After the revolutionary detection of ffDNA (free fetal DNA) in maternal circulation by real-time PCR in 1997 and advances in molecular techniques, NIPD (non-invasive prenatal diagnosis) is now a clinical reality. Non-invasive diagnosis using ffDNA has been implemented, allowing the detection of paternally inherited alleles, sex-linked conditions and some single-gene disorders and is a viable indicator of predisposition to certain obstetric complications [e.g. PET (pre-eclampsia)]. To date, the major use of ffDNA genotyping in the clinic has been for the non-invasive detection of the pregnancies that are at risk of HDFN (haemolytic disease of the fetus and newborn). This has seen numerous clinical services arising across Europe and many large-scale NIPD genotyping studies taking place using maternal plasma. Because of the interest in performing NIPD and the speed at which the research in this area was developing, the SAFE (Special Non-Invasive Advances in Fetal and Neonatal Evaluation) NoE (Network of Excellence) was founded. The SAFE project was set up to implement routine, cost-effective NIPD and neonatal screening through the creation of long-term partnerships within and beyond the European Community and has played a major role in the standardization of non-invasive RHD genotyping. Other research using ffDNA has focused on the amount of ffDNA present in the maternal circulation, with a view to pre-empting various complications of pregnancy. One of the key areas of interest in the non-invasive arena is the prenatal detection of aneuploid pregnancies, particularly Down's syndrome. Owing to the high maternal DNA background, detection of ffDNA from maternal plasma is very difficult; consequently, research in this area is now more focused on ffRNA to produce new biomarkers.

The SAFE (Special Non-Invasive Advances in Fetal and Neonatal Evaluation) NoE (Network of Excellence)

The SAFE network is an EU (European Union) Framework Programme 6-funded NoE (http://www.safenoe.org) established to implement routine, cost-effective NIPD (non-invasive prenatal diagnosis) and neonatal screening through the creation of long-term partnerships within and beyond the EU. The network itself consists of eight workpackages, covering primary scientific research, ethics, psychosocial and socio-economic areas within the arena of fetal and neonatal well-being. SAFE was initiated in March 2004 and was initially scheduled to run for 5 years. To date, SAFE has 50 different partners across Europe, the Middle East and India.

Genotyping for RHD and other antigens that cause HDFN (haemolytic disease of the fetus and newborn)

After the revolutionary detection of ffDNA (free fetal DNA) in maternal circulation by real-time PCR in 1997 [1], and advances in molecular techniques, NIPD is now a clinical reality. Non-invasive diagnosis using ffDNA has been implemented, which allows the detection of paternally inherited alleles, sex-linked conditions and some single-gene disorders and is a viable indicator of predisposition to certain obstetric complications [e.g. PET (pre-eclampsia)].

One of the major areas of clinical utility in the non-invasive prenatal diagnostic era is the advent of non-invasive genotyping for underlying causes of HDFN. HDFN is caused by a maternal alloimmune reaction to a paternally inherited fetal antigen and is therefore more prevalent in second and subsequent pregnancies where alloimmunization occurs during childbirth. The most common cause of HDFN is a reaction to the Rh antigens, predominantly RhD. Pre-1969 alloimmune incompatibility to RhD was the major cause of infant morbidity and mortality. The mortality rate was greatly reduced on the introduction of prophylactic anti-D to RhD-negative mothers. This has been reduced further with the widespread adoption of antenatal prophylaxis. Anti-D is derived from deliberately immunized volunteer donors, but only 40% of RhD-negative pregnancies actually require it as they carry RhD-negative fetuses (in European populations). Genotyping of the fetus to detect the RHD gene would allow anti-D to only be administered to mothers who actually require it.

RHD genotyping using ffDNA obtained from maternal peripheral blood was first described by Lo et al. [2] in 1998. Less than 10 years after the discovery ffDNA in the maternal circulation, the world's first non-invasive genotyping service was integrated into the NHS (National Health Service) in the U.K., where the diagnosis of RHD genotype and fetal gender was offered in Bristol [3]. This service is based on the assays first described in 2002 [4], and utilizes at least two regions of the RHD gene due to complexities at the genetic level of variant forms of RHD. The test also includes a method specific for amplification of the RHD gene in the presence of the African RHD pseudogene (RHDψ). A number of RHD real-time genotyping studies of large cohorts of RhD-negative women have taken place across Europe and the rest of the world. These and subsequent studies have been summarized in Table 1 [231]. Large-scale screening of all RhD-negative pregnant women by real-time PCR is currently being developed in The Netherlands [33], the U.K. [26] and France [27].

Table 1
Published studies on genotyping RHD and other HDFN-causing antigens from ffDNA in maternal plasma

Originally based on a review [32], with the most recent publications added to the contents of the Table.

ReferenceMethodsNo testedGestation (weeks)RHD exonsAccuracy (%)Controls
Lo et al. [2]* Real-time PCR 57 7–41 10 96 HBB 
Faas et al. [5]* PCR 31 16–17 100 None 
Bischoff et al. [6Nested PCR; fluorescent PCR (serum) 20 15–36 70 RHCE 
Zhong et al. [7Nested PCR 22 All trimesters 95 SRY, HBB 
Zhang et al. [8Real-time PCR 58 10–21 98 None 
Nelson et al. [9PCR 26 9–34 10 100 None 
Finning et al. [4]* Real-time PCR 137 8–42 4, 5, 6, 10 100 SRY, CCR5 
Costa et al. [10Real-time PCR 102 8–14 10 100 Mouse galt 
Legler et al. [11]* Real-time PCR 27 11–38 4, 7 96 E.coli plasmid 
Turner et al. [12Real-time PCR 31 <20 10 90 DNA, RHCE, C, c, E 
Johnson et al. [12PCR 47 18–40 4, 5, 10 91 ACTB 
Randen et al. [14Real-time PCR 114 6–38 92 None 
Rijnders et al. [15]* Real-time PCR 72 11–19 99 SRY 
Rouillac-Le Sciellar et al. [16Real-time PCR 893  7, 10 99.5 SRY, ALB 
Harper et al. [17Real-time PCR 13–16 4, 5, 10 100 SRY, CCR5 
Finning et al. [3]* Real-time PCR 283 8–42 4, 5, 10 100 SRY, CCR5 
Gautier et al. [18Real-time PCR 285 8–35 10 99  
Hromanikova et al. [19]* Real-time PCR 28 11–38 7, 10 100 β-globin 
Gonzalez-Gonzalez et al. [20Real-time PCR 20 11–16 90 β-globin 
Hromanikova et al. [21]* Real-time PCR 45 11–40 7,10, RHCE, E, C 100 SRY 
Hromanikova et al. [22]* Real-time PCR 23 11–37 7, 10, CE in 2, 5  GLO 
Zhou et al. [23Real-time PCR 98  4,5,10 94 SRY, biallelic polymorphisms 
Grootkerk-Tax et al. [24]* Real-time PCR 45 28–30 5, 7   
Clausen et al. [25Real-time PCR 56 15–36 7, 10 98 Genomic DNA 
Finning et al. [26]* Real-time PCR 173 10–36 K, C, c, E 99.4 CCR5 
Rouillac-Le Sciellar et al. [27Real-time PCR 300 10–34 7, 10 99+  
Kimura et al. [28PCR, capillary electrophoresis 12 12–39 10 100 Y-STR 
Minon et al. [29Real-time PCR 563 10+ 4, 5, 10 99.8 SRY 
Müller et al. [30]* Real-time PCR 1113 6–32 5, 7 99.8 β-globin 
Finning et al. [31]* Real-time PCR, multiplex PCR 1997 28 4, 5, 10 95.7 SRY 
ReferenceMethodsNo testedGestation (weeks)RHD exonsAccuracy (%)Controls
Lo et al. [2]* Real-time PCR 57 7–41 10 96 HBB 
Faas et al. [5]* PCR 31 16–17 100 None 
Bischoff et al. [6Nested PCR; fluorescent PCR (serum) 20 15–36 70 RHCE 
Zhong et al. [7Nested PCR 22 All trimesters 95 SRY, HBB 
Zhang et al. [8Real-time PCR 58 10–21 98 None 
Nelson et al. [9PCR 26 9–34 10 100 None 
Finning et al. [4]* Real-time PCR 137 8–42 4, 5, 6, 10 100 SRY, CCR5 
Costa et al. [10Real-time PCR 102 8–14 10 100 Mouse galt 
Legler et al. [11]* Real-time PCR 27 11–38 4, 7 96 E.coli plasmid 
Turner et al. [12Real-time PCR 31 <20 10 90 DNA, RHCE, C, c, E 
Johnson et al. [12PCR 47 18–40 4, 5, 10 91 ACTB 
Randen et al. [14Real-time PCR 114 6–38 92 None 
Rijnders et al. [15]* Real-time PCR 72 11–19 99 SRY 
Rouillac-Le Sciellar et al. [16Real-time PCR 893  7, 10 99.5 SRY, ALB 
Harper et al. [17Real-time PCR 13–16 4, 5, 10 100 SRY, CCR5 
Finning et al. [3]* Real-time PCR 283 8–42 4, 5, 10 100 SRY, CCR5 
Gautier et al. [18Real-time PCR 285 8–35 10 99  
Hromanikova et al. [19]* Real-time PCR 28 11–38 7, 10 100 β-globin 
Gonzalez-Gonzalez et al. [20Real-time PCR 20 11–16 90 β-globin 
Hromanikova et al. [21]* Real-time PCR 45 11–40 7,10, RHCE, E, C 100 SRY 
Hromanikova et al. [22]* Real-time PCR 23 11–37 7, 10, CE in 2, 5  GLO 
Zhou et al. [23Real-time PCR 98  4,5,10 94 SRY, biallelic polymorphisms 
Grootkerk-Tax et al. [24]* Real-time PCR 45 28–30 5, 7   
Clausen et al. [25Real-time PCR 56 15–36 7, 10 98 Genomic DNA 
Finning et al. [26]* Real-time PCR 173 10–36 K, C, c, E 99.4 CCR5 
Rouillac-Le Sciellar et al. [27Real-time PCR 300 10–34 7, 10 99+  
Kimura et al. [28PCR, capillary electrophoresis 12 12–39 10 100 Y-STR 
Minon et al. [29Real-time PCR 563 10+ 4, 5, 10 99.8 SRY 
Müller et al. [30]* Real-time PCR 1113 6–32 5, 7 99.8 β-globin 
Finning et al. [31]* Real-time PCR, multiplex PCR 1997 28 4, 5, 10 95.7 SRY 
*

SAFE consortium partner.

Genotyping assay standardization

A key deficiency found in some published RHD NIPD tests that utilize maternal plasma is that there are no controls for identifying the presence of fetal DNA in every sample. It is possible to utilize Y-chromosome-specific markers when a male fetus is present, but is not possible as a control in female fetuses. A set of 11 biallelic markers that can be used as controls to detect fetal alleles [34] has been described. These markers can be utilized for the detection of a RhD-negative female fetus, and are now routinely used in RHD genotyping. Non-invasive genotyping is now the clinical standard of care in the determination of fetal RHD status, and has effectively eliminated amniocentesis as a procedure in the management of HDFN.

When dealing with absolute quantification of low concentrations of DNA (the fetal target constitutes approx. 3% of the total DNA present in maternal plasma), standardization of DNA extraction procedures is important. Standardization of these approaches has been addressed by the SAFE network, and recommended procedures are published on the SAFE website and in a recent paper [35]. One of the key deliverables of the SAFE consortium was standardizing current real-time RHD, SRY and DYS14 (Y chromosome markers) used as controls in the present methods of RHD genotyping as well as routinely introducing the use of the biallelic markers [35].

The placenta is the major source of ffDNA in maternal circulation

Beyond its use in diagnostic approaches to detect paternally inherited genes, the quantification of the concentration of ffDNA may also have some inherent diagnostic properties under certain clinical conditions. Studies have indicated that higher quantities of ffDNA have been associated with trisomy 21 [36], pre-term labour [37], hyperemesis gravidarum [38], feto-maternal haemorrhage, FGR (fetal growth restriction) and PET.

The placenta, or more precisely the syncytiotrophoblast cells of the chorionic villi, were thought to be the major source of ffDNA in the maternal circulation. Through apoptosis, the cells were thought to provide a constant flow of ffDNA into the maternal circulation, which increases with gestational age. This theory was recently strengthened by a study adopting the DYS14 quantitative real-time assay described by Zimmermann et al. [39] and using maternal plasma from blighted ovum or anembryonic pregnancies [40]. These pregnancies at first appear normal but eventually lose the embryonic pole and continue with only a functional placenta. The Y chromosome marker DYS14 was detected in plasma from a number of cases exhibiting this condition in equal or higher quantities than normal pregnancies carrying a male fetus, strongly suggesting that the placenta is the main source of ffDNA [40]. It is therefore not surprising to learn that conditions arising from placental dysfunction such as PET and FGR have higher than normal concentrations of ffDNA.

ffDNA used as a predictor of obstetric conditions of placental dysfunction

PET is a common and potentially fatal condition affecting up to 10% of pregnancies; it is more prevalent in first time pregnancies, normally presenting in the third trimester (after the 32nd week of gestation). It is diagnosed by high blood pressure and proteinuria, with or without oedema, and/or elevated liver and renal function blood tests or low platelet counts. As with some other obstetric complications such as trisomy 21, risk of PET increases with increasing maternal age. Although much research into the aetiology and mechanism of the condition has taken place, its exact pathogenesis remains uncertain, although it is thought that in many cases it arises from a shallowly implanted placenta, which becomes hypoxic, leading to an immune reaction. IUGR (intrauterine growth restriction) or FGR is a term for a fetus that is not growing at the normal rate; it is also a common cause of perinatal mortality and morbidity. A growth-restricted fetus is defined as the fetus whose birth weight or ultrasound measurements are smaller than the 10th percentile. Babies born to mothers who have had FGR normally have a low birth weight and are more likely to suffer from certain health conditions. Both PET and FGR are conditions associated with placental dysfunction that lead to ischaemia and apoptosis of trophoblast cells, thus leading to an increased concentration of ffDNA in maternal circulation. Recent research in this field has focused on utilizing the quantification of ffDNA levels as a marker and/or predictor of disease severity [41].

ffDNA concentrations in diabetic mothers

Another condition with known placentation abnormalities is diabetes. Similar investigations to those used in some of the PET and FGR studies, again employing the marker DYS14 to assess ffDNA concentration, have been conducted using plasma from mothers who are Type 1 and Type 2 diabetics. Preliminary results show that there are no significant differences between ffDNA concentrations of normal pregnancies and those with a diabetic mother. However, there were significant differences (P<0.05) when the pregnancy was exhibiting macrosomia [42]. A macrosomic pregnancy is defined as the pregnancy where the birth weight of the baby is >4.4 kg.

ffDNA in multiple pregnancies

Studies using real-time PCR and plasma from 56 twin pregnancies have been used to test the hypothesis that ffDNA concentration should rise in multiple pregnancies, as there is increased trophoblastic mass [43]. Results showed that there was a significant difference in ffDNA concentration between singleton pregnancies and pregnancies with two male fetuses, and also between twin pregnancies carrying one and two male fetuses as originally hypothesized. No significant differences in ffDNA concentrations were seen between monochorionic and dichorionic pregnancies, probably because of the increased placental mass observed in monochorionic pregnancies [43].

ffDNA, ffRNA (free fetal RNA) and biomarker selection for trisomy 21

The foremost reason that pregnant women opt to enter any kind of prenatal screening programme is for the detection of aneuploidy, in particular, trisomy 21, also known as Down's syndrome. Fetal aneuploidy and other chromosomal aberrations affect 9 of every 1000 live births. Currently, the only definitive prenatal diagnostic tests available are invasive procedures such as amniocentesis and CVS (chorionic villous sampling); these tests themselves pose a small but significant risk of miscarriage. In terms of non-invasive prediction of aneuploid pregnancies, ffDNA, which naturally coexists with a high maternal DNA background, is present at a concentration approx. 1.5-fold that found in normal pregnancies. This difference borders the threshold of real-time detection and is not diagnostic. In these cases, ffRNA has emerged as a potential candidate for providing potential biomarkers of aneuploidies.

The fetal-specific nature (derived from the placenta) of this circulating mRNA is the reason why this mRNA is favoured over ffDNA for use in NIPD, as it removes the current limitations of fetal gender and the need for paternally inherited fetal-specific polymorphisms. As with ffDNA, ffRNA is detectable in the early stages of the first trimester, as early as the fourth week of gestation [44].

After the initial discovery of ffRNA in maternal plasma, mRNA levels of the placentally expressed genes for hPL (human placental lactogen) and β-hCG (β-subunit of human chorionic gonadotrophin) were correlated to the variation of their corresponding protein levels throughout gestation [45], adding to the credibility of ffRNA to provide accurate information on fetal gene expression.

Another approach for addressing the low fractional concentration of fetal DNA is the study of epigenetic markers such as DNA methylation. SERPINB5, coding for maspin, is hypomethylated in placental tissues and hypermethylated in maternal blood cells [46], and RASSF1A is located on chromosome 3 [47]; SERPINB5 and RASSF1A are two examples of such epigenetic markers. Through the targeting of such fetal-specific markers, the detected signal is virtually completely fetal in origin, thus allowing fetal chromosomal dosage via the analysis of the allelic ratio of genetic variations at the detected loci.

More recently published studies have concentrated on the use of SNPs (single-nucleotide polymorphisms) from fetal nucleic acids to determine fetal chromosome dosage. One of them focused on ffDNA using fetal-specific SNPs to be able to distinguish fetal DNA from the masking maternal background and hence establish relative fetal chromosome number [48]. Although promising in theory, numerous groups have been unable to repeat this technique. Reproducibility aside, the method is labour intensive, involving multiple steps and the use of formaldehyde. Implementing a technique of this nature would be clinically difficult.

A more promising breakthrough, again based on SNP analysis, has come from the Lo group [49]. They demonstrated the feasibility of detecting non-invasive aneuploidy pregnancy by analysing the allelic ratio of an SNP of the placentally derived PLAC4 (placenta specific 4) mRNA in maternal plasma by digital PCR [50]. Using this single marker they claim a sensitivity of 90% and specificity of 96%, a result comparable with many multimarker screening approaches. Although a very encouraging, robust and reproducible technique, the particular SNP chosen is not representative of all ethnic backgrounds. It is representative of Caucasian and certain Asian populations but not African or African-American, thereby making it unsuitable as a universal single marker of Down's syndrome. By using an expanded panel of such SNPs, the detection of aneuploidy in all pregnancies should be possible, and is currently undergoing clinical trials at Sequenom.

The latest breakthrough in this area has come from the group of Stephen Quake at Stanford University. They have reported non-invasive diagnosis of aneuploidy by shotgun sequencing of fetal DNA from maternal plasma using Solexa-Illumina and 454/Roche sequencers [51]. This sequencing approach is polymorphism independent and is therefore diagnostically universally applicable. Although very promising, this study was only performed on a small cohort of samples (n=18); shotgun sequencing is also quite a labour-intensive process, but the predicted rapid advancement in this technology may see its routine application.

A long-term goal of the SAFE NoE is to identify a panel of new, more informative, robust trisomy 21 markers. As part of SAFE workpackage 3b transcriptomics and proteomics, we have undertaken a series of parallel microarray and plasma proteomic experiments conducted using identical clinical samples. The transcriptomic contribution to this study has involved the use of an Affymetrix microarray investigation along with a more focused chromosome 21 array. mRNA used for this study was extracted from placenta-derived extravillous tissue from first trimester known trisomy 21 pregnancies, age matched with euploid controls. Analysis of both the Affymetrix and focused chromosome 21 data shows clear differences between the trisomy placental samples and the matched controls (Figure 1). This investigation continues with a larger microarray study being conducted on 40 first trimester CVS samples taken from both normal and trisomy 21 pregnancies.

Hierarchical cluster of normal compared with trisomy 21 placental gene expression data

Figure 1
Hierarchical cluster of normal compared with trisomy 21 placental gene expression data

Hierarchical cluster of Affymetrix microarray data based on RNA taken from six first trimester placental samples: three normal and three trisomy 21 samples. The condition tree was produced using the Spearman correlation. Red indicates the genes that are overexpressed and blue indicates underexpression with respect to the median of the normalized microarray data. The partitioning and grouping of the respective trisomy 21 and normal derived samples at the primary branch of the tree highlights some fundamental differences in placental gene expression between the two sample groups.

Figure 1
Hierarchical cluster of normal compared with trisomy 21 placental gene expression data

Hierarchical cluster of Affymetrix microarray data based on RNA taken from six first trimester placental samples: three normal and three trisomy 21 samples. The condition tree was produced using the Spearman correlation. Red indicates the genes that are overexpressed and blue indicates underexpression with respect to the median of the normalized microarray data. The partitioning and grouping of the respective trisomy 21 and normal derived samples at the primary branch of the tree highlights some fundamental differences in placental gene expression between the two sample groups.

Initial bioinformatics analysis has shown placental expression differences of known placental and chromosome 21 markers such as hCG, PLAC4 and other genes located in the DSCR (Down's syndrome critical region) of chromosome 21. Together with our sister proteomic analysis, we hope to identify some previously unidentified diagnostically important biomarkers of aneuploidy. These are likely to be of placental origin, detectable in maternal plasma and will hopefully augment the current trisomy screening methods.

Advances in Nucleic Acid Detection and Quantification: A joint Biochemical Society and Wellcome Trust Focused Meeting held at Hinxton Hall, Cambridge, U.K. 28–29 October 2008. Organized and Edited by Simon Baker (Oxford Brookes, U.K.), Jeremy Gillespie (Thermo Fisher Scientific, U.K.), Simon Hughes (Oxford Gene Technology, U.K.), Ian Kavanagh (Thermo Fisher Scientific, U.K.) and Devin Leake (Thermo Fisher Scientific, U.S.A.).

Abbreviations

     
  • CVS

    chorionic villous sampling

  •  
  • ffDNA

    free fetal DNA

  •  
  • ffRNA

    free fetal RNA

  •  
  • FGR

    fetal growth restriction

  •  
  • HDFN

    haemolytic disease of the fetus and newborn

  •  
  • NIPD

    non-invasive prenatal diagnosis

  •  
  • NoE

    Network of Excellence

  •  
  • PET

    pre-eclampsia

  •  
  • SAFE

    Special Non-Invasive Advances in Fetal and Neonatal Evaluation

  •  
  • SNP

    single-nucleotide polymorphism

Funding

Work described in this review was partly funded by the Special Non-invasive Advances in Fetal and Neonatal Evaluation Network of Excellence [grant number LSHB-CT-2004-503243], which is funded by the European Union Framework Programme 6.

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