **Table 2.**

*Clinical features of the study population [9].* 


#### **Table 3.**

*Fetal RhD and sex status of maternal plasma samples [9].* 

immunoprophylaxis in RhD-negative women bearing RhD-negative fetuses. We suggest that RHD genotyping should become an essential part of prenatal screening in the management of RhD incompatibility [9].

#### **4. Biosensor in use to detect fetal RhD in maternal blood**

 Nowadays biosensors are universal devices which is used in biomedical diagnosis such as point-of-care monitoring of treatment and disease progression, drug discovery, forensics, and biomedical research [23]. They are widely used in different areas of healthcare [24]. The two main examples of biosensors are pregnancy tests and glucometers which are very successful devices. Biosensors have different transducing mechanisms based on signal generation (such as an electrochemical or optical signal) following the formation of antigen-antibody complexes [25]. Antibodies, enzymes, and synthetic biomolecules that are high-affinity reagents can be coupled to the transducer in order to provide specificity of the biosensors [23, 26].

We designed a new nanopolymer-coated electrochemical biosensor which is specific for the detection of fetal RhD antigens in the blood of pregnant women and results compared with cffDNA RHD genotyping with real-time PCR [26]. Biosensor technology is reproducible which can be used many times. The results can be generated quickly within a few minutes when compared to noninvasive fetal RHD genotyping with real-time PCR-based techniques. We suggest that biosensor technology could become a candidate method in early pregnancy in the management of RhD incompatibility.

#### **4.1 Materials and methods**

 The bioelectrochemical measurements were performed with PalmSens potentiostat systems and gold working electrode combined with the auxiliary Au/Pd (98/2%) electrode and the reference Ag/AgCl electrode. Thermostatic working cell, magnetic stirrer, automatic pipets, and Milli-Q ultrapure water were used in the experiments.

*A New Alternative Approach for RhD Incompatibility; Determination Fetal RhD Status via... DOI: http://dx.doi.org/10.5772/intechopen.84878* 

#### *4.1.1 Preparation procedure of the Au electrode surface*

 Cleaning electrode. First off all, the base of the working electrode surface was polished with alumina. And then the polished working electrode was sonicated in pure ethanol and Milli-Q ultrapure water for 10 min for removing undesired absorbable particles, respectively. For the last step of the electrode cleaning, five successive cyclic voltammogram sweeps were taken with bare working electrode between −1.0 and +1.0 V in 0.1 M HNO3 solution.

RhD antibody immobilization onto Au electrode surface. Poly(Hema-Mac) nanopolymer was immobilized on the clean electrode's surface at room temperature via anilin (20 μL anilin and 20 μL RhD antibody). For trapping the antibody, a cross-linking agent (2.5% glutaraldehyde) was used. The modified working electrode was cleaned with Milli-Q ultrapure water for removing unbinding materials.

Principle of the electrobiochemical measurement. The measurement is based on the oxidation-reduction reactions of the RhD antibodies. All the measurements performed with thermostatic reaction cell included phosphate buffer (50 mM, pH 7.0) and potassium ferrocyanide [K4Fe(CN)6] as mediator complex, at 35°C. The charge transfer capacitance (electrochemical potential difference) of antigen-antibody interaction difference was measured by biosensor system (**Figure 1**).

Preparation of the samples. The working group has 26 RhD-negative primigravidas. All of them were admitted to the Department of Gynecology and Obstetrics and to the Department of Medical Biochemistry for prenatal diagnosis in different gestational ages (8th–36th weeks) that were analyzed in biosensor study for RhD status (**Table 4**). Written informed consent that was approved by the Ethics Committee of the Faculty of Medicine of Cukurova University was obtained from each subject. Blood samples were collected at ethylenediaminetetraacetic acid (EDTA) tube (Becton Dickinson, Bangkok, Thailand). Blood group test was identified by the Blood Bank Centre using slide/tube agglutination test, which includes antibodies against red blood cell antigens.

#### **4.2 RhD antibody immobilization**

UV polymerization of anilin was used for RhD antibody immobilization. Anilin's reduction potential is reducing at the UV light. A reversible manner was showed on the uncovered working electrode of the cyclic voltammogram of redox probe Fe(CN)6 <sup>4</sup><sup>−</sup>/ <sup>3</sup><sup>−</sup> (**Figure 2**). To inhibit the charge transfer among redox probe in solution on the Au electrode, a bioactive layer was applied on the surface of the electrode. The reversible behavior of the cyclic voltammograms turned into a capacitive shape (**Figure 2**).

**Figure 1.**  *The principle of the biosensor [26].* 


#### **Table 4.**

*Clinical features of the samples in biosensor study for RhD status [26].* 

#### **Figure 2.**

*RhD biosensors cyclic voltammogram for the immobilization steps. Red line: uncovered gold electrode; blue line: UV polymerized. (Working conditions: incubation time 1 h for RhD antibody; 50 mM electrochemical redox probe solution; and mediator complex pH 7.0 potassium ferrocyanide [K4Fe(CN)6]). For detection of RhD antigen in maternal sample, the optimal curve of the biosensors potential range was 0.2–1.4 V [26].* 

#### **4.3 Biosensors optimization trials**

Working condition optimization studies were performed to determine the most suitable working conditions for using the biosensor. For this aim, the mediator concentration, cross-linker concentration, RhD antibody concentration, temperature effect, pH, and repeatability were studied.

Concentration of RhD antibody. Determination of the antibody concentration effect on the biosensor response, different RhD antibody concentrations (0.05, 0.10, 0.15, 0.20 ng/mL) were applied on the surface of biosensor. The RhD antibodies optimum concentration was determined at 0.10 ng/mL.

 Mediator and cross-linker concentration. In order to investigate the effect of the mediator concentration on the biosensor response, potassium ferrocyanide of 1.25 and 2.5 mg/dL was used in the preparation of the biosensor. To determine the effect of cross-linker concentration on the biosensor, the concentrations of glutaraldehyde of 12.5 and 2.5% were used. The optimum was value obtained at 2.5%. According to the results obtained from the experiments, the mediator complex of 1.25 mg/dL was assigned as the most effective result for the biosensor.

#### *A New Alternative Approach for RhD Incompatibility; Determination Fetal RhD Status via... DOI: http://dx.doi.org/10.5772/intechopen.84878*

The pH effect. For the pH values' effect on the biosensor response, different buffer systems were investigated. For this aim, acetate (50 mM, pH 5.0 ± 5.5), phosphate (50 mM, pH 6.0 ± 6.5 ± 7.0 ± 7.5), and Tris-HCl (50 mM, 8.0 ± 8.5) buffers were used. The optimum pH value was found at 7.0 due to 100% activity rate. Above and below pH 7.0 can cause a decrease in the biosensor response.

Temperature effect. To examine the temperature effect on the biosensor response, the assay was performed by different temperatures (10 ± 55°C). The optimum working temperature of the biosensor system was detected as 35°C. The biosensor response is directly increased with temperature until 35°C, but further increase in temperature caused a decrease on the biosensor response.

 Repeatability. Determination of the repeatability of the biosensor experiments were also studied for 1 μM RhD concentration (n = 10). From the assays the mean value (*x̄*), standard deviation (SD), and coefficient of variation (CV %) were found to be 2.68 ± 0.06 μM and 2.23%, respectively. From the results, the repeatability of the biosensor response can be accepted as well as within the given concentration of RhD according to the 95% confidence interval.

#### **4.4 Characterization of RhD antibody biosensor**

The graphic shown as **Figure 3** is the concentrations of RhD in different gestational age of pregnant women. The slope of the curves increased with the increasing fetal RhD antigen concentration which depends on gestational ages of the samples (**Figure 3**).

Linearity. The linearity study for the RhD biosensor was obtained in concentration range between 1 and 250 ng/mL. At higher concentrations, standard curve showed a deviation from linearity.

Fetal RHD genotyping. The cffDNA used for fetal RhD status of the fetus is studied in 26 pregnancies with multiplex real-time PCR for RHD gene exons 5 and 7. Twenty-one of 26 cffDNA were detected as RhD positive, and 5 of 26 were detected as RhD negative (the same results as detection with RhD biosensor). The results of the fetuses were confirmed after the delivery by serological and molecular tests.

#### **Figure 3.**

*Detection of increasing fetal RhD antigen with biosensor in different gestational age and mother's blood. Sloped line 1: RhD-negative sample; sloped line 2: sample 8th week of gestation; sloped line 3: sample 13th week of gestation; sloped line 4: sample 21th week of gestation; sloped line 5: sample 36th week of gestation [26].* 

#### **5. Conclusions**

 The new biosensor design, which detects RhD status of the fetus in the early stage of pregnancy in RhD-negative pregnant women blood, is suggested as a candidate method in fetal RhD management. RhD antibody is immobilized using UV polymerization of anilin. To characterize the electrochemical properties of the biosensor surface, impedance measurements were applied. For binding the formed stable bioactive layer showed binding of RhD antigen of fetus. The significant impedance biosensor response concentration to detect RhD antigen-antibody binding was 1 ng/mL RhD. The fetus RhD status was approved with real-time PCR fetal RHD genotyping. The detection of the RhD status of the fetus with antigenantibody biosensor system has more advantage as being fast compared to the noninvasive fetal *RHD* genotyping using fetal DNA. Up to now, common serological-based techniques were used for the detection fetal RhD status on delivery. There is a requirement for fast, sensitive, and low-cost techniques on clinical and molecular diagnostic. Using NIPD for the fetal blood group, detection studies were accelerated after the discovery of fetal DNA in maternal plasma. The noninvasive technique of fetal RhD status of cffDNA with qPCR has been recently introduced and now is a strong alternative for traditional tests in early pregnancy. The early detection of RhD status with NIPD is advantageous and also avoids the mother from anti-RhD prophylaxis [5, 27]. For the detection of fetal *RHD* from maternal plasma, the fetal DNA extraction is a better way. In the last decade, there were significant improvements in the accurate management of pregnancies in RhD-negative pregnant women (not immunized and/or alloimmunized) by noninvasive fetal *RHD* genotyping [12, 28].

 The fetal nucleated red blood cells (RBCs) are well known in maternal blood [29]. Bianchi et al. disclosed that in the first three-month period of the gestation, the fetus blood contains plenty of RBCs [30]. The RBC membrane has the RhD antigen, and when the fetus genotype is RhD positive, the alloimmunization arise when the fetal RBCs enter maternal blood. The cause is the anti-D antibodies developed by RhD-negative mother. The fetal RhD antigens can be detected on the 30–40th day of pregnancy. During the measurement with biosensor, the fetal RhD antigens cause signals (the signals increased in proportion to the gestational week). This chemical signals mean that the fetal RhD antigens on fetal RBCs bind on the surface of the biosensor that is coated with RhD antibodies (antigenantibody complex). In RhD-positive fetuses, this chemical signal is converted into an electrical signal by a transducer. In our RhD-negative samples (five of the fetus were RhD negative), there was no signal change detected on the biosensor. The biosensor detects the fetal RhD-positive antigens in the blood of RhD-negative mothers. This study demonstrates an original, quick, reliable, and easy detection method with biosensor technologies. The design of an immunospecific biosensor offers a candidate noninvasive prenatal detection for fetal RhD status to manage the RhD incompatibility between the fetus and mother. This method is able to capture fetal RhD antigens in maternal blood in the early stage of pregnancy (8th week of pregnancy). The biosensor-based detection of fetal RhD status takes several minutes using a gold electrode covered by RhD antibody. The determined biosensor method is more suitable, simple to construct, sensitive, and specific and does not require any expensive apparatus compared with the routine fetal RhD determination in early pregnancy. The biosensor instrument exhibits low cost with regard to real-time PCR devices. The biosensors can be used several times (up to 400-fold) and so decreases the cost. The most commonly used technique for NIPD is the qRT-PCR. Studies that based on biosensor technologies for NIPD application with cffDNA for monogenic diseases reported previously [31]. Some studies

*A New Alternative Approach for RhD Incompatibility; Determination Fetal RhD Status via... DOI: http://dx.doi.org/10.5772/intechopen.84878* 

demonstrated PCR-free applications by SPR-imaging [32]. We prepared a study which detects fetal RHD genotypes from cffDNA using SPR-based biosensor.

 In conclusion, the biosensor-based technologies which have used less amount of sample and low cost and determine the RhD status of the fetus in a very short time make the biosensors more advantageous than NIPD of RhD based on real-time quantitative PCR technologies.

#### **Acknowledgements**

We thank Cukurova University, Medicine Faculty, Obstetrics and Gynecology and Perinatology Department for sampling (chorionic villi) from mothers in first trimester.

#### **Conflict of interest**

There is no conflict of interest between authors.

#### **Author details**

Ebru Dündar Yenilmez\*, Umut Kökbaş and Abdullah Tuli Department of Medical Biochemistry, Faculty of Medicine, Cukurova University, Adana, Turkey

\*Address all correspondence to: edundar@cu.edu.tr

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **References**

[1] Lo YMD, Corbetta N, Chamberlain PF, Rai V, Sargent IL, Redman CWG, et al. Presence of fetal DNA in maternal plasma and serum. The Lancet. 1997;**350**(9076):485-487

[2] Lo YM, Hjelm NM, Fidler C, Sargent IL, Murphy MF, Chamberlain PF, et al. Prenatal diagnosis of fetal RhD status by molecular analysis. New England Journal of Medicine. 1998;**339**:1734-1738

[3] Legler TJ, Muller SP, Haverkamp A, Grill S, Hahn S. Prenatal RhD testing: A review of studies published from 2006 to 2008. Transfusion Medicine and Hemotherapy. 2009;**36**(3):189-198

[4] Chitty LS, Finning K, Wade A, Soothill P, Martin B, Oxenford K, et al. Diagnostic accuracy of routine antenatal determination of fetal RHD status across gestation: Population based cohort study. BMJ. 2014;**349**:g5243

[5] Daniels G, Finning K, Martin P, Massey E. Noninvasive prenatal diagnosis of fetal blood group phenotypes: Current practice and future prospects. Prenatal Diagnosis. 2009;**29**(2):101-107

[6] van der Schoot CE, Hahn S, Chitty LS. Non-invasive prenatal diagnosis and determination of fetal Rh status. Seminars in Fetal and Neonatal Medicine. 2008

[7] Szczepura A, Osipenko L, Freeman K. A new fetal RHD genotyping test: Costs and benefits of mass testing to target antenatal anti-D prophylaxis in England and Wales. BMC Pregnancy and Childbirth. 2011;**11**(5)

[8] Clausen FB, Christiansen M, Steffensen R, Jørgensen S, Nielsen C, Jakobsen MA, et al. Report of the first nationally implemented clinical routine screening for fetal RHD in D−pregnant

women to ascertain the requirement for antenatal RhD prophylaxis. Transfusion. 2012;**52**(4):752-758

[9] Yenilmez ED, Ozgünen FT, Evrüke IC, Tuli A. Noninvasive fetal RHD genotyping by multiplex real-time PCR in maternal plasma. International Journal of Current Medical Research. 2015;**4**(2):344-347

[10] Mujezinovic FAZ. Procedure-related complications of amniocentesis and chorionic villus sampling. Obstetrics & Gynecology. 2007;**110**(3):687-694

[11] Lo YMD, Bowell PJ, Selinger M, Mackenzie IZ, Chamberlain P, Gillmer MDG, et al. Prenatal determination of fetal RhD status by analysis of peripheral blood of rhesus negative mothers. The Lancet. 1993;**341**(8853):1147-1148

[12] Parchure DS, Kulkarni SS. Noninvasive fetal RHD genotyping from maternal plasma. Global Journal of Transfusion Medicine. 2016;**1**(1):21

[13] Benachi A, Delahaye S, Leticee N, Jouannic JM, Ville Y, Costa JM. Impact of non-invasive fetal RhD genotyping on management costs of rhesus-D negative patients: Results of a French pilot study. European Journal of Obstetrics, Gynecology, and Reproductive Biology. 2012;**162**(1):28-32

 [14] Sbarsi I, Isernia P, Montanari L, Badulli C, Martinetti M, Salvaneschi L. Implementing non-invasive RHD genotyping on cell-free foetal DNA from maternal plasma: The Pavia experience. Blood Transfusion. 2012;**10**(1):34-38

[15] Ordonez E, Rueda L, Canadas MP, Fuster C, Cirigliano V. Evaluation of sample stability and automated DNA extraction for fetal sex determination using cell-free fetal DNA in maternal plasma. BioMed Research International. 2013;**2013**:195363

*A New Alternative Approach for RhD Incompatibility; Determination Fetal RhD Status via... DOI: http://dx.doi.org/10.5772/intechopen.84878* 

[16] Oepkes D, Seaward PG, Vandenbussche FP, Windrim R, Kingdom J, Beyene J, et al. Doppler ultrasonography versus amniocentesis to predict fetal anemia. New England Journal of Medicine. 2006;**355**(2):156-164

[17] Avent ND, Reid ME. The Rh blood group system: A review. Blood. 2000;**95**(2):375-387

[18] Lo YM. Recent developments in fetal nucleic acids in maternal plasma: implications to noninvasive prenatal fetal blood group genotyping. Transfusion Clinique et Biologique. 2006;**13**(1-2):50-52

 [19] Van der Schoot CE, Soussan AA, Koelewijn J, Bonsel G, Paget-Christiaens LGC, de Haas M. Non-invasive antenatal RHD typing. Transfusion Clinique et Biologique. 2006;**13**(1-2):53-57

[20] Boggione C, Luján BM, Mattaloni S, Di Mónaco R, García BS, Biondi C, et al. Genotyping approach for non-invasive foetal RHD detection in an admixed population. Blood Transfusion;**2016**:1-8

[21] Yenilmez ED, Tuli A, Evruke IC. Noninvasive prenatal diagnosis experience in the Cukurova Region of Southern Turkey: Detecting paternal mutations of sickle cell anemia and betathalassemia in cell-free fetal DNA using high-resolution melting analysis. Prenatal Diagnosis. 2013;**33**(11):1054-1062

[22] Yenilmez ED, Tuli A. A noninvasive prenatal diagnosis method: Free fetal DNA in maternal plasma. Archives Medical Review Journal. 2013;**22**(3):317-334

[23] Bhalla N, Jolly P, Formisano N, Estrela P. Introduction to biosensors. Essays in Biochemistry. 2016;**60**(1):1-8

[24] Akkaya A, Altug C, Pazarlioglu NK, Dinckaya E. Determination of 5-aminosalicylic acid by catalaseperoxidase based biosensor. Electroanalysis. 2009;**21**(16):1805-1810

[25] Moina C, Gabriel Y. Fundamentals and applications of immunosensors. In: Chiu DNHL, editor. Advances in Immunoassay Technology. InTech; 2012. pp. 65-80

 [26] Dundar Yenilmez E, Kokbas U, Kartlasmis K, Kayrin L, Tuli A. A new biosensor for noninvasive determination of fetal RHD status in maternal blood of RhD negative pregnant women. PloS one. 2018;**13**(6):e0197855

 [27] Muller SP, Bartels I, Stein W, Emons G, Gutensohn K, Kohler M, et al. The determination of the fetal D status from maternal plasma for decision making on Rh prophylaxis is feasible. Transfusion. 2008;**48**(11):2292-2301

[28] Oxenford K, Silcock C, Hill M, Chitty L. Routine testing of fetal Rhesus D status in Rhesus D negative women using cell-free fetal DNA: An investigation into the preferences and information needs of women. Prenatal Diagnosis. 2013;**33**(7):688-694

 [29] Sohda S, Arinami T, Hamada H, Nakauchi H, Hamaguchi H, Kubo T. The proportion of fetal nucleated red blood cells in maternal blood: Stimation by FACS analysis. Prenatal Diagnosis. 1997;**17**(8):743-752

[30] Bianchi DW. Fetal cells in the maternal circulation: Feasibility for prenatal diagnosis. British Journal of Haematology. 1999;**105**:574-583

[31] Feriotto G, Breveglieri G, Finotti A, Gardenghi S, Gambari R. Real-time multiplex analysis of four betathalassemia mutations employing surface plasmon resonance and biosensor technology. Laboratory Investigation. 2004;**84**(6):796-803

[32] Brouard D, Ratelle O, Perreault J, Boudreau D, St-Louis M. PCRfree blood group genotyping using a nanobiosensor. Vox Sanguinis. 2015;**108**(2):197-204

**63**

**Chapter 5**

**Abstract**

biological activity

**1. Introduction**

X and Y chromosomes.

Interactions

ABO Blood Group Antigens as a

Model of Studying Protein-Protein

*Frida N. Gylmiyarova, Elena Ryskina, Nataliya Kolotyeva,* 

This work presents a research of intermolecular interactions on the example of the antigen antibody interactions of the ABO system. This model could be successfully used in the future due to the lack of knowledge in the area of the ABO antigen's behavior as a biomolecule and the integration of these structures into chain of metabolic processes in a human being. Using computer PASS system ("in silico" research), we describe the possible biological effects of pyruvate, lactate, and antigen determinants A and B. Glycoproteins A and B are very perspective to study as biological active connectors due to the wide range of their biological effects. The obtained knowledge proves that ABO antigen, as well as other glycoprotein conjugates, could play an important role in intercellular adhesion and signal transmission, which could be used in perspective in personalized medicine, target therapy,

**Keywords:** ABO blood groups, protein-protein interaction, computer modeling,

Erythrocytes are the most common blood cells in the human body. They present on their surface a huge number of different receptors and antigens, which explain their multiple biological functions. Since 1900, when Carl Landsteiner first found red blood cell antigens and named them ABO blood group system, a big step forward was made by scientists in this direction. Up to now, over 35 blood groups are registered in the International Society of Blood Transfusion. Some of them as ABO blood groups, MNS, Rh, Lutheran, Kell, Lewis, and Duffy are well studied and found their place in clinical practice, but others as Ok, Scianna, Colton, and Knops are on their way to be fully understood. The genes of the blood groups are mainly autosomal with the exception of XG, XK, and MIC2 genes as they are presented on

The biochemical structure of blood group antigens differs a lot; they can either

ABO blood group system consists of three alleles, dominant A and B, and reces-

be proteins (Rh, Kell) or glycoproteins and glycolipids (ABO) [1].

sive O, and it is controlled by a gene located on chromosome 9 (9q34.2).

*Valeriia Kuzmicheva and Oksana Gusyakova*

and evaluation of lab results in clinical practice.

#### **Chapter 5**

## ABO Blood Group Antigens as a Model of Studying Protein-Protein Interactions

*Frida N. Gylmiyarova, Elena Ryskina, Nataliya Kolotyeva, Valeriia Kuzmicheva and Oksana Gusyakova* 

#### **Abstract**

This work presents a research of intermolecular interactions on the example of the antigen antibody interactions of the ABO system. This model could be successfully used in the future due to the lack of knowledge in the area of the ABO antigen's behavior as a biomolecule and the integration of these structures into chain of metabolic processes in a human being. Using computer PASS system ("in silico" research), we describe the possible biological effects of pyruvate, lactate, and antigen determinants A and B. Glycoproteins A and B are very perspective to study as biological active connectors due to the wide range of their biological effects. The obtained knowledge proves that ABO antigen, as well as other glycoprotein conjugates, could play an important role in intercellular adhesion and signal transmission, which could be used in perspective in personalized medicine, target therapy, and evaluation of lab results in clinical practice.

**Keywords:** ABO blood groups, protein-protein interaction, computer modeling, biological activity

#### **1. Introduction**

Erythrocytes are the most common blood cells in the human body. They present on their surface a huge number of different receptors and antigens, which explain their multiple biological functions. Since 1900, when Carl Landsteiner first found red blood cell antigens and named them ABO blood group system, a big step forward was made by scientists in this direction. Up to now, over 35 blood groups are registered in the International Society of Blood Transfusion. Some of them as ABO blood groups, MNS, Rh, Lutheran, Kell, Lewis, and Duffy are well studied and found their place in clinical practice, but others as Ok, Scianna, Colton, and Knops are on their way to be fully understood. The genes of the blood groups are mainly autosomal with the exception of XG, XK, and MIC2 genes as they are presented on X and Y chromosomes.

The biochemical structure of blood group antigens differs a lot; they can either be proteins (Rh, Kell) or glycoproteins and glycolipids (ABO) [1].

ABO blood group system consists of three alleles, dominant A and B, and recessive O, and it is controlled by a gene located on chromosome 9 (9q34.2).

These genes (A and B) code different glycosyltransferases: glycosyltransferase A which adds N-acetylgalactosamine and glycosyltransferase B which adds d-galactose to H-substance. O allele is inactive and does not encode an enzyme, so that H substance remains unmodified with a fucose moiety. Combinations of these three alleles give us four different blood groups O (I), A (II), B (III), and AB (IV) [2].

It is notable that ABO antigens are expressed not only on the surface of erythrocyte, but they can also be found in a variety of tissues and cells, such as the endothelium of blood vessels, neurons, epithelium, platelets, etc.

In recent years, the amount of research on protein-small molecule (metabolite) interactions has increased significantly. However, the study of these interactions, according to the 2011 Wiley Online Library, is lagging far behind other types of interactions, such as protein-protein, protein-DNA, and protein-RNA, in terms of publications. Only in 2009 the first publications about protein-metabolite interactions appeared.

 From a biochemical point of view, most biological systems work by fulfilling their diverse functions by proteins. Due to the revolutionary progress in the study of genomics and proteomics, a more accurate idea of the amount of proteins synthesized in the body has now been formed, but there is a weak idea of which proteins nonspecifically interact with metabolites [3].

 It has been established that intermolecular interactions play a crucial role in almost all major biological processes, such as cell regulation, biosynthesis and biodegradation, signal transmission, transcription and translation processes, the formation of oligomers and multimolecular complexes, packaging of viruses, and the immune response, are protein-ligand interactions [4]. The polyfunctionality of proteins is due to their ability to change the conformation of a molecule when interacting with ligands. Proteins can interact with almost all types of molecules: from small compounds—water, metal ions, carbohydrates, fatty acids, and cell membrane phospholipids—to high molecular weight proteins and nucleic acids. Disruption of protein interactions underlies some diseases [5].

This fact provides a key argument that biological and clinical significance of blood groups in general and ABO especially extends far beyond our expectations and needs to be clarified.

#### **2. Computer modeling of antigen determinants A and B**

#### **2.1 Antigen A and its predicted biological activity**

It is almost impossible to evaluate the specific properties of the terminal fragments of antigenic structures in an experiment, but with the method of computer simulation, one can predict the biological effects of substances.

Glycosylation of protein molecules significantly affects their ability to contact with other molecules, which is important for understanding the mechanisms of intermolecular interactions, signaling, and adhesion at the cellular and molecular levels. The group-specific antigens of the blood ABO system are formed by the glycosylation of transmembrane proteins presented on the surface of red blood cells.

A-Antigen terminal monosaccharide N-acetylgalactosamine contains in the position C2 NHCOCH3 group, and B-antigen terminal monosaccharide d-galactose, in position C2, contains OH group. Functional groups confer variability in the structure of antigens and provide specificity for binding ligands. The presence in the structure of the N-acetylgalactosamine acetyl group leads to the disappearance of the positive charge.

#### *ABO Blood Group Antigens as a Model of Studying Protein-Protein Interactions DOI: http://dx.doi.org/10.5772/intechopen.82541*

N-Acetylgalactosamine is an amino sugar found in almost all glycoproteins. The immediate precursor of N-acetylgalactosamine is fructose-6-phosphate. Amino sugar is further acetylated with acetyl-CoA.

Monosaccharides can take part in all reactions which are typical for hydroxylcontaining compounds: they form esters and ethers, acetals and ketals, undergo substitution and elimination reactions. An important property of monosaccharides is their ability to form glycosides due to hydroxyl at the first carbon atom. Elucidation of the potential biological activity of antigens, determined by the structural characteristic of the antigenic determinants of the ABO system, is an important task.

We used a program for computer modeling called PASS. Prediction of Activity Spectra for Substances (PASS) is a program designed for computer modeling created by a group of Russian scientists. This tool is based on the dependence between chemical formula of a random substance and its functional activity. Chemical formula is described using Multilevel Neighborhoods of Atom (MNA) descriptors, the combination of which is unique for each substance. The user gets the list of probable activities based on the program's self-educating "training set," which aggregates data on active compounds from databases, publications, and patents, marked with Pa (probability to be active) and Pi (probability to be inactive), placed in the order from the maximum Pa to the minimum one. The program uses the Bayesian approach with some modifications for calculating the Pa and Pi (for more detailed information, see [6, 7]). In our study we chose effects with Pa >0.5. Total number of biological activities in the database is 4130, 501 of them are pharmacological effects, 3295 are molecular mechanisms of action, 57 are toxic effects, 199 are mediated metabolic actions, and 29 are influences on gene expression.

 We have identified a significant number of previously unknown properties and mechanisms of action for the antigenic determinant antigen A, namely, 99 out of 501 possible pharmacological effects, 304 out of 3295 possible molecular mechanisms of action, 17 out of 57 adverse and toxic effects, 12 of 199 metabolic-related activities, 2 of 29 effects regulating the expression of genes, and 5 of 49 effects associated with the transport of substances. We chose biological activities with a probability of Pa greater than 0.5. The PASS program allowed us to establish that the antigenic determinant of antigen A exhibits the following pharmacological effects (**Table 1**).

 It is predicted that the antigenic determinant of antigen A exhibits antibacterial, immunostimulating, antifungal, antiviral, and pharmacological effects, as well as antibiotic properties.


#### **Table 1.**

*Predicted pharmacological effects of antigen A.* 

ABO phenotypic analysis of blood groups is often used to detect the degree of susceptibility of a person to infectious diseases. In the literature, there are data in which it is noted that the interaction of certain parasites and bacteria with human cells depends on the presence of certain blood groups [8].

Thus, antigen A exhibits high adhesive activity against lactic acid bacteria. Some of the antigens affect the humoral and cellular response [9].

For the oligosaccharide of the antigen A, the antineoplastic effect on the cancer of various etiologies and localization is predicted: gastric and lung cancer, sarcoma, leukemia, and cancer of the brain and ovaries. Numerous studies have shown an association between ABO blood groups and the risk of developing various types of cancer [10].

With a high degree of probability, the antigen A is able to regulate angiogenesis and has a potential vasoprotective effect, as well as an effect of inhibitor of membrane permeability and integrity. The formation of new blood vessels in the organ or tissue is activated only when the damaged tissues are regenerated. Some factors, depending on the dose, can be both inducers of angiogenesis and inhibitors [11]. Many predicted effects of the antigenic determinant of antigen A are realized through the molecular mechanisms of its action (**Table 2**).

It is predicted that the oligosaccharide of the antigen A can act as an agonist of nerve growth factor, tumor necrosis factor, hyaluronic acid, α-interferon, interleukin-2, and tissue kallikrein inhibitor. The stimulating effect of the oligosaccharide antigen A on the activity of caspases 3, 8, and 9, participants in the apoptosis process, is predicted. As it is known, all caspases are synthesized in an inactive form and are activated when necessary by initiating caspases in the process of partial proteolysis. Probably, the antigenic determinant A, by activating caspases, can trigger a signal chain of programmed cell death.

Analyzing molecular mechanisms of action of antigenic determinants A, we paid attention to the inhibitory effect, to a number of carbohydrate metabolism enzymes, complex lipid metabolism, and protein biosynthesis process.


**Table 2.** 

 *Predicted molecular mechanisms of action of antigen A.* 

#### *ABO Blood Group Antigens as a Model of Studying Protein-Protein Interactions DOI: http://dx.doi.org/10.5772/intechopen.82541*

Oligosaccharide antigen A may inhibit the activity of enzymes involved in the metabolism of simple carbohydrates, such as α-glucosidase, β-glucuronidase, and β-galactosidase, and in the exchange of complex carbohydrates, predominantly heteropolysaccharides—α-N-acetyl-glucosaminidase, dolichol glycosyltransferase, and GDP-mannose-6-dehydrogenase, which creates the possibility of inhibiting the metabolism of the components of the extracellular matrix of connective tissue glycoproteins and proteoglycans. Dolichol glycosyltransferase plays a leading role in the glycosylation of membrane proteins.

It is predicted that the oligosaccharide of the antigen A can be an agonist of hyaluronic acid. Hyaluronic acid belongs to the innate immunity system and is involved in tissue regeneration, as evidenced by the likely manifestation of the pharmacological effects of tetrasaccharide A, as immunostimulating and vasoprotective.

Also, we predicted possible toxic effects (**Table 3**).

The effect of carbohydrate determinants of antigen A on the metabolism of complex lipids is predicted, and the molecular mechanism of action is inhibition of the activity of CDP-glycerol glycerophosphotransferase, ceramide glycosyltransferases, ganglioside galactosyltransferases, and galactosylglucosylceramidase, involved in the synthesis of phospho- and glycolipids, necessary for the construction of cell membrane structures. Glycolipids play an important role in making cell-to-cell contacts; some serve as a kind of receptor for a number of bacterial toxins.

 The possibility of an inhibitory effect on a gene expressing telomerase was predicted. About 85% of cancer cells acquire unlimited replicative potential due to the reactivation of a specific telomerase enzyme [12].

After analyzing the data obtained by computer prediction, we can conclude that the immunochemical specificity of the antigenic determinant of antigen A is realized by the characteristic and diverse biological activity and toxicity.

#### **2.2 Antigen B and its biological activity**

The antigenic determinant of antigen B contains terminal d-galactose, at position C2 where it has a hydroxyl group.

d-Galactose itself can enter into the reactions of alkylation, acylation, reduction, and oxidation. Analysis of the data of the probable biological activities of the antigenic determinant of antigen B showed 106 out of 501 possible pharmacological effects, 311 of the 3295 probable molecular mechanisms of action, 16 of 57 adverse and toxic effects, 15 of 199 metabolically mediated actions, 3 of 29 effects regulating gene expression, and 6 of 49 effects associated with transport of substances. We chose biological activities with a probability of Pa greater than 0.5. The pharmacological effects of the antigenic determinant of antigen B are shown in **Table 4**.


**Table 3.**  *Possible toxic effects of antigen A.* 


#### **Table 4.**

*Predicted pharmacological effects of antigen B.* 

Many effects and mechanisms of action are common for both antigen A and antigen B, but they are characterized by different degrees of probability of manifestation (Pa value). It is predicted that the antigen B is able to exhibit antibacterial, antiviral, antifungal, and pharmacological effects. According to the literature, group-specific antigens A and B can play a direct role in the susceptibility of the infection, acting as receptors or co-receptors for microorganisms, parasites, and viruses (**Table 5**).

In the study, Kato shows that carbohydrates can act not only as receptors for various microbes but also function as a barrier to infection [13].

 Numerous molecular mechanisms of the action of antigen B tetrasaccharide have been predicted, in particular the inhibitory effect on the activity of a number of enzymes involved in the metabolism and stimulating effects on various bioregulators.


**Table 5.** 

 *Predicted molecular mechanisms of action of antigen B.* 

#### *ABO Blood Group Antigens as a Model of Studying Protein-Protein Interactions DOI: http://dx.doi.org/10.5772/intechopen.82541*

 The stimulating effect of oligosaccharide B on the activity of caspases 3 and 8 is predicted; it can act as an agonist of hyaluronic acid, nerve growth factor, interleukin-2, and interferon antagonist. It has been established that galactooligosaccharides selectively increase the content of useful intestinal microbes, as well as C-reactive protein and interleukins [14].

The probable effect of oligosaccharide B on the expression of the telomerase gene (Pa 0.785) and the transport of electrons in mitochondria (Ra 0.504) is shown. Oligosaccharide B is highly likely to be a substrate for cytochrome P-450 2J2 (Pa 0.980), glutathione-S-transferase (0.907), and cyclooxygenase (Pa 0.759).

Using PASS we also identified possible toxic effects (**Table 6**).

The PASS program revealed that the antigenic determinant of antigen B can exhibit a variety of biological effects and molecular mechanisms of action that regulate various physiological and metabolic processes in the body.

The role of carbohydrates as key biological ligands is well known. This is due to the high degree of isomerism, possible within individual carbohydrate units, in a variety of ways to combine monosaccharides, among themselves, different variations of substituents (acetyl, sulfate) and flexibility of carbohydrate chains.

With the development of computational methods for studying protein-ligand interactions, it became possible to determine the type of bonds and the most important positions of atoms "C" in monosaccharides for the formation of an antigen-antibody complex. Using the molecular docking method, Stanca-Kaposta with a group of scientists found that hydrogen bonds and hydrophobic and van der Waals interactions are involved in the formation of an antigen-antibody association [15]. Monosaccharides of antigens can act as acceptors of hydrogen, and amino acid residues of paratope antibodies can serve as hydrogen donors. Most commonly, hydrogen bonds form atoms "C" at positions 3 and 5 in terminal galactose (antigen B) and atoms "C" at position 5 in N-acetylgalactosamine (antigen A). Accessibility of the nitrogen atom in the GalNAc epitope to participate in the formation hydrogen bonds are hampered by the presence of an acetyl group. The "C" atoms in position 6 are involved in hydrophobic and van der Waals interactions, both in galactose and in N-acetylgalactosamine [16].

In studies by J. Milland, it was found that in the N-acetylated version of the epitope of antigen A, the interaction of the acetyl group of the epitope with the tyrosine 35 of the immunoglobulin heavy chain precludes further penetration of the antigen into the antibody's binding site. In contrast, the Gal epitope of antigen B penetrates deeper into the antibody's binding site, and the second galactose antigenic determinant participates in hydrophobic interactions with tryptophan at position 36 of the immunoglobulin heavy chain [17]. ABO antigens, like other glycoconjugates, are important intercellular adhesion mediators and participants in signal transduction. Due to the diversity of biological effects manifested, oligosaccharides A and B are evaluated from a new perspective side as biologically active compounds and not only blood group antigens that protect blood cells [18].


**Table 6.**  *Possible toxic effects of antigen B.* 

 Computer modeling exists as a way of combining the microscopic world of molecules and experimental results, which helps to confirm our understanding of metabolic processes and propose new directions for research. For many of the predicted types of activity of compounds in the available literature, experimental evidence has not been found, since these organic compounds are difficult for conformational analysis. Computational methodology makes it possible to obtain structures of compounds at the atomic level and information about activity with an accuracy equivalent to or greater than can be obtained in an experiment. The prognostic and interpretative program PASS has helped to better present the mechanisms of action of the studied metabolites and carbohydrate determinants of antigens A and B in relation to the main body systems.

#### **3. ABO system as a marker of metabolic state**

 We selected 3678 healthy people with no chronic somatic and dental diseases, as well as latent socially significant viral infections (hepatitis B and C, HIV). Then, we performed a complex biochemical testing of blood with 40 parameters, complete blood count with 21 parameters, and hemostasiograms with 8 parameters. Studies of concentration of total protein; albumin; immunoglobulins A, G, and M; urea; creatinine; uric acid; total and direct bilirubin; C-reactive protein; alanine aminotransferase; aspartate aminotransferase; gamma-glutamyltranspeptidase; creatine kinase and creatine kinase-MB fraction; total cholesterol content; triglycerides; high-density lipoprotein and low-density lipoprotein; lipase activity; the coefficient of atherogenicity; glucose concentration; lactate dehydrogenase; alpha-amylase; alkaline phosphatase activity; and magnesium, calcium, phosphorus, and iron levels were carried out on an automatic biochemical analyzer "Hitachi-902" and "Integra 800" ("Roche," Japan) with the help of a commercial reagent kit from the company "Roche" (Germany). Intra-laboratory quality control when performing studies was carried out using control serum Precinorm and Precipath (Roche, Germany).

Complete blood count was performed using an automated hematology analyzer Sysmex KX-21 (Roche, Japan) using a commercial set of reagents produced by Roche (Germany). We measured distribution curves for the size of erythrocytes, leukocytes, and platelets, as well as analytical results for 18 parameters: the number of erythrocytes, leukocytes, and platelets; the content of hemoglobin and hematocrit; the average volume of erythrocytes and platelets; the average content and average concentration of hemoglobin in the erythrocyte; the width of the distribution of erythrocytes and platelets by volume; the relative and absolute content of neutrophils, medium cells, and lymphocytes; and the ratio of large platelets. The morphological study of blood cells was performed using a Zeiss light microscope using a unified method. The erythrocyte sedimentation rate was determined using a Panchenkov unified micromethod. The functionality of platelets was assessed by the method of visual detection of the start time of aggregation with different inductors (ADP, with a universal aggregation inducer (UIF), collagen).

 Statistical processing of the results was carried out using the statistical package SPSS 12.0 and Microsoft Excel 2007. The statistical characteristics were used: arithmetic average (M), standard arithmetic average error (m), median (Me), max, min, and 95% interval. Indicators of skewness and steepness reflect the asymmetry of distribution; normality tests were evaluated using the Kolmogorov-Smirnov test with the Lilyfors and Shapiro-Wilkie corrections. We used the nonparametric Mann-Whitney U test with the amendment of Bonferroni as an alternative to the Student's t-test. Taking into account the deviation from normality of various values of dispersions, a nonparametric analogue of dispersive analysis was used—the

#### *ABO Blood Group Antigens as a Model of Studying Protein-Protein Interactions DOI: http://dx.doi.org/10.5772/intechopen.82541*

Kruskal-Wallis analysis. To study the correlation of blood parameters, Spearman's correlation analysis was used.

The data we obtained supplemented information about the connection of certain diseases with blood groups (**Table 7**).

On the basis of the study of the metabolism in the O (I)—AB (IV) blood groups, we determined the trends characterizing their biological variability and identified the parameters associated with a specific blood group (**Table 8**).

 According to the specifics of these indicators, we attributed the owners of AV (IV) blood groups to the protein type, since they have the highest protein availability and they are less likely to get ill. It is known that A (II) carriers of the second blood group suffer from a wide range of diseases, including infectious diseases. They have an immunological memory of old and fresh contacts with bacterial and viral agents. The level of lipids can be conditionally attributed to the lipid type. In the presence of the first blood group, a high level of specific and nonspecific protection factors is characteristic. For them, a preferential connection with somatic pathology has been identified. Owners of the third blood group are characterized by sufficient good health. They have the highest level of albumin, cholesterol [19].

The identified features of the metabolic profile in individuals with different blood groups are the rationale for the individualization of standards for each person. In the future, every citizen should have his own health passport.

In accordance with the results obtained, in persons with O (I) blood group, a lower number of erythrocytes are noted with a relatively small volume of cells.



**Table 7.**  *Possibility of pathological process development in ABO blood groups.* 


#### *ABO Blood Group Antigens as a Model of Studying Protein-Protein Interactions DOI: http://dx.doi.org/10.5772/intechopen.82541*

#### **Table 8.**

*Metabolic characteristic of ABO blood groups (N stands for average results for general population; "+" and "−"—the degree of deviation compared to general population).* 

The level of hemoglobin in the blood is the lowest, while the saturation of each erythrocyte with hemoglobin is maximum, which ensures full blood transport of gases. A (II) of the second blood group is characterized by the lowest hematocrit value, the average hemoglobin content in one erythrocyte, the average platelet volume, and the maximum indicator of the number of leukocytes, neutrophils, and lymphocytes. Carriers of B (III) blood group showed the largest volume of platelets and the maximum of the average concentration of hemoglobin in one erythrocyte. In persons with AB (IV) blood group, the highest absolute and relative content of lymphocytes, which are the basis of cellular and humoral immunity, is noted. Tendency to lymphocytosis, a higher level of the spectrum of immunoglobulins is an indicator of the intensity of specific resistance and sufficient compensatory reserve in patients with AB (IV) blood group (**Figure 1**).

**Figure 1.**  *Biological diversity associated with ABO blood groups.* 

#### **4. Conclusions**

The results obtained in silico by computer prediction with PASS program show that antigens A and B influence on intermolecular processes, protein-protein interaction, maintain balance by regulating protein, carbohydrate, lipid metabolism, antioxidant processes, and tissue respiration quite differently which can be explained with their structure and conformational features.

The series of experiments clearly showed biodiversity in metabolic state of different ABO groups which allow us to create metabolic passport for each blood group summarizing the key data [19].

We found out that the carriers of O (I) blood group suffer from somatic diseases more recently than the other blood group carriers, while the carriers of A blood group are predisposed for infectious diseases, and the carriers B (III) and AB (IV) blood groups are more likely to show metabolic stability.

To summarize, methods of molecular modeling and forecasting allow us to broaden the fundamental knowledge about the molecules' properties and to

*ABO Blood Group Antigens as a Model of Studying Protein-Protein Interactions DOI: http://dx.doi.org/10.5772/intechopen.82541* 

successfully predict new possible biological effects, as well as molecular mechanisms for its realization in complex interactions of ligands and their targets.

### **Acknowledgements**

The authors appreciate the help and support of the head of Samara State Medical University, professor G.P. Kotelnikov. Our work is dedicated to the 100th anniversary of Samara State Medical University.

#### **Conflict of interest**

The authors state that they have no conflicts of interest.

#### **Thanks**

 The authors thank the chair of Fundamental and Clinical Biochemistry with laboratory diagnostic of Samara State Medical University.

### **Author details**

Frida N. Gylmiyarova1 \*, Elena Ryskina<sup>2</sup> , Nataliya Kolotyeva1 , Valeriia Kuzmicheva1 and Oksana Gusyakova1

1 Samara State Medical University, Samara, Russia

2 Peoples' Friendship University of Russia, Moscow, Russia

\*Address all correspondence to: bio-sam@yandex.ru

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **References**

[1] Franchini M, Bonfanti C. Evolutionary aspects of ABO blood group in humans. Clinica Chimica Acta. 2015;**444**:66-71

[2] Franchini M, Liumbruno G. ABO blood group: Old dogma, new perspectives. Clinical Chemistry and Laboratory Medicine. 2013;**51**(8):1545-1553

[3] Gylmiyarova F, Ryskina E, Kolotieva N, Potekhina V, Gorbacheva I. Proteinligand interactions: The influence of minor components of metabolism. Siberian Medical Review. 2017;(6):12-21

[4] Kastritis P, Bonvin A. On the binding affinity of macromolecular interactions: Daring to ask why proteins interact. Journal of the Royal Society Interface. 2012;**10**(79):20120835

 [5] Muronetz V, Barinova K, Stroylova Y, Semenyuk P, Schmalhausen E. Glyceraldehyde-3-phosphate dehydrogenase: Aggregation mechanisms and impact on amyloid neurodegenerative diseases. International Journal of Biological Macromolecules. 2017;**100**:55-66

[6] Stepanchikova AV, Lagunin AA, Filimonov DA, Poroikov VV. Prediction of biological activity spectra for substances: Evaluation on the diverse set of drugs-like structures. Current Medicinal Chemistry. 2003;**10**:225-233

[7] Poroikov VV, Akimov D, Shabelnikova E, Filimonov D. Top 200 medicines: Can new actions be discovered through computeraided prediction? SAR and QSAR in Environmental Research. 2001;**12**(4):327-344

[8] Rios M, Bianca C. The role of blood group antigens in infectious diseases. Seminars in Hematology. 2000;**37**(2):177-185

[9] Daniels G, Fletcher A, Garratty G, Henry S, Jorgensen J, Judd W, et al. Blood group terminology 2004: From the International Society of Blood Transfusion committee terminology for red cell surface antigens. Vox Sanguinis. 2004;**87**(4):304-316

[10] Risch H, Lu L, Wang J, Zhang W, Ni Q, Gao Y, et al. ABO blood group and risk of pancreatic cancer: A study in Shanghai and meta-analysis. American Journal of Epidemiology. 2013;**177**(12):1326-1337

[11] Malecki M, Kolsut P, Proczka R. Angiogenic and antiangiogenic gene therapy. Gene Therapy. 2005;**12**(S1):S159-S169

[12] Bell R, Rube H, Kreig A, Mancini A, Fouse S, Nagarajan R, et al. Abstract B12: GABP selectively binds and activates the mutant TERT promoter across multiple cancer types. Cancer Research. 2015;**75**(23 Supplement):B12-B12

[13] Kato K, Ishiwa A. The role of carbohydrates in infection strategies of enteric pathogens. Tropical Medicine and Health. 2015;**43**(1):41-52

[14] Gao J, He H, Jiang W, Chang X, Zhu L, Luo F, et al. Salidroside ameliorates cognitive impairment in a d-galactoseinduced rat model of Alzheimer's disease. Behavioural Brain Research. 2015;**293**:27-33

[15] Cristina Stanca-Kaposta E, Gamblin D, Screen J, Liu B, Snoek L, Davis B, et al. Carbohydrate molecular recognition: A spectroscopic investigation of carbohydrate-aromatic interactions. Physical Chemistry Chemical Physics. 2007;**9**(32):4444

[16] Milland EY, Xing PX, McKenzie FC, et al. Carbohydrate residues downstream of the terminal Galα(1, 3)

*ABO Blood Group Antigens as a Model of Studying Protein-Protein Interactions DOI: http://dx.doi.org/10.5772/intechopen.82541* 

Gal epitope modulate the specificity of xenoreactive antibodies. Immunology and Cell Biology. 2007;**85**:623-632

[17] Agostino M, Sandrin M, Thompson P, Yuriev E, Ramsland P. Identification of preferred carbohydrate binding modes in xenoreactive antibodies by combining conformational filters and binding site maps. Glycobiology. 2010;**20**(6):724-735

[18] Gylmiyarova F, Radomskaya N, Gergel N, Kotelknikov G, editors. Blood Groups: Biological Variability of Cellular Metabolism in Norm and Pathology. Moscow: Izvestiya; 2007

[19] Gylmiyarova F, Kolotyeva N, Potekhina V, Baisheva G, Ryskina E. The lactate role in intramolecular regulation of proteins interaction. Meditsinskiy Al'manakh. 2017;**2**(47):99-101

### *Edited by Anil Tombak*

Blood groups, erythrocyte antigens, and transfusion are fundamental areas of medicine and are related to many disciplines of science like hematology, immunology, surgery, and genetics. Tis book is a collection of information related to blood groups and transfusion, and a practical resource for all concerned physicians. Te book is divided into two sections. Te frst section includes chapters on blood transfusion reactions and hemolytic disease of the fetus. Te second section includes information for the future perspectives of blood group antigens. Tis book will be a stepping stone for scientists who are rapidly advancing their science journey.

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